Method and system for obtaining electric quantity in different time periods, chip and storage medium

Abstract

The invention discloses an electric quantity time-sharing acquisition method, an electric quantity time-sharing acquisition system, a chip and a computer readable storage medium. The method comprises the following steps: acquiring electricity meter code data of a user and a preset big data algorithm; processing the electricity meter code data of the user according to the preset big data algorithm to obtain a time-phased electricity utilization curve of the user; and obtaining the time-phased electricity consumption of the user according to the time-phased electricity consumption curve of the user. According to the method, the accuracy of the time-sharing electricity consumption curve of the user can be improved, so that the accurate time-sharing electricity consumption of the user can be obtained.

Description

Technical Field

[0001] The present application relates to the technical field of power consumption calculation by time period, and more specifically, to a method for obtaining power consumption by time period, a system, a chip and a computer-readable storage medium for obtaining power consumption by time period. Background Art

[0002] With the growth of new energy installed capacity, the peak load regulation pressure of the power system has increased sharply. For corporate users who need a lot of electricity, my country has implemented a time-based electricity trading mechanism, which adjusts the peak demand of electricity trading in each period by setting different prices for each period. At present, in order to realize the time-based electricity fee settlement, it is necessary to first realize the time-based electricity consumption calculation. The relevant personnel collect the user's electricity consumption data according to the selected data collection time, and obtain the user's electricity consumption curve by manual fitting calculation, and then obtain the user's electricity consumption in different periods. However, this method has low calculation efficiency, large errors in the fitting curve, and it is difficult to obtain accurate time-based electricity consumption. Summary of the invention

[0003] Embodiments of the present application provide a method for acquiring electric quantity by time period, a system, a chip, and a computer-readable storage medium for acquiring electric quantity by time period.

[0004] The method for obtaining electricity consumption by time period in the implementation mode of the present application includes: obtaining the user's electricity meter code data and a preset big data algorithm; processing the user's electricity meter code data according to the preset big data algorithm to obtain the user's time-based electricity consumption curve; and obtaining the user's time-based electricity consumption according to the user's time-based electricity consumption curve.

[0005] The system for acquiring electricity consumption by time period in the implementation mode of the present application includes an acquisition module and a processing module, wherein the acquisition module is used to acquire the user's electricity meter code data and a preset big data algorithm; the processing module is used to process the user's electricity meter code data according to the preset big data algorithm to obtain the user's electricity consumption curve by time period; and obtain the user's electricity consumption by time period according to the user's electricity consumption curve by time period.

[0006] The chip of some embodiments of the present application includes a memory and a processor. The memory is configured to store a computer program, and when the processor executes the computer program, it implements the following method for obtaining the amount of electricity by time period: obtaining the user's electricity meter code data and a preset big data algorithm; processing the user's electricity meter code data according to the preset big data algorithm to obtain the user's time-divided electricity consumption curve; and obtaining the user's time-divided electricity consumption according to the user's time-divided electricity consumption curve.

[0007] The computer-readable storage medium of the embodiment of the present application stores a computer program thereon, and when the program is executed by a processor, the method for obtaining the following time-phased electricity consumption is implemented: obtaining the user's electricity meter code data and a preset big data algorithm; processing the user's electricity meter code data according to the preset big data algorithm to obtain the user's time-phased electricity consumption curve; and obtaining the user's time-phased electricity consumption according to the user's time-phased electricity consumption curve.

[0008] In the method for obtaining electricity consumption by time period, the system, chip and computer-readable storage medium provided by the present application, after obtaining the user's electricity meter code data and a preset big data algorithm, the user's electricity meter code data is processed according to the preset big data algorithm to obtain the user's time-based electricity consumption curve, thereby realizing automatic fitting of the user's time-based electricity consumption curve and improving the accuracy of the fitting curve, and then according to the user's time-based electricity consumption curve, the user's time-based electricity consumption is obtained, thereby obtaining the accurate time-based electricity consumption of the user.

[0009] Additional aspects and advantages of the embodiments of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0011] Figure 1 It is a flowchart of a method for obtaining electricity in different time periods in some implementation modes of the present application;

[0012] Figure 2 It is a structural schematic diagram of a system for acquiring power in different time periods in some implementation modes of the present application;

[0013] Figure 3 is a schematic diagram of a time-divided electricity consumption curve of a user in some implementation modes of the present application;

[0014] Figure 4 is a flowchart of a method for acquiring electricity in different time periods in other implementation modes of the present application;

[0015] Figure 5 It is a structural schematic diagram of a system for acquiring electric power in different time periods in other implementation modes of the present application;

[0016] Figure 6 It is a flow chart of storing the user's electricity meter code data in a preset database in a method for acquiring electricity by time period in certain embodiments of the present application;

[0017] Figure 7It is a schematic diagram of a flow chart of obtaining the user's electricity meter code data in a method for obtaining electricity by time period in certain implementation modes of the present application;

[0018] Figure 8 It is a flow chart of obtaining the user's time-divided electricity consumption curve by processing the user's electricity meter code data according to a preset big data algorithm in a method for obtaining the electricity consumption by time period in certain embodiments of the present application;

[0019] Fig. 9 It is a schematic diagram of the time-divided electricity consumption curve of users in other implementation modes of the present application;

[0020] Fig.10 It is a schematic diagram of a flow chart of checking a user's time-divided electricity consumption curve in a method for obtaining electricity consumption in time periods in certain implementation modes of the present application;

[0021] Fig.11 It is a schematic diagram of a flow chart of checking a user's time-based electricity consumption curve in a method for obtaining electricity consumption in time periods in other implementation modes of the present application;

[0022] Fig.12 is a structural schematic diagram of a system for acquiring electricity in different time periods in some implementation modes of the present application;

[0023] Fig.13 It is a comparative schematic diagram of the time-divided electricity consumption curves of users in certain implementation modes of the present application;

[0024] Fig.14 is a schematic diagram of the structure of a chip in some embodiments of the present application;

[0025] Fig.15 It is a schematic diagram of the connection status of a computer-readable storage medium and a processor in certain embodiments of the present application.

[0026] Description of main component symbols:

[0027] Chip 100;

[0028] Memory 101, processing unit 102;

[0029] System 10 for obtaining electricity in different time periods;

[0030] Acquisition module 11; processing module 12; user's electric meter 13; preset database 14; power station's electric meter 15;

[0031] Processor 20;

[0032] Computer readable storage medium 200 ; computer program 202 . DETAILED DESCRIPTION

[0033] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions from beginning to end. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the embodiments of the present application, and cannot be understood as limiting the embodiments of the present application.

[0034] In recent years, with the growth of new energy installed capacity, the power supply and demand situation has changed dramatically, and the peak load regulation pressure of the power system has increased sharply. It is urgent to release price signals by optimizing and improving market trading rules, and guide the source, network, load and storage to promote the safe and stable operation of the power system. For corporate users who need a lot of electricity, my country implements a time-sharing power trading mechanism, and adjusts the peak power trading demand in each time period by setting different prices for different time periods. At present, in order to realize the time-sharing electricity fee settlement, it is necessary to first realize the time-sharing power consumption calculation. The relevant personnel collect the user's power consumption data according to the selected data collection time, and obtain the user's power consumption curve by manual fitting calculation, and then obtain the user's power consumption in different time periods. However, the curve fitted by this method often has incomplete curve data, disconnected head and tail, data reverse (abnormal curve drop), data flying (sudden sharp increase in the slope of the curve) due to human errors. At the same time, the calculation efficiency of this method is low, the error of the fitting curve is large, and it is difficult to obtain accurate time-sharing power consumption. How to solve the problems of low calculation efficiency, large error of fitting curve, and difficulty in obtaining accurate time-sharing power consumption caused by manual curve fitting has become a difficult problem that technicians in this field need to solve urgently. To solve this problem, the present application provides a method for obtaining power consumption in different time periods (such as Figure 1 and Figure 4 As shown in the figure), the system for obtaining power in different time periods (such as Figure 2 , Figure 5 and Fig.12 As shown), chips (such as Fig.14 ) and computer readable storage medium 200 (as shown Fig.15 shown).

[0035] See also Figure 1 and Figure 2 The method for acquiring the amount of electricity in different time periods according to the implementation mode of the present application includes:

[0036] 02: Obtain the user's electricity meter data and the preset big data algorithm;

[0037] 03: Process the user's electricity meter data according to the preset big data algorithm to obtain the user's time-based electricity consumption curve; and

[0038] 091: According to the user's time-based electricity consumption curve, obtain the user's time-based electricity consumption.

[0039] The above-mentioned method for acquiring electricity in different time periods can be applied to a system 10 for acquiring electricity in different time periods. The system 10 for acquiring electricity in different time periods of the implementation mode of the present application includes an acquisition module 11 and a processing module 12. The acquisition module 11 is used to acquire the user's electricity meter code data and a preset big data algorithm; the processing module 12 is used to process the user's electricity meter code data according to the preset big data algorithm to obtain the user's electricity consumption curve in different time periods; and obtain the user's electricity consumption in different time periods according to the user's electricity consumption curve in different time periods.

[0040] The method in step 02 belongs to the method of the acquisition module 11, and the methods in steps 03 and 091 belong to the method of the processing module 12. Specifically, the acquisition module 11 is used to execute step 02, and the processing module 12 is used to execute steps 03 and 091. Figure 2 It can be seen that the system 10 for acquiring electricity in different time periods may also include the user's electricity meter 13. The user's electricity meter 13 is a device for measuring the electrical energy consumed in a circuit. It may be an electricity meter or a meter. The user's electricity meter 13 determines how much electrical energy the user has consumed by detecting factors such as current, voltage, and power, and usually uses kilowatt-hours (kWh) as the unit of measurement.

[0041] The system 10 for obtaining the amount of electricity in different time periods is a system that calculates the amount of electricity consumed at different electricity consumption times according to the electricity meter data of the user, thereby supporting the collection of electricity charges in different time periods. The main purpose of the system 10 for obtaining the amount of electricity in different time periods is to encourage users to use electricity during periods with low electricity demand (usually referred to as "low-peak" or "night" periods) to reduce the peak load pressure of the power grid and improve the operating efficiency and stability of the power system. Among them, for the division of time periods, the system 10 for obtaining the amount of electricity in different time periods usually divides a day into multiple time periods, such as peak time periods and low-peak time periods, wherein the peak time period is a time period with high electricity demand, usually during the daytime of working days (such as 9 am to 6 pm), and the peak period of electricity consumption at night (such as 7 pm to 10 pm). The low-peak period is a time period with relatively low electricity demand, usually during the night of working days (such as 10 pm to 7 am the next day), as well as all day on weekends and holidays. After the system 10 for obtaining the amount of electricity in different time periods obtains the electricity consumption of users in different time periods, the relevant personnel can set different electricity prices in different time periods, thereby realizing accurate time-based billing. Generally speaking, the electricity price during peak hours is higher than that during off-peak hours to encourage users to use more electricity during off-peak hours. In addition, some regions will insert "peak hours" during peak hours to increase electricity prices when electricity demand reaches its peak during peak hours to further regulate electricity demand.

[0042] The acquisition module 11 is responsible for the acquisition and transmission of power data in the power system and is a key component of the power system. The acquisition module 11 realizes information exchange between the electric meter and the power system through a variety of communication technologies, which is crucial for the efficient operation, fault diagnosis, remote monitoring and optimization management of the power system. The acquisition module 11 can be a communication module, a concentrator or a collector, wherein the communication module includes but is not limited to an optical fiber communication module, a wireless communication module, a microwave communication module and a satellite communication module. Since optical fiber can provide a high-speed, low-latency and high-bandwidth communication channel, the optical fiber communication module is usually used to connect the communication link between large power facilities or between the control center to ensure the reliability of data transmission. The wireless communication module applies a variety of wireless communication technologies and can realize wireless communication between the power system and the electric meter in remote areas where it is difficult to lay wired communications. The microwave communication module can be used for long-distance communication, especially in power systems with complex terrain and geographical dispersion. The microwave communication module provides an economical and efficient solution for connecting power facilities in different geographical locations. For remote areas that cannot be covered by ground communication technology, the satellite communication module provides a backup or supplementary communication means to ensure remote monitoring and control of the power system. Concentrators and collectors are usually used to collect data from remote power metering points and transmit them to the central power system. Concentrators are usually used to cover larger areas, while collectors are used in smaller areas or specific power metering points. In the present application, the acquisition module 11 is connected to both the user's electric meter 13 and the processing module 12. The acquisition module 11 acquires the user's electricity meter code data through the connection with the user's electric meter 13, and transmits the user's electricity meter code data to the processing module 12 for subsequent processing through the connection with the processing module 12.

[0043] The processing module 12 is a key component in the power system to ensure stable operation and efficient management of the system. The processing module 12 is used to analyze and store a large amount of data collected by the power system to support the operation of functions such as real-time monitoring, fault diagnosis, predictive maintenance, and optimized scheduling of the power system, thereby ensuring the smooth operation of the entire smart grid. In the present application, the processing module 12 is connected to the acquisition module 11, so that it can receive the user's electricity meter code data and the preset big data algorithm acquired by the acquisition module 11, and process the user's electricity meter code data. The processing process of the user's electricity meter code data will be described in detail below.

[0044] Specifically, in step 03, after obtaining the user's electricity meter code data and the preset big data algorithm through the acquisition module 11, the processing module 12 processes the user's electricity meter code data according to the preset big data algorithm to obtain the user's time-divided electricity consumption curve. Among them, the preset big data algorithm is usually a preset JAVA curve fitting algorithm, such as polynomial regression, linear regression, exponential regression, logarithmic regression and other curve fitting algorithms. In step 091, the processing module 12 can obtain the user's time-divided electricity consumption curve according to the user's time-divided electricity consumption curve. For more details, please refer to Figure 3 , Figure 3 The horizontal axis represents time, and the vertical axis represents the user's power indication. For example, assuming Figure 3 Represents a user's electricity consumption curve for a day. Figure 3 Time periods 1 and 3 are set as off-peak hours, and time periods 2 and 4 are set as peak hours. The difference between the vertical axis values ​​of the starting and ending points of time periods 1, 2, 3, and 4 can be used to obtain the user's time-based electricity consumption in time periods 1, 2, 3, and 4 of the day, thereby ensuring the accuracy of subsequent time-based electricity charges and improving the calculation efficiency of time-based electricity consumption.

[0045] In certain embodiments, see Figure 2 , Figure 4 and Figure 5 , the method of obtaining the power consumption in different time periods also includes:

[0046] 01: Store the user's electricity meter data in the preset database.

[0047] The above-mentioned method for acquiring electricity in different time periods can be applied to a system 10 for acquiring electricity in different time periods. The system 10 for acquiring electricity in different time periods in the embodiment of the present application further includes a preset database 14, which is used to store the user's electricity meter code data.

[0048] The preset database 14 is a core component of the operation and management of the system 10 for acquiring electricity in different time periods. It is mainly used to store, manage and retrieve big data generated during the operation of the power system. These data mainly include equipment data, user data and various business process data. The design and implementation of the preset database 14 need to take into account the high availability, security, real-time and scalability of the data to support key functions such as efficient operation, fault diagnosis, optimized scheduling, customer service and market transactions of the power system. In this application, the preset database 14 mainly includes a distributed file system (HDFS), a distributed data warehouse (Hive), and a column storage library (HBase). In step 01, in order to acquire the electricity in different time periods in the subsequent steps, the acquisition module 11 needs to first store the user's electricity meter code data in the preset database 14 for subsequent use.

[0049] See also Figure 5 In some implementations, step 01 specifically includes:

[0050] 011: Under the preset data dispatching platform, the preset data import tool and the preset distributed message queue are used to store the user's electricity meter code data into the preset database.

[0051] The above-mentioned method for acquiring electricity in different time periods can be applied to a system 10 for acquiring electricity in different time periods. The acquisition module 11 is used to: under a preset data scheduling platform, use a preset data import tool and a preset distributed message queue to store the user's electricity meter code data into a preset database.

[0052] It is understandable that in step 011, the preset data scheduling platform provides an operating environment for the preset data import tool and the preset distributed message queue. In this application, DolphinScheduler is usually used as the core scheduling platform for the acquisition system big data, that is, the preset data scheduling platform. The preset data scheduling platform is used to deploy programs and configure relative time scheduling tasks. The time period data mainly uses T-1 to T-5 (T represents today's data, T-1 represents data one day ago, and T-5 represents data five days ago) data calculation to ensure the validity of the data. In this application, Sqoop is usually used as the preset data import tool, and Kafka is also usually used as the preset distributed message queue, wherein Sqoop is a tool designed for efficient batch transmission of data. Sqoop can import data into the preset database 14, and also supports exporting data from the preset database 14 to other databases. This two-way data migration capability makes Sqoop very flexible and convenient when processing data interaction. Sqoop can adapt to different data source environments and meet diverse data migration needs. It also supports incremental import, which allows you to manually specify the starting position or automatically record the last end position, so that only newly added or modified data can be synchronized. Kafka is a distributed publish-subscribe messaging system, usually used as a distributed message queue to assist the preset data import tool to achieve real-time transmission of users' electricity meter data.

[0053] See also Figure 2 , Figure 5 ,and Figure 6 In some embodiments, the preset database 14 includes a distributed file system (HDFS), a distributed data warehouse (Hive), and a column storage library (HBase), and step 011 includes:

[0054] 0111: Under the preset data dispatching platform, using the preset data import tool and the preset distributed message queue, the user's electricity meter code data is stored in the distributed file system; and

[0055] 0113: According to the preset data distribution program, the preset data import tool and the preset distributed message queue are used to store the user's electricity meter code data stored in the distributed file system into the distributed data warehouse and the column storage library.

[0056] The above-mentioned method for acquiring electricity in different time periods can be applied to a system 10 for acquiring electricity in different time periods, and the acquisition module 11 is also used for: under a preset data scheduling platform, using a preset data import tool and a preset distributed message queue to store the user's electricity meter code data in a distributed file system; and according to a preset data distribution program, using a preset data import tool and a preset distributed message queue to store the user's electricity meter code data stored in the distributed file system in a distributed data warehouse and a column storage library.

[0057] Specifically, since the distributed data warehouse and the column storage library are both databases based on the distributed file system at the bottom layer, when the acquisition module 11 stores the user's electricity meter code data, it is necessary to first use the preset data import tool and the preset distributed message queue under the preset data scheduling platform to store the user's electricity meter code data in the distributed file system, and then use the preset data import tool and the preset distributed message queue according to the preset data distribution program to store the user's electricity meter code data stored in the distributed file system into the distributed data warehouse and the column storage library.

[0058] See also Figure 2 , Figure 5 ,and Figure 7 In some implementations, the user's electricity meter code data includes historical electricity meter codes and current electricity meter codes, and step 02 includes:

[0059] 021: when receiving an application for suspending electricity use, reading a historical electricity meter code within a predetermined time window from a preset database;

[0060] 0231: If the historical electricity meter code can be read within the scheduled time window, the frozen meter code of the day can be obtained;

[0061] 0232: If the historical electricity meter code cannot be obtained within the scheduled time window, the electricity meter code for the day is recorded as 0;

[0062] 0251: when the last meter code in the historical electricity meter code is greater than the frozen meter code of the day, the last meter code in the historical electricity meter code is determined to be the electricity meter code of the day;

[0063] 0252: When the last meter code in the historical electricity meter code is smaller than the frozen meter code of the day, the frozen meter code of the day is determined as the electricity meter code of the day.

[0064] The above-mentioned method for acquiring electricity in different time periods can be applied to a system 10 for acquiring electricity in different time periods. The acquisition module 11 is also used to read the historical electricity meter code within a predetermined time window from a preset database when an application for suspending electricity use is received; when the historical electricity meter code can be read within the predetermined time window, the frozen meter code for the day is acquired; when the historical electricity meter code cannot be acquired within the predetermined time window, the electricity meter code for the day is recorded as 0; when the last meter code in the historical electricity meter code is greater than the frozen meter code for the day, the last meter code in the historical electricity meter code is determined to be the electricity meter code for the day; when the last meter code in the historical electricity meter code is less than the frozen meter code for the day, the frozen meter code for the day is determined to be the electricity meter code for the day.

[0065] Specifically, when the user has determined that he will not use electricity for a period of time in the future, he will usually directly send an application for suspending electricity use to the system 10 for obtaining electricity in different time periods, and settle the electricity charges. Therefore, when the acquisition module 11 receives the application for suspending electricity use, it is necessary to directly determine the user's electricity usage to determine the time-period electricity usage of the user who submitted the application for suspending electricity use, so as to settle the electricity charges for the user who submitted the application for suspending electricity use. In step 0231 and step 0232, if the acquisition module 11 can read the historical electricity meter code within the predetermined time window, the frozen meter code of the day is obtained, wherein the predetermined time window represents the duration of the historical electricity meter code read, which can be freely set by relevant personnel according to actual conditions. Under normal circumstances, the relevant personnel will set the predetermined time window to two years. The acquisition module 11 reads the historical electricity meter code of the user who submitted the application for suspending electricity use within two years from the preset database 14, which means that the user who submitted the application for suspending electricity use has used electricity within two years, and the acquisition module 11 obtains the frozen meter code of the day. However, if the acquisition module 11 cannot read the historical electricity meter code from the preset database 14, it means that the user who submitted the application for suspension of electricity use has not used electricity within two years, so the acquisition module 11 directly records the electricity meter code of the day as 0. The frozen meter code is usually the meter code determined by the meter at zero o'clock every day through the freeze reading function. The frozen meter code will not be changed unless a specific operation (such as reset) is performed, so the accuracy of the frozen meter code is relatively high. In steps 0251 and 0252, since the meter code increases with the increase of the user's electricity consumption, in order to determine the last meter code of the user's meter, that is, to determine the user's final electricity consumption, if the frozen meter code is greater than the last meter code in the historical electricity meter code, it means that the electricity consumption behavior of the user who submitted the application for suspension of electricity use continued before the meter was frozen and after the meter collected data for the last time, so the frozen meter code can more accurately reflect the user's total electricity consumption. If the frozen meter code is smaller than the last meter code in the historical electricity meter code, it indicates that the electricity consumption behavior of the user who submitted the electricity suspension application continued until after the meter was frozen and before the meter collected data for the last time on that day. Therefore, the last meter code in the historical electricity meter code can more accurately reflect the user's total electricity consumption. In summary, the acquisition module 11 selects the meter code with the largest value between the last meter code in the historical electricity meter code and the frozen meter code of the day as the electricity meter code of the day, thereby helping to complete the determination of the final time-based electricity consumption of the user who submitted the electricity suspension application.

[0066] See also Figure 2 , Figure 5 ,and Figure 7 In some implementations, the user's electricity meter code data includes historical electricity meter codes and current electricity meter codes, and step 02 further includes:

[0067] 027: In the case where no application for suspending electricity use is received, the historical electricity meter code within the predetermined time window and the user's current electricity meter code are read from the preset database 14 to serve as the user's electricity meter code data.

[0068] The above-mentioned method for acquiring electricity in different time periods can be applied to the system 10 for acquiring electricity in different time periods. The acquisition module 11 is also used to read the historical electricity meter codes within a predetermined time window and the user's current electricity meter code from a preset database when no application for suspending electricity use is received, so as to serve as the user's electricity meter code data together.

[0069] Specifically, if the acquisition module 11 does not receive the application for suspending electricity use, the user's time-divided electricity consumption is calculated normally, that is, the historical electricity meter code within the predetermined time window and the user's current electricity meter code are read from the preset database 14 as the user's electricity meter code data.

[0070] See also Figure 2 , Figure 5 ,and Figure 8 In some embodiments, the preset big data algorithm includes an offline calculation algorithm executed by a big data offline calculation framework and a real-time calculation algorithm executed by a big data real-time calculation framework, and the user's electricity meter code data includes the electricity meter code data of the day and the historical electricity meter code data. Step 03 includes:

[0071] 031: The big data offline computing framework uses offline computing algorithms to process historical electricity meter data to obtain the user's historical electricity consumption curve by time period;

[0072] 033: The big data real-time computing framework uses real-time computing algorithms to process the electricity meter data of the day to obtain the user's electricity consumption curve for the day by time period; and

[0073] 035: Obtain the user's time-based electricity consumption curve based on the current day's electricity consumption curve and historical electricity consumption curve.

[0074] The above-mentioned method for obtaining electricity quantity by time period can be applied to the system 10 for obtaining electricity quantity by time period, wherein the processing module 12 is used to process the historical electricity meter code data using an offline computing algorithm based on a big data offline computing framework to obtain the user's historical electricity consumption curve by time period; to process the current day's electricity meter code data using a real-time computing algorithm based on a big data real-time computing framework to obtain the user's current day's electricity consumption curve by time period; and to obtain the user's current day's electricity consumption curve based on the current day's electricity consumption curve and the historical electricity consumption curve.

[0075] Specifically, in this application, Spark is usually used as a big data offline computing framework. Spark can perform offline computing on massive data, and combined with its own SparkSQL, it can solve related business problems more quickly and efficiently. In addition, Flink is usually used as a big data real-time computing framework. Flink can update and store data in real time. The system 10 that obtains electricity in different time periods has a probability of performing data supplementation every day. Through Flink, data changes can be updated and stored more efficiently. Therefore, the processing module 12 uses an offline computing algorithm based on the big data offline computing framework to process historical electricity meter code data to obtain the user's historical electricity consumption curve for different time periods, and uses a real-time computing algorithm based on the big data real-time computing framework to process the electricity meter code data for the day to obtain the user's electricity consumption curve for the day for different time periods. Please refer to Fig. 9 The processing module 12 combines the current day's electricity consumption curve with the historical electricity consumption curve to obtain the user's time-divided electricity consumption curve.

[0076] See also Figure 2 , Figure 4 ,and Figure 5 In some embodiments, the method for acquiring the power consumption in different time periods further includes:

[0077] 04: Verify the user’s electricity consumption curve by time period.

[0078] The above method for acquiring electric power in different time periods can be applied to the system 10 for acquiring electric power in different time periods. The processing module 12 is further used to check the electric power consumption curve of the user in different time periods.

[0079] It is understandable that before obtaining the electricity consumption by time period, it is necessary to check the user's electricity consumption curve by time period to avoid errors when charging electricity fees by time period. The steps of the check part will be described in detail below.

[0080] See also Figure 2 , Figure 5 ,and Fig.10 In some embodiments, step 04 includes:

[0081] 041: Get the frozen table code for the current month;

[0082] 042: Process the freezing table code of the current month according to the preset big data algorithm to obtain the freezing curve of the current month; and

[0083] 043: Check whether the user’s time-based electricity consumption curve is abnormal based on the frozen curve of the current month.

[0084] The above-mentioned method for obtaining electricity consumption by time period can be applied to the system 10 for obtaining electricity consumption by time period, and the processing module 12 is also used to: obtain the frozen meter code of the current month; process the frozen meter code of the current month according to a preset big data algorithm to obtain the frozen curve of the current month; and check whether the user's time-based electricity consumption curve is abnormal according to the frozen curve of the current month.

[0085] Specifically, the electricity bill is usually settled once a month, so the time-based calculation of electricity consumption is also usually set to be once a month. In step 041 and step 042, the processing module 12 first obtains the frozen meter code of the month through the acquisition module 11, and processes the frozen meter code of the month through a preset big data algorithm to obtain the frozen curve of the month. Since the frozen meter code is usually regarded as a more accurate meter code, in step 043, the processing module 12 implements the verification of the user's time-based electricity usage curve according to the frozen curve of the month. When the difference between the two curves is less than a first preset difference threshold, it is determined that there is no abnormality in the user's time-based electricity usage curve. When the difference between the two curves is equal to the first preset difference threshold, it can be determined that there is an abnormality in the user's time-based electricity usage curve, or it can be determined that there is no abnormality in the user's time-based electricity usage curve. When the difference between the two curves is greater than the first preset difference threshold, it is determined that there is an abnormality in the user's time-based electricity usage curve. At this time, relevant personnel are usually dispatched to perform on-site maintenance on the user's electricity meter 13.

[0086] See also Figure 2 , Fig.11 and Fig.12 In some embodiments, step 04 further includes:

[0087] 044: Get the electricity meter data of the power station;

[0088] 045: Processing the power meter data of the power station according to the preset big data algorithm to obtain the power consumption curve of the power station by time period; and

[0089] 046: Check whether the user's time-based electricity consumption curve is abnormal based on the power station's time-based electricity consumption curve.

[0090] The above-mentioned method for obtaining electricity in different time periods can be applied to the system 10 for obtaining electricity in different time periods, and the processing module 12 is also used to: obtain the electricity meter code data of the power station; process the electricity meter code data of the power station according to a preset big data algorithm to obtain the power consumption curve of the power station in different time periods; and check whether the user's power consumption curve in different time periods is abnormal according to the power consumption curve of the power station in different time periods.

[0091] Specifically, in addition to using frozen meter codes to check the user's time-based electricity consumption curve, you can also use the power station's electricity meter code data to complete the verification step. Fig.12Since the electric meter 15 of the power station is connected to the acquisition module 11, the processing module 12 can first obtain the electric meter code data of the power station from the electric meter 15 of the power station through the acquisition module 11, and process the electric meter code data of the power station through the preset big data algorithm to obtain the time-divided electricity consumption curve of the power station. Since the accuracy and durability of the electric meter 15 of the power station are better than the electric meter 13 of the user under normal circumstances, in step 046, the processing module 12 verifies the time-divided electricity consumption curve of the user according to the time-divided electricity consumption curve of the power station to determine whether the time-divided electricity consumption curve of the user is abnormal. When the difference between the two curves is less than the second preset difference threshold, it is determined that there is no abnormality in the time-divided electricity consumption curve of the user. When the difference between the two curves is equal to the second preset difference threshold, it can be determined that there is an abnormality in the time-divided electricity consumption curve of the user, or it can be determined that there is no abnormality in the time-divided electricity consumption curve of the user. When the difference between the two curves is greater than the second preset difference threshold, it is determined that there is an abnormality in the time-divided electricity consumption curve of the user, and at this time, relevant personnel are usually sent to perform on-site maintenance on the electric meter 13 of the user.

[0092] See also Figure 2 and Figure 5 In some embodiments, when there is a zero value in the user's electricity meter code data, the steps of obtaining the user's electricity meter code data and the preset big data algorithm are re-executed, or the steps of storing the user's electricity meter code data in a preset database are re-executed.

[0093] The above-mentioned method for acquiring electricity in different time periods can be applied to the system 10 for acquiring electricity in different time periods. When there is a zero value in the user's electricity meter code data, the acquisition module 11 is also used to re-execute the steps of acquiring the user's electricity meter code data and the preset big data algorithm, or re-execute the steps of storing the user's electricity meter code data in a preset database.

[0094] Specifically, for example, the user's electricity meter code data will also be sent to the marketing department to obtain the marketing monthly table, and the user's electricity meter code data recorded in the marketing monthly table will be rechecked. Under normal circumstances, the user's electricity meter 13 continuously records the user's electricity consumption, so the user's electricity meter 13 should not suddenly have a zero value. The presence of a zero value in the user's electricity meter code data usually indicates that the user's electricity meter 13 has a fault, or that the system 10 for acquiring electricity in different time periods has a fault. Therefore, when there is a zero value in the user's electricity meter code data, the acquisition module 11 re-executes the steps of acquiring the user's electricity meter code data and the preset big data algorithm, or re-executes the steps of storing the user's electricity meter code data in a preset database. If there is still a zero value in the user's electricity meter code data, it indicates that the user's electricity meter 13 has a fault. At this time, relevant personnel will usually be sent to perform on-site repairs on the user's electricity meter 13.

[0095] See also Figure 2 , Figure 4 ,and Figure 5 In some embodiments, the method for acquiring the power consumption in different time periods further includes:

[0096] 05: Get at least two frozen table codes;

[0097] 06: Process at least two frozen table codes according to the preset big data algorithm to obtain the frozen curve for verification;

[0098] 07: Determine whether the freezing function of the meter is abnormal based on the user's time-based electricity consumption curve and the freezing curve for calibration.

[0099] The above-mentioned method for acquiring electricity in different time periods can be applied to the system 10 for acquiring electricity in different time periods, and the acquisition module 11 is also used to acquire at least two frozen table codes; process at least two frozen table codes according to a preset big data algorithm to obtain a frozen curve for verification; the processing module 12 is also used to determine whether the freezing function of the meter is abnormal based on the user's time-based electricity consumption curve and the frozen curve for verification.

[0100] Specifically, when the freezing function of the electric meter is abnormal, the freezing meter code collected every day will be fixed at a certain value (the electric meter does not move), and in step 05 and step 06, the acquisition module 11 first acquires at least two freezing meter codes to ensure that the curve can be fitted later. The processing module 12 processes at least two freezing meter codes through a preset big data algorithm to obtain a frozen curve for verification. In step 07, the processing module 12 verifies the frozen curve for verification according to the user's time-divided electricity consumption curve. When the difference between the two curves is less than the third preset difference threshold, it is determined that there is no abnormality in the frozen curve for verification. When the difference between the two curves is equal to the third preset difference threshold, it can be determined that there is an abnormality in the frozen curve for verification, or it can be determined that there is no abnormality in the frozen curve for verification. When the difference between the two curves is greater than the third preset difference threshold, it is determined that there is an abnormality in the frozen curve for verification. At this time, relevant personnel are usually sent to the user's electric meter 13 for on-site maintenance to ensure that the freezing function of the user's electric meter 13 can be restored to normal.

[0101] See also Figure 2 , Figure 4 ,and Figure 5 In some embodiments, the method for acquiring the power consumption in different time periods further includes:

[0102] 08: Analyze the user's electricity meter data; and

[0103] 092: When the difference between the two adjacent meter codes in the user's electricity meter code data is greater than the preset difference, an abnormal prompt is issued and / or a maintenance work order is dispatched.

[0104] The above-mentioned method for acquiring electricity in different time periods can be applied to the system 10 for acquiring electricity in different time periods, and the processing module 12 is also used to analyze the user's electricity meter code data; the acquisition module 11 is also used to issue an abnormal prompt and / or dispatch a maintenance work order when the difference between the two adjacent meter codes in the user's electricity meter code data is greater than a preset difference.

[0105] Specifically, in step 081 and step 082, the processing module 12 will also analyze the user's electricity meter code data to further ensure whether there are any abnormalities in the user's electricity meter code data, so as to ensure the accuracy of the calculation of electricity in different time periods. When the difference between the two adjacent meter codes in the user's electricity meter code data is greater than the preset difference, the acquisition module 11 issues an abnormality prompt and / or dispatches a maintenance work order to remind relevant personnel to perform on-site maintenance on the user's electricity meter 13. When the difference between the two adjacent meter codes in the user's electricity meter code data is equal to the preset difference, it can be determined that the user's electricity meter code data has an abnormality, or it can be determined that the electricity meter code data does not have an abnormality. When the difference between the two adjacent meter codes in the user's electricity meter code data is less than the preset difference, the subsequent step 091 is performed normally.

[0106] In summary, in the method, system, chip and computer-readable storage medium provided by the present application, after obtaining the user's electricity meter code data and the preset big data algorithm, the user's electricity meter code data is processed according to the preset big data algorithm to obtain the user's electricity consumption curve by time period. Fig.13 , Fig.13 The curve on the left is the curve manually calculated and fitted by relevant personnel. Fig.13 The curve on the right is the curve fitted by the method provided by this application, combined with Fig.13 It can be seen that the curve fitted using the method provided in the present application is smoother and more suitable for time-divided electricity consumption calculation. Therefore, the present application realizes the automatic fitting of the user's time-divided electricity consumption curve and improves the accuracy of the fitting curve. Afterwards, based on the user's time-divided electricity consumption curve, the user's time-divided electricity consumption can be obtained, thereby obtaining the user's accurate time-divided electricity consumption.

[0107] See also Fig.14 In some embodiments, the present application further provides a chip 100, the chip 100 includes a memory 101 and a processing unit 102, the memory 101 is configured to store a computer program, and the processing unit 102 implements the method in any of the above embodiments when executing the computer program.

[0108] For example, please combine Figure 2 When the processing unit 102 executes the computer program stored in the memory 101, the following method is implemented:

[0109] 02: Obtain the user's electricity meter data and the preset big data algorithm;

[0110] 03: Process the user's electricity meter data according to the preset big data algorithm to obtain the user's time-based electricity consumption curve; and

[0111] 091: According to the user's time-based electricity consumption curve, obtain the user's time-based electricity consumption.

[0112] For another example, when the processing unit 102 executes the computer program stored in the memory 101, the following method is implemented:

[0113] 01: Store the user's electricity meter data in the preset database.

[0114] For another example, when the processing unit 102 executes the computer program stored in the memory 101, the methods in 011, 0111, 0113, 021, 0231, 0232, 0251, 0252, 027, 031, 033, 035, 04, 041, 042, 043, 044, 045, 046, 05, 06, 07, 08 and 092 can also be implemented.

[0115] The chip 100 provided in the present application, after acquiring the user's electricity meter code data and the preset big data algorithm, processes the user's electricity meter code data according to the preset big data algorithm to obtain the user's time-divided electricity consumption curve, see Fig.13 , Fig.13 The curve on the left is the curve manually calculated and fitted by relevant personnel. Fig.13 The curve on the right is the curve fitted by the method provided by this application, combined with Fig.13 It can be seen that the curve fitted using the method provided in the present application is smoother and more suitable for time-divided electricity consumption calculation. Therefore, the present application realizes the automatic fitting of the user's time-divided electricity consumption curve and improves the accuracy of the fitting curve. Afterwards, based on the user's time-divided electricity consumption curve, the user's time-divided electricity consumption can be obtained, thereby obtaining the user's accurate time-divided electricity consumption.

[0116] See also Figure 2 and Fig.15 In some embodiments, the present application further provides a computer-readable storage medium 200 on which a computer program 202 is stored, and when the program is executed by a processor, the method in any of the above embodiments is implemented.

[0117] For example, when the computer program 202 is executed by the processor 20, the following method is implemented:

[0118] 02: Obtain the user's electricity meter data and the preset big data algorithm;

[0119] 03: Process the user's electricity meter data according to the preset big data algorithm to obtain the user's time-based electricity consumption curve; and

[0120] 091: According to the user's time-based electricity consumption curve, obtain the user's time-based electricity consumption.

[0121] For another example, when the computer program 202 is executed by the processor 20, the following method is implemented:

[0122] 01: Store the user's electricity meter data in the preset database.

[0123] For another example, when computer program 202 is executed by processor 20, it can also implement the methods in 011, 0111, 0113, 021, 0231, 0232, 0251, 0252, 027, 031, 033, 035, 04, 041, 042, 043, 044, 045, 046, 05, 06, 07, 08 and 092.

[0124] In the computer-readable storage medium 200 of the present application, after obtaining the user's electricity meter code data and the preset big data algorithm, the user's electricity meter code data is processed according to the preset big data algorithm to obtain the user's time-divided electricity consumption curve, see Fig.13 , Fig.13 The curve on the left is the curve manually calculated and fitted by relevant personnel. Fig.13 The curve on the right is the curve fitted by the method provided by this application, combined with Fig.13 It can be seen that the curve fitted using the method provided in the present application is smoother and more suitable for time-divided electricity consumption calculation. Therefore, the present application realizes the automatic fitting of the user's time-divided electricity consumption curve and improves the accuracy of the fitting curve. Afterwards, based on the user's time-divided electricity consumption curve, the user's time-divided electricity consumption can be obtained, thereby obtaining the user's accurate time-divided electricity consumption.

[0125] In the description of this specification, the descriptions with reference to the terms "certain embodiments", "in an example", "exemplarily", etc., mean that the specific features, structures, materials or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0126] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0127] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for obtaining power consumption in different time periods, characterized in that: The method comprises: Obtain the user's electricity meter data and preset big data algorithm; Processing the user's electricity meter data according to the preset big data algorithm to obtain the user's time-divided electricity consumption curve; and The time-divided electricity consumption of the user is obtained according to the time-divided electricity consumption curve of the user.

2. The method according to claim 1, characterized in that The method further comprises: The user's electricity meter data is stored in a preset database.

3. The method according to claim 2, characterized in that The storing of the user's electricity meter code data in a preset database includes: Under the preset data dispatching platform, the user's electricity meter code data is stored in a preset database using a preset data import tool and a preset distributed message queue.

4. The method according to claim 3, characterized in that: The preset database includes a distributed file system, a distributed data warehouse, and a column storage library. Under the preset data scheduling platform, the preset data import tool and the preset distributed message queue are used to store the user's electricity meter code data into the preset database, including: Under the preset data scheduling platform, the preset data import tool and the preset distributed message queue are used to store the user's electricity meter code data into the distributed file system; and According to the preset data distribution program, the preset data import tool and the preset distributed message queue are used to store the user's electricity meter code data stored in the distributed file system into the distributed data warehouse and the column storage library.

5. The method according to claim 1, characterized in that The user's electricity meter code data includes historical electricity meter codes and current electricity meter codes; the obtaining of the user's electricity meter code data includes: When receiving an application for suspending electricity use, reading historical electricity meter codes within a predetermined time window from a preset database; When the historical electricity meter code can be read within the predetermined time window, the frozen meter code of the day is obtained; In the case that the last table code in the historical electricity meter codes is greater than the frozen table code of the day, the last table code in the historical electricity meter codes is determined to be the electricity meter code of the day.

6. The method according to claim 5, characterized in that The step of obtaining the user's electricity meter code data further includes: When the last table code in the historical electricity meter code is smaller than the frozen table code of the day, the frozen table code of the day is determined to be the electricity meter code of the day.

7. The method according to claim 5, characterized in that The step of obtaining the user's electricity meter code data further includes: When the historical electricity meter code cannot be obtained within the predetermined time window, the electricity meter code for the day is recorded as 0.

8. The method according to claim 5, characterized in that The step of obtaining the user's electricity meter code data further includes: In the case that no application for suspending electricity use is received, the historical electricity meter code within a predetermined time window and the current electricity meter code of the user are read from the preset database to serve together as the electricity meter code data of the user.

9. The method according to claim 1, characterized in that: The preset big data algorithm includes an offline calculation algorithm executed by a big data offline calculation framework and a real-time calculation algorithm executed by a big data real-time calculation framework. The user's electricity meter code data includes the electricity meter code data of the day and the historical electricity meter code data. The user's electricity meter code data is processed according to the preset big data algorithm to obtain the user's time-divided electricity consumption curve, including: The big data offline computing framework uses the offline computing algorithm to process the historical electricity meter code data to obtain the user's historical electricity consumption curve by time period; The big data real-time computing framework uses the real-time computing algorithm to process the electricity meter data of the day to obtain the user's electricity consumption curve for the day by time period; and The time-divided electricity consumption curve of the user is obtained according to the current day electricity consumption curve and the historical electricity consumption curve.

10. The method according to claim 1, characterized in that Also includes: The time-divided electricity consumption curve of the user is verified.

11. The method according to claim 10, characterized in that The checking of the time-divided electricity consumption curve of the user includes: Get the frozen table code for the current month; Processing the freezing table code of the current month according to the preset big data algorithm to obtain the freezing curve of the current month; and The time-based electricity consumption curve of the user is checked to see whether it is abnormal based on the frozen curve of the current month.

12. The method according to claim 10, characterized in that The checking of the time-divided electricity consumption curve of the user includes: Obtain the power meter data of the power station; Processing the electricity meter data of the power station according to the preset big data algorithm to obtain the time-divided electricity consumption curve of the power station; and The time-division electricity consumption curve of the user is checked to see whether it is abnormal according to the time-division electricity consumption curve of the power station.

13. The method according to claim 1, characterized in that In the case where there is a zero value in the user's electricity meter code data, the steps of obtaining the user's electricity meter code data and the preset big data algorithm are re-executed, or the steps of storing the user's electricity meter code data in a preset database are re-executed.

14. The method according to claim 1, characterized in that Also includes: Obtain at least two frozen table codes; Processing the at least two frozen table codes according to the preset big data algorithm to obtain a frozen curve for verification; Whether the freezing function of the electric meter is abnormal is determined according to the time-division electricity consumption curve of the user and the freezing curve for calibration.

15. The method according to claim 1, characterized in that Also includes: Analyzing the electricity meter code data of the user; and When the difference between two adjacent meter codes in the user's electricity meter code data is greater than a preset difference, an abnormal prompt is issued and / or a maintenance work order is dispatched.

16. A time-division electricity calculation system, characterized in that: The system comprises: An acquisition module is used to obtain the user's electricity meter code data and a preset big data algorithm; A processing module, used for processing the user's electricity meter code data according to the preset big data algorithm to obtain the user's electricity consumption curve by time period; and The time-divided electricity consumption of the user is obtained according to the time-divided electricity consumption curve of the user.

17. The system according to claim 16, characterized in that The system further comprises: The acquisition module is used to store the user's electricity meter code data in a preset database.

18. The system according to claim 17, characterized in that The acquisition module is also used for: Under the preset data dispatching platform, the user's electricity meter code data is stored in a preset database using a preset data import tool and a preset distributed message queue.

19. The system according to claim 18, characterized in that The preset database includes a distributed file system, a distributed data warehouse, and a column storage library, and the acquisition module is also used for: Under the preset data scheduling platform, the preset data import tool and the preset distributed message queue are used to store the user's electricity meter code data into the distributed file system; and According to the preset data distribution program, the preset data import tool and the preset distributed message queue are used to store the user's electricity meter code data stored in the distributed file system into the distributed data warehouse and the column storage library.

20. The system according to claim 16, characterized in that The user's electricity meter code data includes historical electricity meter codes and current electricity meter codes; the acquisition module is also used for: When receiving an application for suspending electricity use, reading historical electricity meter codes within a predetermined time window from a preset database; If the historical electricity meter code can be read within the predetermined time window, obtaining the frozen meter code for the day; In the case that the last table code in the historical electricity meter codes is greater than the frozen table code of the day, the last table code in the historical electricity meter codes is determined to be the electricity meter code of the day.

21. The system according to claim 20, characterized in that The acquisition module is also used for: When the last table code in the historical electricity meter code is smaller than the frozen table code of the day, the frozen table code of the day is determined to be the electricity meter code of the day.

22. The system according to claim 20, characterized in that The acquisition module is also used for: When the historical electricity meter code cannot be obtained within the predetermined time window, the electricity meter code for the day is recorded as 0.

23. The system according to claim 20, characterized in that The acquisition module is also used for: In the case that no application for suspending electricity use is received, the historical electricity meter code within a predetermined time window and the current electricity meter code of the user are read from the preset database to serve together as the electricity meter code data of the user.

24. The system according to claim 16, characterized in that The preset big data algorithm includes an offline calculation algorithm executed by a big data offline calculation framework and a real-time calculation algorithm executed by a big data real-time calculation framework. The user's electricity meter code data includes the electricity meter code data of the day and the historical electricity meter code data. The processing module is also used for: Based on the big data offline computing framework, the offline computing algorithm is used to process the historical electricity meter code data to obtain the user's historical electricity consumption curve by time period; Based on the big data real-time computing framework, the real-time computing algorithm is used to process the electricity meter data of the day to obtain the user's electricity consumption curve for the day by time period; and The time-divided electricity consumption curve of the user is obtained according to the current day electricity consumption curve and the historical electricity consumption curve.

25. The system according to claim 16, characterized in that Also includes: The processing module is also used to check the time-divided electricity consumption curve of the user.

26. The system according to claim 25, characterized in that The processing module is also used for: Get the frozen table code for the current month; Processing the freezing table code of the current month according to the preset big data algorithm to obtain the freezing curve of the current month; The time-based electricity consumption curve of the user is checked to see whether it is abnormal based on the frozen curve of the current month.

27. The system according to claim 25, characterized in that The processing module is also used for: Obtain the power meter data of the power station; Processing the electricity meter data of the power station according to the preset big data algorithm to obtain the time-divided electricity consumption curve of the power station; The time-division electricity consumption curve of the user is checked to see whether it is abnormal according to the time-division electricity consumption curve of the power station.

28. The system according to claim 16, characterized in that When there is a zero value in the user's electricity meter code data, the acquisition module is also used to re-execute the steps of acquiring the user's electricity meter code data and the preset big data algorithm, or re-execute and store the user's electricity meter code data in a preset database.

29. The system according to claim 16, characterized in that Also includes: The acquisition module is further used to acquire at least two frozen table codes; The acquisition module is further used to process the at least two frozen table codes according to the preset big data algorithm to obtain a frozen curve for verification; The processing module is further used to determine whether the freezing function of the electric meter is abnormal according to the time-divided electricity consumption curve of the user and the freezing curve for calibration.

30. The system of claim 16, wherein: Also includes: The processing module is further used to analyze the user's electricity meter code data; and The acquisition module is further configured to issue an abnormality prompt and / or dispatch a maintenance work order when the difference between two adjacent meter codes in the user's electricity meter code data is greater than a preset difference.

31. A chip, characterized in that: The chip includes a memory and a processor, the memory is configured to store a computer program, and the processor implements the method according to any one of claims 1 to 15 when executing the computer program.

32. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method described in any one of claims 1 to 15 is implemented.

Images