Method for solving electrical degree data transmission error of cloud platform

Abstract

The invention discloses a method for solving an electrical degree data transmission error of a cloud platform, and the method comprises the steps: 1, monitoring the communication state of the cloud platform in real time, and sampling electrical degree values at equal intervals; step 2, acquiring a normal historical meter reading electrical degree value Wh of a previous sampling node; 3, obtaining an automatic adjustment parameter value K of each sampling node; step 4, calculating an estimated electrical degree value We of the electrical degree meter reading of each interruption point; 5, when the two continuous electrical degree values Wr are both determined to be valid, it is determined that the communication is recovered to be normal; 6, when the difference between the two exceeds a preset threshold value, smoothing is carried out; step 7, storing the processed electrical degree value data in a database; according to the method, when communication of the cloud platform is interrupted, historical data and real-time data are effectively fused, and accurate electrical degree value data are estimated; meanwhile, when communication is recovered, smoothing processing is carried out by comparing an actual value with an estimated value, and the phenomenon of data hopping is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things energy monitoring, and specifically to a method for solving the problem of incorrect electricity meter data transmission in the cloud platform. Background Art

[0002] In Internet of Things energy monitoring, electricity meter data is the key information reflecting the energy consumption of equipment. The accuracy and continuity of electricity meter data are crucial for energy management and energy-saving decision-making. However, in practical applications, due to the less stable communication of the cloud platform compared to dedicated networks, the interruption of electricity meter data has become a relatively common phenomenon. Since electricity meter data is the cumulative meter reading value, once interrupted, the electricity meter reading value may become 0 or an unknowable data, which will seriously affect the accuracy and continuity of energy monitoring. Therefore, solving the problem of incorrect electricity meter data in the cloud platform has important practical significance for improving the accuracy and reliability of energy monitoring. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for solving the problem of incorrect electricity meter data transmission in the cloud platform to solve the problems raised in the above background art.

[0004] To achieve the above purpose, the present invention provides the following technical solution: A method for solving the problem of incorrect electricity meter data transmission in the cloud platform, including:

[0005] Step 1: Real-time monitor the communication status of the cloud platform and sample the electricity meter values at equal intervals;

[0006] Step 2: When the communication of the cloud platform is interrupted, start the data processing program. In the data processing program, if the electricity meter value at this moment is not received, obtain the normal historical meter reading electricity value Wh of the previous sampling node and the historical meter reading electricity value W(h-i) of the previous preset comparison point;

[0007] Step 3: According to the duration of the communication interruption, obtain the automatic adjustment parameter value K for each sampling node;

[0008] Step 4: According to the duration, use the obtained normal historical meter reading electricity value Wh of the previous sampling node, the historical meter reading electricity value W(h-i) of the previous preset comparison point, and the real-time adjustment parameter value K to calculate the estimated electricity value We of the electricity meter reading at each interruption point;

[0009] Step 5: When the communication is restored, receive the real-time electricity meter value Wr and detect the electricity meter value Wr. When Wr < Wh, it is determined to be invalid; when Wr > Wh, it is determined to be valid, and only when two consecutive electricity meter values Wr are both determined to be valid, the communication is considered to have returned to normal;

[0010] Step 6: When the communication is restored to normal, the real-time electricity value Wr of the electricity meter reading data is obtained, and the estimated electricity value We of the electricity meter reading of the last sampling node is compared with the actual electricity value Wr. When the difference between the two exceeds the preset threshold, smoothing is performed;

[0011] Step 7: Store the processed electricity value data in a database, and view and export it through a user interface.

[0012] Among them, in step three, the automatic adjustment parameter value K is the fitting curve value of the normal historical meter reading electricity value Wh of the previous sampling node and the historical meter reading electricity value W(hi) of the previous preset comparison point.

[0013] The automatic adjustment parameter value K is a coefficient greater than 0, which is used to determine the proportion of historical data used to estimate the current electricity value. When the interruption time is short, the K value is close to 0; when the interruption time is long, the K value gradually increases.

[0014] Among them, in step 4, the calculation formula of the estimated electric value We is: We=Wh+K*(Wh-W(hi));

[0015] Where Wh is the normal historical meter reading electricity value of the previous sampling node; K is the automatic adjustment parameter value of each sampling node; W(hi) is the historical meter reading electricity value of the previous preset comparison point.

[0016] Among them, in step six, the smoothing process adopts the linear interpolation method to make the transition between the estimated energy value We and the actual energy value Wr of the electricity meter reading smoother.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] The present invention can effectively integrate historical data and real-time data to estimate more accurate electricity value data when the cloud platform communication is interrupted; at the same time, when the communication is restored, the actual value and the estimated value are compared for smoothing, thereby avoiding the jump phenomenon of data;

[0019] In addition, this method is highly versatile and extensible, and can be applied to various types of energy monitoring systems and equipment. It can be integrated into existing energy monitoring systems or used as an independent data processing module, improving the accuracy and continuity of electricity data, thereby providing more reliable support for energy management and energy-saving decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flow chart of electricity data transmission according to the present invention. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0022] In the actual cloud platform electricity data transmission process, the current electricity value Wt, the real-time meter reading electricity value of the previous sampling node at time interval i is W(ti); when the sampling interval is i, the current energy consumption in time period i is W=Wt-W(ti), where Wt is the meter reading electricity value at the current moment, and W(ti) is the real-time meter reading electricity value of the previous sampling node;

[0023] When communication is interrupted or the communication information is poor, transmission errors may cause Wt to ​​jump to 0 or an unknown data, and the energy power W will become a huge data or a negative value, which is obviously unreasonable; in order to solve the above phenomenon, the present application adopts a specific data processing method when the cloud platform communication is interrupted, and performs smoothing when the communication is restored to solve the unreasonable jump phenomenon of electricity data.

[0024] The specific embodiments are as follows

[0025] See also Figure 1 , the present invention provides a technical solution: a method for solving the error of electricity data transmission on cloud platform, comprising:

[0026] Step 1: Monitor the communication status of the cloud platform in real time and sample the electricity value at equal intervals;

[0027] Step 2: When the cloud platform communication is interrupted, the data processing program is started. In the data processing program, if the current electricity value is not received, the normal historical meter reading electricity value Wh of the previous sampling node and the historical meter reading electricity value W(hi) of the previous preset comparison point are obtained;

[0028] Step 3: According to the duration of the communication interruption, the automatic adjustment parameter value K of each sampling node is obtained; the automatic adjustment parameter value K is the fitting curve value of the normal historical meter reading electricity value Wh of the previous sampling node and the historical meter reading electricity value W(hi) of the previous preset comparison point. The automatic adjustment parameter value K is a coefficient greater than 0, which is used to determine the proportion of historical data to be used to estimate the current electricity value. When the interruption time is short, the K value is close to 0; when the interruption time is long, the K value gradually increases.

[0029] Step 4: According to the time length, use the normal historical meter reading kWh value Wh of the previous sampling node, the historical meter reading kWh value W(h-i) of the previous preset comparison point, and the real-time adjustment parameter value K to calculate the estimated kWh value We of the meter reading at each breakpoint; the calculation formula for the estimated kWh value We is: We = Wh + K * (Wh - W(h-i));

[0030] In the formula, Wh is the normal historical meter reading kWh value of the previous sampling node; K is the automatic adjustment parameter value of each sampling node; W(h-i) is the historical meter reading kWh value of the previous preset comparison point.

[0031] Step 5: When the communication resumes, be able to receive the real-time kWh value Wr, and detect the kWh value Wr. When Wr < Wh, it is determined to be invalid; when Wr > Wh, it is determined to be valid, and only when two consecutive kWh values Wr are both determined to be valid, the communication is considered to have resumed normally;

[0032] Step 6: After the communication resumes normally, obtain the real-time kWh value Wr of the meter reading data, compare the estimated kWh value We of the meter reading of the last sampling node with the actual kWh value Wr. When the difference between the two exceeds the preset threshold, perform smoothing processing; the smoothing processing uses the linear interpolation method to make the transition between the estimated kWh value We of the meter reading and the actual kWh value Wr smoother.

[0033] Step 7: Store the processed kWh value data in the database and view and export it through the user interface.

[0034] Working principle: When the communication of the cloud platform is interrupted, use the K times of the fitting curve value of the normal historical meter reading kWh value Wh of the previous sampling node and the historical meter reading kWh value W(h-i) of the previous preset comparison point as the estimated kWh value We; where K is a coefficient value greater than 0; during the communication interruption, use the electricity consumption trend prediction technology to predict the electricity consumption data; when the communication interruption resumes, compare the estimated value with the actual value and perform smoothing processing, which can effectively integrate historical data and real-time data when the communication of the cloud platform is interrupted, estimate relatively accurate electricity consumption value data, and avoid data jump phenomena; this method also has strong versatility and scalability, and can be applied to various different types of energy monitoring systems and devices. It can be integrated into the existing energy monitoring system or used as an independent data processing module, improving the accuracy and continuity of the electricity consumption data, thereby providing more reliable support for energy management and energy-saving decision-making.

[0035] Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

Claims

1. A method for solving the transmission error of electricity data on a cloud platform, characterized in that: Including: Step 1: Real-time monitor the communication status of the cloud platform and sample the electricity meter values at equal intervals. Step 2: When the communication of the cloud platform is interrupted, start the data processing program. In the data processing program, if the electricity meter value at this moment is not received, obtain the normal historical meter reading electricity value Wh of the previous sampling node and the historical meter reading electricity value W(h-i) of the previous preset comparison point. Step 3: According to the duration of the communication interruption, obtain the automatic adjustment parameter value K for each sampling node. Step 4: According to the duration, use the normal historical meter reading electricity value Wh of the previous sampling node, the historical meter reading electricity value W(h-i) of the previous preset comparison point, and the real-time adjustment parameter value K to calculate the estimated electricity value We of the electricity meter reading at each interruption point. Step 5: When the communication is restored, be able to receive the real-time electricity value Wr and detect the electricity value Wr. When Wr < Wh, it is determined to be invalid; when Wr > Wh, it is determined to be valid, and only when two consecutive electricity values Wr are determined to be valid, is the communication considered to have returned to normal. Step 6: After the communication returns to normal, obtain the real-time electricity value Wr of the electricity meter reading data, compare the estimated electricity value We of the electricity meter reading of the last sampling node with the actual electricity value Wr. When the difference between the two exceeds the preset threshold, perform smoothing processing. Step 7: Store the processed electricity value data in the database and view and export it through the user interface.

2. A method for solving cloud platform electricity data transmission errors according to claim 1, characterized in that: In Step 3, the automatic adjustment parameter value K is the fitting curve value of the normal historical meter reading electricity value Wh of the previous sampling node and the historical meter reading electricity value W(h-i) of the previous preset comparison point.

3. A method for solving cloud platform electricity data transmission errors according to claim 2, characterized in that: The automatic adjustment parameter value K is a coefficient greater than 0, which is used to determine what proportion of historical data is used to estimate the current electricity value. When the interruption time is short, the value of K is close to 0. When the interruption time is long, the value of K gradually increases.

4. A method for solving cloud platform electricity data transmission errors according to claim 1, characterized in that: In Step 4, the calculation formula for the estimated electricity value We is: We = Wh + K * (Wh - W(h-i)). In the formula, Wh is the normal historical meter reading electricity value of the previous sampling node; K is the automatic adjustment parameter value for each sampling node; W(h-i) is the historical meter reading electricity value of the previous preset comparison point.

5. The method for solving the cloud platform electricity data transmission error according to claim 1 is characterized in that: In Step 6, linear interpolation is used for smoothing processing to make the transition between the estimated electricity value We of the electricity meter reading and the actual electricity value Wr smoother.

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