Method, system and equipment for checking input accuracy of bottom data of disassembled electric energy meter and medium

Abstract

The invention provides a method, a system, equipment and a medium for verifying the input accuracy of bottom data of a disassembled electric energy meter, and belongs to the technical field of electric energy metering asset management.The method comprises the steps that the disassembly time of the electric energy meter is obtained, and the disassembly date of the electric energy meter and data within a set calculation period from the disassembly date are collected; according to a load change trend, calculating a meter dismounting time period of a meter dismounting date and a fitting increment parameter within a set calculation period from the dismounting date, and according to the calculated fitting increment parameter, calculating a base estimation general value, a base estimation average value, a base estimation maximum value and a base input reference value; and inputting base data, calculating a base data error estimation value according to the input base data and the base input reference value, verifying whether the base data input value is between the freezing indicating value of the latest day before table disassembly and the base estimation maximum value, and verifying whether the base data error estimation value is smaller than a preset verification threshold value. According to the invention, automatic verification is carried out when the bottom data of the detached electric energy meter is input, and hidden troubles are eliminated in advance.

Description

Background Art

[0002] When power departments and power supply units at all levels install and remove electric energy meters, they need to enter the data of the removed electric energy meters into the marketing business system. When the staff sort the equipment of the removed electric energy meters in the later stage, they need to take pictures of the data of the electric energy meters through the electric energy meter sorting device and upload them to the marketing business system. However, due to the experience of power workers, especially in the process of large-scale and centralized replacement of electric energy meters, the data of the removed electric energy meters may be inaccurately entered due to the negligence of power workers. On the one hand, it will cause the accuracy index of the entry of the electric energy meter bottom data to decline, affecting the standardization rate of the overall measurement asset management. On the other hand, it is likely that the user's electricity bill will be wrong due to the error in the entry of the electric energy meter bottom data, causing user complaints and other service problems. The current solution is manual verification and analysis. At the beginning of each month, the power staff will import the sorted bottom data and the entered bottom data of the removed electric energy meters of the previous month into the statistical table, and calculate the difference between the sorted bottom data and the entered bottom data in the statistical table. After taking into account the comprehensive multiple, the accuracy of the entered bottom data will be manually analyzed. If the absolute value of the difference and the comprehensive multiplier product is not less than the preset verification threshold, it is judged as data abnormality. If it is verified that the input base data is incorrect, if the electricity meter is a user billing electricity meter, the electricity fee refund procedure and system process will be initiated, and the situation will be explained to the user.

[0003] The current method of preventing errors in the entry of bottom-level data of electric energy meters has the following defects: First, the time interval from the disassembly of the electric energy meter to the data export in the background by the staff is long and the work efficiency is low. If the bottom-level data is entered incorrectly, it is easy to cause errors in the user's electricity bill, thus causing complaints. Secondly, after the electric energy meter is sorted by equipment, the data is exported from the system background for difference calculation and manual analysis. After the data is determined to be abnormal, verification and processing are carried out, which is a post-control. It is impossible to eliminate the risk of errors in the entry of bottom-level data and related hidden dangers in advance. Even if the abnormal data is processed and targeted measures are taken later, it is difficult to significantly improve the accuracy of bottom-level data entry in a short period of time.

[0004] This is a shortcoming of the prior art. Therefore, in view of the above-mentioned defects in the prior art, it is very necessary to provide a method, system, equipment and medium for verifying the accuracy of data entry of dismantled electric energy meters. Summary of the invention

[0005] In view of the defects that the current method of solving the error in entering the bottom data of the dismantled electric energy meter belongs to post-control, cannot eliminate the risk of entering low bottom data, and is difficult to significantly improve the accuracy of entering bottom readings in a short time, the present invention provides a method, system, equipment and medium for checking the accuracy of entering the bottom data of the dismantled electric energy meter to solve the above technical problems.

[0006] In a first aspect, the present invention provides a method for verifying the accuracy of data entry of a dismantled electric energy meter, comprising the following steps:

[0007] S1. Obtain the time of meter removal, collect the meter removal date and the data within the calculation period set from the removal date;

[0008] S2. Calculate the meter removal date, meter removal period and fitting increment parameters within the set calculation period from the removal date according to the load change trend, and calculate the general value of the bottom degree estimation, the average value of the bottom degree estimation, the maximum value of the bottom degree estimation and the reference value of the bottom degree entry according to the calculated fitting increment parameters;

[0009] S3. Enter the bottom degree data, calculate the bottom degree data error estimate based on the entered bottom degree data and the bottom degree entry reference value, verify whether the bottom degree data entry value is between the most recent daily frozen indication before meter removal and the bottom degree estimated maximum value, and verify whether the bottom degree data error estimate is less than the preset verification threshold.

[0010] Furthermore, the specific steps of step S1 are as follows:

[0011] S11. Obtain the time of meter removal, identify the date and time of removal;

[0012] S12. Acquire the collected data of each sampling point before the meter removal period on the removal date of the electric energy meter and the collected data of each sampling point within the set calculation period from the removal date.

[0013] Furthermore, the specific steps of step S2 are as follows:

[0014] S21. Identify the type of data collected;

[0015] When the collected data is electric energy indication data, proceed to step S22;

[0016] When the collected data is power data, proceed to step S24;

[0017] S22. Obtain the two most recent collected power data before meter removal, the meter removal time point, and the most recent collection time point before meter removal, calculate the fitting increment value of the meter removal period on the day of meter removal in combination with the sampling period, and calculate the fitting increment value, fitting increment mean, and fitting increment maximum value of the same period within the calculation period set from the removal date;

[0018] S23. Calculate the general value of the bottom degree estimation based on the latest data collected before the meter is removed and the fitted increment value of the meter removal period, calculate the average value of the bottom degree estimation based on the latest data collected before the meter is removed and the mean value of the fitted increment of the same period within the calculation period set to the removal date, calculate the maximum value of the bottom degree estimation based on the latest data collected before the meter is removed and the maximum value of the fitted increment of the same period within the calculation period set to the removal date, and calculate the bottom degree entry reference value based on the general value of the bottom degree estimation and the average value of the bottom degree estimation;

[0019] S24. Obtain the power data collected at each sampling point on the day of meter removal, calculate the incremental value of power collected on the day of meter removal, and obtain the power data collected for the last two times before meter removal, the time point of meter removal, and the time point of the last collection before meter removal, calculate the fitting incremental value of the meter removal period on the day of meter removal in combination with the sampling period, and calculate the fitting incremental value, fitting incremental mean and maximum value of the same period within the calculation period set from the removal date;

[0020] S25. Calculate the general value of the bottom degree estimate based on the latest daily frozen indication of meter removal, the latest power data collected before meter removal and the fitted incremental value of the meter removal period; calculate the average value of the bottom degree estimate based on the latest daily frozen indication of meter removal, the latest data collected before meter removal and the mean of the fitted incremental values ​​of the same period within the set calculation period from the removal date; calculate the maximum value of the bottom degree estimate based on the latest daily frozen indication of meter removal, the latest data collected before meter removal and the maximum value of the fitted incremental values ​​of the same period within the set calculation period from the removal date; calculate the reference value for bottom degree entry based on the general value of the bottom degree estimate and the average value of the bottom degree estimate.

[0021] Furthermore, the specific steps of step S22 are as follows:

[0022] S221. Obtain the two most recent collected power data W(n) and W(n-1) before meter removal, the meter removal time point t(x), and the most recent collection time point t(n) before meter removal, and calculate the fitting increment ΔW(x) within the meter removal period on the day of meter removal by the following formula:

[0023] ΔW(x)=[(W(n)-W(n-1)) / zt]*(t(x)-t(n))

[0024] Among them, zt is the sampling period;

[0025] S222. Calculate the fitting increment value ΔW(x) of the same period within the calculation cycle m set from the demolition date by the following formula: _m :

[0026] ΔW(x) _m =[(W(n) _m -W(n-1) _m ) / zt]*(t(x)-t(n));

[0027] S223. Calculate the mean value of the fitted increment value ΔW(x) in the same period within the calculation period m set from the demolition date by the following formula: _average :

[0028]

[0029] S224. Calculate the maximum value of the fitting increment value ΔW(x) in the same period within the calculation cycle m set from the demolition date by the following formula_ max :

[0030] ΔW(x) _max =max(ΔW(x) _1 ,…ΔW(x) _m );

[0031] The specific steps of step S23 are as follows:

[0032] S231. Calculate the general value W(x) of the base estimate based on the most recent data W(n) collected before meter removal and the fitting increment value ΔW(x) during the meter removal period using the following formula:

[0033] W(x)=W(n)+ΔW(x);

[0034] S232. According to the most recent data W(n) collected before the meter was removed and the fitted increment mean ΔW(x) of the same period within the calculation period set from the removal date _average The estimated mean value W(x) of the base is calculated by the following formula: _average :

[0035] W(x) _average =W(n)+ΔW(x) _average ;

[0036] S233. According to the most recent data W(n) collected before the meter was removed and the maximum value of the fitted increment ΔW(x) in the same period of the calculation cycle set from the removal date _max The maximum value of the base estimation W(x) is calculated by the following formula: _max :

[0037] W(x) _max =W(n)+ΔW(x) _max ;

[0038] S234. Calculate the base depth input reference value W(c) according to the base depth estimated general value W(x) and the base depth estimated average value W(x)_average by the following formula:

[0039] W(c)=(W(x)+W(x) _average ) / 2.

[0040] Furthermore, the specific steps of step S24 are as follows:

[0041] S241. Obtain the power data collected at each sampling point on the day of meter removal, and calculate the power collection increment value ΔW(n) on the day of meter removal using the following formula:

[0042]

[0043] Wherein, n and i are both integers and 1<n≤1440 / zt, P(i) is the power value at the i-th time point, and P(1) is the power value at the first sampling point, i.e., 0:00;

[0044] S242. Obtain the two most recent power data collected before meter removal, P(n) and P(n-1), the meter removal time t(x), and the most recent collection time t(n) before meter removal, and calculate the fitted power value P(x) at the meter removal time on the day of meter removal by the following formula:

[0045] P(x)=P(n)+[(P(n)-P(n-1)) / zt]*(t(x)-t(n))

[0046] Among them, zt is the sampling period;

[0047] S243. Calculate the fitting increment value ΔW(x) within the meter removal time period on the day of meter removal based on the fitted power value P(x) at the meter removal time point on the day of meter removal and the most recently collected power data P(n) before meter removal by the following formula:

[0048] ΔW(x)=((P(x)+P(n)) / 2)*(t(x)-t(n)) / 60

[0049] =(P(x)+P(n))*(t(x)-t(n)) / 120;

[0050] S244. Calculate the fitted power value P(x) at the same point within the calculation period m from the demolition date by the following formula: _m :

[0051] P(x) _m =P(n) _m +[(P(n) _m -P(n-1) _m ) / zt]*(t(x)-t(n))

[0052] Among them, P(n) _m 、P(n-1) _m are respectively the most recent data collected before and after the same time point m days before the meter is removed; S245. Calculate the fitting increment value ΔW(x) of the same period within the calculation cycle m set from the removal date by the following formula: _m :

[0053] ΔW(x) _m =((P(x) _m +P(n) _m ) / 2)*(t(x)-t(n)) / 60

[0054] =(P(x)_m +P(n) _m )*(t(x)-t(n)) / 120

[0055] Among them, P(n) _m The value of the most recently collected data before the same time point m days before the meter was removed;

[0056] S246. Calculate the mean value of the fitted increment value ΔW(x) in the same period within the calculation period m set from the demolition date by the following formula: _average :

[0057] Among them, 1≤i≤m;

[0058] S247. Calculate the maximum value of the fitting increment value ΔW(x) in the same period within the calculation cycle m set from the demolition date by the following formula_ max :

[0059] ΔW(x) _max =max(ΔW(x) _1 ,…ΔW(x) _m );

[0060] The specific steps of step S25 are as follows:

[0061] S251. Calculate the general value W(x) of the base estimate based on the most recent daily frozen indication W(d) of the meter removal, the power collection increment value ΔW(n) on the day of meter removal, and the fitting increment value ΔW(x) during the meter removal period by the following formula:

[0062] W(x)=W(d)+ΔW(n)+ΔW(x);

[0063] S252. According to the most recent daily frozen indication W(d) of the meter removal, the power collection increment value ΔW(n) on the day of meter removal, and the fitted increment mean ΔW(x) of the same period within the calculation cycle set from the removal date _average The estimated mean value W(x) of the base is calculated by the following formula: _average :

[0064] W(x) _average =W(d)+ΔW(n)+ΔW(x) _average ;

[0065] S253. According to the most recent daily frozen indication W(d) of the meter removal, the power collection increment value ΔW(n) on the day of meter removal, and the maximum value of the fitted increment ΔW(x) in the same period within the calculation cycle set from the removal date _max The maximum value of the base estimation W(x) is calculated by the following formula: _max :

[0066] W(x) _max=W(d)+ΔW(n)+ΔW(x) _max ;

[0067] S254. Estimate the general value W(x) and the average value W(x) based on the base degree _average The bottom depth input reference value W(c) is calculated by the following formula:

[0068] W(c)=(W(x)+W(x) _average ) / 2.

[0069] Furthermore, the specific steps of step S3 are as follows:

[0070] S31. Input bottom data;

[0071] S32. Obtain the most recent daily frozen indication before meter removal and the calculated maximum value of the bottom density estimate and the reference value for bottom density entry;

[0072] S33. Verify whether the bottom meter data is not less than the most recent daily frozen value before meter reading and is not greater than the maximum value of the bottom meter estimate;

[0073] If yes, go to step S34;

[0074] If not, proceed to step S36;

[0075] S34. Calculate the difference between the input base data and the reference value of the base input to obtain the base error;

[0076] S35. Verify whether the bottom data error is less than a preset verification threshold;

[0077] If so, it is determined that the verification has been passed;

[0078] If not, proceed to step S36;

[0079] S36. Add data anomaly flag and end.

[0080] Furthermore, the specific steps of step S36 are as follows:

[0081] S361 counts the number of bottom data entries and detects whether the number of entries exceeds the maximum number of entries; if so, proceeds to step S362;

[0082] If not, return to step S31;

[0083] S362. Add data exception mark and end.

[0084] In a second aspect, the present invention provides a system for checking the accuracy of data entry of a dismantled electric energy meter, including: a data acquisition module for obtaining the time of dismantling the electric energy meter, collecting the date of dismantling the electric energy meter and data within a set calculation period from the date of dismantling;

[0085] The bottom load data estimation parameter calculation module is used to calculate the meter removal date, meter removal period and fitting increment parameters within the set calculation period from the removal date according to the load change trend, and calculate the bottom load estimation general value, bottom load estimation average value, bottom load estimation maximum value and bottom load entry reference value according to the calculated fitting increment parameters;

[0086] The bottom data verification module is used to enter the bottom data, calculate the bottom data error estimate based on the entered bottom data and the bottom data entry reference value, verify whether the bottom data entry value is between the most recent daily frozen indication before the meter is removed and the bottom data estimated maximum value, and verify whether the bottom data error estimate is less than a preset verification threshold.

[0087] In a third aspect, the present invention provides an electronic device, including a processor and a memory;

[0088] The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the electronic device executes the method described in the first aspect above.

[0089] In a fourth aspect, the present invention provides a storage medium,

[0090] The storage medium stores instructions, which, when executed on a computer, enable the computer to execute the method described in the first aspect.

[0091] The beneficial effects of the present invention are:

[0092] The accuracy verification method, system, equipment and medium for entering the basic data of the returned electric energy meters provided by the present invention can greatly improve the accuracy of basic data entry and eliminate potential problems in advance by performing advance control of basic data verification through data verification; the present invention is based on a large amount of historical data and performs data fitting according to the load change trend, the calculation method is scientific and reasonable, and the calculation result is accurate and reliable; data verification is automatically performed in real time according to the judgment basis, which can greatly reduce the workload of personnel, shorten data verification time and improve work efficiency; the present invention has strong practicality and operability, and can be promoted and applied to the business scenarios of electric energy meters returned and sorted by power supply companies at all levels, and improve the assessment indicators of the standardization rate of metering asset management.

[0093] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect.

[0094] It can be seen that compared with the prior art, the present invention has outstanding substantive features and significant progress, and the beneficial effects of its implementation are also obvious. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0096] Figure 1 The present invention is a flow chart of a method for verifying the accuracy of data entry of a dismantled electric energy meter.

[0097] Figure 2 It is a schematic diagram of the accuracy verification system for entering data of dismantled electric energy meters according to the present invention. DETAILED DESCRIPTION

[0098] The method for verifying the accuracy of data entry of dismantled electric energy meters can be applied to one or more electronic devices, wherein the electronic device is an electronic device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASIC), programmable gate arrays (FPGA), digital signal processors (DSP), embedded devices, etc.

[0099] An electronic device can be any electronic product that can interact with a user, such as a personal computer, a tablet computer, a smart phone, a personal digital assistant (PDA), an interactive network television (IPTV), etc. The network in which the electronic device is located includes but is not limited to the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), etc.

[0100] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention. Please refer to Figure 1 The figure shows a method for checking the accuracy of data entry of dismantled electric energy meters in a specific implementation, including the following steps:

[0101] S1. Obtain the time of meter removal, collect the meter removal date and the data within the calculation period set from the removal date;

[0102] S2. Calculate the meter removal date, meter removal period and fitting increment parameters within the set calculation period from the removal date according to the load change trend, and calculate the general value of the bottom degree estimation, the average value of the bottom degree estimation, the maximum value of the bottom degree estimation and the reference value of the bottom degree entry according to the calculated fitting increment parameters;

[0103] S3. Enter the bottom degree data, calculate the bottom degree data error estimate based on the entered bottom degree data and the bottom degree entry reference value, verify whether the bottom degree data entry value is between the most recent daily frozen indication before meter removal and the bottom degree estimated maximum value, and verify whether the bottom degree data error estimate is less than the preset verification threshold.

[0104] In this embodiment, the specific steps of step S1 are as follows:

[0105] S11. Obtain the time of meter removal, identify the date and time of removal;

[0106] S12. Obtain the data collected from each sampling point before the meter removal period on the date of meter removal and the data collected from each sampling point within the calculation period set from the removal date; for example, the data from each sampling point within 30 from the removal date; the specific steps of step S2 are as follows:

[0107] S21. Identify the type of data collected;

[0108] When the collected data is electric energy indication data, proceed to step S22;

[0109] When the collected data is power data, proceed to step S24;

[0110] S22. Obtain the two most recent collected power data before meter removal, the meter removal time point, and the most recent collection time point before meter removal, calculate the fitting increment value of the meter removal period on the day of meter removal in combination with the sampling period, and calculate the fitting increment value, fitting increment mean, and fitting increment maximum value of the same period within the calculation period set from the removal date;

[0111] It should be noted that the sampling period is determined according to the specific type of electric energy data collected, including 96-point curve data, 48-point curve data and 24-point curve data; among them, the 96-point curve data is to collect electric energy meter data every 15 minutes starting from 0:00, the 48-point curve data is to collect electric energy meter data every 30 minutes starting from 0:00, and the 24-point curve data is to collect electric energy meter data every 60 minutes starting from 0:00;

[0112] S23. Calculate the general value of the bottom degree estimation based on the latest data collected before the meter is removed and the fitted increment value of the meter removal period, calculate the average value of the bottom degree estimation based on the latest data collected before the meter is removed and the mean value of the fitted increment of the same period within the calculation period set to the removal date, calculate the maximum value of the bottom degree estimation based on the latest data collected before the meter is removed and the maximum value of the fitted increment of the same period within the calculation period set to the removal date, and calculate the bottom degree entry reference value based on the general value of the bottom degree estimation and the average value of the bottom degree estimation;

[0113] S24. Obtain the power data collected at each sampling point on the day of meter removal, calculate the incremental value of power collected on the day of meter removal, and obtain the power data collected for the last two times before meter removal, the time point of meter removal, and the time point of the last collection before meter removal, calculate the fitting incremental value of the meter removal period on the day of meter removal in combination with the sampling period, and calculate the fitting incremental value, fitting incremental mean and maximum value of the same period within the calculation period set from the removal date;

[0114] S25. Calculate the general value of the bottom degree estimation based on the latest daily frozen indication of meter removal, the latest power data collected before meter removal and the fitted increment value of the meter removal period; calculate the average value of the bottom degree estimation based on the latest daily frozen indication of meter removal, the latest data collected before meter removal and the mean value of the fitted increment of the same period within the calculation period set to the removal date; calculate the maximum value of the bottom degree estimation based on the latest daily frozen indication of meter removal, the latest data collected before meter removal and the maximum value of the fitted increment of the same period within the calculation period set to the removal date; calculate the bottom degree entry reference value based on the general value of the bottom degree estimation and the average value of the bottom degree estimation;

[0115] The specific steps of step S3 are as follows:

[0116] S31. Input bottom data;

[0117] S32. Obtain the most recent daily frozen indication before meter removal and the calculated maximum value of the bottom density estimate and the reference value for bottom density entry;

[0118] S33. Verify whether the bottom meter data is not less than the most recent daily frozen value before meter reading and is not greater than the maximum value of the bottom meter estimate;

[0119] If yes, go to step S34;

[0120] If not, proceed to step S36;

[0121] S34. Calculate the difference between the input base data and the reference value of the base input to obtain the base error;

[0122] S35. Verify whether the bottom data error is less than a preset verification threshold;

[0123] If so, it is determined that the verification has been passed;

[0124] If not, proceed to step S36;

[0125] S36. Add data anomaly flag and end.

[0126] In some embodiments, step S22 includes the following steps:

[0127] S221. Obtain the two most recent collected power data W(n) and W(n-1) before meter removal, the meter removal time point t(x), and the most recent collection time point t(n) before meter removal, and calculate the fitting increment ΔW(x) within the meter removal period on the day of meter removal by the following formula:

[0128] ΔW(x)=[(W(n)-W(n-1)) / zt]*(t(x)-t(n))

[0129] Among them, zt is the sampling period; taking the 96-point curve data as an example, data is collected every 15 minutes, and zt is 15. Similarly, the corresponding zt of the 48-point curve data is 30, and the corresponding zt of the 24-point curve data is 60;

[0130] S222. Calculate the fitting increment value ΔW(x) of the same period within the calculation cycle m set from the demolition date by the following formula: _m :

[0131] ΔW(x) _m =[(W(n) _m -W(n-1) _m ) / zt]*(t(x)-t(n));

[0132] S223. Calculate the mean value of the fitted increment value ΔW(x) in the same period within the calculation period m set from the demolition date by the following formula: _average :

[0133] Among them, 1≤i≤m;

[0134] S224. Calculate the maximum value of the fitting increment value ΔW(x) in the same period within the calculation cycle m set from the demolition date by the following formula_ max :

[0135] ΔW(x) _max =max(ΔW(x) _1 ,…ΔW(x) _m );

[0136] The specific steps of step S23 are as follows:

[0137] S231. Calculate the general value W(x) of the base estimate based on the most recent data W(n) collected before meter removal and the fitting increment value ΔW(x) during the meter removal period using the following formula:

[0138] W(x)=W(n)+ΔW(x);

[0139] S232. According to the most recent data W(n) collected before the meter was removed and the fitted increment mean ΔW(x) of the same period within the calculation period set from the removal date _average The estimated mean value W(x) of the base is calculated by the following formula: _average :

[0140] W(x) _average =W(n)+ΔW(x) _average ;

[0141] S233. According to the most recent data W(n) collected before the meter was removed and the maximum value of the fitted increment ΔW(x) in the same period of the calculation cycle set from the removal date _max The maximum value of the base estimation W(x) is calculated by the following formula: _max :

[0142] W(x) _max =W(n)+ΔW(x) _max ;

[0143] S234. Calculate the base depth input reference value W(c) according to the base depth estimated general value W(x) and the base depth estimated average value W(x)_average by the following formula:

[0144] W(c)=(W(x)+W(x) _average ) / 2;

[0145] The specific steps of step S24 are as follows:

[0146] S241. Obtain the power data collected at each sampling point on the day of meter removal, and calculate the power collection increment value ΔW(n) on the day of meter removal using the following formula:

[0147]

[0148] Wherein, n and i are both integers and 1<n≤1440 / zt, P(i) is the power value at the i-th time point, and P(1) is the power value at the first sampling point, i.e., 0:00;

[0149] It should be noted that the 60 in the formula is the conversion of the power value P into the electric energy indication value W;

[0150] S242. Obtain the two most recent power data collected before meter removal, P(n) and P(n-1), the meter removal time t(x), and the most recent collection time t(n) before meter removal, and calculate the fitted power value P(x) at the meter removal time on the day of meter removal by the following formula:

[0151] P(x)=P(n)+[(P(n)-P(n-1)) / zt]*(t(x)-t(n))

[0152] Among them, zt is the sampling period;

[0153] S243. Calculate the fitting increment value ΔW(x) within the meter removal time period on the day of meter removal according to the fitted power value P(x) at the meter removal time point on the day of meter removal and the most recently collected power data P(n) before meter removal by the following formula:

[0154] ΔW(x)=((P(x)+P(n)) / 2)*(t(x)-t(n)) / 60

[0155] =(P(x)+P(n))*(t(x)-t(n)) / 120;

[0156] S244. Calculate the fitted power value P(x) at the same point within the calculation period m from the demolition date by the following formula: _m :

[0157] P(x) _m =P(n) _m +[(P(n) _m -P(n-1) _m ) / zt]*(t(x)-t(n))

[0158] Among them, P(n) _m 、P(n-1) _m are respectively the most recent data collected before and after the same time point m days before the meter is removed; S245. Calculate the fitting increment value ΔW(x) of the same period within the calculation cycle m set from the removal date by the following formula: _m :

[0159] ΔW(x) _m =((P(x) _m +P(n) _m ) / 2)*(t(x)-t(n)) / 60

[0160] =(P(x) _m +P(n) _m )*(t(x)-t(n)) / 120

[0161] Among them, P(n) _m The value of the most recently collected data before the same time point m days before the meter was removed;

[0162] S246. Calculate the mean value of the fitted increment value ΔW(x) in the same period within the calculation period m set from the demolition date by the following formula: _average :

[0163] Among them, 1≤i≤m;

[0164] S247. Calculate the maximum value of the fitting increment value ΔW(x) in the same period within the calculation cycle m set from the demolition date by the following formula_ max :

[0165] ΔW(x) _max =max(ΔW(x) _1 ,…ΔW(x) _m );

[0166] The specific steps of step S25 are as follows:

[0167] S251. Calculate the general value W(x) of the base estimate based on the most recent daily frozen indication W(d) of the meter removal, the power collection increment value ΔW(n) on the day of meter removal, and the fitting increment value ΔW(x) during the meter removal period by the following formula:

[0168] W(x)=W(d)+ΔW(n)+ΔW(x);

[0169] It should be noted that the daily frozen indication is the electric energy indication collected at 0:00 every day;

[0170] S252. According to the most recent daily frozen indication W(d) of the meter removal, the power collection increment value ΔW(n) on the day of meter removal, and the fitted increment mean ΔW(x) of the same period within the calculation cycle set from the removal date _average The estimated mean value W(x) of the base is calculated by the following formula: _average :

[0171] W(x) _average =W(d)+ΔW(n)+ΔW(x) _average ;

[0172] S253. According to the most recent daily frozen indication W(d) of the meter removal, the power collection increment value ΔW(n) on the day of meter removal, and the maximum value of the fitted increment ΔW(x) in the same period within the calculation cycle set from the removal date _max The maximum value of the base estimation W(x) is calculated by the following formula: _max :

[0173] W(x) _max =W(d)+ΔW(n)+ΔW(x) _max ;

[0174] S254. Estimate the general value W(x) and the average value W(x) based on the base degree _average The bottom depth input reference value W(c) is calculated by the following formula:

[0175] W(c)=(W(x)+W(x)_average ) / 2;

[0176] In step S33, the bottom depth data is verified by the following formula:

[0177] W(d)≤W≤W(x) _max ;

[0178] Among them, W is the input bottom data, W(d) is the most recent daily frozen indication before the meter is removed, and W(x) _max Estimate the maximum value for the base degree;

[0179] After the comprehensive multiple is taken into account in step S35, the bottom depth data is verified by the following formula:

[0180] δ=|WW(c)|*K=|WW(c)|*K(TV)*K(TA)

[0181] δ<A

[0182] Among them, δ is the error estimate, A is the preset verification threshold, W(c) is the reference value for bottom input, K is the comprehensive multiplier, K(TV) and K(TA) are the voltage transformer and current transformer ratios respectively.

[0183] In some embodiments, step S36 includes the following steps:

[0184] S361. Count the number of bottom data entries and detect whether the number of entries exceeds the maximum number of entries; for example, the maximum number of entries can be set to 3 times;

[0185] If yes, go to step S362;

[0186] If not, return to step S31;

[0187] S362. Add data exception mark and end.

[0188] It should be understood that the order of execution of the steps in the above embodiment does not mean that the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0189] The following is an embodiment of a system for verifying the accuracy of data entry of the bottom measurement of a returned electric energy meter provided in an embodiment of the present disclosure. This system and the method for verifying the accuracy of data entry of the bottom measurement of a returned electric energy meter in the above-mentioned embodiments belong to the same inventive concept. For details not fully described in the embodiment of the system for verifying the accuracy of data entry of the bottom measurement of a returned electric energy meter, reference may be made to the embodiment of the method for verifying the accuracy of data entry of the bottom measurement of a returned electric energy meter in the above-mentioned embodiments.

[0190] like Figure 2 As shown, the system includes:

[0191] A data collection module is used to obtain the removal time of the electric energy meter, collect the removal date of the electric energy meter and the data within a set calculation period from the removal date;

[0192] The bottom load data estimation parameter calculation module is used to calculate the meter removal date, meter removal period and fitting increment parameters within the set calculation period from the removal date according to the load change trend, and calculate the bottom load estimation general value, bottom load estimation average value, bottom load estimation maximum value and bottom load entry reference value according to the calculated fitting increment parameters;

[0193] The bottom data verification module is used to enter the bottom data, calculate the bottom data error estimate based on the entered bottom data and the bottom data entry reference value, verify whether the bottom data entry value is between the most recent daily frozen indication before the meter is removed and the bottom data estimated maximum value, and verify whether the bottom data error estimate is less than a preset verification threshold.

[0194] The method for checking the accuracy of data entry of the bottom of the dismantled electric energy meter provided by the present invention is a module and algorithm step of each example described in combination with the embodiments disclosed in this article, and can be implemented by electronic hardware, computer software or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0195] Those skilled in the art will appreciate that the various aspects of the method for checking the accuracy of data entry of the dismantled electric energy meter provided by the present invention can be implemented as a system, method or program product. Therefore, various aspects of the present disclosure can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software, which can be collectively referred to as "circuit", "module" or "system" here.

[0196] In the non-transitory computer-readable storage medium storing the method for checking the accuracy of data entry of the disassembled electric energy meter, it can be, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0197] Although the present invention has been described in detail with reference to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person of ordinary skill in the art may easily think of changes or substitutions within the technical scope disclosed by the present invention, and these shall be within the scope of protection of the present invention.

Claims

1. A method for checking the accuracy of data entry of a dismantled electric energy meter, characterized in that: The steps include: S1. Obtain the time of meter removal, collect the meter removal date and the data within the calculation period set from the removal date; S2. Calculate the meter removal date, meter removal period and fitting increment parameters within the set calculation period from the removal date according to the load change trend, and calculate the general value of the bottom degree estimation, the average value of the bottom degree estimation, the maximum value of the bottom degree estimation and the reference value of the bottom degree entry according to the calculated fitting increment parameters; S3. Enter the bottom degree data, calculate the bottom degree data error estimate based on the entered bottom degree data and the bottom degree entry reference value, verify whether the bottom degree data entry value is between the most recent daily frozen indication before meter removal and the bottom degree estimated maximum value, and verify whether the bottom degree data error estimate is less than the preset verification threshold.

2. The method for verifying the accuracy of data entry of a dismantled electric energy meter according to claim 1, characterized in that: The specific steps of step S1 are as follows: S11. Obtain the time of meter removal, identify the date and time of removal; S12. Acquire the collected data of each sampling point before the meter removal period on the removal date of the electric energy meter and the collected data of each sampling point within the set calculation period from the removal date.

3. The method for verifying the accuracy of data entry of a dismantled electric energy meter according to claim 1, characterized in that: The specific steps of step S2 are as follows: S21. Identify the type of data collected; When the collected data is electric energy indication data, proceed to step S22; When the collected data is power data, proceed to step S24; S22. Obtain the two most recent collected power data before meter removal, the meter removal time point, and the most recent collection time point before meter removal, calculate the fitting increment value of the meter removal period on the day of meter removal in combination with the sampling period, and calculate the fitting increment value, fitting increment mean, and fitting increment maximum value of the same period within the calculation period set from the removal date; S23. Calculate the general value of the bottom degree estimation based on the latest data collected before the meter is removed and the fitted increment value of the meter removal period, calculate the average value of the bottom degree estimation based on the latest data collected before the meter is removed and the mean value of the fitted increment of the same period within the calculation period set to the removal date, calculate the maximum value of the bottom degree estimation based on the latest data collected before the meter is removed and the maximum value of the fitted increment of the same period within the calculation period set to the removal date, and calculate the bottom degree entry reference value based on the general value of the bottom degree estimation and the average value of the bottom degree estimation; S24. Obtain the power data collected at each sampling point on the day of meter removal, calculate the incremental value of power collected on the day of meter removal, and obtain the power data collected for the last two times before meter removal, the time point of meter removal, and the time point of the last collection before meter removal, calculate the fitting incremental value of the meter removal period on the day of meter removal in combination with the sampling period, and calculate the fitting incremental value, fitting incremental mean and maximum value of the same period within the calculation period set from the removal date; S25. Calculate the general value of the bottom degree estimate based on the latest daily frozen indication of meter removal, the latest power data collected before meter removal and the fitted incremental value of the meter removal period; calculate the average value of the bottom degree estimate based on the latest daily frozen indication of meter removal, the latest data collected before meter removal and the mean of the fitted incremental values ​​of the same period within the set calculation period from the removal date; calculate the maximum value of the bottom degree estimate based on the latest daily frozen indication of meter removal, the latest data collected before meter removal and the maximum value of the fitted incremental values ​​of the same period within the set calculation period from the removal date; calculate the reference value for bottom degree entry based on the general value of the bottom degree estimate and the average value of the bottom degree estimate.

4. The method for verifying the accuracy of data entry of a dismantled electric energy meter as claimed in claim 3, characterized in that: The specific steps of step S22 are as follows: S221. Obtain the two most recent collected power data W(n) and W(n-1) before meter removal, the meter removal time point t(x), and the most recent collection time point t(n) before meter removal, and calculate the fitting increment ΔW(x) within the meter removal period on the day of meter removal by the following formula: ΔW(x)=[(W(n)-W(n-1)) / zt]*(t(x)-t(n)) Among them, zt is the sampling period; S222. Calculate the fitting increment value ΔW(x) of the same period within the calculation cycle m set from the demolition date by the following formula: _m : ΔW(x) _m =[(W(n) _m -W(n-1) _m ) / zt]*(t(x)-t(n)); S223. Calculate the mean value of the fitted increment value ΔW(x) in the same period within the calculation period m set from the demolition date by the following formula: _average : Among them, 1≤i≤m; S224. Calculate the maximum value of the fitting increment value ΔW(x) in the same period within the calculation cycle m set from the demolition date by the following formula_ max : ΔW(x) _max =max(ΔW(x) _1 ,…ΔW(x) _m ); The specific steps of step S23 are as follows: S231. Calculate the general value W(x) of the base estimate based on the most recent data W(n) collected before meter removal and the fitting increment value ΔW(x) during the meter removal period using the following formula: W(x)=W(n)+ΔW(x); S232. According to the most recent data W(n) collected before the meter was removed and the fitted increment mean ΔW(x) of the same period within the calculation period set from the removal date _average The estimated mean value W(x) of the base is calculated by the following formula: _average : W(x) _average =W(n)+ΔW(x) _average ; S233. According to the most recent data W(n) collected before the meter was removed and the maximum value of the fitted increment ΔW(x) in the same period of the calculation cycle set from the removal date _max The maximum value of the base estimation W(x) is calculated by the following formula: _max : W(x) _max =W(n)+ΔW(x) _max ; S234. Calculate the base depth input reference value W(c) according to the base depth estimated general value W(x) and the base depth estimated average value W(x)_average by the following formula: W(c)=(W(x)+W(x) _average ) / 2。 5. The method for verifying the accuracy of data entry of a dismantled electric energy meter as claimed in claim 3, characterized in that: The specific steps of step S24 are as follows: S241. Obtain the power data collected at each sampling point on the day of meter removal, and calculate the power collection increment value ΔW(n) on the day of meter removal using the following formula: Wherein, n and i are both integers and 1<n≤1440 / zt, P(i) is the power value at the i-th time point, and P(1) is the power value at the first sampling point, i.e., 0:00; S242. Obtain the power data P(n) and P(n-1) collected the last two times before the meter is removed, the time point t(x) of the meter removal, and the time point t(n) of the last collection before the meter is removed, and calculate the fitted power value P(x) at the time point of the meter removal on the day of the meter removal by the following formula: P(x)=P(n)+[(P(n)-P(n-1)) / zt]*(t(x)-t(n)) Among them, zt is the sampling period; S243. Calculate the fitting increment value ΔW(x) within the meter removal time period on the day of meter removal according to the fitted power value P(x) at the meter removal time point on the day of meter removal and the most recently collected power data P(n) before meter removal by the following formula: ΔW(x)=((P(x)+P(n)) / 2)*(t(x)-t(n)) / 60 =(P(x)+P(n))*(t(x)-t(n)) / 120; S244. Calculate the fitted power value P(x) at the same point within the calculation period m from the demolition date by the following formula: _m : P(x) _m =P(n) _m +[(P(n) _m -P(n-1) _m ) / zt]*(t(x)-t(n)) Among them, P(n) _m 、P(n-1) _m They are the most recently collected data values ​​before and after the same time point m days before the meter was removed; S245. Calculate the fitting increment value ΔW(x) of the same period within the calculation cycle m set from the demolition date by the following formula: _m : ΔW(x) _m =((P(x) _m +P(n) _m ) / 2)*(t(x)-t(n)) / 60 =(P(x) _m +P(n) _m )*(t(x)-t(n)) / 120 Among them, P(n) _m The value of the most recently collected data before the same time point m days before the meter was removed; S246. Calculate the mean value of the fitted increment value ΔW(x) in the same period within the calculation period m set from the demolition date by the following formula: _average : Among them, 1≤i≤m; S247. Calculate the maximum value of the fitting increment value ΔW(x) in the same period within the calculation cycle m set from the demolition date by the following formula_ max : ΔW(x) _max =max(ΔW(x) _1 ,…ΔW(x) _m ); The specific steps of step S25 are as follows: S251. Calculate the general value W(x) of the base estimate based on the most recent daily frozen indication W(d) of the meter removal, the power collection increment value ΔW(n) on the day of meter removal, and the fitting increment value ΔW(x) during the meter removal period by the following formula: W(x)=W(d)+ΔW(n)+ΔW(x); S252. According to the most recent daily frozen indication W(d) of the meter removal, the power collection increment value ΔW(n) on the day of meter removal, and the fitted increment mean ΔW(x) of the same period within the calculation cycle set from the removal date _average The estimated mean value W(x) of the base is calculated by the following formula: _average : W(x) _average =W(d)+ΔW(n)+ΔW(x) _average ; S253. According to the most recent daily frozen indication W(d) of the meter removal, the power collection increment value ΔW(n) on the day of meter removal, and the maximum value of the fitted increment ΔW(x) in the same period within the calculation cycle set from the removal date _max The maximum value of the base estimation W(x) is calculated by the following formula: _max : W(x) _max =W(d)+ΔW(n)+ΔW(x) _max ; S254. Estimate the general value W(x) and the average value W(x) based on the base degree _average The bottom depth input reference value W(c) is calculated by the following formula: W(c)=(W(x)+W(x) _average ) / 2。 6. The method for verifying the accuracy of data entry of a dismantled electric energy meter as claimed in claim 3, characterized in that: The specific steps of step S3 are as follows: S31. Input bottom data; S32. Obtain the most recent daily frozen indication before meter removal and the calculated maximum value of the bottom density estimate and the reference value for bottom density entry; S33. Verify whether the bottom meter data is not less than the most recent daily frozen value before meter reading and is not greater than the maximum value of the bottom meter estimate; If yes, go to step S34; If not, proceed to step S36; S34. Calculate the difference between the input base data and the reference value of the base input to obtain the base error; S35. Verify whether the bottom data error is less than a preset verification threshold; If so, it is determined that the verification has passed; If not, proceed to step S36; S36. Add data anomaly flag and end.

7. The method for verifying the accuracy of data entry of a dismantled electric energy meter as claimed in claim 6, characterized in that: The specific steps of step S36 are as follows: S361 counts the number of bottom data entries and detects whether the number of entries exceeds the maximum number of entries; if so, proceeds to step S362; If not, return to step S31; S362. Add data exception mark and end.

8. A system for checking the accuracy of data entry of dismantled electric energy meters, characterized in that: include: A data collection module is used to obtain the removal time of the electric energy meter, collect the removal date of the electric energy meter and the data within a set calculation period from the removal date; The bottom load data estimation parameter calculation module is used to calculate the meter removal date, meter removal period and fitting increment parameters within the set calculation period from the removal date according to the load change trend, and calculate the bottom load estimation general value, bottom load estimation average value, bottom load estimation maximum value and bottom load entry reference value according to the calculated fitting increment parameters; The bottom data verification module is used to enter the bottom data, calculate the bottom data error estimate based on the entered bottom data and the bottom data entry reference value, verify whether the bottom data entry value is between the most recent daily frozen indication before the meter is removed and the bottom data estimated maximum value, and verify whether the bottom data error estimate is less than a preset verification threshold.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the electronic device executes the method described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores instructions, which, when executed on a computer, enable the computer to execute the method described in any one of claims 1 to 7.

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