Electric energy meter based on Internet of Things

Abstract

The invention relates to the technical field of intelligent power grids, in particular to an electric energy meter based on the Internet of Things, which comprises an electric energy data real-time monitoring module, an abnormal load identification module, a user notification module, a user demand processing module, a data security management module and an electric energy data transmission module. According to the invention, through analyzing the electric energy use data, adjusting the abnormal identification threshold and matching the power consumption modes in different seasons and time periods, the condition deviating from the conventional power consumption mode is found in real time, the abnormal condition can be timely notified to the user, the management capability of the user on the electric energy use is improved, and the user experience is improved. By collecting energy information requirements of users in different time periods, providing customized energy consumption data, optimizing user satisfaction and encrypting electric energy data, security and integrity of data in a transmission process are ensured, data tampering or unauthorized access is effectively prevented, and efficiency and reliability of a whole power grid are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and particularly to an electricity meter based on the Internet of Things. Background Art

[0002] The technical field of smart grids involves using information and communication technologies to enhance the operation, maintenance, and management efficiency of power systems. This technology enables real-time monitoring and control of power supply, and through the introduction of automated systems, realizes the optimal dispatching and energy efficiency management of the power grid. Smart grid technology can support the integration of renewable energy, improve the reliability and security of the system, and optimize power consumption through demand response mechanisms. In addition, smart grids also include metering infrastructure, power electronic devices, and data analysis and management tools for grid modernization.

[0003] Among them, an electricity meter based on the Internet of Things is a system that uses Internet of Things technology to achieve remote monitoring and management of power consumption. The electricity meter can provide real-time data monitoring, and users and energy suppliers can view the power consumption situation in real time through the Internet. This technology not only improves the transparency of power use, but also helps optimize energy use by analyzing consumption data, thereby reducing waste and improving energy efficiency. In addition, the electricity meter based on the Internet of Things also supports functions such as remote meter reading, fault detection, and load control, which significantly enhances the intelligent level of power management and plays a key role in the construction and optimization of modern power grid management systems.

[0004] Traditional electricity meters rely on periodic data reading rather than real-time data processing, which results in insufficiently timely response to sudden anomalies and inability to effectively prevent power losses or equipment failures. Traditional electricity meters cannot accurately adapt to seasonal and time-of-day changes, making it difficult to detect abnormal electricity consumption behavior in a timely manner, leading to unnecessary energy waste and potential system overload problems. The security of traditional electricity meter data during transmission is not fully guaranteed, and it is easily tampered with or accessed without authorization, affecting the overall credibility of the system, the operation efficiency of the power grid, and having a negative impact on the reliability of the power grid and the trust of users. Summary of the Invention

[0005] The purpose of the present invention is to solve the drawbacks existing in the prior art, and to propose an electricity meter based on the Internet of Things.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: An electricity meter based on the Internet of Things, the electricity meter based on the Internet of Things includes:

[0007] The real-time power data monitoring module, based on the measurement sensors in the electricity meter, collects current and voltage data in real time, evaluates the real-time power consumption, and sorts out the data to obtain a power consumption record;

[0008] The abnormal load identification module, based on the power consumption record, analyzes the power consumption data of users in different seasons and time periods, dynamically adjusts the abnormal identification threshold according to statistical parameters, identifies abnormal power consumption patterns, and obtains abnormal power consumption information;

[0009] The user notification module, based on the abnormal power consumption information, evaluates the abnormal level according to the deviation between the real-time power consumption number and the abnormal identification threshold, sends an abnormal alarm to the user's mobile phone, and obtains user alarm information;

[0010] The user demand processing module, based on the power consumption record, collects the user's demand for power information in different time periods, evaluates the average energy consumption and energy consumption volatility in the corresponding time periods, and displays the energy consumption information to the user, obtaining customized energy consumption data information;

[0011] The data security management module, based on the power consumption record, encrypts the power data collected by the electric energy meter, optimizes the security of the data during storage and transmission, and obtains an encrypted data record;

[0012] The electric energy data transmission module, based on the encrypted data record, transmits the encrypted data to the power company according to the preset transmission frequency, and adjusts the transmission data frequency according to the power data statistics requirements of the power company, obtaining the power data transmission result.

[0013] As a further solution of the present invention, the power consumption record includes real-time power consumption, total cumulative power consumption, and power peak records. The abnormal power consumption information includes the time stamp of abnormal consumption and the exceeded electric energy. The user alarm information includes the confirmation status of the user receiving the alarm and the response time of the user to the alarm. The customized energy consumption data information includes the energy consumption statistics within the user-specified time period, the energy consumption volatility within the specified time period, and the energy consumption peak. The encrypted data record includes the encrypted data type, the key information used for encryption, and the time stamp of data encryption. The power data transmission result includes the number of transmitted data packets, the proportion of successfully transmitted data packets, and the average delay time of data transmission.

[0014] As a further solution of the present invention, the electric energy data real-time monitoring module includes:

[0015] The current and voltage sampling sub-module, based on the measurement sensors in the electric energy meter, continuously samples the current and voltage, and through an analog-to-digital converter, converts the analog signal into a digital signal in real time, obtaining digitized sampling data;

[0016] The electric energy calculation sub-module, based on the digitized sampling data, calculates the product of the current and voltage according to the electric energy measurement formula, calculates the real-time power consumption, and obtains the calculation result of the instantaneous power consumption;

[0017] Based on the calculated results of the instantaneous power consumption, the data statistics and sorting sub-module combines the power consumption measurement obtained each time with the corresponding time stamp, formats and sorts the data, and obtains the power consumption record.

[0018] As a further solution of the present invention, the abnormal load identification module includes:

[0019] Based on the power consumption record, the time period impact analysis sub-module calculates the average value and standard deviation of the power consumption data of users in different seasons and time periods, and evaluates the distribution characteristics of the consumption data through statistical methods, including standard deviation and coefficient of variation, to obtain the statistical analysis results;

[0020] Based on the statistical analysis results, the threshold adjustment sub-module dynamically adjusts the abnormal identification threshold according to the seasonal and time period characteristics of power consumption, optimizes the sensitivity and accuracy of abnormal detection, and obtains the adjusted abnormal detection threshold;

[0021] Based on the adjusted abnormal detection threshold, the abnormal judgment sub-module compares the real-time power consumption data with the adjusted abnormal detection threshold to identify abnormal power consumption patterns and obtain abnormal power consumption information.

[0022] As a further solution of the present invention, the formula for dynamically adjusting the abnormal identification threshold is:

[0023]

[0024] where T is the adjusted abnormal identification threshold, μ T represents the average value of the power consumption data within the selected time period, σ T represents the standard deviation of the power consumption data within the selected time period, k T represents the adjustment coefficient, D represents the average power consumption data of the current season, D L represents the average power consumption data of the same season in history.

[0025] As a further solution of the present invention, the user notification module includes:

[0026] Based on the abnormal power consumption information, the abnormal level evaluation sub-module analyzes the real-time power consumption data and the adjusted abnormal detection threshold, and evaluates the abnormal level according to the deviation size to obtain the abnormal level data.

[0027] Based on the abnormal level data, the alarm trigger sub-module compares the preset alarm trigger conditions according to different abnormal levels, evaluates whether the conditions for sending an alarm are met, and obtains the alarm start decision;

[0028] The notification sending sub-module initiates a decision based on the alert, and uses a communication interface, including SMS and email, to encode and send the alert content to the user's mobile device according to the preset exception notification information, obtaining user alert information.

[0029] As a further aspect of the present invention, the user demand processing module includes:

[0030] The user request analysis sub-module collects and analyzes the user's query requests for electricity usage information in different time periods based on the power consumption record, identifies the critical time periods of the user's demand, and obtains the user demand analysis result;

[0031] The demand content compilation sub-module extracts the energy consumption data for the required time periods based on the user demand analysis result, evaluates the average energy consumption and energy consumption volatility of the target time period, and constructs the energy consumption information content that matches the user's demand, obtaining the customized energy consumption information content;

[0032] The content output sub-module displays the energy consumption data through the user interface based on the customized energy consumption information content, matches the user's viewing demand, and obtains the customized energy consumption data information.

[0033] As a further aspect of the present invention, the data security management module includes:

[0034] The data encryption sub-module encrypts the electricity data end-to-end based on the power consumption record using the Advanced Encryption Standard algorithm, optimizing the security and integrity of the data during storage and transmission, obtaining the encrypted power data information;

[0035] The security inspection sub-module conducts security inspections on the encrypted power data information, including integrity verification and tampering detection, and uses a hashing algorithm to check that the data has not been accessed and modified without authorization, obtaining the data security status information;

[0036] The security log recording sub-module records the activities and results of the security inspection based on the data security status information, creates the corresponding security log, optimizing the transparency and traceability of data security management, obtaining the encrypted data record.

[0037] As a further aspect of the present invention, the electricity data transmission module includes:

[0038] The data packaging sub-module packages the data based on the encrypted data record, including compression and formatting, optimizing the data packet size, matching the network bandwidth and reducing the transmission cost, obtaining the data transmission packet;

[0039] The transmission adjustment sub-module dynamically adjusts the data transmission frequency based on the data transmission packet according to the power data statistics requirements of the power company and in combination with the network conditions, obtaining the transmission strategy adjustment result;

[0040] The receiving confirmation sub-module monitors the sending and receiving status of data packets based on the transmission policy adjustment result, verifies that the data is correctly transmitted to the power company's server, and verifies the integrity and accuracy of data transmission to obtain the power data transmission result.

[0041] As a further solution of the present invention, the formula for dynamically adjusting the data transmission frequency is:

[0042]

[0043] where F new represents the adjusted data transmission frequency, F base represents the reference data transmission frequency, R req represents the current data demand rate of the power company, R cur represents the current actual data transmission rate, R base represents the reference data demand rate, L represents the packet loss rate of the current network, and α F and β F are adjustment coefficients respectively.

[0044] The beneficial effects brought by the technical solution provided by the embodiment of the present invention at least include:

[0045] In the present invention, by analyzing the electricity usage data, adjusting the anomaly recognition threshold, and matching the electricity usage patterns in different seasons and time periods, the situation deviating from the conventional electricity usage pattern can be discovered in real time, users can be notified of abnormal situations in a timely manner, and the user's management ability of electricity usage can be increased. By collecting the energy information needs of users at different time periods and providing customized energy consumption data, user satisfaction can be optimized. By encrypting the electricity data, the security and integrity of the data during transmission are ensured, effectively preventing data from being tampered with or unauthorized access, and improving the efficiency and reliability of the overall power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0047] Figure 1 is the flowchart of the electric energy meter of the present invention;

[0048] Figure 2 is the schematic diagram of the system framework of the present invention;

[0049] Figure 3This is the flowchart of the real-time power data monitoring module of the present invention;

[0050] Figure 4 This is the flowchart of the abnormal load identification module of the present invention;

[0051] Figure 5 This is the flowchart of the user notification module of the present invention;

[0052] Figure 6 This is the flowchart of the user demand processing module of the present invention;

[0053] Figure 7 This is the flowchart of the data security management module of the present invention;

[0054] Figure 8 This is the flowchart of the power data transmission module of the present invention. Detailed implementation manners

[0055] The following describes the technical solutions in the present invention with reference to the accompanying drawings.

[0056] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two can be selected.

[0057] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when their differences are not emphasized, the meanings they express are the same. "(of)", "corresponding" and "corresponding" can sometimes be used interchangeably. It should be noted that when their differences are not emphasized, the meanings they express are the same.

[0058] In the embodiments of the present invention, sometimes subscripts such as W 1 may be written in a non-subscript form such as W1. When their differences are not emphasized, the meanings they express are the same.

[0059] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0060] Please refer to Figure 1 , an electricity meter based on the Internet of Things. The electricity meter based on the Internet of Things includes:

[0061] The real-time power data monitoring module is based on the measurement sensors in the electricity meter, which collects current and voltage data in real time. By calculating the product of the current and voltage data, it evaluates the real-time power consumption, marks the data with timestamps, and organizes the data to obtain power consumption records.

[0062] The abnormal load identification module is based on the power consumption records. By analyzing the power usage data of users in different seasons and time periods, it identifies the average value and standard deviation of power usage in each season and time period, dynamically adjusts the abnormal identification threshold according to the statistical parameters, and compares the real-time monitored power consumption data with the adjusted abnormal identification threshold to identify abnormal power usage patterns and obtain abnormal power usage information.

[0063] The user notification module is based on the abnormal power usage information. According to the deviation between the real-time power consumption number and the abnormal identification threshold, it evaluates the abnormal level and sends an abnormal alarm to the user's mobile phone according to the preset abnormal notification information to obtain user alarm information.

[0064] The user demand processing module is based on the power consumption records. It collects the user's power information requirements for different time periods, extracts the energy consumption data for the corresponding time periods, evaluates the average energy consumption and energy consumption volatility for the corresponding time periods, and displays the energy consumption information to the user to obtain customized energy consumption data information.

[0065] The data security management module is based on the power consumption records. It encrypts the power data collected by the electricity meter, optimizes the security of the data during storage and transmission, prevents the data from being tampered with and unauthorized access during transmission, and optimizes the security and integrity of data transmission to obtain encrypted data records.

[0066] The power data transmission module is based on the encrypted data records. According to the preset transmission frequency, it transmits the encrypted data to the power company, and adjusts the data transmission frequency according to the power data statistics requirements of the power company to optimize the real-time performance of the data and obtain the power data transmission result.

[0067] The power consumption records include real-time power consumption, total cumulative power consumption, and power peak records. The abnormal power usage information includes the timestamp of abnormal consumption and the excessive power energy. The user alarm information includes the confirmation status of the user receiving the alarm and the response time of the user to the alarm. The customized energy consumption data information includes the energy consumption statistics within the user-specified time period, the energy consumption volatility within the specified time period, and the energy consumption peak. The encrypted data records include the encrypted data type, the key information used for encryption, and the timestamp of data encryption. The power data transmission result includes the number of data packets transmitted, the proportion of successfully transmitted data packets, and the average delay time of data transmission.

[0068] Please refer to Figure 2 and Figure 3, the real-time power data monitoring module includes a current and voltage sampling sub-module, a power calculation sub-module, and a data statistics and sorting sub-module;

[0069] The current and voltage sampling sub-module continuously samples the current and voltage based on the measurement sensors inside the electricity meter. Through an analog-to-digital converter, it converts the analog signal into a digital signal in real time to obtain digital sampling data;

[0070] Based on the measurement sensors inside the electricity meter, the continuous sampling of current and voltage is achieved by connecting to the current and voltage measurement sensors inside the electricity meter. The sensors can capture the current and voltage fluctuations in the transmission line. To ensure the accuracy of the data, data acquisition is carried out at a predetermined time interval (such as every millisecond). Each acquisition includes the peak value, average value of the current, and the real-time voltage value. Through a precisely synchronized clock, the data sampling points will be accurately marked to ensure the time accuracy of the sampling data. Subsequently, the analog signal is sent into the analog-to-digital converter. In the analog-to-digital converter, the analog signal is converted into a digital signal through a multi-stage quantization process. The quantization process involves signal amplification, filtering, and digital encoding to ensure that the converted digital signal can accurately reflect the voltage and current characteristics of the original analog signal, obtaining digital sampling data.

[0071] The power calculation sub-module calculates the product of the current and voltage based on the digital sampling data according to the power measurement formula to calculate the real-time power consumption and obtain the calculation result of the instantaneous power consumption;

[0072] Based on the digital sampling data, the power calculation software first reads each sampling point of the voltage and current from the dataset, and then uses the power measurement formula. The formula multiplies the current and voltage values by the corresponding time interval to calculate the instantaneous power of each sampling point. Then, the software accumulates these instantaneous power values to calculate the total energy consumption of the entire sampling period. This process involves repeated reading and multiplication operations on the data to ensure high precision in each calculation. By accumulating the power values of all sampling points, the calculation result of the instantaneous power consumption is obtained.

[0073] The data statistics and sorting sub-module combines the power consumption measurement obtained from each calculation with the corresponding timestamp based on the calculation result of the instantaneous power consumption, formats and sorts the data to obtain the power consumption record.

[0074] Based on the calculation result of the instantaneous power consumption, each result is marked with the corresponding timestamp, and these data are arranged and stored in a preset format, including data classification, sorting, and summarization. To ensure the queryability and sortability of the data, each record will be indexed and compared according to the timestamp and consumption amount, thus generating neatly ordered power consumption data to obtain the power consumption record.

[0075] Please refer to Figure 2 and Figure 4 , the abnormal load identification module includes a time period impact analysis sub-module, a threshold adjustment sub-module, and an abnormal judgment sub-module;

[0076] Based on the power consumption records, the time period impact analysis sub-module calculates the average value and standard deviation of the user's power consumption data for different seasons and time periods. Through statistical methods, including standard deviation and coefficient of variation, it evaluates the distribution characteristics of the consumption data to obtain the statistical analysis results;

[0077] Based on the power consumption records, using time series data processing, the power consumption record data is divided into time periods. For example, the data is classified according to time periods such as morning peak, evening peak, and night valley. Then, statistical analysis is performed on the power consumption data within each time period, including calculating the average power consumption for each time period, collecting all the power records for each time period, and then calculating the arithmetic mean of the records. For the calculation of the standard deviation, the system will first calculate the deviation of each data point from the average value, then find the sum of the squares of the deviations, and finally divide by the total number of data points. The calculated standard deviation is used to evaluate the volatility of the consumption, and the coefficient of variation, which is the ratio of the standard deviation to the average value, is used to analyze the dispersion degree of the data set. The statistical analysis results provide a basis for subsequent data applications to obtain the statistical analysis results.

[0078] Based on the statistical analysis results, the threshold adjustment sub-module dynamically adjusts the abnormal identification threshold according to the seasonal and time period characteristics of power consumption to optimize the sensitivity and accuracy of abnormal detection, and obtains the adjusted abnormal detection threshold;

[0079] The formula for dynamically adjusting the abnormal identification threshold is:

[0080]

[0081] where T is the adjusted abnormal identification threshold, μ T represents the average value of the power consumption data within the selected time period, σ T represents the standard deviation of the power consumption data within the selected time period, k T represents the adjustment coefficient, D represents the average power consumption data of the current season, and D L represents the average power consumption data of the same season in history;

[0082] Formula:

[0083]

[0084] Meaning and acquisition method of parameters:

[0085] μ TRepresents the average value of the electricity consumption data within the selected time period. The parameter is calculated by collecting the electricity consumption data over a certain period and then using statistical methods to obtain the arithmetic mean of these data.

[0086] σ T Represents the standard deviation of the electricity consumption data within the selected time period. The standard deviation is a statistic that measures the degree of dispersion of the data.

[0087] k T Is an adjustment coefficient, which is a preset coefficient and can be adjusted according to the historical data of abnormal behaviors in different seasons or time periods. Usually, this coefficient is set according to the abnormal frequency and severity in the historical data to adapt to the abnormal detection sensitivity in different seasons.

[0088] D and D L Represent the average electricity consumption data of the current season and the average electricity consumption data of the same season in history, respectively.

[0089] Calculation example:

[0090] Assume the parameters are: μ T = 200 kWh (kilowatt-hour), σ T = 50 kWh, k T = 1.5, D = 250 kWh, D L = 210 kWh.

[0091] Calculation process:

[0092]

[0093] The calculation result T = 232.7375 kWh indicates that considering the deviation between the current data and the historical data and the influence of the standard deviation, the adjusted abnormal recognition threshold is 232.7375 kWh. This means that if any electricity consumption data within this time period exceeds this threshold, the system will identify it as abnormal and trigger further inspections or alarms.

[0094] Based on the adjusted abnormal detection threshold, the abnormal judgment sub-module compares the real-time electricity consumption data with the adjusted abnormal detection threshold to identify abnormal electricity consumption patterns and obtain abnormal electricity consumption information.

[0095] Based on the adjusted anomaly detection threshold, real-time power consumption data is received and compared with the adjusted anomaly detection threshold. For each data point, it is checked whether it exceeds the threshold. The comparison operation is carried out continuously to ensure real-time monitoring. Once it is found that the power consumption data exceeds the threshold, the system determines it as an anomaly and records the detailed information of the event, including the time, consumption amount, and the degree of exceeding the threshold. The judgment process is automated and implemented through software programming to ensure that all data can be evaluated quickly and accurately, so as to identify and respond to any possible abnormal power consumption patterns in a timely manner, and obtain abnormal power consumption information.

[0096] Please refer to Figure 2 and Figure 5 , the user notification module includes an anomaly level assessment sub-module, an alarm trigger sub-module, and a notification sending sub-module;

[0097] The anomaly level assessment sub-module analyzes the real-time power consumption data and the adjusted anomaly detection threshold based on the abnormal power consumption information, and evaluates the anomaly level according to the size of the deviation to obtain anomaly level data;

[0098] Based on the abnormal power consumption information, analyze the real-time power consumption data and its deviation from the threshold. According to the absolute size of the deviation, the system classifies and sets the anomaly level. For example, a deviation within a certain range may be marked as "minor", a larger deviation is marked as "medium", and a large deviation beyond the normal range is defined as "severe". The process involves continuous data input and real-time processing, ensuring the timeliness and accuracy of data evaluation. By comparing each real-time data point with the set threshold, the system can dynamically classify the anomaly level of each power consumption, ensuring that each level is adjusted according to the latest data, and obtaining anomaly level data.

[0099] The alarm trigger sub-module, based on the anomaly level data, compares the preset alarm trigger conditions according to the differentiated anomaly levels to evaluate whether the conditions for sending an alarm are met, and obtains an alarm activation decision;

[0100] Based on the anomaly level data, determine whether to trigger an alarm through preset logic, including the comparative analysis of the anomaly level and the preset alarm conditions. The alarm conditions may be set based on the severity of the anomaly level. For example, an alarm is triggered only when the anomaly level reaches "severe", ensuring that only abnormal events that meet all conditions will trigger an alarm. Such processing not only improves the reliability of the system but also reduces the possibility of false alarms, and obtains an alarm activation decision.

[0101] The notification sending sub-module, based on the alarm activation decision, uses communication interfaces, including SMS and email, encodes and sends the alarm content to the user's mobile device according to the preset abnormal notification information, and obtains user alarm information.

[0102] Based on the alarm activation decision, using the integrated communication interfaces such as SMS and email services, according to the alarm activation decision, call the functions of the communication interface, encode the preset alarm information, including determining the priority of the information and the recipients, and send the alarm information through the specified channels. The process ensures that the information is sent according to the highest security and speed standards. By dynamically configuring the recipient list and alarm levels, ensure that the right people receive the alarm at the appropriate time, enabling users to be informed and respond in a timely manner to potential abnormal electricity usage, and obtain user alarm information.

[0103] Please refer to Figure 2 and Figure 6 , the user demand processing module includes a user request analysis sub-module, a demand content compilation sub-module, and a content output sub-module;

[0104] The user request analysis sub-module, based on the power consumption records, collects and analyzes the user's query requests for electricity usage information in different time periods, identifies the critical time periods of the user's needs, and obtains the user demand analysis results;

[0105] Based on the power consumption records, extract the power consumption records related to the user's query from the database, including the power consumption data for each time period. The process involves data access and retrieval techniques, perform keyword matching according to the user's query request, identify the time periods that the user is concerned about. For example, the user may be particularly concerned about the electricity consumption on holidays or weekdays. By analyzing the data access frequencies of these critical time periods, determine the hot spots of the user's needs. The analysis results include the frequencies of queries for each time period and the statistical data of the user's preferences. These data are processed to form the user demand analysis results, and obtain the user demand analysis results.

[0106] The demand content compilation sub-module, based on the user demand analysis results, extracts the energy consumption data for the required time periods, evaluates the average energy consumption and energy consumption volatility of the target time periods, and constructs the energy consumption information content that matches the user's needs, and obtains the customized energy consumption information content;

[0107] Based on the user demand analysis results, according to the hot spot analysis data of the user's needs, extract the power data for specific time periods from the overall power consumption database, including the average power consumption and consumption volatility, and use statistical analysis methods to evaluate these data to determine the typical energy consumption patterns and possible abnormal fluctuations. Based on the analysis results, construct the energy consumption information that matches the user's query needs, including visualizing the data, such as charts and trend lines, to clearly present the energy consumption changes within the target time periods, and obtain the customized energy consumption information content.

[0108] The content output sub-module, based on the customized energy consumption information content, displays the energy consumption data through the user interface to match the user's viewing needs, and obtains the customized energy consumption data information.

[0109] Based on the customized energy consumption information content, the processed energy consumption data is displayed through the user interface. The customized energy consumption information is integrated into the appropriate parts of the user interface, including dynamic charts and real-time updated data sections. Through these interfaces, users can intuitively see the power consumption in different time periods. The interface design takes into account the convenience of user interaction and the readability of data, ensuring the accurate transmission and efficient access of information, so as to meet the user's viewing needs for electrical energy usage information and obtain the customized energy consumption data information.

[0110] Please refer to Figure 2 and Figure 7 , the data security management module includes a data encryption sub-module, a security check sub-module, and a security log recording sub-module;

[0111] Based on the power consumption records, the data encryption sub-module uses the Advanced Encryption Standard algorithm to perform end-to-end encryption processing on the electrical energy data, optimizing the security and integrity of the data during storage and transmission, and obtaining the encrypted electrical energy data information.

[0112] Based on the power consumption records, collect the electrical energy data that needs to be encrypted. The data includes the user's consumption, time tags, and other relevant information. Perform end-to-end encryption processing using the Advanced Encryption Standard algorithm. During the encryption process, each piece of data is encrypted with a generated key to ensure the security of each data packet. This key is unique for each session, thus increasing security and ensuring the encrypted integrity of the data during storage and the security during transmission, generating the encrypted electrical energy data information.

[0113] Based on the encrypted electrical energy data information, the security check sub-module conducts security checks, including integrity verification and tampering detection. Use the hash algorithm to check that the data has not been accessed and modified without authorization, and obtain the data security status information.

[0114] Based on the encrypted electrical energy data information, use a preset hash algorithm, such as SHA-256, to perform integrity verification on the data, including comparing the hash values before and after data encryption to detect whether the data has been tampered with during transmission or storage. If the hash values do not match, it is marked as a potential security issue. The security check is not limited to integrity verification but also includes tampering detection. The system further enhances security by recording and analyzing access patterns and unauthorized access attempts to ensure that the data has not been accessed and modified without authorization, and obtain the data security status information.

[0115] Based on the data security status information, the security log recording sub-module records the activities and results of the security checks, creates corresponding security logs, and optimizes the transparency and traceability of data security management, obtaining the encrypted data records.

[0116] Based on the data security status information, record each security inspection activity and result, including the timestamp, the participating system components, and the details of specific security events. All security events are recorded in a security log database. The log provides a way to trace data security events. Regularly audit the log to detect security vulnerabilities or improper data access attempts. The records help improve the transparency and traceability of overall data security management. Through these detailed records and systematic monitoring, a high level of data security and management is ensured, and encrypted data records are obtained.

[0117] Please refer to Figure 2 and Figure 8 , the electric energy data transmission module includes a data packaging sub-module, a transmission adjustment sub-module, and a receiving confirmation sub-module;

[0118] The data packaging sub-module packs the data based on the encrypted data records, including compression and formatting, optimizes the size of the data packet, matches the network bandwidth, and reduces the transmission cost to obtain a data transmission packet;

[0119] Based on the encrypted data records, perform data packaging processing. The process includes using data compression algorithms such as ZIP or GZIP to compress the data to reduce the size of the data packet. The formatting of the data packet involves converting the data into a format more suitable for network transmission, such as JSON or XML. Such formatting helps maintain the structure and meaning of the data during transmission. In addition, optimize the size of the data packet to match the network bandwidth, thereby reducing the transmission cost and ensuring the efficient transmission of data over the network to obtain a data transmission packet.

[0120] The transmission adjustment sub-module, based on the data transmission packet, dynamically adjusts the data transmission frequency according to the power data statistics requirements of the power company and in combination with the network conditions to obtain the result of the transmission strategy adjustment;

[0121] The formula for dynamically adjusting the data transmission frequency is:

[0122]

[0123] where, F new represents the adjusted data transmission frequency, F base represents the reference data transmission frequency, R req represents the current data demand rate of the power company, R cur represents the current actual data transmission rate, R base represents the reference data demand rate, L represents the packet loss rate of the current network, α F and β F are adjustment coefficients respectively.

[0124] The formula is:

[0125]

[0126] Meaning and acquisition method of parameters:

[0127] F base is the reference data transfer frequency, which is usually set according to the design under the optimal state of the network or historical highest efficiency data. For example, the transfer frequency at the highest packet-loss-free time can be obtained from previous network performance evaluations.

[0128] R req represents the current data demand rate of the power company, which is usually provided by the power company and determined based on their real-time data processing requirements and frequency requirements of statistical analysis.

[0129] R cur represents the current actual data transfer rate, which can be measured in real time through network monitoring tools and reflects the actual data transfer speed under the current network conditions.

[0130] R base is the reference data demand rate, which is the ideal demand rate preset when designing the system and may be set based on data collection requirements during peak periods.

[0131] L represents the packet loss rate of the current network. This parameter is measured in real time through network performance monitoring tools and is usually expressed as the percentage of lost data packets during the data sending and receiving process.

[0132] α F and β F are adjustment coefficients, which are set according to historical data analysis and network performance adjustment experience and are used to adjust the impact of demand changes and packet loss rate on the transfer frequency. These coefficients help adjust the reaction sensitivity of the algorithm to network condition changes.

[0133] Calculation example:

[0134] Set the parameters as follows: F base = 100Hz, R req = 120Mbps, R cur = 80Mbps, R base = 100Mbps, L = 10%, α F = 0.5, β F = 0.3.

[0135] Calculate the adjusted data transfer frequency:

[0136]

[0137] Calculation result F new= 116.4 Hz, indicating that the adjusted data transmission frequency is 116.4 Hz. This frequency reflects the adjustment of the current network packet loss rate and the difference between data demand and actual transmission rate. The increased transmission frequency aims to bridge the gap between data demand and current transmission capacity, taking into account the impact of network packet loss to ensure stable and punctual data transmission, thereby optimizing the response efficiency and data integrity of the entire system.

[0138] The receiving confirmation sub-module monitors the sending and receiving status of data packets based on the transmission strategy adjustment result, verifies that the data is correctly transmitted to the power company's server, and verifies the integrity and accuracy of data transmission to obtain the power data transmission result.

[0139] Based on the transmission strategy adjustment result, monitor the sending and receiving status of data packets to ensure that each data packet is correctly sent from the local server to the power company's server, including using network protocols such as TCP / IP for data sending, checking whether the data packet arrives successfully through the confirmation mechanism, and verifying the integrity and accuracy of the data. Use hash algorithms and checksum techniques to check whether the data is intact during transmission. The process ensures the reliability and security of data transmission to obtain the power data transmission result.

[0140] It should be understood that the term "and / or" in this article is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood with reference to the context.

[0141] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or its similar expressions refer to any combination of these items, including any combination of single item (s) or plural item (s). For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0142] It should be understood that in various embodiments of the present invention, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0143] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0144] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0145] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0146] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0147] In addition, the functional units in various embodiments of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0148] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0149] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. An electric energy meter based on the Internet of Things, characterized in that: The electric energy meter based on the Internet of Things includes: The real-time monitoring module of electric energy data collects current and voltage data in real time based on the measurement sensors in the electric energy meter, evaluates the real-time electric energy consumption, and organizes the data to obtain the electric energy consumption record; The abnormal load identification module is based on the power consumption record, analyzes the power usage data of users in different seasons and time periods, dynamically adjusts the abnormal identification threshold according to statistical parameters, identifies abnormal power consumption patterns, and obtains abnormal power consumption information; The user notification module evaluates the abnormal level based on the abnormal power consumption information and the deviation between the real-time power consumption and the abnormal recognition threshold, sends an abnormal alarm to the user's mobile phone, and obtains the user alarm information; The user demand processing module collects the user's demand for electric energy information in differentiated time periods based on the power consumption record, evaluates the average energy consumption and energy consumption volatility in the corresponding time periods, displays energy consumption information to the user, and obtains customized energy consumption data information; The data security management module encrypts the electric energy data collected by the electric energy meter based on the electric energy consumption record, optimizes the security of the data during storage and transmission, and obtains an encrypted data record; The electric energy data transmission module transmits the encrypted data to the power company based on the encrypted data record at a preset transmission frequency, and adjusts the transmission data frequency according to the power data statistical requirements of the power company to obtain the power data transmission result.

2. The electric energy meter based on the Internet of Things according to claim 1, characterized in that: The power consumption record includes real-time power consumption, cumulative total power consumption and power peak record; the abnormal power consumption information includes the timestamp of abnormal consumption and the amount of power exceeding the standard; the user alarm information includes the confirmation status of the user receiving the alarm and the user's response time to the alarm; the customized energy consumption data information includes energy consumption statistics within a user-specified time period, energy consumption volatility and energy consumption peak within a specified time period; the encrypted data record includes the encrypted data type, the key information used for encryption and the timestamp of data encryption; the power data transmission result includes the number of transmitted data packets, the proportion of successfully transmitted data packets and the average delay time of data transmission.

3. The electric energy meter based on the Internet of Things according to claim 1, characterized in that: The electric energy data real-time monitoring module includes: The current and voltage sampling submodule continuously samples the current and voltage based on the measurement sensor in the electric energy meter, and converts the analog signal into a digital signal in real time through the analog-to-digital converter to obtain digital sampling data; The electric energy calculation submodule calculates the product of current and voltage based on the digital sampling data and the electric energy measurement formula, calculates the real-time electric energy consumption, and obtains the instantaneous electric energy consumption calculation result; The data statistics and sorting submodule combines the electric energy consumption amount calculated each time with the corresponding timestamp based on the instant electric energy consumption calculation result, formats and sorts the data, and obtains the electric energy consumption record.

4. The electric energy meter based on the Internet of Things according to claim 1, characterized in that: The abnormal load identification module includes: The time period impact analysis submodule calculates the average and standard deviation of the user's power usage data in different seasons and time periods based on the power consumption records, evaluates the distribution characteristics of the consumption data through statistical methods, including standard deviation and coefficient of variation, and obtains statistical analysis results; The threshold adjustment submodule dynamically adjusts the abnormality recognition threshold based on the statistical analysis results and the seasonal and periodic characteristics of the electric energy consumption, optimizes the sensitivity and accuracy of the abnormality detection, and obtains the adjusted abnormality detection threshold; The abnormality judgment submodule compares the real-time power consumption data with the adjusted abnormality detection threshold based on the adjusted abnormality detection threshold, identifies the abnormal power consumption pattern, and obtains abnormal power consumption information.

5. The electric energy meter based on the Internet of Things according to claim 4, characterized in that: The formula for dynamically adjusting the abnormality recognition threshold is: Among them, T is the adjusted anomaly recognition threshold, μ T Represents the average value of the power usage data in the selected period, σ T Represents the standard deviation of the energy usage data in the selected period, k T represents the adjustment factor, D represents the average power consumption data of the current season, and D L Represents the average electricity consumption data for the same season in history.

6. The electric energy meter based on the Internet of Things according to claim 1, characterized in that: The user notification module comprises: The abnormal level assessment submodule analyzes the real-time power consumption data and the adjusted abnormal detection threshold based on the abnormal power consumption information, assesses the abnormal level according to the deviation, and obtains abnormal level data; The alarm triggering submodule compares the preset alarm triggering conditions based on the abnormality level data and the differentiated abnormality levels, evaluates whether the conditions for sending an alarm are met, and obtains an alarm activation decision; The notification sending submodule encodes the alarm content and sends it to the user's mobile device according to the preset abnormal notification information based on the alarm initiation decision, using the communication interface, including SMS and email, to obtain the user alarm information.

7. The electric energy meter based on the Internet of Things according to claim 1, characterized in that: The user demand processing module includes: The user request analysis submodule collects and analyzes the user's query requests for the power usage information in different time periods based on the power consumption records, identifies the key time periods of user needs, and obtains the user demand analysis results; The demand content compilation submodule extracts the energy consumption data of the required period based on the user demand analysis results, evaluates the average energy consumption and energy consumption volatility of the target period, constructs energy consumption information content that matches the user's needs, and obtains customized energy consumption information content; The content output submodule displays the energy consumption data through a user interface based on the customized energy consumption information content to match the user's viewing needs and obtain customized energy consumption data information.

8. The electric energy meter based on the Internet of Things according to claim 1, characterized in that: The data security management module includes: The data encryption submodule uses the advanced encryption standard algorithm based on the power consumption record to perform end-to-end encryption processing on the power data, optimize the security and integrity of the data during storage and transmission, and obtain power data encryption information; The security check submodule performs a security check based on the power data encryption information, including integrity verification and tampering detection, and uses a hash algorithm to verify that the data has not been unauthorizedly accessed and modified, thereby obtaining data security status information; The security log recording submodule records the activities and results of the security check based on the data security status information, creates a corresponding security log, optimizes the transparency and traceability of data security management, and obtains encrypted data records.

9. The electric energy meter based on the Internet of Things according to claim 1, characterized in that: The electric energy data transmission module comprises: The data packaging submodule packages the data based on the encrypted data record, including compression and formatting, optimizing the data packet size, matching the network bandwidth and reducing the transmission cost, and obtaining a data transmission packet; The transmission adjustment submodule dynamically adjusts the frequency of data transmission based on the data transmission packet, according to the power data statistical requirements of the power company and in combination with network conditions, and obtains a transmission strategy adjustment result; The receiving confirmation submodule monitors the sending and receiving status of the data packet based on the transmission strategy adjustment result, verifies that the data is correctly transmitted to the power company's server, and verifies the integrity and accuracy of the data transmission to obtain the power data transmission result.

10. The electric energy meter based on the Internet of Things according to claim 9, characterized in that: The formula for dynamically adjusting the frequency of data transmission is: Among them, F new Represents the adjusted data transmission frequency, F base Represents the base data transmission frequency, R req represents the current data demand rate of the power company, R cur Represents the current actual data transmission rate, R base represents the benchmark data demand rate, L represents the current network packet loss rate, α F and β F are the adjustment coefficients respectively.

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