Electric meter intelligent dynamic complementary reading method and equipment based on big data analysis

Abstract

According to the electric meter intelligent dynamic complementary reading method and device based on big data analysis, the method comprises the steps that historical data are analyzed, the reading data items and the reading priority of an electric meter are intelligently adjusted, task data sets needing complementary reading are combined into a composite task according to requirements, automatic execution is executed in the non-busy time period, and the intelligent dynamic complementary reading efficiency of the electric meter is improved. And the data acquisition efficiency and accuracy are improved. A master station system is used for issuing a configuration scheme and a task to terminal equipment, data items of different electricity meters are sorted, and deep mining and analysis are carried out based on collected data within a past set time (flexible adjustment can be carried out according to specific field requirements); and a set of personalized complementary reading configuration strategy suitable for various ammeters is deeply combed and constructed, so that intelligent dynamic complementary reading is realized. The measure not only effectively guarantees the basic data support of big data processing, but also assists a decision application level to obtain more accurate and effective results.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power data collection, and in particular to a method and device for intelligent dynamic supplementary reading of electric meters based on big data analysis. Background Art

[0002] With the rapid evolution of big data technology, the requirements of smart grid for big data processing capabilities have been raised to an unprecedented level. The core task is to ensure that the huge amount of electric energy data can be captured and safely stored in real time and efficiently through a variety of collection terminals. However, in the current environment, facing a variety of meter types - including but not limited to high-precision three-phase meters, high-precision single-phase meters, low-precision three-phase meters, low-precision single-phase meters, Internet of Things three-phase meters, Internet of Things single-phase meters, microgrid power generation high-precision three-phase meters, microgrid power generation high-precision single-phase meters, microgrid power generation ordinary three-phase meters, microgrid power generation ordinary single-phase meters and other energy metering tools such as water, gas, heat, etc. - there are two key problems in the data collection process:

[0003] 1) Differentiation of data items and personalized supplementary reading requirements: The data attributes required to be collected by various types of meters vary greatly. If the supplementary reading specifications in the DL / T 698.45 protocol issued by the State Grid are followed, specific supplementary reading plans and tasks must be independently designed and executed for each meter. At the same time, considering the actual conditions of local power grids and personalized needs, these supplementary reading plans must be regionally adaptable and planned and deployed in a customized manner.

[0004] 2) Reading integrity and dynamic change challenges: Due to the complex and changeable power grid environment, it is impossible to ensure that all types of meters can fully and accurately capture data in the same reading cycle. At the same time, with the evolution of business forms and technological innovations, the actual reading needs will continue to change, and the data items required to be collected may also be adjusted. This requires that the supplementary reading plans and tasks have a high degree of flexibility and instant update mechanism to adapt to the current changes in data collection needs. Summary of the invention

[0005] The present invention provides an intelligent dynamic supplementary reading method, device and storage medium for electric meters based on big data analysis, which can solve at least one of the technical problems in the background technology.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for intelligent dynamic supplementary reading of electric meters based on big data analysis includes the following steps:

[0008] S1. First, the intelligent data collection terminal needs to sort out the historical reading activity records of each independent meter. This process is based on the characteristics of the carrier channel and comprehensively considers factors such as the reading success rate and time consumption, so as to calculate the reading priority index of each meter. This step is the basis for the subsequent intelligent reading strategy formulation, ensuring the pertinence and effectiveness of subsequent operations.

[0009] S2. After obtaining the reading priority of each meter, the intelligent data collection terminal intelligently analyzes the list of meters and their data items to be read according to the reading plan and task requirements issued by the master station, and integrates these meters into an intelligent supplementary reading plan list as needed in combination with the reading priority index calculated previously. This ensures that when executing tasks, the reading priority order of the meters is followed, thereby improving reading efficiency.

[0010] S3. Then, for the list of meters that have been sorted out, the intelligent data collection terminal deeply mines and summarizes the historical data of each meter according to the data requirements specified by the master station. By comparing the theoretical data point distribution with the actual stored historical data, the system can accurately identify the specific missing data point time period and the corresponding data item content between the past set time and the current moment. The analysis at this stage is not limited to determining the missing time period, but also further refines the specific data items missing in each cycle to enable accurate supplementary copying.

[0011] S4. Finally, after completing the above preparations, the intelligent data collection terminal will automatically execute the supplementary meter reading plan and tasks during idle time without affecting the normal meter reading tasks. The priority of these supplementary meter reading tasks is set lower than any pre-configured tasks, so they can be flexibly interrupted by higher priority tasks, ensuring the overall operation efficiency and data integrity of the system.

[0012] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0013] It can be seen from the above technical solution that the present invention aims to improve the completeness and success rate of meter data collection by introducing intelligent scheduling strategy algorithms and task aggregation. Specifically, by analyzing historical data, intelligently adjusting the meter reading data items and reading priorities, and merging the task data sets that need to be supplemented into a composite task according to demand, and automatically executing it during non-busy hours, thereby improving the efficiency and accuracy of data collection.

[0014] The intelligent dynamic supplementary reading method and device of the electric meter based on big data analysis of the present invention uses the master station system to send configuration plans and tasks to the terminal equipment, sorts out the data items of different electric meters, and conducts in-depth mining and analysis based on the data collected within the past set time (which can be flexibly adjusted according to the specific needs of the site), deeply combs and builds a set of personalized supplementary reading configuration strategies suitable for various types of electric meters, and realizes intelligent dynamic supplementary reading. This measure not only effectively guarantees the basic data support for big data processing, but also helps to achieve more accurate and effective results at the decision-making application level.

[0015] Specifically, the present invention proposes a personalized supplementary reading strategy applicable to various types of electric meters, realizing intelligent dynamic supplementary reading. Compared with the prior art, the main advantages and beneficial effects are as follows:

[0016] a. Comprehensiveness and success rate improvement: The intelligent supplementary reading strategy proposed in the present invention aims to solve the problem of meter data loss in the user-side terminal. By intelligently analyzing and integrating meter reading tasks, the completeness and success rate of curve data collection are improved.

[0017] b. Refined task planning: Develop personalized intelligent supplementary reading plans for different electricity meters. Combined with the task requirements of the master station, the specific cycles and data items that need to be supplemented are clarified, which improves the precision of meter reading task allocation.

[0018] c. Intelligent detection of missing data: Through the detailed division of the benchmark period and the data integrity verification mechanism, the time period and specific content of missing data points within 24 hours (which can be flexibly adjusted according to specific on-site needs) can be quickly and accurately located, significantly enhancing the accuracy of missing data analysis.

[0019] d. Dynamic priority management: The system conducts statistical analysis on the historical reading records of each meter, calculates the reading priority in real time according to the characteristics of the carrier channel, and arranges the meter reading sequence reasonably, which can not only avoid the impact of poor communication meters on the overall meter reading efficiency, but also reduce invalid waiting time.

[0020] e. Carrier environment adaptability: Introduce a meter reading priority coefficient to dynamically adjust the meter reading strategy based on the carrier environment quality feedback from the meter reading equipment. When the carrier environment is poor, the meter reading times can be increased or the meter reading strategy can be adjusted automatically to effectively deal with environmental interference during data collection and further improve data collection integrity.

[0021] f. Efficient use of resources: The make-up copying task is performed during idle time, which not only ensures the priority execution of normal pre-configured tasks, but also has the ability to flexibly schedule tasks. The make-up copying task can be interrupted at any time according to the new configuration of the master station to achieve effective use of resources.

[0022] In summary, the present invention innovatively solves the problem of integrity of curve data collection in the power system and realizes intelligent, efficient and reliable data collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flowchart of an embodiment of the present invention;

[0024] Figure 2 This is a flow chart of intelligent sorting and task aggregation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0026] like Figure 1 As shown, the method for intelligent dynamic supplementary reading of electric meters based on big data analysis described in this embodiment includes the following steps:

[0027] Step 1. Priority index calculation: At the beginning, the intelligent data collection terminal will conduct a detailed historical reading activity record sorting for each independent meter and calculate the reading priority index of each meter; based on the characteristics of the carrier channel, the terminal conducts in-depth analysis of key factors such as reading success rate and reading time, and scientifically calculates the reading priority index of each meter. The higher this index is, the higher the reading priority of the meter; otherwise, it indicates that its priority is lower. The core of this strategy is to prevent meters with poor communication quality from affecting the overall reading efficiency, thereby reducing unnecessary waiting time and improving system operation efficiency.

[0028] Step 2: Generate an intelligent supplementary reading plan: After obtaining the reading priority index of each meter, the intelligent data collection terminal will receive and parse the reading plan and task requirements issued by the master station. Based on these requirements, the terminal intelligently analyzes the list of meters and their data items to be read, and combines the reading priority index obtained in the first step, taking the electric energy meter as the basic unit, to generate an intelligent supplementary reading plan list that meets actual needs. This step ensures the efficiency and pertinence of the supplementary reading work.

[0029] Step 3: Locate data requirements: Next, the intelligent data collection terminal digs into the historical record data of each meter according to the supplementary reading plan generated in the previous step and the data requirements specified by the master station. By comparing the theoretical data point distribution with the actual stored historical data, the terminal can accurately find the specific missing data point time period and its corresponding data item content between the past 24 hours and the current moment. The detailed analysis of this link helps to accurately identify and locate the missing time period and data items of each meter, providing detailed guidance for the next step of supplementary reading.

[0030] Step 4: Perform supplementary meter reading during non-busy hours: After completing all the previous preparations, the intelligent data collection terminal will execute the automatically generated supplementary meter reading scheme and tasks during non-busy hours (i.e. after the normal meter reading task is completed). The priority of these supplementary meter reading tasks is set lower than any pre-configured tasks, which means that they can be interrupted by higher priority tasks at any time, thereby ensuring the effective allocation and utilization of system resources.

[0031] Through the above process, the system can not only significantly enhance the effectiveness and accuracy of meter data collection, but also ensure the overall integrity and reliability of the data, thus providing a solid foundation for data analysis and management decisions.

[0032] The following are detailed descriptions:

[0033] (1) Description of intelligent scheduling strategy algorithm (priority scheduling algorithm based on historical efficiency):

[0034] The system uses the startup time of the intelligent data collection terminal as the benchmark starting point. On this basis, the system will collect and analyze the operation data of the electric meter in the previous 24 hours (which can be flexibly adjusted according to the specific needs of the site) as the initial operation parameters of each electric meter. In this process, the focus will be on counting the number of successful readings of each electric meter in the past continuous reading cycle and the actual time consumed for each reading. Based on these key initial parameters, the priority calculation formula set by the intelligent scheduling strategy algorithm can be used to determine the priority order of each electric meter.

[0035] The goal of this algorithm is to determine the meter reading order based on the historical meter reading efficiency, so as to give priority to those meters with better communication quality, thereby maximizing the overall meter reading efficiency. The core idea of ​​the intelligent scheduling strategy algorithm is to introduce the time variable t. The steps of the algorithm are as follows:

[0036] 1. Data collection: Collect the historical meter reading success rate Si(t) and average response time and average meter reading time Ti(t) for each meter over the past period of time.

[0037] 2. Establish a priority queue: Calculate a priority score for each meter. The scoring formula is as follows.

[0038]

[0039] Where t0 is the starting time point, w s is the weighted copying success rate, w t Weighting for response time, which can be adjusted as needed;

[0040] 3. Establish a priority queue: Based on the scoring results, sort all the meters by Pi(t) to form a priority queue Q(t).

[0041] (2) Task aggregation (step 2 to generate an intelligent supplementary copying plan):

[0042] 1. Parse the master station instructions

[0043] Receiving instructions: The intelligent data collection terminal first analyzes the reading plan and task requirements from the master station. These requirements may include the reading frequency within a specific time period, the data items to be read (such as electrical energy, voltage, current, etc.), the reading time window, etc.

[0044] Parsing instructions: The terminal parses the received instructions and extracts key information, such as reading schedule, data item list, reading frequency, etc., in preparation for subsequent analysis.

[0045] 2. Intelligent analysis of copying and reading needs

[0046] Task requirement analysis: The terminal analyzes the specific objectives of the reading task according to the requirements of the master station. For example, if the master station requires reading once every hour in a day, the terminal needs to ensure that each meter has at least one successful reading record every hour.

[0047] Meter list generation: The terminal generates a list of meters to be read according to the requirements of the master station. The list includes all the meter IDs that need to be read in this reading task.

[0048] 3. Combined Priority Index

[0049] Priority sorting: The terminal sorts the meter list according to the reading priority index calculated in the first step. Meters with high priority will be placed at the front and read first.

[0050] Weight adjustment: If the master station's requirements include special weights for certain meters (for example, meters for certain important customers), the terminal will give these meters higher priority when sorting.

[0051] 4. Data item requirements matching

[0052] Data item matching: The terminal determines the data items that need to be read for each meter according to the requirements of the master station. For example, some meters may only need to read the electrical energy, while other meters need to read the complete data items (electrical energy, voltage, current, etc.). Some meters may be gas meters or water meters, which need to read different data items, etc.

[0053] Data item priority: The terminal can also assign a priority to each data item based on its importance. For example, electrical energy may be the most important data item, followed by voltage and current.

[0054] 5. Generate a list of intelligent supplementary copying plans

[0055] Plan list creation: Based on the above analysis results, the terminal creates an intelligent supplementary reading plan list, which includes the ID of each meter, the reading time window, the data items that need to be read, and the priority information.

[0056] Time window allocation: The terminal allocates reading time windows reasonably according to the priority of the meter and the requirements of the data items. For example, meters with high priority can be processed first at the beginning of the reading task, while meters with lower priority can be scheduled for a later time period.

[0057] 6. Plan list optimization

[0058] Conflict detection: The terminal checks whether there are time conflicts or other unreasonable aspects in the generated supplementary copying plan list. If there are conflicts, adjustments will be made.

[0059] Dynamic adjustment: The terminal also has dynamic adjustment capabilities. If new requirements or problems are found during the execution process, the supplementary copying plan list can be adjusted in real time.

[0060] 7. Preparation for plan execution

[0061] Task scheduling: The terminal schedules the supplementary copying tasks according to priority and time window based on the final generated intelligent supplementary copying plan list.

[0062] (3) Data requirement positioning (Step 3 Data requirement positioning):

[0063] Historical data acquisition: The intelligent data collection terminal first obtains all historical reading records of each meter within the specified time range from the local database. These records include but are not limited to changes in parameters such as electrical energy, voltage, and current.

[0064] Theoretical data point generation: Based on the requirements of reading frequency and time interval, the intelligent data collection terminal generates a theoretical data point distribution model. The model defines how many data points should be within a given time range and at what time these data points should appear.

[0065] Data comparison and analysis: The terminal compares the actual stored historical data with the theoretical data point distribution model to identify which data points were not collected on time. Through this comparison and analysis, breakpoints and anomalies in the actual data can be found.

[0066] Missing data location: Once missing data is found, the intelligent data collection terminal will accurately locate the specific time period of each missing data point based on the timestamp of the missing data. For example, if the data of a meter between 8:00 and 9:00 yesterday morning was not recorded, then this period will be marked as the time period that needs to be supplemented.

[0067] Data item confirmation: In addition to time positioning, the terminal also needs to confirm the specific data items involved in each missing time period. For example, in some time periods, only the electric energy may be missing, while other data items such as voltage and current are collected normally. Through detailed analysis, more specific guidance can be provided for subsequent supplementary copying tasks.

[0068] Periodicity analysis: The terminal will further analyze whether the missing data shows a certain regularity or periodicity to better understand the reasons for the missing data. For example, some meters may frequently experience communication failures during a specific period of time, resulting in data loss. This periodicity analysis helps to develop more effective preventive measures.

[0069] For periodicity analysis, Fourier transform is used to identify periodic patterns in time series with missing data. Assuming there is a time series M with missing data, discrete Fourier transform (DFT) can be applied to identify periodic features:

[0070]

[0071] Where m[n] is the state (presence or absence) of the nth data point in the time series M, N is the length of the time series, and X[k] is the result of the Fourier transform. By analyzing the spectrum of X[k], the periodic characteristics of the missing data can be identified.

[0072] (4) Compensation task planning (step 4): Based on the above analysis results, the intelligent data collection terminal will formulate a detailed compensation plan for each missing data point. This includes determining details such as how to make up the missing data point and the priority of the missing data point. This information will be integrated into the intelligent compensation plan list as the basis for the next step.

[0073] During the meter reading process, the priority of the meter is dynamically adjusted according to the actual meter reading situation. If a high-priority meter suddenly becomes difficult to read, its priority is lowered; otherwise, its priority is increased.

[0074] Execute copy:

[0075] Task priority setting: The intelligent data collection terminal sets the priority of the make-up task to be lower than any pre-configured task. This means that the make-up task can be interrupted by a higher priority task (such as an emergency reading request or other important task) during execution. This design ensures that the system can respond quickly to other important events when necessary, maintaining flexibility and responsiveness.

[0076] Identification of idle periods: The intelligent data collection terminal identifies idle periods during the day by analyzing the schedule of daily meter reading tasks. These periods are usually after the normal meter reading tasks are completed, when the system load is low, such as at night or during low electricity consumption periods. Selecting these periods for supplementary reading can minimize the impact on normal user use.

[0077] Supplementary reading task scheduling: After identifying the idle time period, the intelligent data collection terminal will automatically schedule the supplementary reading tasks according to the previously generated supplementary reading plan list. The terminal will give priority to those meter supplementary reading tasks with higher priority to ensure that important data can be supplemented in time.

[0078] Task execution and monitoring: Once the supplementary copying mode is entered, the intelligent data collection terminal will execute the supplementary copying tasks one by one according to the predetermined supplementary copying plan. During the execution process, the terminal will monitor each copying activity in real time to ensure that the copying is successful and record the relevant data. If a communication failure or other problem occurs, the terminal will automatically try to retry and record the reason for the failure for subsequent analysis.

[0079] Dynamic adjustment and optimization: During the re-copying process, the intelligent data collection terminal will dynamically adjust the re-copying plan according to the actual situation. For example, if a new high-priority task appears within a certain period of time, the terminal will suspend the current re-copying task and execute the new task first. In addition, the terminal will continuously optimize the re-copying strategy based on the actual effect of the re-copying to improve the overall operating efficiency of the system.

[0080] Task completion and reporting: When all supplementary reading tasks are completed, the intelligent data collection terminal will update the meter data status in the database and generate supplementary reading task completion information. The information will include a list of completed tasks, supplementary reading data items, supplementary reading time, and any problems encountered. The information will provide data support for the next round of supplementary reading.

[0081] Combination Figure 2 , the following are examples:

[0082] Basic environment settings

[0083] Simulation scenario: A large community has 650 electricity meters, including:

[0084] 300 three-phase electricity meters, 200 single-phase electricity meters, 50 water meters, 50 gas meters, and 50 IoT;

[0085] Simulation time: reading attempts of common meters every 15 minutes in the past 24 hours (can be adjusted flexibly according to specific needs on site).

[0086] Data items:

[0087] Three-phase energy meter: energy, voltage, current, power factor;

[0088] Single-phase energy meter: energy, voltage, current;

[0089] Water meter: flow rate;

[0090] Gas meter: flow rate;

[0091] Device Type:

[0092] Internet of Things (IoT) meters: with remote control and monitoring capabilities, enabling more advanced data processing and analysis.

[0093] Ordinary meter: only supports basic reading functions.

[0094] Step 1: Priority Index Calculation

[0095] Historical record sorting: The intelligent data collection terminal collects the reading records of each meter in the past week, including reading time, reading results, time consumption, signal strength and other information.

[0096] Key factors analysis:

[0097] Reading success rate: 85% on average, but some meters have a success rate of less than 60%.

[0098] Reading time: 10 seconds on average, but some meters take more than 30 seconds.

[0099] Signal strength: The communication quality is evaluated by the signal strength (RSSI).

[0100] Priority index calculation:

[0101] Each meter is given a priority index ranging from 0 to 100. The highest index is 90 and the lowest is 20.

[0102] Step 2: Generate an intelligent supplementary copying plan

[0103] Copy and read plan: The requirements issued by the master station are:

[0104] Three-phase energy meter: 4 successful readings per hour.

[0105] Single-phase energy meter: 4 successful readings per hour.

[0106] Water meter: One successful reading record per day.

[0107] Gas meter: successful reading record once a month.

[0108] IoT meter: real-time data monitoring every 15 minutes.

[0109] Analysis results:

[0110] The terminal generates a supplementary reading plan list based on the reading priority index, among which the meters with higher priority are given priority for supplementary reading.

[0111] IoT meters have a higher priority because they require real-time monitoring. Even if they have a higher historical reading success rate, they will be given priority.

[0112] Task Type:

[0113] 15-minute task: Real-time data monitoring of IoT meters.

[0114] Daily task: Daily reading of water meters.

[0115] Monthly task: Monthly reading of gas meters.

[0116] Step 3: Identify data requirements

[0117] Theoretical data points:

[0118] Three-phase energy meter: There should be 96 data points in 24 hours (one every 15 minutes).

[0119] Single-phase energy meter: There should be 96 data points in 24 hours (one every 15 minutes).

[0120] Water meter: Should have 1 data point in 24 hours.

[0121] Gas meter: should have 1 data point in a month.

[0122] IoT meter: There should be 96 data points in 24 hours (one every 15 minutes).

[0123] Actual data points:

[0124] Three-phase energy meter: There are actually 85 data points.

[0125] Single-phase energy meter: There are actually 88 data points.

[0126] Water meter: There are actually 0.8 data points (i.e., no successful readings were made for two days).

[0127] Gas meter: There are actually 0.9 data points (i.e., no successful reading for one month).

[0128] IoT meter: There are actually 94 data points.

[0129] Missing data location:

[0130] By comparing the theoretical data points with the actual data points, it was found that the three-phase electricity meter had 11 missing data points, concentrated between 10 pm and 2 am.

[0131] The single-phase energy meter has 8 missing data points, concentrated between 6 and 8 in the morning.

[0132] The water meter has two days of missing data.

[0133] The gas meter has one month of missing data.

[0134] The IoT meter has 2 missing data points, concentrated between 5pm and 6pm.

[0135] Data item confirmation:

[0136] The missing data points of the three-phase energy meter mainly involve the energy and current data.

[0137] The missing data points of single-phase energy meters mainly involve energy data.

[0138] The missing data points for water meters mainly concern flow data.

[0139] The missing data points for gas meters mainly involve flow data.

[0140] The missing data points for IoT meters mainly involve electrical energy and voltage data.

[0141] Step 4: Perform supplementary copying during non-peak hours

[0142] Execution of copying tasks:

[0143] Start the supplementary reading task during the idle period, giving priority to the meters with the highest priority.

[0144] Task monitoring:

[0145] The success rate of the make-up copying task was 95%, and the average time taken was 12 seconds.

[0146] At 3 a.m., due to a temporary emergency task (such as the power company executing a penetration task), the supplementary copying task was temporarily interrupted and continued after the emergency task was completed.

[0147] In summary, the intelligent dynamic supplementary reading method of electric meter based on big data analysis according to the embodiment of the present invention has the following specific advantages:

[0148] Improve system efficiency: Through priority index calculation, frequent attempts on meters with poor communication quality are reduced, saving a lot of waiting time.

[0149] Data integrity: Through precise positioning of data requirements, timely supplementation of missing data is ensured, thus improving the overall integrity and reliability of data.

[0150] Flexibility: Priority setting of make-up copy tasks allows the system to quickly respond to other important tasks when necessary, enhancing the flexibility and responsiveness of the system.

[0151] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0152] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0153] In another embodiment provided in the present application, a computer program product including instructions is also provided. When the computer is run on a computer, the computer executes any one of the intelligent dynamic supplementary reading methods for electric meters based on big data analysis in the above embodiments.

[0154] It is understandable that the system, device and storage medium provided in the embodiments of the present invention correspond to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above methods.

[0155] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0156] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0157] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0158] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent dynamic supplementary reading of electric meters based on big data analysis, characterized in that: The following steps are included: S1. First, the intelligent data collection terminal sorts out the historical reading activity records of each independent meter and calculates the reading priority index of each meter; S2. After obtaining the reading priority of each meter, the intelligent data collection terminal intelligently analyzes the list of meters and their data items to be read according to the reading plan and task requirements issued by the master station, and integrates these meters into an intelligent supplementary reading plan list as needed based on the reading priority index calculated previously; S3. Subsequently, for the sorted meter list, the intelligent data collection terminal deeply mines and summarizes the historical record data of each meter according to the data requirements specified by the master station; by comparing the theoretical data point distribution with the actual stored historical data, the system can accurately identify the specific missing data point time period and the corresponding data item content between the past set time and the current time; S4. Finally, the intelligent data collection terminal will automatically execute supplementary meter reading plans and tasks during idle time without affecting normal meter reading tasks; the priority of these supplementary meter reading tasks is set lower than any pre-configured tasks.

2. The method for intelligent dynamic supplementary reading of electric meters based on big data analysis according to claim 1 is characterized in that: Step S1 specifically includes: S11. Data collection: Collect the historical meter reading success rate for each meter over a period of time Average response time and average copy time ; S12. Establish a priority queue: Calculate a priority score for each meter. The scoring formula is as follows: in is the starting time point, is the weighted copying success rate, Weighting for response time, which can be adjusted as needed; S13, establish a priority queue: according to the scoring results, all the meters are ranked Sorting to form a priority queue .

3. The method for intelligent dynamic supplementary reading of electric meters based on big data analysis according to claim 2 is characterized in that: Step S2 specifically includes: S21. Analyze the master station command Receiving instructions: The intelligent data collection terminal first analyzes the reading scheme and task requirements from the master station; these tasks include the reading frequency within a specific time period, the data items to be read, and the reading time window; Parsing instructions: The terminal parses the received plans and tasks and extracts key information, including the reading schedule, data item list, and reading frequency, in preparation for subsequent analysis; S22, Intelligent analysis of copying and reading needs Task requirement analysis: The terminal analyzes the specific objectives of the copying task according to the requirements of the master station; Meter list generation: The terminal generates a list of meters that need to be read according to the requirements of the master station; S23, combined priority index Priority sorting: The terminal sorts the meter list according to the reading priority index calculated in the first step. Meters with high priority will be placed in front and read first. Weight adjustment: If the master station's requirements include special weights for certain meters, the terminal will give these meters higher priority when sorting; S24, Data item requirements matching Data item matching: The terminal determines the data items that need to be read for each meter according to the requirements of the master station; Data item priority: The terminal assigns a priority to each data item based on its importance; S25. Generate intelligent supplementary copying plan list Plan list creation: The terminal creates an intelligent supplementary copying plan list based on the above analysis results; The list contains the ID of each meter, the reading time window, the data items to be read, and the priority information; Time window allocation: The terminal reasonably allocates the reading time window according to the priority of the meter and the requirements of the data items; S26. Plan list optimization Conflict detection: The terminal checks whether there are time conflicts or other unreasonable places in the generated supplementary copying plan list. If there are conflicts, adjustments will be made; Dynamic adjustment: The terminal also has the ability to dynamically adjust. If new requirements or problems are found during the execution process, the supplementary copying plan list can be adjusted in real time; S27. Preparation for Plan Implementation Task scheduling: The terminal schedules the supplementary copying tasks according to priority and time window based on the final generated intelligent supplementary copying plan list.

4. The method for intelligent dynamic supplementary reading of electric meters based on big data analysis according to claim 3 is characterized in that: Step S3 specifically includes: Historical data acquisition: The intelligent data collection terminal first obtains all historical reading records of each meter within the specified time range from the local database; Theoretical data point generation: Based on the requirements of reading frequency and time interval, the intelligent data collection terminal generates a theoretical data point distribution model; the model defines how many data points should be within a given time range and at which time these data points should appear; Data comparison and analysis: The terminal compares the actual stored historical data with the theoretical data point distribution model to identify which data points have not been collected on time. Through this comparison and analysis, breakpoints and anomalies in the actual data can be found. Missing data location: Once missing data is found, the intelligent data collection terminal will accurately locate the specific time period of each missing data point based on the timestamp of the missing data; Data item confirmation: In addition to the time location, the terminal also confirms the specific data items involved in each missing time period; Periodicity analysis: The terminal will further analyze whether the missing data shows a certain regularity or periodicity, so as to better understand the reasons for the missing data; For periodicity analysis, Fourier transform is used to identify periodic patterns in time series with missing data; suppose there is a time series M with missing data, discrete Fourier transform (DFT) is applied to identify periodic features: Where m[n] is the presence or absence of the nth data point in the time series M, N is the length of the time series, and X[k] is the result of the Fourier transform; by analyzing the spectrum of X[k], the periodic characteristics of the missing data can be identified.

5. The method for intelligent dynamic supplementary reading of electric meters based on big data analysis according to claim 4 is characterized in that: Step S4 specifically includes: Based on the analysis results of step S3, the intelligent data collection terminal will formulate a detailed supplementary copying plan for each missing data point, including determining how to make up the missing data point and the priority of the missing data point. This information will be integrated into the intelligent supplementary copying plan list as the basis for the next action; During the meter reading process, the priority of the meter is dynamically adjusted according to the actual meter reading situation. If a high-priority meter suddenly becomes difficult to read, its priority is lowered; otherwise, its priority is increased. Task priority setting: The intelligent collection terminal sets the priority of the supplementary copying task to be lower than any pre-configured task; Idle time period identification: The intelligent data collection terminal identifies idle time periods during the day by analyzing the schedule of daily reading tasks; Supplementary reading task scheduling: After identifying the idle time period, the intelligent data collection terminal will automatically schedule the supplementary reading tasks according to the previously generated supplementary reading plan list. The terminal will give priority to those meter supplementary reading tasks with higher priority to ensure that important data can be supplemented in time; Task execution and monitoring: Once the supplementary copying mode is entered, the intelligent data collection terminal will execute the supplementary copying tasks one by one according to the predetermined supplementary copying plan. During the execution process, the terminal will monitor each copying activity in real time to ensure the success of the copying and record the relevant data. If there is a communication failure or other problems, the terminal will automatically try to retry and record the failure reason for subsequent analysis; Task completion and reporting: When all supplementary reading tasks are completed, the intelligent data collection terminal will update the meter data status in the database and generate supplementary reading task completion information. The information will include a list of completed tasks, supplementary reading data items, supplementary reading time, and related problems encountered.

6. The method for intelligent dynamic supplementary reading of electric meters based on big data analysis according to claim 5 is characterized in that: The idle period in step S4 refers to a period when the system load is low after the normal meter reading task is completed, including nighttime or low-peak electricity consumption period.

7. The method for intelligent dynamic supplementary reading of electric meters based on big data analysis according to claim 5 is characterized by: Step S4 also includes dynamic adjustment and optimization: during the supplementary copying process, the intelligent data collection terminal will dynamically adjust the supplementary copying plan according to the actual situation.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the computer program is executed by the processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

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