Low-voltage meter state monitoring system and method based on Internet of Things

Abstract

The invention relates to the technical field of electric power monitoring, in particular to a low-voltage meter state monitoring system and method based on the Internet of Things, which is characterized in that a power consumption floating value of each minimum monitoring unit in a past monitoring period is obtained through calculation, the abnormality of an electric energy meter is judged according to an abnormal power consumption increment, and meanwhile, a numerical value is corrected through a discrete value; and according to the corrected numerical value, polling is carried out on the transformer area electric energy meter, so that polling is carried out in a targeted manner. The line loss rate obtained through calculation is compared with the actual line loss rate, the severity degree of abnormity of the transformer area electric energy meters in the transformer area is evaluated, the number of the transformer area electric energy meters needing to be inspected is adjusted in real time according to the obtained data, and compared with a preset inspection number and traversal inspection, the number of the transformer area electric energy meters needing to be inspected is adjusted in real time. The method can reduce the detection number and the detection cost, reduces the missing detection of abnormal electric energy meters as much as possible, and guarantees the accuracy and safety of electric energy detection in a transformer area.

Description

Technical Field

[0001] The present invention relates to the technical field of power monitoring, and is a low-voltage meter status monitoring system and method based on the Internet of Things. Background Art

[0002] With the continuous advancement of the construction of smart grids, the power system is developing towards a highly intelligent and automated direction. In this huge power system, as a key device at the end of the power grid directly facing users, the stable operation of low-voltage electric meters plays a crucial role in power supply quality and user electricity safety.

[0003] The accurate measurement of electricity consumption by low-voltage electric meters is an important basis for power enterprises to settle electricity bills. If a low-voltage electric meter fails or the measurement is inaccurate, it will not only cause economic losses to power enterprises, but may also lead to users' doubts about electricity bills, affecting the harmonious relationship between power supply and use parties. At the same time, some abnormally operating low-voltage electric meters may have safety hazards such as electric leakage and overheating, seriously threatening the life and property safety of users. Therefore, regular inspections of low-voltage electric meters to timely detect and handle abnormal situations have become a necessary measure to ensure power supply quality and user electricity safety.

[0004] However, the number of low-voltage electric meters is extremely large. In large residential communities or commercial areas in cities, there are often thousands of low-voltage electric meters operating simultaneously. The traditional status monitoring of low-voltage electric meters mainly relies on manual inspection methods, which have many drawbacks. First, the efficiency of manual inspection is low. Staff need to check each household and each electric meter one by one, which not only consumes a lot of time but also is prone to omissions during the inspection process. Second, the cost of manual inspection is high. It involves various costs such as labor costs and transportation costs. In the long run, this is a significant expense for power enterprises. Third, the real-time performance of manual inspection is poor. Since it is impossible to obtain the operation data of electric meters in real time, it is very difficult to timely detect sudden failures of electric meters. Often, it is only after users find abnormal electricity consumption or the electric meter failure has caused serious consequences that fault investigation and repair are carried out, which greatly reduces the reliability and stability of power supply.

[0005] In addition, the development of smart grids has put forward higher requirements for the monitoring and management of the power system. Smart grids need to realize the real-time collection, analysis, and processing of power data in order to quickly respond to various changes in the power system and achieve the optimal allocation of power resources. However, the traditional manual inspection method cannot meet this requirement and is difficult to adapt to the development pace of smart grids. Summary of the Invention

[0006] The present invention provides a low-voltage meter status monitoring system and method based on the Internet of Things, which overcomes the deficiencies of the above-mentioned prior art and can effectively solve the problems of low efficiency, high cost, and poor real-time performance in the status monitoring of low-voltage electric meters in the prior art, which mainly relies on manual inspections.

[0007] One of the technical solutions of the present invention is achieved through the following measures: A low-voltage meter status monitoring system based on the Internet of Things, including:

[0008] A total power meter for the substation area, which is used to monitor the total power consumption in the substation area;

[0009] A substation area power meter for each minimum monitoring unit, which is used to monitor the power consumption of each minimum monitoring unit;

[0010] A data storage unit, which is used to store the power consumption information collected by the total power meter for the substation area and the substation area power meters;

[0011] A processor, which is used to analyze and identify abnormal substation area power meters and determine the substation area power meters that need to be inspected and corrected;

[0012] Among them, the total power meter for the substation area and the substation area power meters are respectively communicatively connected to the processor through the Internet of Things, and the data storage unit is electrically connected to the processor; the total power meter for the substation area and the substation area power meters respectively transmit the collected total power consumption data and sub-user power consumption data to the processor, and the processor analyzes and identifies abnormal substation area power meters based on the historical data stored in the data storage unit.

[0013] The following is a further optimization or / and improvement of one of the above-mentioned technical solutions of the present invention:

[0014] The above-mentioned processor may include an abnormality recognition module, which is used to generate a power consumption change trend graph based on the historical and current power consumption data of each minimum monitoring unit, and identify abnormal substation area power meters with abnormal fluctuations by analyzing the dispersion degree of the slopes in the trend graph.

[0015] The above-mentioned processor may also include an inspection priority determination module, which is used to calculate an optimization comparison coefficient according to the power consumption fluctuation amplitude and historical power consumption stability of the abnormal substation area power meters, and determine the inspection priority of each abnormal substation area power meter based on this coefficient.

[0016] The above-mentioned processor may also include an inspection quantity dynamic adjustment module, which is used to calculate an inspection quantity threshold according to the real-time change of the line loss rate of the substation area, and select no more than this threshold of substation area power meters from high to low based on the optimization comparison coefficient as the final inspection objects.

[0017] Another technical solution of the present invention is achieved through the following measures: A low-voltage meter status monitoring method based on the Internet of Things, including:

[0018] Step 1: Collect the total power consumption data within the substation area through the total power meter of the substation area, and collect the power consumption data of each minimum monitoring unit through the substation area's sub-meter.

[0019] Step 2: Transmit the total power consumption data and the power consumption data of each minimum monitoring unit to the processor and store them in the data storage unit.

[0020] Step 3: Based on the historical power consumption data stored in the data storage unit, construct a power consumption change trend model for each minimum monitoring unit.

[0021] Step 4: Analyze and identify abnormal substation area sub-meters and determine the substation area sub-meters that need to be inspected and corrected.

[0022] The following is a further optimization or / and improvement of the second technical solution of the above invention:

[0023] The method for determining the substation area sub-meters that need to be inspected and corrected in the above Step 4 may be:

[0024] Step 4.1: Determine the abnormal substation area sub-meters according to the power consumption increase.

[0025] Step 4.2: Obtain the power consumption floating value R of each abnormal substation area sub-meter 浮动j ;

[0026] Step 4.3: Obtain the discrete value S of the power consumption of an abnormal substation area sub-meter in the past m cycles.

[0027] Step 4.4: According to the formula T 优化 = R 浮动j -(θ S -1) calculate the optimization comparison coefficient T of each minimum monitoring unit 优化 , where θ is a preset value greater than 1 and less than 1.1. Sort each minimum monitoring unit in descending order according to the corresponding T 优化 value, and the higher the serial number, the greater the priority of inspection and correction.

[0028] The method for determining the abnormal substation area sub-meters in the above Step 4.1 may be:

[0029] Obtain the power consumption Qi of each minimum monitoring unit within a monitoring cycle, where i ranges from 1 to n, and n is the number of sub-meters in the corresponding substation area;

[0030] For the power consumption Qi corresponding to a minimum monitoring unit, obtain its power consumption Qik in the past m cycles, where k ranges from 1 to m;

[0031] Obtain the line graph Ri of the power consumption change corresponding to each minimum monitoring unit, where the line graph Ri of the power consumption change has the number of cycles as the abscissa and the power consumption Qi as the ordinate;

[0032] Obtain the slope of the straight line connected between each adjacent two points in Ri, and mark the corresponding m - 1 straight line slopes in the order from left to right as xi1, xi2, …, xi m-1 ;

[0033] Divide the n straight line slope values with the same subscript into the same evaluation group, and obtain the coefficient of variation S1 of the m - 1 straight line slope values within the same evaluation group;

[0034] If the value of the coefficient of variation S1 is greater than the preset value S1y, delete the straight line slope values in the order of the absolute value of the difference between the straight line slope and the average value of the n straight line slopes from large to small, and update the value of S1 according to the remaining undeleted straight line slope values each time a straight line slope value is deleted until the calculated coefficient of variation S1 is less than or equal to S1y. At this time, the sub - district electricity meter corresponding to the deleted straight line slope value is an abnormal sub - district electricity meter.

[0035] The method for obtaining the power consumption floating value R of each abnormal sub - district electricity meter in step 4.2 above 浮动j can be:

[0036] Statistically analyze all the deleted straight line slope values to obtain the number e of the deleted straight line slope values;

[0037] According to the formula R 浮动j = (the difference between the straight line slope and the average value of the n straight line slopes) / (the average value of the n straight line slopes) × 100% to calculate the power consumption floating value R of the corresponding j minimum monitoring units, 浮动j where the value of j ranges from 1 to e.

[0038] The method for obtaining the discrete value S of the power consumption of an abnormal sub - district electricity meter in the past m cycles in step 4.3 above can be:

[0039] Mark an abnormal minimum monitoring unit as the target monitoring unit;

[0040] According to the formula calculate the discrete value S of the m power consumptions Qik corresponding to the target monitoring unit in the past m cycles, where Qip is the average value of the corresponding m Qik values.

[0041] After step 4.4 above, it may further include step 4.5 of determining the specific sub - district electricity meter for inspection and correction. Step 4.5 specifically includes the following steps:

[0042] Step 4.51: Calculate the line loss rate β of the power distribution area based on the total power consumption in the area and the power consumption of each minimum monitoring unit.

[0043] Step 4.52: Obtain the line loss rate β in each monitoring period, and at the same time obtain the total power consumption Qz in the power distribution area corresponding to each monitoring period. Taking the total power consumption Qz in the power distribution area as the abscissa and the line loss rate β as the ordinate, and using the (Qz, β) corresponding to a monitoring period as the coordinate point, fit a line loss rate change curve.

[0044] Step 4.53: At the end of a monitoring period, obtain the corresponding real-time total power consumption Qzs in the power distribution area, then obtain the corresponding line loss rate calculated value β1 according to the line loss rate change curve, and then calculate the real-time line loss rate value β2 according to the method in Step 4.41; According to the formula G 巡检数 = g + (|β2 - β1| / β3) 取整 calculate the detection quantity of the minimum monitoring unit in the power distribution area at the end of the current monitoring period, where g is the basic detection quantity, and (|β2 - β1| / β3) 取整 represents the integer value of the calculated value of |β2 - β1| / β3.

[0045] Step 4.54: Select the sub-metering electric energy meters of G 优化 minimum monitoring units in the power distribution area for inspection and correction in descending order according to the corresponding T 巡检数 value.

[0046] The present invention calculates the power consumption floating values of each minimum monitoring unit in the past monitoring period, and uses the abnormal power consumption increment to judge the abnormality of the electricity meter. At the same time, the discrete value is used to correct the numerical value, reducing the influence of the minimum monitoring unit with poor power consumption regularity on the final judgment result. According to the principle that the earlier the serial number, the higher the priority of the inspection, the sub-meter of the transformer area is inspected. Conducting inspections in this targeted manner is conducive to making full use of the existing inspection resources and is more likely to detect abnormal sub-meters of the transformer area. The present invention utilizes the relationship between the line loss rate and the load, fits to obtain the relationship curve between the total power consumption and the line loss rate within the same transformer area, and compares the line loss rate calculated using this relationship curve with the actual line loss rate to evaluate the possibility or severity of the abnormality of the sub-meter of the transformer area, and thereby determines the number of sub-meters of the transformer area that need to be specifically inspected. Thus, the number of sub-meters of the transformer area that need to be inspected is adjusted in real time according to the obtained data. Compared with presetting an inspection number and traversing inspections, the present invention can reduce the inspection number and inspection cost while minimizing the missed inspection of abnormal electricity meters as much as possible, ensuring the accuracy and safety of power detection within the transformer area. The present invention monitors the number of times the sub-meter of the transformer area appears abnormal, and determines the electricity meter that needs to be inspected only when the number of times meets certain requirements, which can further reduce the interference of the randomness of power consumption on the determination of abnormal electricity meters and is suitable for implementation in transformer areas with relatively short monitoring periods or relatively high tolerance for abnormal electricity meters. Description of the Drawings

[0047] Figure 1 It is a schematic diagram of the overall structure of the low-voltage meter status monitoring system based on the Internet of Things in the embodiment of the present invention.

[0048] Figure 2 It is a schematic diagram of the method flow for the processor to determine the sub-meter of the transformer area that needs to be inspected and corrected in the embodiment of the present invention. Detailed Embodiment

[0049] The present invention is not limited by the following embodiments, and the specific implementation manner can be determined according to the technical solution of the present invention and the actual situation.

[0050] The following further describes the present invention in conjunction with embodiments:

[0051] Embodiment 1: As shown in the attached Figure 1 、 2 The low-voltage meter status monitoring system based on the Internet of Things includes:

[0052] The total transformer area electricity meter is used to monitor the total power consumption within the transformer area;

[0053] The sub-meter of the transformer area is used to monitor the power consumption of each minimum monitoring unit;

[0054] A data storage unit for storing the electricity consumption information collected by the total electricity meter and the sub-meter of the transformer area.

[0055] A processor for analyzing and identifying abnormal sub-meter of the transformer area and determining the sub-meter of the transformer area that needs to be inspected and corrected. Specifically, the processor can be used to receive the electricity consumption information collected in real time by the total electricity meter of the transformer area and the sub-meter of the transformer area, read the electricity consumption information stored in the data storage unit, identify the abnormal sub-meter of the transformer area after analysis, determine the sub-meter of the transformer area that needs to be inspected and corrected, and transmit the information of the abnormal sub-meter of the transformer area to the interaction unit for display, and the staff can inspect and correct the abnormal sub-meter of the transformer area.

[0056] Among them, the total electricity meter of the transformer area and the sub-meter of the transformer area are respectively connected to the processor through the Internet of Things, and the data storage unit is electrically connected to the processor; the total electricity meter of the transformer area and the sub-meter of the transformer area respectively transmit the collected total electricity consumption data and sub-user electricity consumption data to the processor, and the processor analyzes and identifies the abnormal sub-meter of the transformer area based on the historical data stored in the data storage unit. Through the Internet of Things, three-level data collection (total meter → sub-meter → processor) is realized, and abnormal conditions are identified by comparing historical data with real-time data. In this way, a complete low-voltage meter monitoring network can be constructed to realize centralized management of electricity consumption data and abnormal warning.

[0057] In the present invention, the processor includes an abnormal identification module for generating a power consumption change trend chart based on the historical and current power consumption data of each minimum monitoring unit, and identifying the abnormal sub-meter of the transformer area with abnormal fluctuations by analyzing the dispersion degree of the slope in the trend chart. The electricity consumption data is converted into a visual trend chart, and the abnormal fluctuations are quantified through mathematical statistics. In this way, automatic screening of abnormal meters can be realized, the workload of manual inspection can be reduced, and the detection efficiency can be improved.

[0058] In the present invention, the processor further includes an inspection priority determination module for calculating an optimization comparison coefficient according to the power consumption fluctuation amplitude and historical power consumption stability of the abnormal sub-meter of the transformer area, and determining the inspection priority of each abnormal sub-meter of the transformer area based on this coefficient. The priority is calculated by integrating two dimensions: the fluctuation amplitude (short-term abnormality) and the historical stability (long-term performance). In this way, inspection resources can be scientifically allocated, high-risk meters can be processed first, and the risk of power loss can be reduced.

[0059] In the present invention, the processor further includes an inspection quantity dynamic adjustment module for calculating an inspection quantity threshold according to the real-time change of the line loss rate of the transformer area, and selecting no more than this threshold of the sub-meter of the transformer area from high to low based on the optimization comparison coefficient as the final inspection object. The inspection scale is dynamically adjusted by using the line loss rate (the difference between the total meter and the sub-meter) to balance the cost and risk. In this way, the waste of resources or missed inspections caused by a fixed inspection quantity can be avoided, and the inspection benefit can be maximized.

[0060] Embodiment 2: As shown in the appendix Figure 1 、 2 This embodiment discloses a method for monitoring the status of low-voltage meters based on the Internet of Things, including the following steps:

[0061] Step 1: Collect the total power consumption data in the area through the total power meter of the substation area, and collect the power consumption data of each minimum monitoring unit through the substation area power sub-meter;

[0062] Step 2: Transmit the total power consumption data and the power consumption data of each minimum monitoring unit to the processor and store them in the data storage unit;

[0063] Step 3: Based on the historical power consumption data stored in the data storage unit, construct a power consumption change trend model for each minimum monitoring unit;

[0064] Step 4: Analyze and identify abnormal substation area power sub-meters, and determine the substation area power sub-meters that need to be inspected and corrected. Through the closed-loop process of data acquisition → transmission → modeling → analysis, intelligent diagnosis of the meter status is realized. In this way, a standardized monitoring process can be established, and the accuracy and repeatability of abnormal identification can be improved.

[0065] As Figure 2 shown, the method for the processor to identify abnormal substation area power sub-meters and determine the substation area power sub-meters that need to be inspected and corrected includes the following steps:

[0066] Step 4.1: Take a substation area as an independent monitoring object, monitor the total power consumption in the substation area through the total power meter of the substation area, and monitor the power consumption of each minimum monitoring unit through the substation area power sub-meter;

[0067] Then, monitor the power consumption Qi of each minimum monitoring unit within a monitoring period through the substation area power sub-meter, where i ranges from 1 to n, and n is the number of power sub-meters in the corresponding substation area; A monitoring period can be one day, one week, one month or one quarter, etc., and is specifically set according to the usage scenario; In addition, it should be noted that Qi here is the detection value of the substation area power sub-meter;

[0068] Step 4.2: For the power consumption Qi corresponding to a minimum monitoring unit, obtain its power consumption in the past m cycles, and mark it as Qik in sequence according to the time sequence, where k ranges from 1 to m;

[0069] Obtain the power consumption change line graph Ri corresponding to each minimum monitoring unit, where the power consumption change line graph Ri takes the number of cycles as the abscissa and the power consumption Qi as the ordinate, and the power consumption change line graph Ri is formed by connecting m coordinate points in sequence;

[0070] Obtain the slope of the straight line connecting adjacent two points in the electricity consumption change line graph Ri, and sequentially label the corresponding m - 1 straight line slopes as xi1, xi2, …, xi from left to right. m-1 ;

[0071] Divide the n straight line slope values with the same subscript into the same evaluation group. For example, n corresponding xi1 form an evaluation group.

[0072] According to the formula Calculate the coefficient of variation S1 of the m - 1 straight line slope values within the same evaluation group.

[0073] If the value of the coefficient of variation S1 is greater than the preset value S1y, it is considered that the electricity consumption change amplitude between the cycles represented by some of the smallest monitoring units from i - 1 to i is abnormal. At this time, delete xi k in descending order of |xi m-1 - xip|. And each time an xi k value is deleted, update the S1 value according to the remaining undeleted xi k values until the calculated coefficient of variation S1 is less than or equal to S1y.

[0074] Statistically analyze all the deleted xik values to obtain the number e of the deleted xi k values.

[0075] According to the formula R 浮动j =(xi k - xip) / xip×100%, calculate the electricity consumption floating value R 浮动j of the corresponding j smallest monitoring units, where the value of j ranges from 1 to e.

[0076] Step 4.3: Mark a corresponding smallest monitoring unit as the target monitoring unit.

[0077] According to the formula calculate the discrete value S of the m electricity consumption Qik corresponding to the target monitoring unit in the past m cycles, where Qip is the average value of the corresponding m Qik values, that is, the average electricity consumption of the target monitoring unit in the past m cycles.

[0078] Specifically, before calculating the discrete value S, the corresponding m electricity consumption Qik can also be data - cleaned according to the situation, deleting the data that is obviously abnormal, such as some parameters that are significantly different from the average level, so as to ensure the accuracy of the result.

[0079] Step 4.4: According to the formula T 优化 =R 浮动j -(θ S-1) Calculate the optimization comparison coefficient T of the target monitoring unit 优化 , where θ is a preset value greater than 1 and less than 1.1. In an embodiment of the present invention, θ is taken as 1.05;

[0080] Calculate the optimization comparison coefficient T of each minimum monitoring unit in sequence 优化 After that, sort each minimum monitoring unit in descending order according to the corresponding T 优化 value.

[0081] In the present invention, the power consumption floating value R of each minimum monitoring unit in the past monitoring period is calculated 浮动j , and the abnormality of the electric energy meter is judged by the abnormal power consumption increment. At the same time, the value is corrected by the discrete value S to reduce the influence of the minimum monitoring unit with poor power consumption regularity on the final judgment result. During the subsequent inspection of the substation area electric energy meters, the earlier the serial number, the higher the inspection priority, that is, it is the substation area electric energy meter that is more likely to have abnormalities.

[0082] Embodiment 3: On the basis of Embodiment 2, this embodiment discloses a method for arranging the inspection of the substation area electric energy meters, including the following steps:

[0083] Step 4.51: Calculate the line loss rate of the substation area according to the total power consumption in the substation area and the power consumption of each minimum monitoring unit; specifically, monitor the total power consumption Qz in the monitored substation area within a monitoring period through the substation area total electric energy meter; it should be noted that Qz here is the detection value of the substation area total electric energy meter; due to line losses during the operation of the power grid, Qz and should not be equal, and Qz is greater than Then we use the formula to calculate the line loss rate β of the substation area in this monitoring period;

[0084] Step 4.52: Obtain the line loss rate β in each monitoring period, and at the same time obtain the total power consumption Qz in the corresponding substation area in each monitoring period; take the total power consumption Qz in the substation area as the abscissa and the line loss rate β as the ordinate, and use the (Qz, β) corresponding to a monitoring period as the coordinate point to fit and form a line loss rate change curve;

[0085] Step 4.53: At the end of a monitoring period, obtain the corresponding real-time total power consumption Qzs in the substation area, then obtain the corresponding line loss rate calculation value β1 according to the line loss rate change curve, and then calculate the real-time line loss rate value β2 according to the method in Step 4.21; According to the formula G 巡检数 = g + (|β2 - β1| / β3) 取整Calculate the detection quantity of the minimum monitoring unit in the substation area at the end of the current monitoring period, where g is the basic detection quantity, indicating that regardless of the value of the optimization comparison coefficient T 优化 The value of is, the minimum number of inspections of the substation area electric energy meters, (|β2 - β1| / β3) 取整 Indicates that the calculated value of |β2 - β1| / β3 is rounded, and it can be rounded according to rounding methods such as rounding up or down.

[0086] Step 4.54. Select G 优化 substation area electric energy meters of the minimum monitoring unit in descending order according to the corresponding T 巡检数 values for inspection and correction.

[0087] This embodiment utilizes the relationship between the line loss rate and the load, fits to obtain the relationship curve between the total power consumption and the line loss rate in the same substation area, and compares the line loss rate calculated using this relationship curve with the actual line loss rate to evaluate the possibility or severity of abnormal substation area electric energy meters in the substation area, and thereby determines the number of substation area electric energy meters that need to be specifically inspected, so as to adjust the number of substation area electric energy meters that need to be inspected in real time according to the obtained data. Compared with presetting an inspection quantity and traversing inspections, the present invention can reduce the inspection quantity and inspection cost while minimizing the missed inspection of abnormal electric energy meters as much as possible, ensuring the accuracy and safety of electric energy detection in the substation area.

[0088] Embodiment 4: Even if the optimization comparison coefficient T of each minimum monitoring unit is calculated according to the methods in Embodiment 2 and Embodiment 3 优化 , in some usage scenarios, the electricity consumption randomness of some minimum monitoring units will be relatively large. Therefore, there may still be a situation where the number of substation area electric energy meters for inspection and correction is relatively large. Therefore, this embodiment further provides a method for determining the number of substation area electric energy meters for final inspection and correction, as follows:

[0089] After determining the substation area electric energy meters of G 巡检数 minimum monitoring units according to the method in Embodiment 3, do not perform inspection and correction, and add 1 to the marking times of the substation area electric energy meters included therein. When a substation area electric energy meter is continuously marked k1 times or is marked k1 times in k2 consecutive monitoring periods, then perform inspection and correction on the corresponding substation area electric energy meter. In the present invention, both k1 and k2 are preset values. By doing so, the interference of electricity consumption randomness on the determination of abnormal electric energy meters can be further reduced, which is suitable for implementation in some substations with shorter monitoring periods or higher tolerance for abnormal electric energy meters.

[0090] The above technical features constitute an embodiment of the present invention, which has strong adaptability and implementation effects. Non-essential technical features can be added or subtracted according to actual needs to meet the requirements of different situations.

Claims

1. A low-voltage meter status monitoring system based on the Internet of Things, characterized in that Including: The total power meter for the substation area is used to monitor the total power consumption in the substation area; The substation area sub-meter is used to monitor the power consumption of each minimum monitoring unit; The data storage unit is used to store the power consumption information collected by the total power meter for the substation area and the sub-meter for the substation area; The processor is used to analyze and identify abnormal substation area sub-meters and determine the substation area sub-meters that need to be inspected and corrected; Among them, the total power meter for the substation area and the sub-meter for the substation area are respectively communicatively connected to the processor through the Internet of Things, and the data storage unit is electrically connected to the processor; the total power meter for the substation area and the sub-meter for the substation area respectively transmit the collected total power consumption data and sub-user power consumption data to the processor, and the processor analyzes and identifies abnormal substation area sub-meters based on the historical data stored in the data storage unit.

2. The low-voltage meter status monitoring system based on the Internet of Things according to claim 1, wherein The processor includes an abnormal identification module, which is used to generate a power consumption change trend chart based on the historical and current power consumption data of each minimum monitoring unit, and identify abnormal fluctuating substation area sub-meters by analyzing the dispersion degree of the slopes in the trend chart.

3. The low-voltage meter status monitoring system based on the Internet of Things according to claim 1 or 2, characterized in that The processor further includes an inspection priority determination module, which is used to calculate an optimized comparison coefficient according to the power consumption fluctuation amplitude and historical power consumption stability of the abnormal substation area sub-meters, and determine the inspection priorities of each abnormal substation area sub-meter based on this coefficient.

4. The low-voltage meter status monitoring system based on the Internet of Things according to claim 1 or 2, characterized in that The processor further includes an inspection quantity dynamic adjustment module, which is used to calculate an inspection quantity threshold according to the real-time change of the line loss rate of the substation area, and select no more than this threshold of substation area sub-meters from high to low based on the optimized comparison coefficient as the final inspection objects.

5. A method for monitoring the status of a low-voltage meter based on the Internet of Things, characterized in that Including: Step 1: Collect the total power consumption data in the substation area through the total power meter for the substation area, and collect the power consumption data of each minimum monitoring unit through the sub-meter for the substation area; Step 2: Transmit the total power consumption data and the power consumption data of each minimum monitoring unit to the processor and store them in the data storage unit; Step 3: Based on the historical power consumption data stored in the data storage unit, construct a power consumption change trend model for each minimum monitoring unit; Step 4: Analyze and identify abnormal substation area sub-meters and determine the substation area sub-meters that need to be inspected and corrected.

6. The method for monitoring the status of a low-voltage meter based on the Internet of Things according to claim 5, wherein The method for determining the substation area sub-meters that need to be inspected and corrected in Step 4 is: Step 4.1: Determine the abnormal substation area sub-meters according to the power consumption increase; Step 4.2: Obtain the power consumption floating value R of the district electricity meters with each anomaly 浮动j ; Step 4.3: Obtain the discrete value S of the power consumption of an abnormal substation area sub-meter in the past m cycles; Step 4.4: According to formula T 优化 =R 浮动j -(θ S -1) Calculate the optimized contrast coefficient T of each minimum monitoring unit 优化 , where θ is a preset value greater than 1 and less than 1.1, according to the corresponding T 优化 The minimum monitoring units are sorted in descending order, and the earlier the sequence number, the greater the priority of inspection and correction.

7. The method for monitoring the status of a low-voltage meter based on the Internet of Things according to claim 5 or 6, characterized in that The method for determining the abnormal substation area sub-meters in Step 4.1 is: Obtain the power consumption Qi of each minimum monitoring unit in a monitoring cycle, where i takes values from 1 to n, and n is the number of sub-meters in the corresponding substation area; For the power consumption Qi corresponding to a minimum monitoring unit, obtain its power consumption Qik in the past m cycles, where k takes values from 1 to m; Obtain the power consumption change line chart Ri corresponding to each minimum monitoring unit, where the power consumption change line chart Ri takes the number of cycles as the abscissa and the power consumption Qi as the ordinate; Obtain the slopes of the straight lines connecting adjacent two points in Ri, and sequentially label the corresponding m - 1 straight - line slopes as xi1, xi2, …, xi from left to right m-1 ; Divide the n straight line slope values with the same subscript into the same evaluation group, and obtain the discrete coefficient S1 of the m-1 straight line slope values in the same evaluation group; If the value of the coefficient of variation S1 is greater than the preset value S1y, the slope values of the lines are deleted in descending order of the absolute value of the difference between the slope of the line and the average value of the slopes of n lines. And each time a slope value of a line is deleted, the value of S1 is updated according to the remaining undeleted slope values of the lines until the calculated coefficient of variation S1 is less than or equal to S1y. At this time, the substation area electricity meter corresponding to the deleted slope value of the line is an abnormal substation area electricity meter.

8. The method for monitoring the status of a low-voltage meter based on the Internet of Things according to claim 5 or 6, characterized in that The method for obtaining the power consumption floating value R of the substation electric energy meters with various anomalies in Step 4.2 浮动j is as follows: Statistically analyze all the deleted slope values of the lines to obtain the number e of the deleted slope values of the lines; According to the formula R 浮动j = (the difference between the slope of the straight line and the average value of the slopes of n straight lines) / (the average value of the slopes of n straight lines) × 100% to calculate the power consumption floating value R of the corresponding j minimum monitoring units 浮动j , where the value range of j is from 1 to e.

9. The method for monitoring the status of a low-voltage meter based on the Internet of Things according to claim 5 or 6, characterized in that The method for obtaining the discrete value S of the power consumption of an abnormal substation area electricity meter in the past m cycles in step 4.3 is as follows: Mark an abnormal minimum monitoring unit as the target monitoring unit; According to the formula Calculate the discrete value S of the m power consumption amounts Qik corresponding to the target monitoring unit in the past m cycles, where Qip is the average value of the corresponding m Qik values.

10. The method for monitoring the status of a low-voltage meter based on the Internet of Things according to claim 6, wherein After step 4.4, it further includes step 4.5 of determining the specific substation area electricity meter for inspection and correction. Step 4.5 specifically includes the following steps: Step 4.51: Calculate the line loss rate β of the substation area according to the total power consumption in the substation area and the power consumption of each minimum monitoring unit; Step 4.52: Obtain the line loss rate β in each monitoring cycle, and at the same time obtain the total power consumption Qz in the substation area corresponding to each monitoring cycle; Taking the total power consumption Qz in the substation area as the abscissa and the line loss rate β as the ordinate, and using the (Qz, β) corresponding to one monitoring cycle as the coordinate point, fit into a line loss rate change curve; Step 4.53: At the end of a monitoring period, obtain the corresponding total power consumption Qzs in the power distribution area in real time, then obtain the corresponding calculated value β1 of the line loss rate according to the line loss rate change curve, and then calculate the real-time line loss rate value β2 according to the method in Step 4.41; According to the formula G 巡检数 = g + (|β2 - β1| / β3) 取整 calculate the detection quantity of the minimum monitoring unit in the power distribution area at the end of the current monitoring period, where g is the basic detection quantity, and (|β2 - β1| / β3) 取整 represents the integer value of the calculated value of |β2 - β1| / β3; Step 4.54, select the substation energy meters of G 优化 minimum monitoring units in descending order of the corresponding T 巡检数 values for inspection and correction.

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