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

Abstract

The invention provides a data acquisition error monitoring method and system based on an intelligent electric energy meter, and relates to the technical field of intelligent meter reading, the data acquisition error monitoring method based on the intelligent electric energy meter comprises the following steps: obtaining offline meter reading data and a timestamp corresponding to the offline meter reading data; after the offline meter reading data is obtained, performing continuous meter reading of the online acquisition terminal to obtain online meter reading data; according to the online meter reading data, performing power consumption condition simulation to obtain a power consumption simulation model; according to the power consumption simulation model, performing meter reading data prediction corresponding to the timestamp to obtain predicted meter reading data; and comparing the offline meter reading data with the predicted meter reading data to obtain a comparison result. According to the monitoring method and system, data acquisition errors possibly existing in the electric energy meter can be analyzed, reasons for the data acquisition errors can be predicted, and therefore a corresponding solution can be found quickly.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent meter reading, and in particular to a method and system for monitoring data collection errors based on intelligent electricity meters. Background Art

[0002] ‌The principle of wireless electricity meter reading‌ is to realize intelligent meter reading of the electricity consumption of intelligent electricity meters through wireless communication technology. The system consists of terminal nodes, data collectors, and a background management system. The terminal nodes are responsible for measuring the consumption and sending the data to the data collectors. After data encryption and compression, the data is stored in the collectors and uploaded to the background management system regularly for users to view and perform statistical analysis.

[0003] When the existing technology is used to perform online meter reading on electricity meters using wireless communication technology, data collection errors may occur in the online meter reading data transmitted to the online collection terminal due to reasons such as data transmission interference and untimely software upgrades. It is usually difficult to quickly and accurately determine the cause of the data collection error, which will not only affect the accuracy of meter reading by the online collection terminal but also make it difficult to quickly provide a solution for the data collection error. Summary of the Invention

[0004] In view of the above-mentioned disadvantages of the prior art, the purpose of the present invention is to provide a method and system for monitoring data collection errors based on intelligent electricity meters, which are used to solve the problem that when the existing technology is used to perform online meter reading on electricity meters using wireless communication technology, data collection errors may occur in the online meter reading data transmitted to the online collection terminal due to reasons such as data transmission interference and untimely software upgrades. It is usually difficult to quickly and accurately determine the cause of the data collection error, which will not only affect the accuracy of meter reading by the online collection terminal but also make it difficult to quickly provide a solution for the data collection error.

[0005] To achieve the above purpose and other related purposes, the present invention provides a method for monitoring data collection errors based on intelligent electricity meters, including: obtaining offline meter reading data and the corresponding timestamp of the offline meter reading data; after obtaining the offline meter reading data, performing continuous meter reading on the online collection terminal to obtain online meter reading data; according to the online meter reading data, simulating the electricity consumption situation to obtain an electricity consumption simulation model; according to the electricity consumption simulation model, predicting the meter reading data corresponding to the timestamp 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 is 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.

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

[0007] In an embodiment of the present invention, according to the online meter reading data, simulating the electricity consumption situation to obtain an electricity consumption simulation model, including: according to the continuous time interval The online meter reading data obtained by meter reading , obtaining the time interval Under, the online meter reading data corresponding to the next meter reading moment And the online meter reading data at the previous meter reading moment The change amplitude between ; according to all continuous time intervals The corresponding change amplitudes , dividing the change amplitude Into flat changes and fluctuating changes, obtaining the average change amplitude corresponding to each flat change period Under, and the initial change amplitude corresponding to each fluctuating change period And the fluctuation factor Under, and the initial change amplitude And the fluctuation factor ; according to the average change amplitude corresponding to each flat change period Under, and the initial change amplitude corresponding to each fluctuating change period And the fluctuation factor Under, and the initial change amplitude And the fluctuation factor , establishing an electricity consumption simulation model .

[0008] In an embodiment of the present invention, according to the electricity consumption simulation model, predicting the meter reading data corresponding to the time stamp to obtain the predicted meter reading data, including: extracting each flat change corresponding period in the electricity consumption simulation model Under, the average change amplitude Under, and the initial change amplitude corresponding to each fluctuating change period And the fluctuation factor Under, and the initial change amplitude And the fluctuation factor ; in the order of approaching the time stamp , each flat change corresponding period Under, the average change amplitude And the initial change amplitude corresponding to each fluctuating change period Under, and the initial change amplitude And the fluctuation factor , successively based on the initial online meter reading data perform reverse prediction on the meter reading data corresponding to the time stamp to obtain predicted meter reading data ; wherein, the time period from the time stamp to the initial online meter reading data satisfies: ; the predicted meter reading data satisfies: . .

[0009] In an embodiment of the present invention, compare the offline meter reading data with the predicted meter reading data to obtain a comparison result, including: comparing each predicted meter reading data that satisfies with the offline meter reading data respectively; when there is a predicted meter reading data and the offline meter reading data meet the preset requirements, then the comparison result is that the predicted meter reading data is consistent with the offline meter reading data .

[0010] In an embodiment of the present invention, compare each predicted meter reading data that satisfies with the offline meter reading data respectively, including: in the order of approaching the time stamp, successively compare each predicted meter reading data that satisfies with the offline meter reading data respectively.

[0011] In an embodiment of the present invention, when there is a predicted meter reading data and the offline meter reading data meet the preset requirements, then the comparison result is that the predicted meter reading data is consistent with the offline meter reading data , including: when there is a predicted meter reading data and the offline meter reading data the difference is less than the set threshold , then the comparison result is that the predicted meter reading data is consistent with the offline meter reading data .

[0012] In an embodiment of the present invention, when the comparison result shows that the offline meter reading data is inconsistent with the predicted meter reading data, data collection error analysis is performed to obtain the 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, the data collection error model corresponding to the comparison result is retrieved for each target data collection error model in; the power consumption simulation model is updated through the target data collection error model to obtain the simulation updated model ; the simulation updated model is returned to the step of predicting the meter reading data corresponding to the time stamp to obtain the compensated meter reading data corresponding to the simulation updated model , and when the comparison result shows that the offline meter reading data is consistent with the compensated meter reading data corresponding to the simulation updated model , the corresponding target data collection error model in the simulation updated model

[0013] is output. In an embodiment of the present invention, the power consumption simulation model is updated through the target data collection error model to obtain the simulation updated model , including: sequentially extracting the compensation factors corresponding to the continuous time interval in each target data collection error model ; the compensation factors for the corresponding time interval are sequentially used to update and adjust the predicted meter reading data corresponding to the power consumption simulation model to obtain the simulation updated model , and the compensated meter reading data corresponding to the simulation updated model

[0014] To achieve the above and other related objectives, the present invention further provides a data collection error monitoring system based on an intelligent electricity meter, including: an acquisition unit for acquiring offline meter reading data and the corresponding timestamp of the offline meter reading data; a meter reading unit for, after acquiring the offline meter reading data, performing continuous meter reading of the online acquisition terminal to obtain online meter reading data; a modeling unit for simulating the electricity consumption status based on the online meter reading data to obtain an electricity consumption simulation model; a prediction unit for predicting the meter reading data corresponding to the timestamp based on the electricity 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, when the comparison result indicates 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.

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

[0016] To achieve the above and other related objectives, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor of a computer, the computer executes the aforementioned data collection error monitoring method based on an intelligent electricity meter.

[0017] As described above, the data collection error monitoring method and system based on an intelligent electricity meter of the present invention have the following beneficial effects: When monitoring data collection errors of the online acquisition terminal, by comparing the offline meter reading data obtained through offline collection and the corresponding timestamp of the offline meter reading data with the predicted meter reading data predicted online through the electricity consumption simulation model, it is possible to accurately predict possible online data collection errors. And when it is predicted that there are online data collection errors, based on different types of data collection error models, it is possible to relatively accurately predict the data collection error analysis results corresponding to the errors, so that it is possible to obtain a solution according to the data collection error analysis result, in order to timely and accurately locate the data collection error and quickly provide a solution for the located data collection error, thereby enabling a quick response to possible data collection errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic flowchart of the data collection error monitoring method based on an intelligent electricity meter provided by an embodiment of the present invention.

[0019] Figure 2It shows a structural block diagram of a data acquisition error monitoring system based on an intelligent electricity meter provided by an embodiment of the present invention.

[0020] Description of component labels 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 implementation manners

[0021] Please refer to Figure 1 , the present invention provides a data acquisition error monitoring method based on an intelligent electricity meter, including: Step S10: Obtain offline meter reading data and the time stamp corresponding to the offline meter reading data; Step S20: After obtaining the offline meter reading data, perform continuous meter reading of the online acquisition terminal to obtain online meter reading data; Step S30: According to the online meter reading data, simulate the electricity consumption situation to obtain an electricity consumption simulation model; Step S40: According to the electricity consumption simulation model, predict the meter reading data corresponding to the time stamp to obtain predicted meter reading data; Step S50: Compare the offline meter reading data with the predicted meter reading data to obtain a comparison result; Step S60: When the comparison result shows that the offline meter reading data is inconsistent with the predicted meter reading data, perform data acquisition error analysis to obtain a data acquisition error analysis result.

[0022] It is not difficult to find through the above steps that in the process of online meter reading through the online collection terminal, the online collection terminal can obtain the electricity consumption data of the electricity meter as the online meter reading data through wireless communication technology. In the process of monitoring data collection errors, when offline personnel conduct irregular spot checks and monitoring on the electricity meter site, they can enter the offline meter reading data of the electricity meter site through a mobile device, etc. When obtaining the offline meter reading data, the corresponding timestamp of the offline meter reading data will also be further obtained. After the offline meter reading data and the timestamp corresponding to the offline meter reading data are transmitted back to the online collection terminal, the online collection terminal will issue continuous meter reading control for the corresponding electricity meter to obtain the online meter reading data corresponding to the continuous meter reading. Then, based on the online meter reading data corresponding to the continuous meter reading, the electricity consumption situation is simulated to obtain an electricity consumption simulation model. And then the electricity consumption simulation model is used to predict the predicted meter reading data. Among them, the prediction time of the predicted meter reading data obtained by the prediction is the timestamp time corresponding to the offline meter reading data. Thus, the offline meter reading data can 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 is that the offline meter reading data is inconsistent with the predicted meter reading data, it is possible to predict the possible problems that may occur in the process of online meter reading. Then, when the comparison result is that the offline meter reading data is inconsistent with the predicted meter reading data, data collection error analysis is performed to determine the data collection error analysis result corresponding to the error, so that a solution can be obtained according to the data collection error analysis result, in order to quickly and accurately locate the data collection error and provide a solution for the located data collection error, so that possible data collection errors can be quickly dealt with through the solution.

[0023] Figure 1 FIG. shows a flowchart of a data collection error monitoring method based on an intelligent electricity meter in an exemplary embodiment of the present application, including steps S10 - step S60. The technical solution of the present application will be elaborated in detail below in conjunction with Figure 1 to elaborate on the technical solution of the present application in detail.

[0024] First, step S10 is executed to obtain the offline meter reading data and the timestamp corresponding to the offline meter reading data.

[0025] The offline meter reading data can be manually entered into the system. Of course, it can also be obtained through intelligent means, such as taking image data through a mobile device camera and identifying the electricity consumption data in the image data to achieve offline meter reading. The timestamp corresponding to the offline meter reading data is the time at the moment of offline meter reading of the electricity meter, so that the accurate prediction of data collection errors can be carried out according to the timestamp.

[0026] In step S10, obtain the offline meter reading data and the time stamp corresponding to the offline meter reading data, including: Step S101: Obtain the shooting signal of the display area of the electric energy meter of the offline specified user, and obtain the image data corresponding to the display area and the time stamp corresponding to the shooting signal ; Step S102: Scan the image data to obtain the electricity consumption data in the image data as the offline meter reading data 。

[0027] In this embodiment, when performing offline meter reading, it can be that when offline personnel conduct irregular spot checks and monitoring on the electric energy meter on-site, they use the camera of the mobile device to take pictures of the display area of the electric energy meter of the offline specified user to obtain the image data corresponding to the display area. And in order to ensure the correspondence between the time stamp and the offline meter reading data, when the shooting signal of the display area of the electric energy meter is obtained, the corresponding time information of the shooting signal will be recorded as the time stamp corresponding to the offline meter reading data. And 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 。In the above way, the offline meter reading data and the time stamp corresponding to the offline meter reading data can be obtained quickly and accurately.

[0028] Of course, when obtaining the offline meter reading data and the time stamp corresponding to the offline meter reading data, it can also be collected by using a handheld meter reading terminal. However, obtaining the offline meter reading data from the image data of the display area of the electric energy meter is a direct acquisition method, so the data accuracy is better than that of the handheld meter reading terminal.

[0029] Next, execute step S20: After obtaining the offline meter reading data, perform continuous meter reading of the online acquisition terminal to obtain the online meter reading data.

[0030] Since the acquisition time of the offline meter reading data of the specified user is uncontrollable, and the online acquisition terminal also regularly performs online meter reading of the corresponding specified user, therefore, the meter reading of the online acquisition terminal can be performed after obtaining the offline meter reading data, so as to obtain the online meter reading data. Because when the online acquisition terminal performs meter reading, if the obtained online meter reading data is obtained after the offline meter reading data, there may be certain differences between the two. Therefore, continuous meter reading of the online acquisition terminal can be performed to predict the online meter reading data corresponding to the time stamp through the respective online meter reading data corresponding to the continuous meter reading, so as to obtain the predicted meter reading data.

[0031] Then, execute step S30, and perform an electricity consumption condition simulation based on the online meter reading data to obtain an electricity consumption simulation model.

[0032] After obtaining the online meter reading data corresponding to each time interval through continuous meter reading of the online collection terminal, the electricity consumption status is simulated for each piece of online meter reading data, so as to obtain an electricity consumption simulation model based on the online meter reading data, which is convenient for predicting the predicted meter reading data corresponding to the time stamp.

[0033] In step S30, according to the online meter reading data, the electricity consumption status is simulated to obtain an electricity consumption simulation model, including: Step S301: According to the continuous time intervals The online meter reading data obtained by meter reading , obtain the time interval At the next meter reading time corresponding to the online meter reading data And the online meter reading data at the previous meter reading time The change amplitude between ; Step S302: According to all continuous time intervals The corresponding change amplitudes , divide the change amplitude Into flat changes and fluctuating changes, and obtain the average change amplitude Under each flat change corresponding time period , and the initial change amplitude Under each fluctuating change corresponding time period And the fluctuation factor ; Step S303: According to the average change amplitude Under each flat change corresponding time period , and the initial change amplitude Under each fluctuating change corresponding time period And the fluctuation factor , establish an electricity consumption simulation model .

[0034] In this embodiment, during the process of simulating the electricity consumption status, by according to the continuous time intervals The corresponding online meter reading data obtained by continuous meter reading , it is possible to obtain the online meter reading data At the next meter reading time after the time interval , and the online meter reading data At the previous meter reading time before the time interval Perform a difference calculation, so as to obtain the change amplitude Between the two. Under all time intervals Corresponding to continuous meter reading, all change amplitudes Can be obtained. Because in the change amplitude There are a flat change state in which the change range of the electricity consumption data remains basically unchanged, and a fluctuating change state in which the change range of the electricity consumption data changes linearly. Therefore, the average change range corresponding to each time period of the flat change can be obtained ; The average change range corresponding to each time period showing a fluctuating change can also be obtained ; The initial change range and the fluctuation factor . Thus, it is possible to establish an electricity consumption simulation model according to the average change range corresponding to each flat change time period , the initial change range corresponding to each fluctuating change time period , and the fluctuation factor . Through this electricity consumption simulation model , all situations of the predicted meter reading data corresponding to the timestamp moment can be comprehensively back-simulated and predicted

[0035] It should be noted that the moment corresponding to the timestamp can be an integer multiple of the time interval , and of course it can also be an integer multiple of the time interval close to the moment corresponding to the timestamp .

[0036] Next, execute step S40: According to the electricity consumption simulation model, perform meter reading data prediction corresponding to the timestamp, and obtain the predicted meter reading data

[0037] After obtaining the electricity consumption simulation model by simulating the electricity consumption situation using continuous meter readings, according to the timestamp corresponding to the offline meter reading data , the meter reading data corresponding to this timestamp can be predicted through the electricity consumption simulation model, so as to predict the predicted meter reading data , in order to compare the predicted meter reading data with the offline meter reading data corresponding to the corresponding timestamp .

[0038] In step S40, according to the electricity consumption simulation model, perform meter reading data prediction corresponding to the timestamp, and obtain the predicted meter reading data, including: Step S401: Extract the average change range corresponding to each flat change time period in the electricity consumption simulation model , and the initial change range corresponding to each fluctuating change time period and the fluctuation factor ; Step S402: In the order close to the time stamp , for each flat change corresponding time period , the average change amplitude , and for each fluctuating change corresponding time period , the initial change amplitude and the fluctuation factor , based on the initial online meter reading data in sequence, perform reverse prediction of the meter reading data corresponding to the time stamp to obtain the predicted meter reading data ; wherein, the time period from the time stamp to the initial online meter reading data satisfies: ; the predicted meter reading data satisfies: . .

[0039] In this embodiment, when predicting the predicted meter reading data according to the power consumption simulation model, by using each flat change corresponding time period in the power consumption simulation model , the average change amplitude (the average change amplitude is the average value of each change amplitude corresponding to the time period in the change amplitude ), and for each fluctuating change corresponding time period , the initial change amplitude and the fluctuation factor , based on the initially obtained online meter reading data , respectively process the meter reading data through the average change amplitude corresponding to the flat change time period , the initial change amplitude corresponding to the fluctuating change time period and the fluctuation factor to obtain the predicted meter reading data . And the predicted meter reading data satisfies: , that is to say, the calculation formula of the predicted meter reading data is: , wherein, is the flat change corresponding time period, is the fluctuating change corresponding time period.

[0040] Next, execute step S50 to compare the offline meter reading data with the predicted meter reading data to obtain a comparison result.

[0041] According to the power consumption simulation model The average change amplitude during each flat change corresponding time period And the initial change amplitude during each fluctuation change corresponding time period And the fluctuation factor After predicting the predicted meter reading data Then, each predicted meter reading data Is compared with the off-line meter reading data respectively. And when there is a predicted meter reading data That is consistent with the off-line meter reading data Then, it can be predicted that there is no data collection error in the comparison result. When the predicted meter reading data Are all inconsistent with the off-line meter reading data Then, it can be predicted that there is a data collection error in the comparison result, and further analysis of the data collection error analysis result is required.

[0042] In step S50, the off-line meter reading data is compared with the predicted meter reading data to obtain a comparison result, including: Step S501: Each predicted meter reading dataThat meets the requirements Is compared with the off-line meter reading data respectively; Step S502: When there is a predicted meter reading data That meets the preset requirements with the off-line meter reading data Then, the comparison result is that the predicted meter reading data Is consistent with the off-line meter reading data

[0043] In this embodiment, when analyzing and comparing the off-line meter reading data With the predicted meter reading data By comparing each predicted meter reading data With the off-line meter reading data respectively. And when there is a predicted meter reading data That meets the preset requirements with the off-line meter reading data Then, the comparison result is that the predicted meter reading data Is consistent with the off-line meter reading data And when the comparison result is that the predicted meter reading data Is consistent with the off-line meter reading data Then, it can be concluded that there is no data collection error.

[0044] In step S501, the requirements that are met Each predicted meter reading data is respectively compared with the off-line meter reading data including: In the order of approaching the timestamp each predicted meter reading data that meets the requirements is respectively compared with the off-line meter reading data in sequence. In this embodiment, when comparing the off-line meter reading data

[0045] with the predicted meter reading data it can be in the order of approaching the timestamp each predicted meter reading data that meets the requirements is respectively compared with the off-line meter reading data in sequence. That is to say, when the initial on-line meter reading data closest to the off-line meter reading data corresponds to a flat change, the predicted meter reading data is first predicted by the formula and for other cases far from the timestamp of the off-line meter reading data the prediction is made by analogy using the formula Similarly, when the initial on-line meter reading data closest to the off-line meter reading data corresponds to a fluctuating change, the predicted meter reading data is first predicted by the formula and for other cases far from the timestamp of the off-line meter reading data the prediction is made by analogy using the formula In step S502, when there is a predicted meter reading data that meets the preset requirements with the off-line meter reading data the comparison result is that the predicted meter reading data is consistent with the off-line meter reading data including:

[0046] When there is a predicted meter reading data whose difference from the off-line meter reading data is less than the set threshold the comparison result is that the predicted meter reading data is consistent with the off-line meter reading data When there is a predicted meter reading data whose difference from the off-line meter reading data is less than the set threshold the comparison result is that the predicted meter reading data is consistent with the off-line meter reading data In this embodiment, when it is determined that the comparison result is that the predicted meter reading data

[0047] ​When comparing with the offline meter reading data to determine if they are consistent, it can be done by checking if there is predicted meter reading data and the difference between it and the offline meter reading data is less than a set threshold. If there is predicted meter reading data and the difference between it and the offline meter reading data is less than the set threshold then the comparison result is that the predicted meter reading data is consistent with the offline meter reading data. Based on this, it is possible to more accurately predict whether there are data collection errors.

[0048] Next, execute step S60: When the comparison result is that the offline meter reading data is inconsistent with the predicted meter reading data, perform data collection error analysis to obtain a data collection error analysis result.

[0049] When the comparison result is that the offline meter reading data is inconsistent with the predicted meter reading data, it indicates that there are data collection errors in the predicted online meter reading process. Then, through further data collection error analysis, a data collection error analysis result can be determined, and based on the data collection error analysis result, a solution can be obtained to quickly and accurately locate the data collection error and provide a solution for the located data collection error in a timely manner to quickly respond to possible data collection errors.

[0050] In step S60, when the comparison result is that the offline meter reading data is inconsistent with the predicted meter reading data, perform data collection error analysis to obtain a data collection error analysis result, including: Step S601: When the comparison result is that the offline meter reading data is inconsistent with the predicted meter reading data, retrieve each target data collection error model in the data collection error model corresponding to the comparison result ; Step S602: Update the power consumption simulation model using the target data collection error model to obtain an updated simulation model ; Step S603: Return the updated simulation model to the step of predicting meter reading data corresponding to the time stamp to obtain compensated meter reading data corresponding to the updated simulation model , and when the comparison result is that the offline meter reading data is compared with the compensated meter reading data corresponding to the updated simulation model When they are consistent, output the simulation update model The corresponding target data acquisition error model in .

[0051] In this embodiment, when the comparison result shows that the off-line meter reading data is inconsistent with the predicted meter reading data and data acquisition error analysis is performed, based on the possible data acquisition errors corresponding to the comparison result, the impact on the meter reading data is formed, and a data acquisition error model is established . And during the analysis, by combining each target data acquisition error model in with the power consumption simulation model , the adjustment of the prediction algorithm for the meter reading data is updated, so that a simulation update model can be obtained. And with the simulation update model , continue to perform the prediction of the meter reading data corresponding to the time stamp , and the compensated meter reading data corresponding to the simulation update model can be obtained . Then, determine whether the comparison result shows that the off-line meter reading data is consistent with the compensated meter reading data corresponding to the simulation update model . If the comparison result shows that the off-line meter reading data is consistent with the compensated meter reading data corresponding to the simulation update model , it means that the target data acquisition error model where the data acquisition error occurs has been found, and output the corresponding target data acquisition error model in the simulation update model . If the comparison result shows that the off-line meter reading data is inconsistent with the compensated meter reading data corresponding to the simulation update model , then continue to extract the target data acquisition error model from the data acquisition error model until the off-line meter reading data is consistent with the compensated meter reading data corresponding to the simulation update model , and output the corresponding target data acquisition error model in the simulation update model . If all the target data acquisition error models in the data acquisition error model cannot make the off-line meter reading data consistent with the compensated meter reading data corresponding to the simulation update model When they are consistent, the processing can be notified to the manual side to improve the processing efficiency of data acquisition errors.

[0052] In step S602, the target data acquisition error model is used to update the power consumption simulation model to obtain a simulation update model , including: Step S6021: Sequentially extract the compensation factors corresponding to the continuous time intervals in each target data acquisition error model ; Step S6022: Use the compensation factors corresponding to the corresponding time intervals to sequentially update and adjust the predicted meter reading data corresponding to the power consumption simulation model to obtain a simulation update model , and generate compensated meter reading data corresponding to the simulation update model .

[0053] In this embodiment, when updating the simulation update model , for each target data acquisition error model , the compensation factor corresponding to each continuous time interval may not be a unique value. Therefore, according to the compensation factor corresponding to each time interval t, the power consumption simulation model corresponding to each time interval is processed to obtain compensated meter reading data corresponding to the simulation update model , that is, the formula is , so as to predict whether the data acquisition error that appears with the compensated meter reading data is caused by the corresponding target data acquisition error model .

[0054] Referring to FIG. 2, the present invention further provides a data collection error monitoring system 11 based on an intelligent electricity meter, including: an acquisition unit 111 for acquiring offline meter reading data and the time stamp corresponding to the offline meter reading data; a meter reading unit 112 for, after acquiring the offline meter reading data, performing continuous meter reading of an online acquisition terminal to obtain online meter reading data; a modeling unit 113 for simulating the electricity consumption status according to the online meter reading data to obtain an electricity consumption simulation model; a prediction unit 114 for predicting the meter reading data corresponding to the time stamp according to the electricity consumption simulation model to obtain predicted meter reading data; a comparison unit 115 for comparing the offline meter reading data with the predicted meter reading data to obtain a comparison result; and an analysis unit 116 for, 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.

[0055] It should be noted that the data collection error monitoring system 11 based on the intelligent electricity meter provided in the above embodiment and the data collection error monitoring method based on the intelligent electricity meter provided in the above embodiment belong to the same concept. The specific manners in which each module and unit perform operations have been described in detail in the method embodiment and will not be elaborated herein. In practical applications, the data collection error monitoring system 11 based on the intelligent electricity meter provided in the above embodiment may, according to needs, allocate the above functions to different functional modules, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above. No limitation is imposed herein either.

[0056] In summary, for the data collection error monitoring method and system based on an intelligent electricity meter disclosed in the present invention, when monitoring data collection errors of an online acquisition terminal, by comparing the offline meter reading data collected offline and the time stamp corresponding to the offline meter reading data with the predicted meter reading data predicted online through an electricity consumption simulation model, accurate prediction of possible online data collection errors can be achieved. And when it is predicted that there are online data collection errors, based on different types of data collection error models, the data collection error analysis results corresponding to the errors can be predicted relatively accurately, so that solutions can be obtained according to the data collection error analysis results, facilitating timely and accurate positioning of data collection errors and quickly providing solutions for the located data collection errors, thereby enabling quick response to possible data collection errors. Therefore, the present invention effectively overcomes various drawbacks in the prior art and has high industrial utilization value.

[0057] The above embodiments are only illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical idea disclosed by the present invention should still be covered by the claims of the present invention.

Claims

1. A data acquisition error monitoring method based on an intelligent electricity meter, characterized in that, Including: Obtain offline meter reading data and the time stamp corresponding to the offline meter reading data; After obtaining the offline meter reading data, perform continuous meter reading of the online collection terminal to obtain online meter reading data; According to the online meter reading data, simulate the electricity consumption situation to obtain an electricity consumption simulation model; According to the electricity consumption simulation model, perform prediction of meter reading data corresponding to the time stamp to obtain predicted meter reading data; Compare the offline meter reading data with the predicted meter reading data to obtain a comparison result; When the comparison result is that the offline meter reading data is inconsistent with the predicted meter reading data, perform data collection error analysis to obtain a data collection error analysis result.

2. The data acquisition error monitoring method based on an intelligent electricity meter according to claim 1, wherein: Obtaining offline meter reading data and the time stamp corresponding to the offline meter reading data includes: Obtain the shooting signal of the display area of the electric energy meter of a specified user offline, and obtain the image data corresponding to the display area and the time stamp 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 an intelligent electricity meter according to claim 1, characterized in that: According to the online meter reading data, simulating the electricity consumption situation to obtain an electricity consumption simulation model includes: According to consecutive time intervals the online meter reading data obtained by meter reading , the time interval is obtained, and the online meter reading data corresponding to the next meter reading moment and the online meter reading data at the previous meter reading moment are used to obtain the change range ; According to all continuous time intervals The corresponding change amplitude , divide the change amplitude into flat changes and undulating changes, and obtain the average value of the change amplitude corresponding to each flat change time period , and the initial change amplitude corresponding to each undulating change time period and the undulation factor ; According to the average change amplitude during each corresponding time period of the flat change and the initial change amplitude during each corresponding time period of the fluctuation change as well as the fluctuation factor the electricity consumption simulation model is established .​​ 4. The data acquisition error monitoring method based on an intelligent electricity meter according to claim 3, characterized in that: According to the electricity consumption simulation model, performing prediction of meter reading data corresponding to the time stamp to obtain predicted meter reading data includes: Extract the power consumption simulation model The average change amplitude corresponding to each flat change time period in and the initial change amplitude corresponding to each undulating change time period in and the undulation factor ; According to the proximity to the time stamp in sequence, for each of the average change amplitudes corresponding to the flat change time periods , and for each of the initial change amplitudes corresponding to the undulating change time periods and the undulation factors , based on the initial on-line meter reading data in sequence, perform inverse prediction of the meter reading data corresponding to the time stamp to obtain the predicted meter reading data ; Among them, the time stamp to the meter reading data on the initial line time period satisfies: ; the predicted meter reading data satisfies: .

5. The data acquisition error monitoring method based on an intelligent electricity meter according to claim 4, wherein: Comparing the offline meter reading data with the predicted meter reading data to obtain a comparison result includes: Each predicted meter reading data that meets is compared separately with the offline meter reading data ; for comparison. When there is predicted meter reading data and the offline meter reading data meet the preset requirements, the comparison result is that the predicted meter reading data is consistent with the offline meter reading data is consistent.

6. The data acquisition error monitoring method based on an intelligent electricity meter according to claim 5, wherein: Each predicted meter reading data that meets is compared with the offline meter reading data respectively, including: ​ In the order close to the timestamp , each predicted meter reading data that meets is successively compared with the offline meter reading data respectively .

7. The data acquisition error monitoring method based on an intelligent electricity meter according to claim 1, wherein: When there is predicted meter reading data and the offline meter reading data meet the preset requirements, the comparison result is that the predicted meter reading data is consistent with the offline meter reading data including: When there is predicted meter reading data and the difference from the offline meter reading data is less than the set threshold then the comparison result is that the predicted meter reading data is consistent with the offline meter reading data ​ 8. The data acquisition error monitoring method based on an intelligent electricity meter according to claim 1, wherein: When the comparison result is 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 includes: When the comparison result indicates that the off-line meter reading data is inconsistent with the predicted meter reading data, retrieve the data acquisition error model corresponding to the comparison result for each target data acquisition error model ; Through the target data acquisition error model update the power consumption simulation model to obtain a simulation updated model ; Return the simulation update model to the step of predicting the meter reading data corresponding to the time stamp to obtain the compensated meter reading data corresponding to the simulation update model and, when the comparison result is that the offline meter reading data is consistent with the compensated meter reading data corresponding to the simulation update model output the corresponding target data acquisition error model in the simulation update model when they are consistent .​ 9. The data acquisition error monitoring method based on an intelligent electricity meter according to claim 8, characterized in that: Through the target data acquisition error model Update the power consumption simulation model to obtain a simulation updated model , including: Extract each of the target data acquisition error models in sequence with a continuous time interval corresponding compensation factor ; For the corresponding time interval the compensation factor is successively used to update and adjust the predicted meter reading data corresponding to the electricity consumption simulation model, so as to obtain a simulation update model , and generate compensated meter reading data corresponding to the simulation update model .

10. A data acquisition error monitoring system based on an intelligent electricity meter, characterized in that, Including: An obtaining unit for obtaining offline meter reading data and the time stamp corresponding to the offline meter reading data; A meter reading unit for performing continuous meter reading of the online collection terminal after obtaining the offline meter reading data to obtain online meter reading data; A modeling unit for simulating the electricity consumption situation according to the online meter reading data to obtain an electricity consumption simulation model; A prediction unit for performing prediction of meter reading data corresponding to the time stamp according to the electricity 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 collection error analysis to obtain a data collection error analysis result when the comparison result is that the offline meter reading data is inconsistent with the predicted meter reading data.

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