Data acquisition error monitoring method and system based on smart electric energy meter

Abstract

The present invention provides a data acquisition error monitoring method and system based on a smart electricity meter, which relates to the field of smart meter reading technology. The data acquisition error monitoring method based on a smart electricity meter includes: obtaining offline meter reading data and the timestamp corresponding to the offline meter reading data; after obtaining the offline meter reading data, performing continuous meter reading at an online acquisition terminal to obtain online meter reading data; simulating electricity usage conditions based on the online meter reading data to obtain an electricity usage simulation model; predicting meter reading data corresponding to the timestamp based on the electricity usage simulation model to obtain predicted meter reading data; and comparing the offline meter reading data with the predicted meter reading data to obtain a comparison result. The monitoring method and system of the present invention can analyze possible data acquisition errors in the electricity meter and predict the cause of the data acquisition error, thereby quickly finding a corresponding solution.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent meter reading, and in particular to a data acquisition error monitoring method and system based on an intelligent electric energy meter. Background Art

[0002] Wireless electricity meter reading utilizes wireless communication technology to enable intelligent meter reading of electricity usage. The system consists of terminal nodes, a data collector, and a backend management system. The terminal nodes measure electricity usage and send the data to the data collector. After encryption and compression, the data is stored in the collector and regularly uploaded to the backend management system for user review and statistical analysis.

[0003] When wireless communication technology is used to read electricity meters online, data collection errors may occur in the online meter reading data transmitted to the online collection terminal due to data transmission interference, untimely software upgrades, etc. Such errors are usually difficult to quickly and accurately determine the cause of the data collection error, which not only affects the accuracy of the online meter reading terminal, but also makes it difficult to quickly provide a solution to the data collection error. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide a data collection error monitoring method and system based on a smart electric energy meter, which is used to solve the problem in the prior art that when using wireless communication technology to read the electric energy meter online, the online meter reading data transmitted to the online collection terminal may have data collection errors due to data transmission interference, software not upgraded in time, etc., and such errors are usually difficult to quickly and accurately determine the cause of the data collection error, which not only affects the accuracy of the online collection terminal's meter reading, but also makes it difficult to quickly provide a solution to the data collection error.

[0005] To achieve the above-mentioned purpose and other related purposes, the present invention provides a data acquisition error monitoring method based on a smart electricity meter, comprising: obtaining offline meter reading data and a timestamp corresponding to the offline meter reading data; after obtaining the offline meter reading data, performing continuous meter reading at an online acquisition terminal to obtain online meter reading data; simulating power consumption conditions based on the online meter reading data to obtain a power consumption simulation model; predicting the meter reading data corresponding to the timestamp based on the power consumption simulation model to obtain predicted meter reading data; comparing the offline meter reading data with the predicted meter reading data to obtain a comparison result; and when the comparison result shows that the offline meter reading data is inconsistent with the predicted meter reading data, performing data acquisition error analysis to obtain a data acquisition error analysis result.

[0006] In one embodiment of the present invention, obtaining offline meter reading data and the timestamp corresponding to the offline meter reading data includes: obtaining a shooting signal of the display area of the electric energy meter of the offline specified user, obtaining the image data corresponding to the display area and the timestamp corresponding to the shooting signal ; Scan the image data and obtain the electricity consumption data in the image data as offline meter reading data .

[0007] In one embodiment of the present invention, based on the online meter reading data, the power consumption status is simulated to obtain the power consumption simulation model, including: Online meter reading data obtained from meter reading , get the time interval Next, the online meter reading data corresponding to the next meter reading time Online meter reading data at the last meter reading time The range of change between ; Based on all consecutive time intervals The corresponding change range , the change range Divide into flat change and fluctuating change, and get the corresponding time period of each flat change The mean change in , and the time periods corresponding to each fluctuation The initial change range under and fluctuation factor ;According to the corresponding time period of each level change The mean change in , and the time periods corresponding to each fluctuation The initial change range under and fluctuation factor , establish a power consumption simulation model .

[0008] In one embodiment of the present invention, based on the power consumption simulation model, the meter reading data corresponding to the timestamp is predicted to obtain the predicted meter reading data, including: extracting the power consumption simulation model The corresponding time period of each level change in The mean change in , and the time periods corresponding to each fluctuation The initial change range under and fluctuation factor ;According to the close timestamp The order of each level change corresponds to the time period The mean change in , and the time periods corresponding to each fluctuation The initial change range under and fluctuation factor , based on the initial online meter reading data Corresponding to the timestamp Reverse prediction of meter reading data to obtain predicted meter reading data ; Among them, the timestamp To initial online meter reading data Time period satisfy: ; Predicted meter reading data satisfy: .

[0009] In one embodiment of the present invention, the offline meter reading data is compared with the predicted meter reading data to obtain a comparison result, including: Each predicted meter reading data Separately with offline meter reading data Compare; when there is predicted meter reading data Offline meter reading data When the preset requirements are met, the comparison result is the predicted meter reading data. Offline meter reading data consistent.

[0010] In one embodiment of the present invention, Each predicted meter reading data Separately with offline meter reading data Comparisons include: by close timestamp The order will satisfy Each predicted meter reading data Separately with offline meter reading data Make a comparison.

[0011] In one embodiment of the present invention, when there is predicted meter reading data Offline meter reading data When the preset requirements are met, the comparison result is the predicted meter reading data. Offline meter reading data Consistent, including: when there is predictive meter reading data Offline meter reading data The difference is less than the set threshold When , the comparison result is the predicted meter reading data Offline meter reading data consistent.

[0012] In one embodiment of the present invention, when the comparison result shows that the offline meter reading data is inconsistent with the predicted meter reading data, a data collection error analysis is performed to obtain a data collection error analysis result, including: when the comparison result shows that the offline meter reading data is inconsistent with the predicted meter reading data, calling a data collection error model corresponding to the comparison result Each target data collection error model in ; Through the target data acquisition error model Power consumption simulation model Update and get the simulation update model ; Update the simulation model Return to the time stamp corresponding to The steps of meter reading data prediction to get the updated model with simulation Corresponding compensation meter reading data , and when the comparison result is offline meter reading data Update model with simulation Corresponding compensation meter reading data When consistent, output simulation update model The corresponding target data collection error model .

[0013] In one embodiment of the present invention, the target data acquisition error model Power consumption simulation model Update and get the simulation update model , including: extracting each target data acquisition error model in turn Medium and continuous time intervals Corresponding compensation factor ; The corresponding time interval Compensation factor The power consumption simulation model Corresponding predicted meter reading data Perform updates and adjustments to obtain the simulation update model , and generate and simulate updated models Corresponding compensation meter reading data .

[0014] To achieve the above-mentioned purpose and other related purposes, the present invention also provides a data acquisition error monitoring system based on a smart electric energy meter, comprising: an acquisition unit for acquiring offline meter reading data and a timestamp corresponding to the offline meter reading data; a meter reading unit for performing continuous meter reading at an online acquisition terminal after acquiring the offline meter reading data to obtain online meter reading data; a modeling unit for simulating power consumption conditions based on the online meter reading data to obtain a power consumption simulation model; a prediction unit for predicting the meter reading data corresponding to the timestamp based on the power consumption simulation model to obtain predicted meter reading data; a comparison unit for comparing the offline meter reading data with the predicted meter reading data to obtain a comparison result; and an analysis unit for performing data acquisition error analysis to obtain a data acquisition error analysis result when the comparison result shows that the offline meter reading data is inconsistent with the predicted meter reading data.

[0015] To achieve the above-mentioned objectives and other related objectives, the present invention further provides an electronic device, which includes: one or more processors; a storage device for storing one or more programs. When the one or more programs are executed by one or more processors, the electronic device implements the aforementioned data acquisition error monitoring method based on the smart electricity meter.

[0016] To achieve the above-mentioned purpose and other related purposes, the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a computer processor, the computer executes the aforementioned data acquisition error monitoring method based on a smart electricity meter.

[0017] As described above, the data collection error monitoring method and system based on a smart electric energy meter of the present invention has the following beneficial effects: when monitoring data collection errors on an online collection terminal, by comparing the offline meter reading data obtained from offline collection and the timestamps corresponding to the offline meter reading data with the predicted meter reading data obtained through the online power consumption simulation model, it is possible to accurately predict possible online data collection errors. Furthermore, when it is predicted that an online data collection error exists, the data collection error analysis results corresponding to the error can be more accurately predicted based on different types of data collection error models, thereby achieving a solution based on the data collection error analysis results, so as to timely and accurately locate the data collection error, and quickly provide a solution for the located data collection error, thereby achieving a rapid response to possible data collection errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flow chart of a data acquisition error monitoring method based on a smart electric energy meter provided in an embodiment of the present invention.

[0019] Figure 2Shown is a structural block diagram of a data acquisition error monitoring system based on a smart electric energy meter provided by an embodiment of the present invention.

[0020] Component number description

[0021] Data acquisition error monitoring system 11; acquisition unit 111; meter reading unit 112; modeling unit 113; prediction unit 114; comparison unit 115; analysis unit 116. DETAILED DESCRIPTION

[0022] See also Figure 1 The present invention provides a data acquisition error monitoring method based on a smart electric energy meter, comprising:

[0023] Step S10: Obtain offline meter reading data and a timestamp corresponding to the offline meter reading data;

[0024] Step S20: After obtaining the offline meter reading data, continuous meter reading is performed on the online acquisition terminal to obtain online meter reading data;

[0025] Step S30: simulating the power usage status based on the online meter reading data to obtain a power usage simulation model;

[0026] Step S40: predicting the meter reading data corresponding to the timestamp based on the electricity consumption simulation model to obtain predicted meter reading data;

[0027] Step S50: Compare the offline meter reading data with the predicted meter reading data to obtain a comparison result;

[0028] Step S60: When the comparison result shows that the offline meter reading data is inconsistent with the predicted meter reading data, a data collection error analysis is performed to obtain a data collection error analysis result.

[0029] From the above steps, it is readily apparent that during online meter reading via an online acquisition terminal, the online acquisition terminal can utilize wireless communication technology to acquire electricity meter usage data as online meter reading data. During data acquisition error monitoring, offline personnel can use mobile devices to record offline meter reading data from the offline meter field during irregular spot checks and monitoring of the meter field. When acquiring the offline meter reading data, a timestamp corresponding to the offline meter reading data is also obtained. After the offline meter reading data and the timestamp corresponding to the offline meter reading data are transmitted back to the online acquisition terminal, the online acquisition terminal issues a continuous meter reading control command for the corresponding meter, thereby acquiring online meter reading data corresponding to the continuous meter reading. Subsequently, electricity usage conditions are simulated based on the online meter reading data corresponding to the continuous meter reading, thereby generating an electricity usage simulation model. The electricity usage simulation model is then used to predict predicted meter reading data, wherein the predicted time of the predicted meter reading data is the timestamp corresponding to the offline meter reading data. The offline meter reading data can then be further compared with the predicted meter reading data to determine whether the predicted meter reading data is the same as the offline meter reading data. When the comparison result shows that the offline meter reading data is inconsistent with the predicted meter reading data, a possible problem in the online meter reading process can be predicted. Then, a data collection error analysis can be performed when the comparison result shows that the offline meter reading data is inconsistent with the predicted meter reading data to determine the data collection error analysis result corresponding to the error. This allows solutions to be obtained based on the data collection error analysis results, allowing for timely and accurate location of data collection errors and rapid provision of solutions for the located data collection errors, allowing for rapid response to potential data collection errors through solutions.

[0030] Figure 1 The flowchart of the data acquisition error monitoring method based on the smart energy meter in an exemplary embodiment of the present application is shown, including steps S10 to S60. Figure 1 The technical solution of this application will be described in detail.

[0031] First, step S10 is executed to obtain offline meter reading data and a timestamp corresponding to the offline meter reading data.

[0032] Offline meter reading data can be entered manually into the system, but it can also be intelligently recorded, such as by capturing images with a mobile camera and identifying electricity usage data within them. The timestamp associated with offline meter reading data is the time at which the meter was read offline, enabling accurate prediction of data collection errors based on the timestamp.

[0033] In step S10, obtaining offline meter reading data and a timestamp corresponding to the offline meter reading data includes:

[0034] Step S101: Acquire the shooting signal of the electric energy meter display area of the offline specified user, and obtain the image data corresponding to the display area and the timestamp corresponding to the shooting signal ;

[0035] Step S102: Scan the image data to obtain the electricity consumption data in the image data as offline meter reading data .

[0036] In this embodiment, when performing offline meter reading, offline personnel can use the camera of a mobile device to shoot the electric energy meter display area of an offline designated user to perform irregular spot checks, monitoring, etc. on the electric energy meter site to obtain image data corresponding to the display area. In order to ensure the correspondence between the timestamp and the offline meter reading data, when the shooting signal of the electric energy meter display area is obtained, the time information corresponding to the shooting signal will be recorded as the timestamp corresponding to the offline meter reading data. After obtaining the image data, the image data will also be scanned, so that the electricity consumption data in the image data can be obtained as the offline meter reading data. Through the above method, offline meter reading data and the timestamp corresponding to the offline meter reading data can be obtained quickly and accurately.

[0037] Of course, offline meter reading data and its corresponding timestamps can also be acquired using a handheld meter reading terminal. However, acquiring offline meter reading data from captured images of the meter display area is a more direct method, and therefore provides greater accuracy than using a handheld meter reading terminal.

[0038] Next, step S20 is executed: after acquiring the offline meter reading data, continuous meter reading is performed on the online acquisition terminal to obtain online meter reading data.

[0039] Because the acquisition time of offline meter reading data for a specific user is uncontrollable, and the online collection terminal also periodically performs online meter reading for the corresponding specific user, it is possible to perform meter reading on the online collection terminal after obtaining offline meter reading data to obtain online meter reading data. Because the online meter reading data obtained by the online collection terminal is obtained after the offline meter reading data, there may be some differences between the two. Therefore, by performing continuous meter reading on the online collection terminal, the online meter reading data corresponding to the timestamp can be predicted based on the online meter reading data corresponding to the continuous meter reading data to obtain predicted meter reading data.

[0040] Next, step S30 is executed to simulate the power usage status based on the online meter reading data to obtain a power usage simulation model.

[0041] After continuously reading the meter from the online collection terminal to obtain the online meter reading data corresponding to each time interval, the power consumption status of each online meter reading data is simulated, so that a power consumption simulation model based on the online meter reading data can be obtained to facilitate the prediction of the predicted meter reading data corresponding to the timestamp.

[0042] In step S30, based on the online meter reading data, the power consumption condition is simulated to obtain a power consumption simulation model, including:

[0043] Step S301: according to the continuous time interval Online meter reading data obtained from meter reading , get the time interval Next, the online meter reading data corresponding to the next meter reading time Online meter reading data at the last meter reading time The range of change between ;

[0044] Step S302: According to all continuous time intervals The corresponding change range , the change range Divide into flat change and fluctuating change, and get the corresponding time period of each flat change The mean change in , and the time periods corresponding to each fluctuation The initial change range under and fluctuation factor ;

[0045] Step S303: According to the corresponding time period of each level change The mean change in , and the time periods corresponding to each fluctuation The initial change range under and fluctuation factor , establish a power consumption simulation model .

[0046] In this embodiment, during the power consumption simulation process, the continuous time intervals obtained by continuous meter reading are used. Corresponding online meter reading data , can be obtained in the time interval Online meter reading data corresponding to the next meter reading time , and time interval Online meter reading data of the last meter reading time before Perform difference calculation to get the change range of the two . All time intervals corresponding to continuous meter readings All the changes can be obtained Because the range of change There are two states in which the amplitude of the change of the electricity consumption data remains basically unchanged, and there are two states in which the amplitude of the change of the electricity consumption data changes linearly. Therefore, the corresponding time period of the flat change can be obtained. The mean change in ; You can also get each time period corresponding to the fluctuation The initial change range under and fluctuation factor . So as to realize the corresponding time period according to each level change The mean change in , and the time periods corresponding to each fluctuation The initial change range under and fluctuation factor , establish a power consumption simulation model Through this power consumption simulation model A relatively comprehensive reverse simulation can be performed to predict all situations of the predicted meter reading data corresponding to the timestamp moment.

[0047] It is worth noting that the timestamp corresponds to the moment Can be a time interval An integer multiple of, or a time close to the timestamp Time interval An integer multiple of .

[0048] Next, step S40 is executed: predicting the meter reading data corresponding to the timestamp according to the electricity consumption simulation model to obtain predicted meter reading data.

[0049] After simulating the electricity consumption status by continuous meter reading and obtaining the electricity consumption simulation model, the offline meter reading data can be used to simulate the electricity consumption status by continuous meter reading and obtaining the electricity consumption simulation model. Corresponding timestamp , the timestamp is set by the power simulation model The corresponding meter reading data prediction is obtained, thereby predicting the predicted meter reading data , by predicting meter reading data With the corresponding timestamp Offline meter reading data .

[0050] In step S40, the meter reading data corresponding to the timestamp is predicted based on the power consumption simulation model to obtain the predicted meter reading data, including:

[0051] Step S401: Extracting the power consumption simulation model The corresponding time period of each level change in The mean change in , and the time periods corresponding to each fluctuation The initial change range under and fluctuation factor ;

[0052] Step S402: according to the close timestamp The order of each level change corresponds to the time period The mean change in , and the time periods corresponding to each fluctuation The initial change range under and fluctuation factor , based on the initial online meter reading data Corresponding to the timestamp Reverse prediction of meter reading data to obtain predicted meter reading data ;

[0053] Among them, the timestamp To initial online meter reading data Time period satisfy: ; Predicted meter reading data satisfy: .

[0054] In this embodiment, when predicting meter reading data according to the power consumption simulation model, The corresponding time period of each level change in The mean change in (The mean of the change The range of change Corresponding to the time period The average value of each change amplitude), and the corresponding time period of each fluctuation The initial change range under and fluctuation factor , based on the initial online meter reading data The corresponding time periods are respectively The mean change in , the time period corresponding to the fluctuation The initial change range under and fluctuation factor Process meter reading data to obtain predicted meter reading data . And predict meter reading data satisfy: , that is, the calculation formula for predicting meter reading data is: ,in, is the time period corresponding to the flat change, The time period corresponding to the fluctuation.

[0055] Next, step S50 is executed to compare the offline meter reading data with the predicted meter reading data to obtain a comparison result.

[0056] According to the power consumption simulation model The corresponding time period of each level change in The mean change in , and the time periods corresponding to each fluctuation The initial change range under and fluctuation factor , predict the predicted meter reading data Afterwards, each predicted meter reading data Separately with offline meter reading data Compare and when there is predicted meter reading data Offline meter reading data If the data is consistent, it can be predicted that there will be no comparison results with data collection errors. All the data are the same as offline meter reading data When there is inconsistency, it can be predicted that the comparison result has data collection errors, and further analysis is needed to obtain the data collection error analysis results.

[0057] In step S50, the offline meter reading data is compared with the predicted meter reading data to obtain a comparison result, including:

[0058] Step S501: Satisfy Each predicted meter reading data Separately with offline meter reading data Make comparisons;

[0059] Step S502: When there is predicted meter reading data Offline meter reading data When the preset requirements are met, the comparison result is the predicted meter reading data. Offline meter reading data consistent.

[0060] In this embodiment, the offline meter reading data and predictive meter reading data When analyzing and comparing, the predicted meter reading data Separately with offline meter reading data Compare and when there is predictive meter reading data Offline meter reading data When the preset requirements are met, the comparison result is the predicted meter reading data. Offline meter reading data And when the comparison result is the predicted meter reading data Offline meter reading data If the two data are consistent, it can be concluded that there is no data collection error.

[0061] In step S501, Each predicted meter reading data Separately with offline meter reading data Comparisons include:

[0062] Follow the timestamps The order will satisfy Each predicted meter reading data Separately with offline meter reading data Make a comparison.

[0063] In this embodiment, the offline meter reading data and predictive meter reading data When comparing, you can use the closest timestamp The order will satisfy Each predicted meter reading data Separately with offline meter reading data That is, when the meter reading data closest to the offline Initial online meter reading data When the corresponding change is flat, first use the formula Predicted meter reading data , other data far away from offline meter reading Timestamp The formula is used in the same way as Similarly, when the meter reading data is closest to the offline Initial online meter reading data When the corresponding fluctuation is Predicted meter reading data , other data far away from offline meter reading Timestamp The formula is used in the same way as Make predictions.

[0064] In step S502, when there is predicted meter reading data Offline meter reading data When the preset requirements are met, the comparison result is the predicted meter reading data. Offline meter reading data Consistent, including:

[0065] When there is predictive meter reading data Offline meter reading data The difference is less than the set threshold When , the comparison result is the predicted meter reading data Offline meter reading data consistent.

[0066] In this embodiment, when the comparison result is determined to be the predicted meter reading data Offline meter reading data If they are consistent, we can determine whether there is predicted meter reading data. Offline meter reading data The difference is less than the set threshold , and if there is predicted meter reading data Offline meter reading data The difference is less than the set threshold When , the comparison result is the predicted meter reading data Offline meter reading data Based on this, we can more accurately predict whether there is a data collection error.

[0067] Next, step S60 is executed: when the comparison result shows that the offline meter reading data is inconsistent with the predicted meter reading data, a data collection error analysis is performed to obtain a data collection error analysis result.

[0068] If the comparison result shows that the offline meter reading data is inconsistent with the predicted meter reading data, it indicates that there was a data collection error in the predicted online meter reading process. Further data collection error analysis can be performed to determine the data collection error analysis results. Based on the data collection error analysis results, a solution can be obtained to promptly and accurately locate the data collection error and quickly provide a solution for the located data collection error, so as to quickly address possible data collection errors.

[0069] In step S60, when the comparison result shows that the offline meter reading data is inconsistent with the predicted meter reading data, a data collection error analysis is performed to obtain a data collection error analysis result, including:

[0070] Step S601: When the comparison result shows that the offline meter reading data is inconsistent with the predicted meter reading data, the data acquisition error model corresponding to the comparison result is retrieved. Each target data collection error model in ;

[0071] Step S602: Collect error model through target data Power consumption simulation model Update and get the simulation update model ;

[0072] Step S603: Update the simulation model Return to the time stamp corresponding to The steps of meter reading data prediction to get the updated model with simulation Corresponding compensation meter reading data , and when the comparison result is offline meter reading data Update model with simulation Corresponding compensation meter reading data When consistent, output simulation update model The corresponding target data collection error model .

[0073] In this embodiment, when the comparison result shows that the offline meter reading data is inconsistent with the predicted meter reading data, a data collection error analysis is performed. According to the influence of the possible data collection errors corresponding to the comparison result on the meter reading data, a data collection error model is established. . And when analyzing, by integrating the data acquisition error model Each target data collection error model in Power consumption simulation model Combined with the above, the adjustment of the meter reading data prediction algorithm is updated, so that the simulation update model can be obtained. . And update the model with simulation Continue with the time stamp corresponding to The meter reading data prediction can be obtained by updating the simulation model Corresponding compensation meter reading data , and then judge that the comparison result is offline meter reading data Update model with simulation Corresponding compensation meter reading data Are they consistent? If the comparison result is offline meter reading data Update model with simulation Corresponding compensation meter reading data If they are consistent, it means that the target data collection error model where the data collection error occurs has been found , and output the simulation update model The corresponding target data collection error model If the comparison result is offline meter reading data Update model with simulation Corresponding compensation meter reading data If there is inconsistency, the error model will be collected from the data Continue to extract target data collection error model , until offline meter reading data can be Update model with simulation Corresponding compensation meter reading data When consistent, output simulation update model The corresponding target data collection error model If the data collection error model All target data collection error models in It is impossible to obtain offline meter reading data Update model with simulation Corresponding compensation meter reading data When they are consistent, the manual end can be notified to process them, so as to improve the efficiency of handling data collection errors.

[0074] In step S602, the error model is collected through the target data Power consumption simulation model Update and get the simulation update model ,include:

[0075] Step S6021: Extract each target data acquisition error model in turn Medium and continuous time intervals Corresponding compensation factor ;

[0076] Step S6022: Corresponding time interval Compensation factor The power consumption simulation model Corresponding predicted meter reading data Perform updates and adjustments to obtain the simulation update model , and generate and simulate updated models Corresponding compensation meter reading data .

[0077] In this embodiment, when updating the simulation model When updating, each target data collection error model For each consecutive time interval Compensation factor There are cases where the value is not unique. Therefore, the compensation factor corresponding to each time interval t can be used. For each time interval Corresponding power consumption simulation model Processing to obtain the updated model with simulation Corresponding compensation meter reading data , that is, the formula is , thereby achieving compensation for meter reading data Predict whether the data collection error is the corresponding target data collection error model caused by.

[0078] Please refer to 2. The present invention also provides a data acquisition error monitoring system 11 based on a smart electricity meter, including: an acquisition unit 111, used to acquire offline meter reading data and a timestamp corresponding to the offline meter reading data; a meter reading unit 112, used to perform continuous meter reading of an online acquisition terminal after acquiring the offline meter reading data to obtain online meter reading data; a modeling unit 113, used to simulate the power consumption status according to the online meter reading data to obtain a power consumption simulation model; a prediction unit 114, used to predict the meter reading data corresponding to the timestamp according to the power consumption simulation model to obtain predicted meter reading data; a comparison unit 115, used to compare the offline meter reading data with the predicted meter reading data to obtain a comparison result; and an analysis unit 116, used to perform data acquisition error analysis to obtain a data acquisition error analysis result when the comparison result is that the offline meter reading data is inconsistent with the predicted meter reading data.

[0079] It should be noted that the data acquisition error monitoring system 11 for smart energy meters provided in the above-described embodiment and the data acquisition error monitoring method for smart energy meters provided in the above-described embodiment are based on the same concept. The specific manner in which each module and unit performs operations has been described in detail in the method embodiment and will not be repeated here. In actual applications, the data acquisition error monitoring system 11 for smart energy meters provided in the above-described embodiment can, as needed, allocate the above-described functions to different functional modules, i.e., divide the internal structure of the device into different functional modules to perform all or part of the functions described above. This is not a limitation herein.

[0080] In summary, the present invention discloses a data acquisition error monitoring method and system based on a smart electric energy meter. When monitoring data acquisition errors on an online acquisition terminal, the method and system can accurately predict possible online data acquisition errors by comparing the offline meter reading data obtained through offline acquisition and the timestamps corresponding to the offline meter reading data with the predicted meter reading data obtained through the online power consumption simulation model. Moreover, when it is predicted that there is an online data acquisition error, the data acquisition error analysis results corresponding to the error can be more accurately predicted based on different types of data acquisition error models, thereby obtaining a solution based on the data acquisition error analysis results, so as to timely and accurately locate the data acquisition error, and quickly provide a solution for the located data acquisition error, thereby quickly responding to possible data acquisition errors. Therefore, the present invention effectively overcomes the various shortcomings of the existing technology and has a high industrial utilization value.

[0081] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical principles disclosed herein are intended to be covered by the claims of the present invention.

Claims

1. A data acquisition error monitoring method based on a smart electric energy meter, characterized in that: include: Obtain offline meter reading data and a timestamp corresponding to the offline meter reading data; After obtaining the offline meter reading data, continuous meter reading is performed on the online acquisition terminal to obtain online meter reading data; simulating the power consumption status based on the online meter reading data to obtain a power consumption simulation model; Predicting meter reading data corresponding to the timestamp based on the electricity consumption simulation model to obtain predicted meter reading data; Comparing the offline meter reading data with the predicted meter reading data to obtain a comparison result; When the comparison result shows that the offline meter reading data is inconsistent with the predicted meter reading data, performing data collection error analysis to obtain a data collection error analysis result; Predicting meter reading data corresponding to the timestamp according to the electricity consumption simulation model to obtain predicted meter reading data includes: Extracting the power consumption simulation model The corresponding time period of each level change in The mean change in , and the time periods corresponding to each fluctuation The initial change range under and fluctuation factor ; Follow the timestamps The order of each level change corresponds to the time period The mean change in , and the time period corresponding to each of the fluctuations The initial change range under and fluctuation factor , based on the initial online meter reading data Corresponding to the timestamp Reverse prediction of meter reading data to obtain predicted meter reading data ; Among them, the timestamp To initial online meter reading data Time period satisfy: The predicted meter reading data satisfy: ,in, express The online meter reading data before or after a time interval t corresponds to the time; when the offline meter reading data is closest to Initial online meter reading data When the corresponding change is flat, first use the formula Predicted meter reading data , other data far away from offline meter reading Timestamp The formula is used in the same way as Make predictions; when the data is closest to the offline meter reading Initial online meter reading data When the corresponding fluctuation is Predicted meter reading data , other data far away from offline meter reading Timestamp The formula is used in the same way as Make predictions; When the comparison result shows that the offline meter reading data is inconsistent with the predicted meter reading data, a data collection error analysis is performed to obtain a data collection error analysis result, including: When the comparison result is that the offline meter reading data is inconsistent with the predicted meter reading data, the data acquisition error model corresponding to the comparison result is retrieved. Each target data collection error model in ; The target data acquisition error model The power consumption simulation model Update and get the simulation update model ; The simulation update model Return to the time stamp corresponding to The meter reading data prediction step is to get the updated model with the simulation Corresponding compensation meter reading data , and when the comparison result is offline meter reading data Update the model with the simulation Corresponding compensation meter reading data When consistent, output the simulation update model The target data acquisition error model corresponding to .

2. The data acquisition error monitoring method based on the smart electric energy meter according to claim 1 is characterized in that: Obtaining offline meter reading data and a timestamp corresponding to the offline meter reading data, including: Obtain the shooting signal of the electric energy meter display area of the designated offline user, and obtain the image data corresponding to the display area and the timestamp corresponding to the shooting signal ; Scan the image data to obtain the electricity consumption data in the image data as the offline meter reading data .

3. The data acquisition error monitoring method based on the smart electric energy meter according to claim 1 is characterized in that: Based on the online meter reading data, the power consumption condition is simulated to obtain a power consumption simulation model, including: Based on continuous time intervals Online meter reading data obtained from meter reading , get the time interval Next, the online meter reading data corresponding to the next meter reading time Online meter reading data at the last meter reading time The range of change between ; Based on all consecutive time intervals The corresponding change range , the change range Divide into flat change and fluctuating change, and obtain the corresponding time period of each flat change The mean change in , and the time period corresponding to each of the fluctuations The initial change range under and fluctuation factor ; According to the corresponding time period of each level change The mean change in , and the time period corresponding to each of the fluctuations The initial change range under and fluctuation factor , establish the electricity consumption simulation model .

4. The data acquisition error monitoring method based on the smart electric energy meter according to claim 1 is characterized in that: Comparing the offline meter reading data with the predicted meter reading data to obtain a comparison result includes: will satisfy Each predicted meter reading data Separately with offline meter reading data Make comparisons; When there is predictive meter reading data The offline meter reading data When the preset requirements are met, the comparison result is the predicted meter reading data. The offline meter reading data consistent.

5. The data acquisition error monitoring method based on the smart electric energy meter according to claim 4 is characterized in that: will satisfy Each predicted meter reading data Separately with offline meter reading data Comparisons include: Follow the timestamps The order will satisfy Each predicted meter reading data Separately with offline meter reading data Make a comparison.

6. The data acquisition error monitoring method based on the smart electric energy meter according to claim 1 is characterized in that: When there is predictive meter reading data The offline meter reading data When the preset requirements are met, the comparison result is the predicted meter reading data. The offline meter reading data Consistent, including: When there is predictive meter reading data The offline meter reading data The difference is less than the set threshold When the comparison result is the predicted meter reading data The offline meter reading data consistent.

7. The data acquisition error monitoring method based on the smart electric energy meter according to claim 1 is characterized in that: The target data acquisition error model The power consumption simulation model Update and get the simulation update model ,include: Extract each target data acquisition error model in turn Medium and continuous time intervals Corresponding compensation factor ; The corresponding time interval The compensation factor The power consumption simulation model is sequentially Corresponding predicted meter reading data Perform updates and adjustments to obtain the simulation update model , and generate an updated model with the simulation Corresponding compensation meter reading data .

8. A monitoring system applied to the data acquisition error monitoring method based on a smart electric energy meter according to any one of claims 1 to 7, characterized in that: include: An acquisition unit, configured to acquire offline meter reading data and a timestamp corresponding to the offline meter reading data; A meter reading unit, configured to perform continuous meter reading on an online acquisition terminal after acquiring the offline meter reading data, so as to obtain online meter reading data; A modeling unit, configured to simulate power usage conditions based on the online meter reading data to obtain a power usage simulation model; a prediction unit, configured to predict the meter reading data corresponding to the timestamp based on the electricity consumption simulation model to obtain predicted meter reading data; a comparing unit, configured to compare the offline meter reading data with the predicted meter reading data to obtain a comparison result; as well as The analyzing unit is configured to perform data collection error analysis to obtain a data collection error analysis result when the comparison result shows that the offline meter reading data is inconsistent with the predicted meter reading data.

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