Metering equipment misalignment judgment method and system based on Internet of Things

Abstract

The invention belongs to the field of metering, and particularly relates to a metering equipment misalignment judgment method and system based on the Internet of Things, and the method comprises the steps: obtaining meter error values of metering equipment, comparing the sum of the meter error values in a monitoring period with a meter error value threshold value in the monitoring period, obtaining a metering qualified signal and a metering unqualified signal according to a comparison result; analyzing the coincidence number of the voltage sub-curve and the meter error value sub-curve to obtain a voltage influence value; processing the voltage fluctuation sub-curve and the meter error fluctuation sub-curve, and analyzing the change synchronization value of the voltage fluctuation and the meter error value; on the basis of the change synchronization value, analyzing whether other factors influence meter errors exist or not; according to the method, whether factors causing misalignment of the metering equipment are mainly caused by the voltage or not and the influence degree can be accurately analyzed through a method for judging the double influence between the voltage and the error of the metering equipment, and other factors can be searched.

Description

Technical Field

[0001] The invention belongs to the field of measurement, and specifically relates to a method and system for judging inaccuracy of measurement equipment based on the Internet of Things. Background Art

[0002] Chinese patent CN 114371438 B discloses a method for judging the inaccuracy of metering equipment based on the Internet of Things, which belongs to the technical field of electric energy meter detection. The specific method includes: step 1: obtaining the power supply area to be monitored, dividing the obtained power supply area to form different monitoring areas, and setting fixed management personnel for each monitoring area; step 2: checking the total power error in the monitoring area, and marking the monitoring area with metering error as an abnormal area; step 3: obtaining an analysis model corresponding to the abnormal area, collecting meter metering data of each user in the abnormal area in the current time period, analyzing the collected meter metering data through the analysis model, obtaining the electric energy meter corresponding to the abnormal meter metering data, and marking it as an abnormal meter; step 4: establishing an electric energy meter analysis library, obtaining the metering data of the abnormal meter N days before, N is a positive integer, inputting the obtained metering data into the electric energy meter analysis library for analysis, and obtaining the inaccuracy judgment result of the abnormal meter;

[0003] In the prior art, when a metering device is inaccurate, it is not possible to accurately analyze whether the factor causing the inaccuracy of the metering device is mainly caused by voltage and the extent of the influence, and there is no way to find other factors. Summary of the invention

[0004] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a method for judging inaccuracy of a measuring device based on the Internet of Things, comprising:

[0006] Step 1: Obtain the meter error value of the metering equipment, analyze it, obtain the sum of the meter error values ​​within the monitoring period, compare the sum of the meter error values ​​within the monitoring period with the meter error value threshold within the monitoring period, and obtain a metering qualified signal and a metering unqualified signal according to the comparison result;

[0007] Step 2: Based on the unqualified signal, the meter error value and voltage parameters within the monitoring period are obtained, an XY two-dimensional coordinate system is constructed, the monitoring period is divided to obtain a voltage sub-curve and a meter error value sub-curve, and the voltage impact value is obtained based on the analysis of the number of overlaps between the voltage sub-curve and the meter error value sub-curve;

[0008] Step 3: Compare the obtained voltage impact value with the voltage impact threshold, analyze according to the comparison result, and generate an analysis signal. The comparison process is as follows:

[0009] If the voltage impact value is less than the voltage impact threshold, it is a normal signal;

[0010] If the voltage impact value is greater than or equal to the voltage impact threshold, then an abnormal signal is generated;

[0011] Step 4: Based on the abnormal signal, the voltage fluctuation sub-curve and the meter error fluctuation sub-curve are processed to analyze the change synchronization value of the voltage fluctuation and the meter error value; based on the change synchronization value, analyze whether there are other factors affecting the meter error.

[0012] As a further technical solution of the present invention: in step 1, if the sum of the meter error values ​​within the monitoring period is greater than or equal to the meter error value threshold, a metering failure signal is generated;

[0013] If the sum of the meter error values ​​within the monitoring period is less than the meter error value threshold, a metering qualified signal is generated.

[0014] As a further technical solution of the present invention: In step 2, the voltage impact value is obtained in the following manner:

[0015] The time corresponding to the overlapped part of the voltage abnormal sub-curve and the meter error value abnormal sub-curve is marked as abnormal time; the abnormal time is summed to obtain the total abnormal time value;

[0016] The obtained total abnormal time value is ratioed with the total duration value of the monitoring period to obtain the voltage impact value.

[0017] As a further technical solution of the present invention: the obtained voltage fluctuation curve is divided into a plurality of small segments of equal time, which are marked as voltage fluctuation sub-curves; the voltage average value of the voltage fluctuation sub-curve is obtained, and the voltage average value of the voltage fluctuation sub-curve is subjected to difference processing with the voltage threshold value, and the absolute value is taken to obtain the voltage fluctuation difference;

[0018] The voltage fluctuation difference is compared with the voltage fluctuation threshold, and the comparison result is analyzed to generate an analysis signal. Specifically:

[0019] If the voltage fluctuation difference is less than the voltage fluctuation threshold, a voltage fluctuation normal signal is generated;

[0020] If the voltage fluctuation difference is greater than or equal to the voltage fluctuation threshold, a voltage fluctuation abnormal signal is generated;

[0021] Based on the voltage fluctuation abnormality signal, a voltage fluctuation sub-curve whose voltage fluctuation difference is greater than or equal to the voltage fluctuation threshold is marked as a voltage abnormality sub-curve.

[0022] As a further technical solution of the present invention: the meter error value sub-curve and the meter error value fluctuation difference are obtained in the same way as the voltage fluctuation sub-curve and the voltage fluctuation difference, and the meter error value fluctuation difference is compared with the meter error value fluctuation difference threshold, and an analysis is performed according to the comparison result to generate an analysis signal, specifically:

[0023] If the meter error value fluctuation difference is less than the meter error value fluctuation difference threshold, a meter error value fluctuation normal signal is generated;

[0024] If the meter error value fluctuation difference is greater than or equal to the meter error value fluctuation difference threshold, a meter error value fluctuation abnormal signal is generated;

[0025] Based on the meter error value fluctuation abnormal signal, a meter error value fluctuation sub-curve whose meter error value fluctuation difference is greater than or equal to a meter error value fluctuation difference threshold is marked as a meter error value abnormal sub-curve.

[0026] As a further technical solution of the present invention: in step three, if the voltage impact value is less than the voltage impact threshold, then it is a normal signal;

[0027] If the voltage impact value is greater than or equal to the voltage impact threshold, an abnormal signal is generated.

[0028] As a further technical solution of the present invention: in step 4, based on the abnormal signal, the voltage fluctuation sub-curve and the meter error fluctuation sub-curve are processed to obtain the slope of each section of the voltage fluctuation sub-curve and the meter error fluctuation sub-curve;

[0029] Arranging the slopes of the voltage fluctuation sub-curves in time sequence to obtain a slope group of the voltage fluctuation sub-curves, and arranging the meter error fluctuation sub-curves in time sequence to obtain a slope group of the meter error fluctuation sub-curves;

[0030] The corresponding slopes of the slope group of the voltage fluctuation sub-curve and the slope group of the meter error fluctuation sub-curve are processed by difference in time sequence to obtain a slope difference group; the variance of the data in the slope difference group is calculated to obtain a synchronous change value.

[0031] As a further technical solution of the present invention: if the synchronous change value is less than or equal to the synchronous change threshold, a normal signal is generated;

[0032] If the synchronous change value is greater than the synchronous change threshold, an abnormal signal is generated.

[0033] A measurement equipment inaccuracy judgment system based on the Internet of Things, comprising: a preliminary judgment module: obtaining a meter error value of a measurement equipment, analyzing it, obtaining a sum of the meter error values ​​within a monitoring period, comparing the sum of the meter error values ​​within the monitoring period with a meter error value threshold within the monitoring period, and obtaining a measurement qualified signal and a measurement unqualified signal according to the comparison result;

[0034] Impact analysis module: Based on the unqualified signal, the meter error value and voltage parameters within the monitoring period are obtained, an XY two-dimensional coordinate system is constructed, the monitoring period is divided into voltage sub-curves and meter error value sub-curves, and the voltage impact value is obtained by analyzing the number of overlaps between the voltage sub-curve and the meter error value sub-curve;

[0035] Second judgment module: compare the obtained voltage impact value with the voltage impact threshold, analyze according to the comparison result, and generate an analysis signal. The comparison process is as follows:

[0036] If the voltage impact value is less than the voltage impact threshold, it is a normal signal;

[0037] If the voltage impact value is greater than or equal to the voltage impact threshold, an abnormal signal is generated.

[0038] As a further technical solution of the present invention: the final judgment module: based on the abnormal signal, the voltage fluctuation sub-curve and the meter error fluctuation sub-curve are processed to analyze the change synchronization value of the voltage fluctuation and the meter error value; based on the change synchronization value, it is analyzed whether there are other factors affecting the meter error.

[0039] The beneficial effects of the present invention are as follows:

[0040] The present invention obtains the meter error value of the metering device, analyzes it, obtains the sum of the meter error values ​​within the monitoring period, compares the sum of the meter error values ​​within the monitoring period with the meter error value threshold within the monitoring period, and obtains a metering qualified signal and a metering unqualified signal according to the comparison result; based on the unqualified signal, obtains the meter error value and voltage parameter within the monitoring period, constructs an XY two-dimensional coordinate system, divides the monitoring period to obtain a voltage sub-curve and a meter error value sub-curve, and obtains a voltage influence value based on the analysis of the overlap number of the voltage sub-curve and the meter error value sub-curve; compares and processes the obtained voltage influence value with the voltage influence threshold, analyzes according to the comparison result, generates an analysis signal, processes the voltage fluctuation sub-curve and the meter error fluctuation sub-curve based on the abnormal signal, and analyzes the voltage fluctuation and the change synchronization value of the meter error value; based on the change synchronization value, analyzes whether there are other factors affecting the meter error; the present invention can accurately analyze whether the factor causing the inaccuracy of the metering device is mainly caused by the voltage and the degree of influence through the dual influence judgment method between the voltage and the metering device error, and can also search for other factors, which will greatly improve the accuracy and diversity of the judgment of the inaccuracy of the metering device. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention will be further described below in conjunction with the accompanying drawings.

[0042] Figure 1 It is a module diagram of a method for determining inaccuracy of a metering device based on the Internet of Things according to an embodiment of the present invention;

[0043] Figure 2 The present invention is a system block diagram of a measuring equipment inaccuracy judgment system based on the Internet of Things according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0045] Example 1

[0046] like Figure 1 As shown, a method and system for determining inaccuracy of a metering device based on the Internet of Things according to an embodiment of the present invention include:

[0047] Step 1: Obtain the meter error value of the metering equipment, analyze it, obtain the sum of the meter error values ​​within the monitoring period, compare the sum of the meter error values ​​within the monitoring period with the meter error value threshold within the monitoring period, and obtain a metering qualified signal and a metering unqualified signal according to the comparison result;

[0048] If the sum of the meter error values ​​within the monitoring period is greater than or equal to the meter error value threshold, a metering failure signal is generated;

[0049] If the sum of the meter error values ​​within the monitoring period is less than the meter error value threshold, a metering qualified signal is generated;

[0050] Step 2: Based on the unqualified signal, the meter error value and voltage parameters within the monitoring period are obtained, an XY two-dimensional coordinate system is constructed, the monitoring period is divided to obtain a voltage sub-curve and a meter error value sub-curve, and the voltage impact value is obtained based on the analysis of the number of overlaps between the voltage sub-curve and the meter error value sub-curve;

[0051] With the monitoring period as the X-axis and the voltage parameter within the monitoring period as the Y-axis, an XY two-dimensional coordinate system is constructed, and the obtained voltage parameters within the monitoring period are marked and connected on the XY two-dimensional coordinate system to obtain a fluctuation curve of the voltage parameters within the monitoring period; the obtained voltage fluctuation curve is divided into a number of small segments with equal time, marked as voltage fluctuation sub-curves; the voltage average value of the voltage fluctuation sub-curve is obtained, and the voltage average value of the voltage fluctuation sub-curve is subjected to difference processing with the voltage threshold, and the absolute value is taken to obtain the voltage fluctuation difference;

[0052] The voltage fluctuation difference is compared with the voltage fluctuation threshold, and the comparison result is analyzed to generate an analysis signal. Specifically:

[0053] If the voltage fluctuation difference is less than the voltage fluctuation threshold, a voltage fluctuation normal signal is generated;

[0054] If the voltage fluctuation difference is greater than or equal to the voltage fluctuation threshold, a voltage fluctuation abnormal signal is generated;

[0055] Based on the voltage fluctuation abnormality signal, a voltage fluctuation sub-curve whose voltage fluctuation difference is greater than or equal to the voltage fluctuation threshold is marked as a voltage abnormality sub-curve;

[0056] With the monitoring period as the X-axis and the meter error value within the monitoring period as the Y-axis, an XY two-dimensional coordinate system is constructed, and the meter error values ​​within the monitoring period are marked and connected on the XY two-dimensional coordinate system to obtain a fluctuation curve of the meter error value within the monitoring period;

[0057] The meter error value sub-curve and the meter error value fluctuation difference are obtained in the same way as the voltage fluctuation sub-curve and the voltage fluctuation difference, and the meter error value fluctuation difference is compared with the meter error value fluctuation difference threshold, and an analysis is performed according to the comparison result to generate an analysis signal, specifically:

[0058] If the meter error value fluctuation difference is less than the meter error value fluctuation difference threshold, a meter error value fluctuation normal signal is generated;

[0059] If the meter error value fluctuation difference is greater than or equal to the meter error value fluctuation difference threshold, a meter error value fluctuation abnormal signal is generated;

[0060] Based on the meter error value fluctuation abnormal signal, a meter error value fluctuation sub-curve whose meter error value fluctuation difference is greater than or equal to a meter error value fluctuation difference threshold is marked as a meter error value abnormal sub-curve;

[0061] The time corresponding to the overlapped part of the voltage abnormal sub-curve and the meter error value abnormal sub-curve is marked as abnormal time; the abnormal time is summed to obtain the total abnormal time value;

[0062] The obtained total abnormal time value is compared with the total duration value of the monitoring period to obtain the voltage impact value;

[0063] Step 3: Compare the obtained voltage impact value with the voltage impact threshold, analyze according to the comparison result, and generate an analysis signal. The comparison process is as follows:

[0064] If the voltage impact value is less than the voltage impact threshold, it is a normal signal;

[0065] If the voltage impact value is greater than or equal to the voltage impact threshold, then an abnormal signal is generated;

[0066] It should be noted that the generation of a normal signal indicates that the voltage fluctuation during the monitoring period has no effect on the meter error; the generation of an abnormal signal indicates that the voltage fluctuation during the monitoring period has an effect on the meter error;

[0067] The above technical solution is: obtain the meter error and voltage parameters within the monitoring period, construct an XY two-dimensional coordinate system, divide the monitoring period into a voltage anomaly sub-curve and a meter error anomaly sub-curve, and analyze the number of overlaps between the voltage anomaly sub-curve and the meter error anomaly sub-curve to obtain the voltage impact value; based on data processing of the voltage impact value, it is convenient to determine whether the voltage fluctuation in the current time is affected by the meter error.

[0068] Step 4: Based on the abnormal signal, the voltage fluctuation sub-curve and the meter error fluctuation sub-curve are processed to analyze the change synchronization value of the voltage fluctuation and the meter error value; based on the change synchronization value, whether there are other factors affecting the meter error;

[0069] Based on the abnormal signal, the voltage fluctuation sub-curve and the meter error fluctuation sub-curve are processed to obtain the slope of each section of the voltage fluctuation sub-curve and the meter error fluctuation sub-curve;

[0070] Arranging the slopes of the voltage fluctuation sub-curves in time sequence to obtain a slope group of the voltage fluctuation sub-curves, and arranging the meter error fluctuation sub-curves in time sequence to obtain a slope group of the meter error fluctuation sub-curves;

[0071] The slope group of the voltage fluctuation sub-curve and the slope group of the meter error fluctuation sub-curve are subjected to difference processing of the corresponding slopes in chronological order to obtain a slope difference group; the variance of the data in the slope difference group is calculated to obtain a synchronous change value;

[0072] The obtained synchronous change value is compared with the synchronous change threshold, and it is determined whether there are other factors affecting the meter error according to the comparison result;

[0073] The obtained synchronization change value is compared with the synchronization change threshold, specifically:

[0074] If the synchronous change value is less than or equal to the synchronous change threshold, a normal signal is generated;

[0075] If the synchronous change value is greater than the synchronous change threshold, an abnormal signal is generated;

[0076] It should be noted that when a normal signal is generated, no other factors affect the meter error; when an abnormal signal is generated, other factors affect the meter error;

[0077] The technical solution of the embodiment of the present invention is as follows: obtaining the meter error value of the metering equipment, analyzing it, obtaining the sum of the meter error values ​​within the monitoring period, comparing the sum of the meter error values ​​within the monitoring period with the meter error value threshold within the monitoring period, and obtaining a metering qualified signal and a metering unqualified signal according to the comparison result; based on the unqualified signal, obtaining the meter error value and voltage parameter within the monitoring period, constructing an XY two-dimensional coordinate system, dividing the monitoring period to obtain a voltage sub-curve and a meter error value sub-curve, and obtaining a voltage impact value based on the number of overlaps between the voltage sub-curve and the meter error value sub-curve; and obtaining the obtained voltage The influence value is compared with the voltage influence threshold, and an analysis is performed based on the comparison result to generate an analysis signal. Based on the abnormal signal, the voltage fluctuation sub-curve and the meter error fluctuation sub-curve are processed to analyze the change synchronization value of the voltage fluctuation and the meter error value; based on the change synchronization value, it is analyzed whether there are other factors affecting the meter error; the present invention uses a dual influence judgment method between voltage and metering equipment error to accurately analyze whether the factor causing the inaccuracy of the metering equipment is mainly caused by the voltage, as well as the degree of influence, and can also search for other factors, which will greatly improve the accuracy and diversity of the judgment of the inaccuracy of the metering equipment.

[0078] Example 2

[0079] like Figure 2As shown, a method and system for determining inaccuracy of a metering device based on the Internet of Things according to an embodiment of the present invention include:

[0080] Preliminary judgment module: obtain the meter error value of the metering equipment, analyze it, obtain the sum of the meter error values ​​within the monitoring period, compare the sum of the meter error values ​​within the monitoring period with the meter error value threshold within the monitoring period, and obtain a metering qualified signal and a metering unqualified signal according to the comparison result;

[0081] Impact analysis module: Based on the unqualified signal, the meter error value and voltage parameters within the monitoring period are obtained, an XY two-dimensional coordinate system is constructed, the monitoring period is divided into voltage sub-curves and meter error value sub-curves, and the voltage impact value is obtained by analyzing the number of overlaps between the voltage sub-curve and the meter error value sub-curve;

[0082] Second judgment module: compare the obtained voltage impact value with the voltage impact threshold, analyze according to the comparison result, and generate an analysis signal. The comparison process is as follows:

[0083] If the voltage impact value is less than the voltage impact threshold, it is a normal signal;

[0084] If the voltage impact value is greater than or equal to the voltage impact threshold, then an abnormal signal is generated;

[0085] Finally, the judgment module processes the voltage fluctuation sub-curve and the meter error fluctuation sub-curve based on the abnormal signal, and analyzes the change synchronization value between the voltage fluctuation and the meter error value; based on the change synchronization value, analyzes whether there are other factors affecting the meter error.

[0086] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for judging inaccuracy of measuring equipment based on the Internet of Things, characterized in that: include: Step 1: Obtain the meter error value of the metering equipment, analyze it, obtain the sum of the meter error values ​​within the monitoring period, compare the sum of the meter error values ​​within the monitoring period with the meter error value threshold within the monitoring period, and obtain a metering qualified signal and a metering unqualified signal according to the comparison result; Step 2: Based on the unqualified signal, the meter error value and voltage parameters within the monitoring period are obtained, an XY two-dimensional coordinate system is constructed, the monitoring period is divided to obtain a voltage sub-curve and a meter error value sub-curve, and the voltage impact value is obtained based on the analysis of the number of overlaps between the voltage sub-curve and the meter error value sub-curve; Step 3: Compare the obtained voltage impact value with the voltage impact threshold, analyze according to the comparison result, and generate an analysis signal. The comparison process is as follows: If the voltage impact value is less than the voltage impact threshold, it is a normal signal; If the voltage impact value is greater than or equal to the voltage impact threshold, then an abnormal signal is generated; Step 4: Based on the abnormal signal, the voltage fluctuation sub-curve and the meter error fluctuation sub-curve are processed to analyze the change synchronization value of the voltage fluctuation and the meter error value; based on the change synchronization value, analyze whether there are other factors affecting the meter error.

2. The method for determining inaccuracy of a measuring device based on the Internet of Things according to claim 1, characterized in that: In step 1, if the sum of the meter error values ​​within the monitoring period is greater than or equal to the meter error value threshold, a metering failure signal is generated; If the sum of the meter error values ​​within the monitoring period is less than the meter error value threshold, a metering qualified signal is generated.

3. The method for determining inaccuracy of a measuring device based on the Internet of Things according to claim 2 is characterized in that: In step 2, the voltage impact value is obtained by: The time corresponding to the overlapped part of the voltage abnormal sub-curve and the meter error value abnormal sub-curve is marked as abnormal time; the abnormal time is summed to obtain the total abnormal time value; The obtained total abnormal time value is ratioed with the total duration value of the monitoring period to obtain the voltage impact value.

4. The method for determining inaccuracy of a measuring device based on the Internet of Things according to claim 3 is characterized in that: The obtained voltage fluctuation curve is divided into several small segments of equal time, which are marked as voltage fluctuation sub-curves; the voltage average value of the voltage fluctuation sub-curve is obtained, and the voltage average value of the voltage fluctuation sub-curve is subjected to difference processing with the voltage threshold value, and the absolute value is taken to obtain the voltage fluctuation difference; The voltage fluctuation difference is compared with the voltage fluctuation threshold, and the comparison result is analyzed to generate an analysis signal. Specifically: If the voltage fluctuation difference is less than the voltage fluctuation threshold, a voltage fluctuation normal signal is generated; If the voltage fluctuation difference is greater than or equal to the voltage fluctuation threshold, a voltage fluctuation abnormal signal is generated; Based on the voltage fluctuation abnormality signal, a voltage fluctuation sub-curve whose voltage fluctuation difference is greater than or equal to the voltage fluctuation threshold is marked as a voltage abnormality sub-curve.

5. The method for determining inaccuracy of a measuring device based on the Internet of Things according to claim 4 is characterized in that: The meter error value sub-curve and the meter error value fluctuation difference are obtained in the same way as the voltage fluctuation sub-curve and the voltage fluctuation difference, and the meter error value fluctuation difference is compared with the meter error value fluctuation difference threshold, and an analysis is performed according to the comparison result to generate an analysis signal, specifically: If the meter error value fluctuation difference is less than the meter error value fluctuation difference threshold, a meter error value fluctuation normal signal is generated; If the meter error value fluctuation difference is greater than or equal to the meter error value fluctuation difference threshold, a meter error value fluctuation abnormal signal is generated; Based on the meter error value fluctuation abnormal signal, a meter error value fluctuation sub-curve whose meter error value fluctuation difference is greater than or equal to a meter error value fluctuation difference threshold is marked as a meter error value abnormal sub-curve.

6. The method for determining inaccuracy of a measuring device based on the Internet of Things according to claim 5 is characterized in that: In step three, if the voltage impact value is less than the voltage impact threshold, then it is a normal signal; If the voltage impact value is greater than or equal to the voltage impact threshold, an abnormal signal is generated.

7. The method for determining inaccuracy of a measuring device based on the Internet of Things according to claim 6 is characterized in that: In step 4, based on the abnormal signal, the voltage fluctuation sub-curve and the meter error fluctuation sub-curve are processed to obtain the slope of each section of the voltage fluctuation sub-curve and the meter error fluctuation sub-curve; Arranging the slopes of the voltage fluctuation sub-curves in time sequence to obtain a slope group of the voltage fluctuation sub-curves, and arranging the meter error fluctuation sub-curves in time sequence to obtain a slope group of the meter error fluctuation sub-curves; The corresponding slopes of the slope group of the voltage fluctuation sub-curve and the slope group of the meter error fluctuation sub-curve are processed by difference in time sequence to obtain a slope difference group; the variance of the data in the slope difference group is calculated to obtain a synchronous change value.

8. The method for determining inaccuracy of a measuring device based on the Internet of Things according to claim 7, characterized in that: If the synchronous change value is less than or equal to the synchronous change threshold, a normal signal is generated; If the synchronous change value is greater than the synchronous change threshold, an abnormal signal is generated.

9. A measurement equipment inaccuracy judgment system based on the Internet of Things, characterized by: include: Preliminary judgment module: obtain the meter error value of the metering equipment, analyze it, obtain the sum of the meter error values ​​within the monitoring period, compare the sum of the meter error values ​​within the monitoring period with the meter error value threshold within the monitoring period, and obtain a metering qualified signal and a metering unqualified signal according to the comparison result; Impact analysis module: Based on the unqualified signal, the meter error value and voltage parameters within the monitoring period are obtained, an XY two-dimensional coordinate system is constructed, the monitoring period is divided into voltage sub-curves and meter error value sub-curves, and the voltage impact value is obtained by analyzing the number of overlaps between the voltage sub-curve and the meter error value sub-curve; Second judgment module: compare the obtained voltage impact value with the voltage impact threshold, analyze according to the comparison result, and generate an analysis signal. The comparison process is as follows: If the voltage impact value is less than the voltage impact threshold, it is a normal signal; If the voltage impact value is greater than or equal to the voltage impact threshold, an abnormal signal is generated.

10. The inaccuracy judgment system for measuring equipment based on the Internet of Things according to claim 9 is characterized in that: Also includes: Finally, the judgment module processes the voltage fluctuation sub-curve and the meter error fluctuation sub-curve based on the abnormal signal, and analyzes the change synchronization value of the voltage fluctuation and the meter error value; Based on the changing synchronization value, analyze whether there are other factors affecting the meter error.

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