A cloud computing-based intelligent electric energy meter electricity real-time monitoring system

Abstract

This invention relates to the field of electrical equipment monitoring technology, specifically a cloud-based smart meter real-time electricity consumption monitoring system. The system includes: using smart meters to monitor enterprise electrical equipment in real time within a target monitoring area, collecting real-time electricity consumption data of the equipment; sensors within the smart meters monitoring the meters; analyzing the situation when the collected electricity consumption data exceeds a threshold value to determine if it constitutes an abnormality; obtaining the smart meter's health value, analyzing its health status, and issuing alerts to staff based on the analysis results; calculating the probability of an abnormal electricity consumption based on staff reports of equipment and smart meter maintenance; and establishing an electrical equipment electricity consumption database based on the real-time electricity consumption data collected by the smart meters for managing the enterprise's electrical equipment.

Description

Technical Field

[0001] This invention relates to the field of electrical equipment monitoring technology, specifically to a real-time electricity consumption monitoring system for smart meters based on cloud computing. Background Technology

[0002] With societal development, electricity consumption is constantly increasing, but the methods for collecting electricity data have not been updated for a long time. Currently, my country primarily uses manual collection or smart meter collection for electricity data. Manual data collection has the following drawbacks: It is too costly; a meter reader typically collects no more than 3,000 meters of data per day. Meters are usually installed at high locations, posing a certain degree of danger for meter readers. Manual data collection requires patrolling different locations, observing meter anomalies, and relying solely on on-site feedback and personal experience, which can easily lead to estimations, omissions, and errors. Using traditional smart meters only collects basic data on electrical equipment usage. When anomalies occur, it's impossible to determine whether the anomaly is caused by the smart meter or a problem with the monitored equipment, requiring staff to inspect both the smart meter and the equipment, thus wasting human and financial resources. Summary of the Invention

[0003] The purpose of this invention is to provide a cloud computing-based smart energy meter real-time electricity consumption monitoring system to solve the problems mentioned in the background art.

[0004] To address the aforementioned technical problems, this invention provides the following technical solution: a cloud-based smart energy meter real-time electricity consumption monitoring system, comprising: a data acquisition module, an electricity consumption anomaly detection module, an analysis module, a probability calculation module, and a data management module.

[0005] The data acquisition module is used to monitor the electrical equipment of enterprises within the target monitoring area in real time and collect real-time power consumption data of the electrical equipment within the enterprise; the sensor installed in the smart meter monitors the smart meter and collects smart meter information data;

[0006] The power consumption anomaly detection module is used to analyze the power consumption data of electrical equipment within the target monitored enterprise and determine power consumption anomalies based on the analysis results.

[0007] The analysis module is used to analyze the health status of the smart energy meter and issue prompts to staff based on the analysis results;

[0008] The probability calculation module is used to calculate the probability value of abnormal electricity consumption; the staff will inspect the smart energy meter and electrical equipment according to the prompts, and after the inspection, the staff will report the inspection results and calculate the probability value of abnormal electricity consumption based on the reported inspection results.

[0009] The management module is used to collect real-time electricity consumption data of electrical equipment within the enterprise from smart meters, establish an electrical equipment electricity consumption database, and manage the enterprise's electrical equipment based on the electrical equipment database.

[0010] Furthermore, the data acquisition module includes an electrical equipment data acquisition unit and an energy meter data acquisition unit:

[0011] The electrical equipment data acquisition unit is used to monitor the electrical equipment of enterprises within the target monitoring area of ​​the smart energy meter in real time and collect the electricity consumption data of the electrical equipment in real time.

[0012] The electricity meter data acquisition unit is used to monitor the smart electricity meter via sensors installed on the i-th smart electricity meter and collect information data from the i-th smart electricity meter; the information data includes the electricity meter's operating voltage V. i Load current I i Ambient temperature F i .

[0013] Furthermore, the power consumption anomaly detection module includes a power consumption anomaly detection unit, a threshold acquisition unit, and a data analysis unit:

[0014] The power consumption anomaly detection unit is used to determine whether the power consumption data of electrical equipment within the target monitored enterprise exceeds the power consumption data threshold; when time point T i When the electricity consumption data of electrical equipment within the target monitored enterprise collected by the smart energy meter exceeds the electricity consumption data status threshold, and the probability of electricity consumption anomaly is higher than the electricity consumption anomaly probability threshold, the situation where the electricity consumption data of electrical equipment within the target monitored enterprise collected by the smart energy meter exceeds the electricity consumption data threshold is determined to be an electricity consumption anomaly, and the time point T is recorded. i When an abnormal power consumption is detected, the collected smart meter data is compared with the data threshold. When the collected smart meter data exceeds the data threshold, the smart meter health value is calculated. When the collected smart meter data is within the data threshold, a prompt is issued to the staff, prompting them to check the electrical equipment.

[0015] The threshold acquisition unit is used to obtain threshold data of smart energy meters when an abnormal power consumption is detected, in order to avoid incorrect judgment of the health status of the smart energy meter. This threshold data is acquired via cloud computing. The smart energy meter information data thresholds include: the operating voltage V of the energy meter. a Load current I b Ambient temperature Fc ;

[0016] The data analysis unit is used to analyze the collected information data from smart meters and provide prompts to staff based on the analysis results.

[0017] Furthermore, the analysis module includes a data processing unit, a data calculation unit, and an analysis unit:

[0018] The data processing unit is used to normalize the smart energy meter information data collected on the i-th smart energy meter, perform linear transformation on the original data, and map the data to the range [0,1].

[0019] The data calculation unit is used to calculate the health value of the i-th smart energy meter based on the normalization processing result of the information data.

[0020] The analysis unit is used to calculate the health value of the i-th smart meter and obtain the maximum health threshold H of the i-th smart meter based on cloud computing. w Minimum health threshold H p The analysis was conducted, and the results were used to provide guidance to the staff.

[0021] Furthermore, the probability calculation module includes a probability calculation unit and an anomaly analysis unit:

[0022] The probability calculation unit sets a time error range and calculates the probability based on the time error range and time point T. i Obtain the target time interval to calculate the probability value P of abnormal electricity consumption obtained by the staff after reporting maintenance for the i-th smart meter within the target time interval. i ;

[0023]

[0024] Among them, M i N represents the total number of times staff report maintenance requests for the i-th smart meter within the target time interval; i The total number of times that staff members report the repair of the i-th smart meter within the target time interval, and the feedback result shows that the corresponding electrical equipment or the corresponding smart meter is damaged.

[0025] Anomaly analysis unit, used to analyze anomalies at time point T i If, within the allowable error range, the electricity consumption data of electrical equipment within the target monitored enterprise collected by the i-th smart meter exceeds the electricity consumption data threshold, and the probability value of electricity consumption anomaly is also higher than the electricity consumption anomaly probability threshold, then the electricity consumption data of electrical equipment within the target monitored enterprise collected by the i-th smart meter exceeding the electricity consumption threshold constitutes an electricity consumption anomaly; at time point T... iIf the electricity consumption data of electrical equipment in the target monitored enterprise collected by the i-th smart energy meter exceeds the electricity consumption data threshold while the probability value of electricity consumption abnormality is lower than the electricity consumption abnormality probability threshold, it is determined that the electricity consumption data exceeding the electricity consumption data threshold collected by the i-th smart energy meter is not due to electricity consumption abnormality, but rather an increase in electricity consumption data caused by normal operation.

[0026] In the above unit, the monitoring system prompts staff to inspect the i-th smart energy meter and the number of times the i-th smart energy meter monitors electrical equipment within the enterprise, as well as the number of times staff report damage to the i-th smart energy meter and the number of times the i-th smart energy meter monitors electrical equipment within the enterprise. Calculating the probability value of abnormal power consumption is to prevent the use of normal electrical equipment from being judged as abnormal power consumption, leading to incorrect prompts to staff and wasting human and material resources.

[0027] Furthermore, the data management unit includes a database creation unit and a data management unit:

[0028] The database creation unit is used to create an electricity consumption database for electrical equipment.

[0029] The data management unit is used to manage the company's electrical equipment. The management platform manages the electrical equipment based on the electrical equipment database. Users can view the company's electrical equipment electricity consumption data anytime, anywhere, and pay electricity bills.

[0030] Furthermore, the monitoring system also includes a cloud computing-based real-time electricity consumption monitoring method for smart meters, the monitoring method including:

[0031] Step S100: Use a smart meter to monitor the enterprise's electrical equipment in real time within the target monitoring area and collect real-time power consumption data of the electrical equipment within the enterprise; the sensor inside the smart meter monitors the smart meter and collects smart meter information data; the information data includes the meter's operating voltage, load current, ambient temperature, and power supply frequency;

[0032] Step S200: When the electricity consumption data of electrical equipment within the target monitored enterprise collected by the smart meter exceeds the electricity consumption data status threshold, and the probability of electricity consumption anomaly is higher than the probability threshold, the situation where the electricity consumption data of electrical equipment within the target monitored enterprise collected by the smart meter exceeds the electricity consumption data threshold is determined to be an electricity consumption anomaly; when it is determined to be an electricity consumption anomaly, the collected smart meter information data is compared with the information data threshold; when it is determined that the collected smart meter information data exceeds the information data threshold, the smart meter health value is calculated; when the collected smart meter information data is within the information data threshold, a prompt is issued to the staff, prompting the staff to check the electrical equipment;

[0033] Step S300: Obtain the health value of the smart energy meter, analyze the health status of the smart energy meter, and issue a prompt to the staff based on the analysis results;

[0034] Step S400: Staff members inspect and repair smart meters and electrical equipment according to the prompts, and report the inspection results; based on the inspection and repair status of electrical equipment and smart meters reported by staff members, calculate the probability value of abnormal electricity consumption.

[0035] Step S500: Based on the real-time electricity consumption data of electrical equipment within the enterprise collected by smart meters, establish an electrical equipment electricity consumption database; based on the electrical equipment database, manage the enterprise's electrical equipment.

[0036] Furthermore, step S100 includes:

[0037] Step S101: Use smart meters to monitor the electrical equipment of enterprises within the target monitoring area in real time and collect the power consumption data of the electrical equipment in real time;

[0038] Step S102: The sensor installed on the i-th smart meter monitors the smart meter and collects information data of the i-th smart meter; the information data includes the operating voltage V of the smart meter. i Load current I i Ambient temperature F i Power supply frequency f i .

[0039] Furthermore, step S200 includes:

[0040] Step S201: When the electricity consumption data of electrical equipment within the target monitored enterprise collected by the i-th smart meter exceeds the electricity consumption data threshold and the probability value of electricity consumption anomaly is higher than the electricity consumption anomaly probability threshold, it is determined that the situation where the electricity consumption data of electrical equipment within the target monitored enterprise collected by the smart meter exceeds the electricity consumption data threshold is an electricity consumption anomaly; when the electricity consumption data of electrical equipment within the target monitored enterprise collected by the i-th smart meter exceeds the electricity consumption data threshold and the probability value of electricity consumption anomaly is lower than the electricity consumption anomaly probability threshold, it is determined that the situation where the electricity consumption data of electrical equipment within the target monitored enterprise collected by the smart meter exceeds the electricity consumption data status threshold is not an electricity consumption anomaly, but rather an increase in electricity consumption data due to normal use; record the time point T at which the electricity consumption data of electrical equipment within the target monitored enterprise collected by the i-th smart meter exceeds the electricity consumption data threshold. i ;

[0041] Step S202: When an abnormal power consumption is detected, to avoid misjudging the health status of the smart energy meter, the smart energy meter information data threshold is obtained through cloud computing. The smart energy meter information data threshold includes: the operating voltage V of the energy meter. a Load current I b Ambient temperature Fc Power supply frequency f d ;

[0042] Step S203: When |V i -V a |>V o or |I i -I b |>I e or |F i -F c |>F f or |f i -f d |>f g Calculate the health value of the smart energy meter in real time; when |V i -V a | <V o and|I i -I b | e and |F i -F c | <F f and |f i -f d | <f g The system will issue timely reminders to staff, prompting them to inspect electrical equipment within the company; V o The operating voltage error value of the smart energy meter is obtained through cloud computing; I e The load current error value of the smart energy meter obtained through cloud computing; F f The ambient temperature error value of the smart energy meter obtained through cloud computing; f g The power supply frequency error value of the smart energy meter is obtained through cloud computing;

[0043] Step S204: Normalize the smart energy meter information data collected on the i-th smart energy meter, perform a linear transformation on the original data, and map the data to the range [0,1].

[0044] Step S205: Calculate the health value H of the i-th smart energy meter based on the information data normalization processing result. i :

[0045]

[0046] Among them, V ik I is the normalized mapping value of the operating voltage of the i-th smart energy meter; ik F is the normalized mapping value of the load current of the i-th smart energy meter; ik The normalized mapping value of the ambient temperature of the i-th smart energy meter;

[0047] ​In the above steps, normalizing the information data of the i-th smart energy meter is to address the inconsistency in units of measurement when calculating the health value later. The normalized mapping value is then used to calculate the health value H. i This provides technical support for determining the health status of the i-th smart meter and serves as the basis for determining the health status of the i-th smart meter.

[0048] Furthermore, step S300 includes:

[0049] Step S301: Obtain the maximum health threshold H of the i-th smart energy meter based on cloud computing. w Minimum health threshold H p ;

[0050] Step S302: When H i >H w When H is detected as unhealthy, a notification is sent to the staff, prompting them to inspect and repair the smart meter; i <H p When H is detected as unhealthy, a notification is sent to the staff, prompting them to inspect and repair the smart meter; p ≤H i ≤H w If the i-th smart meter is determined to be in good health and does not require maintenance, and the cause of the abnormal power consumption is a malfunction in the electrical equipment monitored by the i-th smart meter, a prompt is issued to the staff to inspect and repair the electrical equipment.

[0051] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention utilizes computers, communication equipment, metering and protection devices, etc., to monitor smart meters and collect smart meter information data; it monitors electrical equipment in enterprises within a target area and collects real-time electricity consumption data of electrical equipment in the target area; it uploads the collected electricity consumption data to a database for the management of electrical equipment; users can view the electricity consumption data of enterprise electrical equipment anytime, anywhere, and pay their electricity bills; when abnormalities are found in the collected electricity consumption data, the cause of the abnormality is determined based on the real-time collected data, prompting staff to inspect and repair the electrical equipment or smart meters within the enterprise, avoiding waste of human and financial resources and saving expenses. Attached Figure Description

[0052] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0053] Figure 1This is a schematic diagram of a cloud computing-based smart energy meter real-time electricity consumption monitoring system according to the present invention.

[0054] Figure 2 This is a flowchart of a monitoring method in a cloud-based smart energy meter real-time electricity consumption monitoring system according to the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Please see Figures 1-2 This invention provides a technical solution: a real-time electricity consumption monitoring system for smart energy meters based on cloud computing, characterized in that the monitoring system includes: a data acquisition module, an electricity consumption anomaly detection module, an analysis module, a probability calculation module, and a data management module.

[0057] The data acquisition module is used to monitor the electrical equipment of enterprises within the target monitoring area in real time and collect real-time power consumption data of the electrical equipment within the enterprise; the sensor installed in the smart meter monitors the smart meter and collects smart meter information data;

[0058] The power consumption anomaly detection module is used to analyze the power consumption data of electrical equipment within the target monitored enterprise and determine power consumption anomalies based on the analysis results.

[0059] The analysis module is used to analyze the health status of the smart energy meter and issue prompts to staff based on the analysis results;

[0060] The probability calculation module is used to calculate the probability value of abnormal electricity consumption; the staff will inspect the smart energy meter and electrical equipment according to the prompts, and after the inspection, the staff will report the inspection results and calculate the probability value of abnormal electricity consumption based on the reported inspection results.

[0061] The management module is used to collect real-time electricity consumption data of electrical equipment within the enterprise from smart meters, establish an electrical equipment electricity consumption database, and manage the enterprise's electrical equipment based on the electrical equipment database.

[0062] The data acquisition module includes an electrical equipment data acquisition unit and an electricity meter data acquisition unit.

[0063] The electrical equipment data acquisition unit is used to monitor the electrical equipment of enterprises within the target monitoring area of ​​the smart energy meter in real time and collect the electricity consumption data of the electrical equipment in real time.

[0064] The electricity meter data acquisition unit is used to monitor the smart electricity meter through the sensor installed on the i-th smart electricity meter and collect information data of the i-th smart electricity meter; the information data includes the electricity meter operating voltage Vi, load current Ii, and ambient temperature Fi;

[0065] The module for determining abnormal electricity consumption includes an abnormal electricity consumption determination unit, a threshold acquisition unit, and a data analysis unit.

[0066] The power consumption anomaly detection unit is used to determine whether the power consumption data of electrical equipment within the target monitored enterprise exceeds the power consumption data threshold; when time point T i When the electricity consumption data of electrical equipment within the target monitored enterprise collected by the smart energy meter exceeds the electricity consumption data status threshold, and the probability of electricity consumption anomaly is higher than the electricity consumption anomaly probability threshold, the situation where the electricity consumption data of electrical equipment within the target monitored enterprise collected by the smart energy meter exceeds the electricity consumption data threshold is determined to be an electricity consumption anomaly, and the time point T is recorded. i When an abnormal power consumption is detected, the collected smart meter data is compared with the data threshold. When the collected smart meter data exceeds the data threshold, the smart meter health value is calculated. When the collected smart meter data is within the data threshold, a prompt is issued to the staff, prompting them to check the electrical equipment.

[0067] The threshold acquisition unit is used to obtain threshold data of smart energy meters when an abnormal power consumption is detected, in order to avoid incorrect judgment of the health status of the smart energy meter. This threshold data is acquired via cloud computing. The smart energy meter information data thresholds include: the operating voltage V of the energy meter. a Load current I b Ambient temperature F c ;

[0068] The data analysis unit is used to analyze the collected information data from smart energy meters and provide prompts to staff based on the analysis results.

[0069] The analysis module includes a data processing unit, a data calculation unit, and an analysis unit.

[0070] The data processing unit is used to normalize the smart energy meter information data collected on the i-th smart energy meter, perform linear transformation on the original data, and map the data to the range [0,1].

[0071] The data calculation unit is used to calculate the health value of the i-th smart energy meter based on the normalization processing result of the information data.

[0072] The analysis unit is used to calculate the health value of the i-th smart meter and obtain the maximum health threshold H of the i-th smart meter based on cloud computing.w Minimum health threshold H p Analyze the data and provide feedback to staff based on the results;

[0073] The probability calculation module includes a probability calculation unit and an anomaly analysis unit.

[0074] The probability calculation unit sets a time error range and calculates the probability based on the time error range and time point T. i Obtain the target time interval to calculate the probability value P of abnormal electricity consumption obtained by the staff after reporting maintenance for the i-th smart meter within the target time interval. i ;

[0075]

[0076] Among them, M i N represents the total number of times staff report maintenance requests for the i-th smart meter within the target time interval; i The total number of times that staff members report the repair of the i-th smart meter within the target time interval, and the feedback result shows that the corresponding electrical equipment or the corresponding smart meter is damaged.

[0077] Anomaly analysis unit, used to analyze anomalies at time point T i If, within the allowable error range, the electricity consumption data of electrical equipment within the target monitored enterprise collected by the i-th smart meter exceeds the electricity consumption data threshold, and the probability value of electricity consumption anomaly is also higher than the electricity consumption anomaly probability threshold, then the electricity consumption data of electrical equipment within the target monitored enterprise collected by the i-th smart meter exceeding the electricity consumption threshold constitutes an electricity consumption anomaly; at time point T... i If, within the allowable error range, the electricity consumption data of electrical equipment within the target monitored enterprise collected by the i-th smart meter exceeds the electricity consumption data threshold while the electricity consumption anomaly probability value is lower than the electricity consumption anomaly probability threshold, then the excess electricity consumption data collected by the i-th smart meter within the target monitored enterprise is determined not to be an electricity consumption anomaly, but rather an increase in electricity consumption data due to normal operation. For example, if the set time point is 9:00 AM with an allowable error range of 30 minutes, and the period from 8:30 AM to 9:30 AM is within the allowable error range, and the electricity consumption anomaly probability threshold is 0.9, then at 9:20 AM, the electricity consumption data of electrical equipment within the target monitored enterprise collected by the i-th smart meter exceeds the electricity consumption data threshold, and the electricity consumption anomaly probability value P... i If the value is 0.92, which is greater than the power consumption anomaly probability threshold of 0.9, it is determined that the power consumption data of electrical equipment in the target monitored enterprise collected by the i-th smart energy meter exceeds the power consumption data threshold, which constitutes a power consumption anomaly.

[0078] The data management unit includes a database creation unit and a data management unit.

[0079] The database creation unit is used to create an electricity consumption database for electrical equipment.

[0080] The data management unit is used to manage the company's electrical equipment. The management platform manages the electrical equipment based on the electrical equipment database. Users can view the company's electrical equipment electricity consumption data anytime, anywhere, and pay electricity bills.

[0081] The monitoring system also includes a cloud-based real-time electricity consumption monitoring method for smart meters, which includes:

[0082] Step S100: Use a smart meter to monitor the enterprise's electrical equipment in real time within the target monitoring area and collect real-time power consumption data of the electrical equipment within the enterprise; the sensor inside the smart meter monitors the smart meter and collects smart meter information data; the information data includes the meter's operating voltage, load current, ambient temperature, and power supply frequency;

[0083] Step S200: When the electricity consumption data of electrical equipment within the target monitored enterprise collected by the smart meter exceeds the electricity consumption data status threshold, and the probability of electricity consumption anomaly is higher than the probability threshold, the situation where the electricity consumption data of electrical equipment within the target monitored enterprise collected by the smart meter exceeds the electricity consumption data threshold is determined to be an electricity consumption anomaly; when it is determined to be an electricity consumption anomaly, the collected smart meter information data is compared with the information data threshold; when it is determined that the collected smart meter information data exceeds the information data threshold, the smart meter health value is calculated; when the collected smart meter information data is within the information data threshold, a prompt is issued to the staff, prompting the staff to check the electrical equipment;

[0084] Step S300: Obtain the health value of the smart energy meter, analyze the health status of the smart energy meter, and issue a prompt to the staff based on the analysis results;

[0085] Step S400: Staff members inspect and repair smart meters and electrical equipment according to the prompts, and report the inspection results; based on the inspection and repair status of electrical equipment and smart meters reported by staff members, calculate the probability value of abnormal electricity consumption.

[0086] Step S500: Based on the real-time electricity consumption data of electrical equipment within the enterprise collected by smart meters, establish an electrical equipment electricity consumption database; based on the electrical equipment database, manage the enterprise's electrical equipment;

[0087] Step S100 includes:

[0088] Step S101: Use smart meters to monitor the electrical equipment of enterprises within the target monitoring area in real time and collect the power consumption data of the electrical equipment in real time;

[0089] Step S102: The sensor installed on the i-th smart meter monitors the smart meter and collects information data of the i-th smart meter; the information data includes the operating voltage V of the smart meter. i Load current I i Ambient temperature F i Power supply frequency f i ;

[0090] Step S200 includes:

[0091] Step S201: When the electricity consumption data of electrical equipment within the target monitored enterprise collected by the i-th smart meter exceeds the electricity consumption data threshold and the probability value of electricity consumption anomaly is higher than the electricity consumption anomaly probability threshold, it is determined that the situation where the electricity consumption data of electrical equipment within the target monitored enterprise collected by the smart meter exceeds the electricity consumption data threshold is an electricity consumption anomaly; when the electricity consumption data of electrical equipment within the target monitored enterprise collected by the i-th smart meter exceeds the electricity consumption data threshold and the probability value of electricity consumption anomaly is lower than the electricity consumption anomaly probability threshold, it is determined that the situation where the electricity consumption data of electrical equipment within the target monitored enterprise collected by the smart meter exceeds the electricity consumption data status threshold is not an electricity consumption anomaly, but rather an increase in electricity consumption data due to normal use; record the time point T at which the electricity consumption data of electrical equipment within the target monitored enterprise collected by the i-th smart meter exceeds the electricity consumption data threshold. i ;

[0092] Step S202: When an abnormal power consumption is detected, to avoid misjudging the health status of the smart energy meter, the smart energy meter information data threshold is obtained through cloud computing. The smart energy meter information data threshold includes: the operating voltage V of the energy meter. a Load current I b Ambient temperature F c Power supply frequency f d ;

[0093] Step S203: When |V i -V a |>V o or |I i -I b |>I e or |F i -F c |>F f or |f i -f d |>f g Calculate the health value of the smart energy meter in real time; when |V i -V a | <V o and|I i -I b | e and |F i -F c ​| <F f and |f i -f d | <f g The system will issue timely reminders to staff, prompting them to inspect electrical equipment within the company; V o The operating voltage error value of the smart energy meter is obtained through cloud computing; I e The load current error value of the smart energy meter obtained through cloud computing; F f The ambient temperature error value of the smart energy meter obtained through cloud computing; f g The power supply frequency error value of the smart energy meter is obtained through cloud computing;

[0094] Step S204: Normalize the smart energy meter information data collected on the i-th smart energy meter, perform a linear transformation on the original data, and map the data to the range [0,1].

[0095] Step S205: Calculate the health value H of the i-th smart energy meter based on the information data normalization processing result. i :

[0096]

[0097] Among them, V ik I is the normalized mapping value of the operating voltage of the i-th smart energy meter; ik F is the normalized mapping value of the load current of the i-th smart energy meter; ik The normalized mapping value of the ambient temperature of the i-th smart energy meter;

[0098] Step S300 includes:

[0099] Step S301: Obtain the maximum health threshold H of the i-th smart energy meter based on cloud computing. w Minimum health threshold H p ;

[0100] Step S302: When H i >H w When H is detected as unhealthy, a notification is sent to the staff, prompting them to inspect and repair the smart meter; i <H p When H is detected as unhealthy, a notification is sent to the staff, prompting them to inspect and repair the smart meter; p ≤H i ≤H wWhen the i-th smart meter is determined to be in good health and does not require maintenance, the cause of the abnormal power consumption is determined to be a malfunction in the electrical equipment monitored by the i-th smart meter. A prompt is issued to the staff to check and repair the electrical equipment.

[0101] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0102] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cloud computing-based smart energy meter real-time electricity consumption monitoring system, characterized in that, The monitoring system includes a data acquisition module, an abnormal power consumption detection module, an analysis module, a probability calculation module, and a data management module. The data acquisition module is used to monitor the electrical equipment of enterprises within the target monitoring area in real time and collect real-time power consumption data of the electrical equipment within the enterprise. Sensors installed inside smart meters monitor the smart meters and collect information data from them; The power consumption anomaly detection module is used to analyze the power consumption data of electrical equipment within the target monitored enterprise and determine power consumption anomalies based on the analysis results. The analysis module is used to analyze the health status of the smart energy meter and issue prompts to staff based on the analysis results; The probability calculation module is used to calculate the probability value of abnormal electricity consumption; the staff will inspect the smart energy meter and electrical equipment according to the prompts, and after the inspection, the staff will report the inspection results and calculate the probability value of abnormal electricity consumption based on the reported inspection results. The management module is used to collect real-time electricity consumption data of electrical equipment within the enterprise from smart meters, establish an electrical equipment electricity consumption database, and manage the enterprise's electrical equipment based on the electrical equipment database. The probability calculation module includes a probability calculation unit and an anomaly analysis unit: The probability calculation unit includes setting a time error range and calculating based on the time error range and time points. Obtain the target time interval and calculate the probability value of abnormal electricity consumption obtained by the staff after reporting maintenance for the i-th smart meter within the target time interval. ; ； in, The total number of times that staff members report maintenance for the i-th smart energy meter within the target time interval; The total number of times that staff members report the repair of the i-th smart meter within the target time interval, and the feedback result shows that the corresponding electrical equipment or the corresponding smart meter is damaged. The anomaly analysis unit is used to analyze abnormal behavior; at a given time point If, within a unit of time, the electricity consumption data of electrical equipment within the target monitored enterprise collected by the i-th smart meter exceeds the electricity consumption data threshold, and the probability value of electricity consumption anomaly is also higher than the electricity consumption anomaly probability threshold, then the electricity consumption data of electrical equipment within the target monitored enterprise collected by the i-th smart meter exceeding the electricity consumption threshold is determined to be an electricity consumption anomaly; when time point If the electricity consumption data of electrical equipment within the target monitored enterprise collected by the i-th smart meter exceeds the electricity consumption data threshold while the probability value of electricity consumption anomaly is lower than the electricity consumption anomaly probability threshold, it is determined that the electricity consumption data exceeding the electricity consumption data threshold collected by the i-th smart meter does not belong to electricity consumption anomaly, but is an increase in electricity consumption data due to normal operation.

2. The real-time electricity consumption monitoring system for smart energy meters based on cloud computing according to claim 1, characterized in that, The data acquisition module includes an electrical equipment data acquisition unit and an energy meter data acquisition unit. The electrical equipment data acquisition unit is used to monitor the electrical equipment of enterprises within the target monitoring area of ​​the smart energy meter in real time and collect the power consumption data of the electrical equipment in real time. The electricity meter data acquisition unit is used to monitor the smart electricity meter via sensors installed on the i-th smart electricity meter and collect information data from the i-th smart electricity meter; the information data includes the electricity meter's operating voltage. Load current Ambient temperature .

3. The real-time electricity consumption monitoring system for smart energy meters based on cloud computing according to claim 1, characterized in that, The power consumption anomaly detection module includes a power consumption anomaly detection unit, a threshold acquisition unit, and a data analysis unit. The power consumption anomaly determination unit is used to determine whether the power consumption data of electrical equipment within the target monitored enterprise exceeds the power consumption data threshold; when the time point... When the electricity consumption data of electrical equipment within the target monitored enterprise collected by the smart meter exceeds the electricity consumption data status threshold, and the probability of electricity consumption anomaly is higher than the electricity consumption anomaly probability threshold, the situation where the electricity consumption data of electrical equipment within the target monitored enterprise collected by the smart meter exceeds the electricity consumption data threshold is determined to be an electricity consumption anomaly, and the time point is recorded. ; When an abnormal power consumption is identified, the collected smart meter information data is compared with the information data threshold. When it is determined that the collected smart energy meter information data exceeds the information data threshold, the health value of the smart energy meter is calculated. When the collected smart energy meter information data is within the information data threshold, a prompt is issued to the staff, prompting them to check the electrical equipment. The threshold acquisition unit is used to avoid incorrect judgments about the health status of smart energy when an abnormal power consumption is detected. Thresholds for obtaining smart meter information data through cloud computing; The smart meter information data thresholds include: the operating voltage of the smart meter. Load current Ambient temperature ; The data analysis unit is used to analyze the collected smart energy meter information data and provide prompts to staff based on the analysis results.

4. The real-time electricity consumption monitoring system for smart energy meters based on cloud computing according to claim 1, characterized in that, The analysis module includes a data processing unit, a data calculation unit, and an analysis unit: The data processing unit is used to normalize the smart energy meter information data collected on the i-th smart energy meter, perform linear transformation on the original data, and map the data to the range [0,1]. The data calculation unit is used to calculate the health value of the i-th smart energy meter based on the normalization processing result of the information data. The analysis unit is used to calculate the health value of the i-th smart meter and obtain the maximum health threshold of the i-th smart meter based on cloud computing. Minimum health threshold The analysis was conducted, and the results were used to provide guidance to the staff.

5. The real-time electricity consumption monitoring system for smart energy meters based on cloud computing according to claim 1, characterized in that, The data management unit includes a database creation unit and a data management unit. The database establishment unit is used to establish an electricity consumption database for electrical equipment; The data management unit is used to manage the company's electrical equipment. The management platform manages the electrical equipment based on the electrical equipment database. Users can view the company's electrical equipment electricity consumption data and pay electricity bills anytime, anywhere.

6. The real-time electricity consumption monitoring system for smart energy meters based on cloud computing according to claim 1, characterized in that, The monitoring system also includes a cloud computing-based real-time monitoring method for smart energy meter electricity consumption, the monitoring method comprising: Step S100: Use a smart energy meter to monitor the enterprise's electrical equipment in real time within the target monitoring area and collect real-time power consumption data of the electrical equipment within the enterprise; a sensor inside the smart energy meter monitors the smart energy meter and collects smart energy meter information data; the information data includes the energy meter's operating voltage, load current, ambient temperature, and power supply frequency; Step S200: When the electricity consumption data of electrical equipment within the target monitored enterprise collected by the smart meter exceeds the electricity consumption data status threshold, and the probability of electricity consumption anomaly is higher than the probability threshold, it is determined that the situation of the electricity consumption data of electrical equipment within the target monitored enterprise exceeding the electricity consumption data threshold constitutes an electricity consumption anomaly; when it is determined that it is an electricity consumption anomaly, the collected smart meter information data is compared with the information data threshold; when it is determined that the collected smart meter information data exceeds the information data threshold, the health value of the smart meter is calculated; when the collected smart meter information data is within the information data threshold, a prompt is issued to the staff, prompting the staff to check the electrical equipment. Step S300: Obtain the health value of the smart energy meter, analyze the health status of the smart energy meter, and issue a prompt to the staff based on the analysis results; Step S400: Staff members inspect and repair the smart energy meter and electrical equipment according to the prompts, and report the inspection results; based on the inspection status of the electrical equipment and smart energy meter reported by the staff members, calculate the probability value of abnormal electricity consumption. Step S500: Based on the real-time electricity consumption data of electrical equipment within the enterprise collected by smart meters, establish an electrical equipment electricity consumption database; based on the electrical equipment database, manage the enterprise's electrical equipment.

7. The real-time electricity consumption monitoring system for smart energy meters based on cloud computing according to claim 6, characterized in that, Step S100 includes: Step S101: Use a smart energy meter to monitor the electrical equipment of the enterprise within the target monitoring area in real time and collect the power consumption data of the electrical equipment in real time; Step S102: The sensor installed on the i-th smart meter monitors the smart meter and collects information data of the i-th smart meter; the information data includes the operating voltage of the smart meter. Load current Ambient temperature Power supply frequency .

8. The real-time electricity consumption monitoring system for smart energy meters based on cloud computing according to claim 7, characterized in that, Step S200 includes: Step S201: When the electricity consumption data of electrical equipment within the target monitored enterprise collected by the i-th smart meter exceeds the electricity consumption data threshold and the probability value of electricity consumption anomaly is higher than the electricity consumption anomaly probability threshold, it is determined that the situation where the electricity consumption data of electrical equipment within the target monitored enterprise collected by the smart meter exceeds the electricity consumption data threshold is an electricity consumption anomaly; when the electricity consumption data of electrical equipment within the target monitored enterprise collected by the i-th smart meter exceeds the electricity consumption data threshold and the probability value of electricity consumption anomaly is lower than the electricity consumption anomaly probability threshold, it is determined that the situation where the electricity consumption data of electrical equipment within the target monitored enterprise collected by the smart meter exceeds the electricity consumption data status threshold is not an electricity consumption anomaly, but an increase in electricity consumption data due to normal use; record the time point at which the electricity consumption data of electrical equipment within the target monitored enterprise collected by the i-th smart meter exceeds the electricity consumption data threshold. ; Step S202: When an abnormal power consumption is detected, to avoid misjudging the health status of the smart energy meter, the smart energy meter information data threshold is obtained through cloud computing; the smart energy meter information data threshold includes: the operating voltage of the energy meter. Load current Ambient temperature Power supply frequency ; Step S203: When or or or Calculate the health value of the smart energy meter in real time; when and and and The system will issue timely reminders to staff, prompting them to inspect electrical equipment within the company; The operating voltage error value of the smart energy meter is obtained through cloud computing; The load current error value of the smart energy meter is obtained through cloud computing; The ambient temperature error value of the smart energy meter is obtained through cloud computing; The power supply frequency error value of the smart energy meter is obtained through cloud computing; Step S204: Normalize the smart energy meter information data collected on the i-th smart energy meter, perform a linear transformation on the original data, and map the data to the range [0,1]. Step S205: Calculate the health value of the i-th smart energy meter based on the information data normalization processing result. : ; in, This is the normalized mapping value of the operating voltage of the i-th smart energy meter; This is the normalized mapping value of the load current of the i-th smart energy meter. The normalized mapping value of the ambient temperature of the i-th smart energy meter.

9. The real-time electricity consumption monitoring system for a cloud-based smart energy meter according to claim 8, characterized in that, Step S300 includes: Step S301: Obtain the maximum health threshold of the i-th smart meter based on cloud computing. Minimum health threshold ; Step S302: When When the i-th smart meter is deemed unhealthy, a notification is sent to the staff, prompting them to inspect and repair the smart meter; when When the i-th smart meter is deemed unhealthy, a notification is sent to the staff, prompting them to inspect and repair the smart meter; when If the health status of the i-th smart meter is determined to be healthy and no maintenance is required, and the cause of the abnormal power consumption is a malfunction in the electrical equipment monitored by the i-th smart meter, a prompt is issued to the staff to repair the aforementioned electrical equipment.

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