A safety monitoring method and device for electric energy meter and smart electric energy meter

Abstract

The present invention relates to the field of power safety technology, and specifically discloses a safety monitoring method, device and intelligent electric energy meter. The safety monitoring method includes: collecting each type of operation monitoring data during the operation of the electric energy meter, and preprocessing each type of operation monitoring data to obtain each type of effective operation monitoring data; when it is judged based on an anomaly detection model that there is abnormal operation monitoring data in each type of effective operation monitoring data, all abnormal operation monitoring data of the corresponding type of effective operation monitoring data are extracted; based on all abnormal operation monitoring data in all types of effective operation monitoring data and risk assessment fuzzy logic, the safety risk of the electric energy meter is evaluated to determine the risk level of the electric energy meter; when the risk level of the electric energy meter reaches the risk level threshold, an alarm message is issued through a wireless communication module; effective monitoring of the operation of the electric energy meter is achieved, and the safety and stability of the use of the electric energy meter are improved.

Description

Technical Field

[0001] The present invention relates to the field of power safety technology, and in particular to a safety monitoring method and device for an electric energy meter and an intelligent electric energy meter. Background Art

[0002] In today's energy management and power supply landscape, electricity meters are crucial devices for measuring energy usage. Their accuracy and safety are crucial to the stable operation of power systems and the interests of both users and power suppliers. With the continuous development of smart grids, the functionality of electricity meters is becoming increasingly complex, and the amount of data generated during their operation is also increasing. To ensure the proper operation and safe use of electricity meters, effective monitoring is necessary. Currently, the most common method for monitoring electricity meters is through regular manual inspections and simple data collection.

[0003] However, this approach has numerous limitations. Manual inspections are inefficient and difficult to detect problems in real time. Simple data collection cannot fully and deeply reflect the operating status of electricity meters. Furthermore, with the development of intelligent and information-based power systems, the requirements for analyzing and processing electricity meter operating data are becoming increasingly stringent. Existing monitoring methods struggle to quickly and accurately extract valuable information from massive amounts of operational monitoring data. They also fail to effectively and timely identify potential safety hazards and abnormal conditions, and accurately assess risk levels.

[0004] Therefore, the present invention provides a safety monitoring method and device for an electric energy meter and a smart electric energy meter. Summary of the Invention

[0005] The present invention provides a safety monitoring method and device for an electric energy meter, as well as an intelligent electric energy meter. By collecting and preprocessing operational monitoring data, accurate and effective data is obtained, providing a reliable basis for subsequent analysis. Anomalies in various types of operational monitoring data can be promptly detected using an anomaly detection model. Abnormal operational monitoring data is extracted, and the safety risk of the electric energy meter is assessed using risk assessment fuzzy logic to determine the risk level, making the assessment more scientific and comprehensive. When the risk level reaches a threshold, an alarm message is issued, prompting relevant personnel to take measures to prevent risks. This achieves effective monitoring of the electric energy meter's operation, improving the safety and stability of the meter's use.

[0006] The present invention provides a safety monitoring method for an electric energy meter, comprising:

[0007] S1: Collect each type of operation monitoring data during the operation of the electric energy meter, and pre-process each type of operation monitoring data to obtain each type of effective operation monitoring data;

[0008] S2: When it is determined based on the anomaly detection model that there is abnormal operation monitoring data in each category of valid operation monitoring data, all abnormal operation monitoring data in the corresponding category of valid operation monitoring data are extracted;

[0009] S3: Evaluate the safety risk of the electric energy meter based on all abnormal operation monitoring data of all types of effective operation monitoring data and risk assessment fuzzy logic to determine the risk level of the electric energy meter;

[0010] S4: When the risk level of the electric energy meter reaches the risk level threshold, an alarm message is issued.

[0011] Preferably, the safety monitoring method for an electric energy meter, S2: when it is determined based on the anomaly detection model that abnormal operation monitoring data exists in each type of valid operation monitoring data, all abnormal operation monitoring data in the corresponding type of valid operation monitoring data are extracted, including:

[0012] Obtaining a preset number of manual judgment instances of electric energy meter monitoring data, and extracting abnormal operation monitoring data and complete operation monitoring data contained in each manual judgment instance of electric energy meter monitoring data;

[0013] A neural network model is trained based on abnormal operation monitoring data and complete operation monitoring data contained in manual judgment instances of electricity meter monitoring data to obtain an abnormality detection model for the electricity meter;

[0014] Input each type of valid operation monitoring data obtained in real time into the anomaly detection model to determine whether there is abnormal operation monitoring data in each type of valid operation monitoring data;

[0015] If so, all abnormal operation monitoring data in the corresponding class of valid operation monitoring data are obtained based on the output result of the anomaly detection model;

[0016] Otherwise, keep the corresponding model output results.

[0017] Preferably, the safety monitoring method for an electric energy meter, S3: evaluating the safety risk of the electric energy meter based on all abnormal operation monitoring data in all types of effective operation monitoring data and risk assessment fuzzy logic to determine the risk level of the electric energy meter, including:

[0018] Extract the performance features of all abnormal operation monitoring data from all types of valid operation monitoring data, and perform feature quantization and normalization on the performance features of all abnormal operation monitoring data to obtain the standard quantified values ​​of the performance features of all abnormal operation monitoring data;

[0019] Define all fuzzy sets and corresponding membership functions and all fuzzy rules of risk assessment fuzzy logic;

[0020] Fuzzy reasoning and defuzzification are performed based on the standard quantitative values ​​of the performance characteristics of all abnormal operation monitoring data in all types of effective operation monitoring data, all fuzzy sets and corresponding membership functions of risk assessment fuzzy logic, and all fuzzy rules. Combined with the preset risk level classification standards, the risk level of the electricity meter is determined.

[0021] Preferably, the safety monitoring method of the electric energy meter performs fuzzy reasoning and defuzzification based on the standard quantitative values ​​of the performance characteristics of all abnormal operation monitoring data in all types of effective operation monitoring data, all fuzzy sets and corresponding membership functions and all fuzzy rules of the risk assessment fuzzy logic, and combines the preset risk level classification standard to determine the risk level of the electric energy meter, including:

[0022] Based on the performance characteristic standard quantitative values ​​of all abnormal operation monitoring data in all types of effective operation monitoring data, all fuzzy sets of risk assessment fuzzy logic and corresponding membership functions, the membership of each performance characteristic standard quantitative value to each fuzzy set is determined;

[0023] Based on the membership of each performance characteristic standard quantization value to each fuzzy set and the premise parts of all fuzzy rules, all fuzzy rules activated by each performance characteristic standard quantization value are determined, and the membership of each performance characteristic standard quantization value to all fuzzy sets contained in the premise parts of each corresponding activated fuzzy rule is regarded as all activation intensities of the corresponding fuzzy rules;

[0024] Based on all the fuzzy rules activated by the quantitative values ​​of all performance feature standards and all the corresponding activation intensities, combined reasoning and defuzzification are performed, and the risk level of the electricity meter is determined in combination with the preset risk level classification standard.

[0025] Preferably, the safety monitoring method of the electric energy meter performs combined reasoning and defuzzification based on all fuzzy rules activated by the quantitative values ​​of all performance characteristic standards and all corresponding activation intensities, and determines the risk level of the electric energy meter in combination with a preset risk level classification standard, including:

[0026] Obtaining the membership distribution of the fuzzy sets of the conclusion parts of all fuzzy rules activated by all the quantitative values ​​of the performance characteristic standards, taking each activation intensity corresponding to each fuzzy rule activated by all the quantitative values ​​of the performance characteristic standards as a membership adjustment multiple, performing membership adjustment on the membership distribution of the fuzzy sets of the conclusion parts of the corresponding fuzzy rules, and obtaining the adjusted membership distribution of the fuzzy sets of the conclusion parts of the corresponding fuzzy rules under each corresponding activation intensity;

[0027] The adjusted membership distributions of the conclusion parts of all fuzzy rules with the same corresponding conclusion parts in all fuzzy rules activated by the quantitative values ​​of all performance characteristic standards are summarized under all corresponding activation intensities to obtain all the adjusted membership distributions of each conclusion;

[0028] Based on all the adjusted membership distributions of all the conclusions, combined reasoning and defuzzification are performed to obtain the fuzzy logic decision value;

[0029] The preset risk level classification standard is retrieved based on the fuzzy logic decision value to determine the risk level of the electric energy meter.

[0030] Preferably, the safety monitoring method of the electric energy meter performs combined reasoning and defuzzification based on all adjusted membership distributions of all conclusions to obtain a fuzzy logic decision value, including:

[0031]

[0032] Where R u is the fuzzy logic decision value, max is the maximum independent variable value in the membership distribution function, n is the total number of conclusions, m i is the total number of all adjusted membership distributions of the i-th conclusion, θ ij (x) is the function representation of the jth adjusted membership distribution of the i-th conclusion, x is the independent variable of the adjusted membership distribution function, α ij is the preset weight of the fuzzy rule corresponding to the jth adjusted membership distribution of the i-th conclusion, From 0 to max Perform an integration operation with the integral variable x, From 0 to max Performs an integration operation with the integral variable x.

[0033] Preferably, in the safety monitoring method of the electric energy meter, the alarm information is sent through the wireless communication module.

[0034] Preferably, the safety monitoring method of the electric energy meter further includes:

[0035] All types of operation monitoring data and all abnormal operation monitoring data, all risk assessment results and all alarm information obtained in real time are stored in the monitoring background, among which the risk assessment results include the risk level of the electricity meter.

[0036] The present invention provides a safety monitoring device for an electric energy meter, which is used to execute any of the above safety monitoring methods for an electric energy meter, comprising:

[0037] The data acquisition and processing module is used to collect each type of operation monitoring data during the operation of the electric energy meter, and pre-process each type of operation monitoring data to obtain each type of effective operation monitoring data;

[0038] An abnormal data extraction module is used to extract all abnormal operation monitoring data in the corresponding class of valid operation monitoring data when it is determined based on the abnormal detection model that there is abnormal operation monitoring data in each class of valid operation monitoring data;

[0039] A safety risk assessment module is used to assess the safety risk of the electric energy meter based on all abnormal operation monitoring data in all types of effective operation monitoring data and risk assessment fuzzy logic to determine the risk level of the electric energy meter;

[0040] The alarm module is used to issue an alarm message when the risk level of the electric energy meter reaches the risk level threshold.

[0041] The present invention provides an intelligent electric energy meter connected to the above safety monitoring device.

[0042] The beneficial effects of the present invention compared to the existing technology are as follows: by collecting and preprocessing operation monitoring data, accurate and effective data is obtained, providing a reliable basis for subsequent analysis. The anomaly detection model can promptly detect anomalies in various types of operation monitoring data. Abnormal operation monitoring data is extracted and combined with risk assessment fuzzy logic to assess the safety risks of the electricity meter and determine the risk level, making the assessment more scientific and comprehensive. When the risk level reaches the threshold, an alarm message is issued, which can promptly remind relevant personnel to take measures to prevent risks. This achieves effective monitoring of the operation of the electricity meter and improves the safety and stability of the electricity meter.

[0043] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in this application document.

[0044] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0046] Figure 1 Flowchart of a safety monitoring method for an electric energy meter in an embodiment of the present invention;

[0047] Figure 2This is a connection diagram of the smart energy meter, security monitoring device, and monitoring background in an embodiment of the present invention;

[0048] Figure 3 Schematic diagram of a safety monitoring device for an electric energy meter in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0050] Example 1:

[0051] The present invention provides a safety monitoring method for an electric energy meter, referring to Figure 1 ,include:

[0052] S1: Collect each type of operation monitoring data during the operation of the electric energy meter, and pre-process each type of operation monitoring data to obtain each type of effective operation monitoring data;

[0053] S2: When it is determined based on the anomaly detection model that there is abnormal operation monitoring data in each category of valid operation monitoring data, all abnormal operation monitoring data in the corresponding category of valid operation monitoring data are extracted;

[0054] S3: Evaluate the safety risk of the electric energy meter based on all abnormal operation monitoring data of all types of effective operation monitoring data and risk assessment fuzzy logic to determine the risk level of the electric energy meter;

[0055] S4: When the risk level of the electric energy meter reaches the risk level threshold, an alarm message is issued.

[0056] In this embodiment, the operation monitoring data refers to various relevant data reflecting the operation status and performance of the electric energy meter, such as voltage, current, power, etc., collected during the operation of the electric energy meter.

[0057] In this embodiment, effective operation monitoring data refers to data with actual analysis value and accuracy obtained by preprocessing the collected operation monitoring data and removing invalid or erroneous data.

[0058] In this embodiment, the anomaly detection model is a neural network model obtained by training using a preset number of manual judgment examples of electricity meter monitoring data, and is used to determine whether there is an anomaly in the electricity meter operation monitoring data.

[0059] In this embodiment, abnormal operation monitoring data refers to the portion of operation monitoring data that is determined to be inconsistent with the normal operating state after processing and judgment.

[0060] In this embodiment, the risk assessment fuzzy logic is a set of fuzzy rules, fuzzy sets and corresponding membership functions for assessing the safety risk of the electric energy meter, which can comprehensively consider multiple uncertain factors to assess the risk.

[0061] In this embodiment, the risk level of the electric energy meter is a level indicating the degree of safety risk faced by the electric energy meter, such as high, medium, low, etc., divided according to the operation monitoring data and risk assessment results of the electric energy meter.

[0062] In this embodiment, the risk level threshold is a pre-set threshold used to determine whether the risk level of the electric energy meter has reached a critical level requiring an alarm message to be issued.

[0063] In this embodiment, the alarm information is a message sent by the wireless communication module to prompt relevant personnel to take measures when the risk level of the electric energy meter reaches or exceeds the risk level threshold.

[0064] The beneficial effects of the above technology include: by collecting and preprocessing operation monitoring data, accurate and valid data is obtained, providing a reliable foundation for subsequent analysis. The anomaly detection model can promptly detect anomalies in various types of operation monitoring data. Abnormal operation monitoring data is extracted and combined with risk assessment fuzzy logic to assess the safety risk of the electricity meter and determine the risk level, making the assessment more scientific and comprehensive. When the risk level reaches the threshold, an alarm is issued, prompting relevant personnel to take measures to prevent risks. This achieves effective monitoring of electricity meter operation and improves the safety and stability of electricity meter use.

[0065] Example 2:

[0066] Based on Example 1, the safety monitoring method for an electric energy meter, S2: when it is determined based on the anomaly detection model that abnormal operation monitoring data exists in each type of valid operation monitoring data, all abnormal operation monitoring data in the corresponding type of valid operation monitoring data are extracted, including:

[0067] Obtaining a preset number of manual judgment instances of electric energy meter monitoring data, and extracting abnormal operation monitoring data and complete operation monitoring data contained in each manual judgment instance of electric energy meter monitoring data;

[0068] A neural network model is trained based on abnormal operation monitoring data and complete operation monitoring data contained in manual judgment instances of electricity meter monitoring data to obtain an abnormality detection model for the electricity meter;

[0069] Input each type of valid operation monitoring data obtained in real time into the anomaly detection model to determine whether there is abnormal operation monitoring data in each type of valid operation monitoring data;

[0070] If so, all abnormal operation monitoring data in the corresponding class of valid operation monitoring data are obtained based on the output result of the anomaly detection model;

[0071] Otherwise, keep the corresponding model output results.

[0072] In this embodiment, the preset number is a pre-set number value used to specify the number of certain data or instances. For example, if the preset number is 100, it means that 100 electric energy meter monitoring data manual judgment instances are obtained to perform subsequent operations.

[0073] In this embodiment, the manual evaluation example of meter monitoring data is a specific example of manual evaluation and analysis of meter monitoring data, including the determination of whether the data is normal and related instructions. For example, a manual evaluation example of meter monitoring data may be a determination that the meter's voltage data exhibits abnormal fluctuations, while the current data is normal, within a certain time period.

[0074] In this embodiment, the complete operation monitoring data covers all aspects of the operation of the electric energy meter without any missing or omissions. For example, the complete operation monitoring data includes the monitoring values ​​of various parameters such as voltage, current, power, power factor, and frequency over a period of time.

[0075] In this embodiment, a neural network model is trained based on abnormal operation monitoring data and complete operation monitoring data contained in manual judgment instances of electricity meter monitoring data to obtain an abnormality detection model for the electricity meter. The neural network is trained using both the manually judged abnormal data and comprehensive normal data, enabling it to learn and identify abnormalities in the electricity meter operation data, thereby obtaining a model for detecting electricity meter anomalies. For example, assuming 100 manual judgment instances are obtained, including detailed data for 50 abnormal instances and corresponding 50 normal instances, this data is used to train the neural network, ultimately resulting in a model that can automatically determine whether the electricity meter operation data is abnormal and extract abnormal operation monitoring data.

[0076] In this embodiment, all abnormal operation monitoring data within the corresponding category of valid operation monitoring data is obtained based on the output of the anomaly detection model. Based on the judgment result provided by the anomaly detection model, the data determined to be abnormal is extracted from the processed valid operation monitoring data. For example, if the output of the anomaly detection model indicates that voltage data during certain time periods is abnormal, the voltage data during these time periods is extracted from the valid operation monitoring data as abnormal operation monitoring data.

[0077] In this embodiment, the model output result is the result generated by the anomaly detection model, such as the result of determining whether the data is abnormal and marking the abnormal operation monitoring data contained therein. For example, the model output result may be "the current operation data of the electricity meter is abnormal."

[0078] The beneficial effects of the above technology include: obtaining a preset number of manual judgment examples of electricity meter monitoring data, providing rich sample data for training anomaly detection models. Using these examples to train a neural network model to obtain anomaly detection models improves the accuracy and reliability of the models. Inputting real-time data into the model for judgment enables timely detection of anomalies in effective operation monitoring data. When an anomaly is identified, abnormal operation monitoring data is accurately extracted, providing an accurate basis for subsequent risk assessment. This improves the accuracy and efficiency of detecting abnormal operation monitoring data for electricity meters, contributing to more effective safety monitoring.

[0079] Example 3:

[0080] Based on Example 1, the safety monitoring method for an electric energy meter, S3: assessing the safety risk of the electric energy meter based on all abnormal operation monitoring data in all types of effective operation monitoring data and risk assessment fuzzy logic, and determining the risk level of the electric energy meter, includes:

[0081] Extract the performance features of all abnormal operation monitoring data from all types of valid operation monitoring data, and perform feature quantization and normalization on the performance features of all abnormal operation monitoring data to obtain the standard quantified values ​​of the performance features of all abnormal operation monitoring data;

[0082] Define all fuzzy sets and corresponding membership functions and all fuzzy rules of risk assessment fuzzy logic;

[0083] Fuzzy reasoning and defuzzification are performed based on the standard quantitative values ​​of the performance characteristics of all abnormal operation monitoring data in all types of effective operation monitoring data, all fuzzy sets and corresponding membership functions of risk assessment fuzzy logic, and all fuzzy rules. Combined with the preset risk level classification standards, the risk level of the electricity meter is determined.

[0084] In this embodiment, the performance characteristics of the abnormal operation monitoring data refer to the characteristics or attributes of the abnormal operation monitoring data that can reflect the abnormal situation, such as the fluctuation amplitude, frequency, and duration of the abnormal data. For example, if the abnormal operation monitoring data is voltage, its performance characteristics may be a sudden increase in voltage of 50V for 10 seconds.

[0085] In this embodiment, the performance characteristics of all abnormal operation monitoring data are quantified and normalized to obtain standard quantized values ​​for the performance characteristics of all abnormal operation monitoring data. The performance characteristics of the abnormal operation monitoring data are converted into comparable and calculable numerical values ​​through certain mathematical methods, and then normalized to ensure uniform standards and comparability within a specific range. For example, if the performance characteristic is the voltage increase, it is quantized from the range of 0 to 100V to a value between 0 and 1. For example, a 50V increase is quantized to 0.5, which is the standard quantized value.

[0086] In this embodiment, the standard quantized value of the performance characteristic of the abnormal operation monitoring data is the value of the performance characteristic of the abnormal operation monitoring data after quantization and normalization. For example, in the above-mentioned example of voltage increase, 0.5 is the standard quantized value of the performance characteristic.

[0087] In this embodiment, all fuzzy sets, corresponding membership functions, and all fuzzy rules for risk assessment fuzzy logic are defined. This defines the various imprecise or ambiguous sets involved in the fuzzy logic used to assess the safety risk of electricity meters, the membership functions of the elements in the sets, and the assessment rules. For example, fuzzy sets could be "high risk," "medium risk," and "low risk." The corresponding membership functions could determine the degree to which a value belongs to a set based on specific data. A fuzzy rule could be "If the voltage fluctuation is large and lasts for a long time, the risk is high."

[0088] In this embodiment, a fuzzy set is a set without clear boundaries, and the belonging of elements to the set is somewhat vague. For example, the set "voltage fluctuation is large" does not clearly define the fluctuation that is considered large.

[0089] In this embodiment, fuzzy sets and corresponding membership functions are used to represent the degree to which an element in a fuzzy set belongs to that set. For example, for the fuzzy set "large voltage fluctuations," a voltage value of 150V might have a membership of 0.8, indicating a high degree of belonging to the "large fluctuations" set.

[0090] In this embodiment, fuzzy rules are rules for judgment and reasoning based on fuzzy concepts and logic, such as "if the current changes rapidly and the power is unstable, then the risk is medium."

[0091] In this embodiment, the preset risk level classification standard is a pre-set criterion for determining the risk level of the electric energy meter, for example, a fuzzy logic decision value of 0 to 0.3 is low risk, 0.3 to 0.7 is medium risk, and 0.7 to 1 is high risk.

[0092] The beneficial effects of these technologies include: extracting the characteristics of abnormal operation monitoring data and quantifying and normalizing them, making the data more comparable and standardized. Defining the relevant elements of fuzzy logic for risk assessment provides clear rules and standards for risk assessment. Through fuzzy reasoning and defuzzification, the impact of multiple uncertain factors on the safety risks of electricity meters can be comprehensively considered. Combined with pre-set risk classification standards, the risk level of electricity meters can be accurately determined, improving the accuracy and scientific nature of the assessment. This enhances the comprehensiveness and accuracy of electricity meter safety risk assessments, providing a reliable basis for timely implementation of appropriate measures.

[0093] Example 4:

[0094] On the basis of Example 3, the safety monitoring method of the electric energy meter performs fuzzy reasoning and defuzzification based on the standard quantitative values ​​of the performance characteristics of all abnormal operation monitoring data in all types of effective operation monitoring data, all fuzzy sets and corresponding membership functions of the risk assessment fuzzy logic, and all fuzzy rules, and combines the preset risk level classification standard to determine the risk level of the electric energy meter, including:

[0095] Based on the performance characteristic standard quantitative values ​​of all abnormal operation monitoring data in all types of effective operation monitoring data, all fuzzy sets of risk assessment fuzzy logic and corresponding membership functions, the membership of each performance characteristic standard quantitative value to each fuzzy set is determined;

[0096] Based on the membership of each performance characteristic standard quantization value to each fuzzy set and the premise parts of all fuzzy rules, all fuzzy rules activated by each performance characteristic standard quantization value are determined, and the membership of each performance characteristic standard quantization value to all fuzzy sets contained in the premise parts of each corresponding activated fuzzy rule is regarded as all activation intensities of the corresponding fuzzy rules;

[0097] Based on all the fuzzy rules activated by the quantitative values ​​of all performance feature standards and all the corresponding activation intensities, combined reasoning and defuzzification are performed, and the risk level of the electricity meter is determined in combination with the preset risk level classification standard.

[0098] In this embodiment, the premise of the fuzzy rule is the part of the fuzzy rule used to determine whether the rule conditions are met. For example, for the fuzzy rule "If the voltage fluctuation amplitude is large and the duration is long, then it is a high risk", "the voltage fluctuation amplitude is large and the duration is long" is the premise.

[0099] In this embodiment, based on the membership of each performance feature standard quantitative value to each fuzzy set and the premise part of all fuzzy rules, all fuzzy rules activated by each performance feature standard quantitative value are determined: according to the degree of membership of the performance feature standard quantitative value to the fuzzy set and the premise of the fuzzy rule, the fuzzy rule that is satisfied and thus takes effect is found.

[0100] For example, suppose the standardized quantitative value of a voltage fluctuation characteristic has a high membership in the fuzzy set "Large Voltage Fluctuation Amplitude." If a fuzzy rule exists with the premise that "Large voltage fluctuation amplitude indicates medium risk," then this rule is activated. In this embodiment, the activation strength of all fuzzy rules is a numerical value used to measure the degree of activation of the fuzzy rule. For example, for the activated fuzzy rule "If the voltage fluctuation amplitude is large and lasts for a long time, then it indicates high risk," the activation strength might be 0.8, indicating a high degree of activation.

[0101] The beneficial effects of the above technology include: by determining the membership of each performance characteristic standard quantitative value to each fuzzy set, basic data support is provided for subsequent reasoning. The fuzzy rules activated by each performance characteristic standard quantitative value and their activation strength are clarified to make the reasoning process more accurate and targeted. Based on the combined reasoning and defuzzification of all activated fuzzy rules and activation strengths, the impact of multiple factors on the risk level of the electricity meter can be comprehensively considered. Combined with the preset risk level classification standards, the risk level of the electricity meter is accurately determined, which improves the accuracy and reliability of risk assessment. The scientificity and rationality of fuzzy reasoning and defuzzification in the safety risk assessment of electricity meters are enhanced, providing a more effective decision-making basis for the safety monitoring of electricity meters.

[0102] Example 5:

[0103] Based on Example 4, the safety monitoring method for an electric energy meter performs combined reasoning and defuzzification based on all fuzzy rules activated by the quantitative values ​​of all characteristic standards and all corresponding activation intensities, and determines the risk level of the electric energy meter in combination with a preset risk level classification standard, including:

[0104] Obtaining the membership distribution of the fuzzy sets of the conclusion parts of all fuzzy rules activated by all the quantitative values ​​of the performance characteristic standards, taking each activation intensity corresponding to each fuzzy rule activated by all the quantitative values ​​of the performance characteristic standards as a membership adjustment multiple, performing membership adjustment on the membership distribution of the fuzzy sets of the conclusion parts of the corresponding fuzzy rules, and obtaining the adjusted membership distribution of the fuzzy sets of the conclusion parts of the corresponding fuzzy rules under each corresponding activation intensity;

[0105] The adjusted membership distributions of the conclusion parts of all fuzzy rules with the same corresponding conclusion parts in all fuzzy rules activated by the quantitative values ​​of all performance characteristic standards are summarized under all corresponding activation intensities to obtain all the adjusted membership distributions of each conclusion;

[0106] Based on all the adjusted membership distributions of all the conclusions, combined reasoning and defuzzification are performed to obtain the fuzzy logic decision value;

[0107] The preset risk level classification standard is retrieved based on the fuzzy logic decision value to determine the risk level of the electric energy meter.

[0108] In this embodiment, the conclusion part of the fuzzy rule is the result part obtained when the premise conditions of the fuzzy rule are met. For example, for the fuzzy rule "If the voltage fluctuation is large and lasts for a long time, then it is a high risk", "is a high risk" is the conclusion part.

[0109] In this embodiment, the fuzzy set of the conclusion of the fuzzy rule is a set of unclear boundaries involved in the conclusion of the fuzzy rule. For example, if the conclusion is "belongs to high risk", then "high risk" is a fuzzy set.

[0110] In this embodiment, the membership distribution of the fuzzy set in the conclusion of the fuzzy rule represents the distribution of the membership of the elements in the fuzzy set in the conclusion of the fuzzy rule. For example, for the fuzzy set "high risk", its membership distribution may be a linear increase from 0 to 1 within a certain numerical range.

[0111] In this embodiment, the activation strength corresponding to each fuzzy rule activated by all characteristic standard quantized values ​​is used as a membership adjustment factor. The membership distribution of the fuzzy set corresponding to the conclusion of the fuzzy rule is adjusted to obtain the adjusted membership distribution of the fuzzy set corresponding to the conclusion of the fuzzy rule at each activation strength. The activation strength is used as a coefficient to modify the original membership distribution of the fuzzy set. For example, if the activation strength of a fuzzy rule is 0.6 and the original membership distribution is [0.2, 0.5, 0.8], the adjusted membership distribution may be [0.12, 0.3, 0.48].

[0112] In this embodiment, the adjusted membership distribution of the fuzzy set in the conclusion part of the fuzzy rule at each corresponding activation intensity is the membership distribution of the fuzzy set after the activation intensity adjustment. For example, the adjusted membership distribution of [0.12, 0.3, 0.48] is the adjusted membership distribution.

[0113] In this embodiment, each conclusion is a different final judgment result obtained by different fuzzy rules. For example, "belongs to high risk", "belongs to medium risk", and "belongs to low risk" are three different conclusions.

[0114] In this embodiment, the fuzzy logic decision value is a value used for the final decision obtained by comprehensively calculating and processing various adjusted membership distributions. For example, a value such as 0.7 is obtained based on a series of complex calculations to determine the final risk level of the electricity meter.

[0115] The beneficial effects of the above technology include: by adjusting the membership distribution of the conclusion part of the fuzzy rule, the influence of the activation intensity on the evaluation results is fully considered. The adjusted membership distribution of fuzzy rules with the same conclusion part is aggregated to integrate the evaluation results of similar rules. The fuzzy logic decision value is obtained by combined reasoning and defuzzification, making the evaluation results more accurate and reliable. The risk level of the electricity meter is determined based on the decision value retrieval risk level classification standard, which improves the accuracy and scientificity of the risk level determination. The combined reasoning and defuzzification process in the safety risk assessment of the electricity meter is optimized, providing a more accurate and effective risk level assessment for the safety monitoring of the electricity meter.

[0116] Example 6:

[0117] Based on Example 5, the safety monitoring method of the electric energy meter performs combined reasoning and defuzzification based on all adjusted membership distributions of all conclusions to obtain a fuzzy logic decision value, including:

[0118]

[0119] Where R u is the fuzzy logic decision value, max is the maximum independent variable value in the membership distribution function, n is the total number of conclusions, m i is the total number of all adjusted membership distributions of the i-th conclusion, θ ij (x) is the function representation of the jth adjusted membership distribution of the i-th conclusion, x is the independent variable of the adjusted membership distribution function, α ij is the preset weight of the fuzzy rule corresponding to the jth adjusted membership distribution of the i-th conclusion, From 0 to max Perform an integration operation with the integral variable x, From 0 to max Performs an integration operation with the integral variable x.

[0120] In this embodiment, the maximum independent variable value in the membership distribution function refers to the maximum value that the independent variable can take in the function describing the membership distribution. For example, assuming that the membership distribution function is θ(x), where x is the independent variable, if the value range of x is [0, 1], then 1 is the maximum independent variable value.

[0121] In this embodiment, the preset weights of the fuzzy rules corresponding to the adjusted membership distribution are: When performing fuzzy logic decision calculations, the importance of each fuzzy rule corresponding to the adjusted membership distribution is pre-set. For example, if there are two fuzzy rules, Rule A has a preset weight of 0.7 and Rule B has a preset weight of 0.3, indicating that Rule A is relatively more important in the decision. The sum of the preset weights of all fuzzy rules is 1.

[0122] The benefits of this technology include: By combining inference and defuzzification using complex mathematical formulas, fuzzy logic decision values ​​can be accurately calculated. This approach considers multiple factors, including membership distribution, conclusion type, adjusted membership distribution totals, and preset weights, making the decision value calculation more comprehensive and accurate. This precise calculation method facilitates a more scientific assessment of the safety risks of electricity meters and provides a reliable numerical basis for accurately determining the risk level of electricity meters. This improves the accuracy and reliability of risk assessment in electricity meter safety monitoring, enhancing the effectiveness of safety monitoring.

[0123] Example 7:

[0124] Based on Example 1, in the safety monitoring method of the electric energy meter, the alarm information is sent through the wireless communication module.

[0125] In this embodiment, the wireless communication module is a hardware component that enables wireless data transmission between the energy meter and other devices or systems. Common examples of wireless communication modules include Bluetooth modules, Wi-Fi modules, and Zigbee modules. For example, the energy meter can send alarm information to the user's mobile phone via the Bluetooth module, or transmit operational monitoring data to a remote monitoring server via the Wi-Fi module.

[0126] The benefits of these technologies include: Using wireless communication modules to transmit alarm information eliminates the limitations of wired connections, improving the flexibility and convenience of alarm information transmission. Alarm information can be quickly transmitted to the receiving devices of relevant personnel, shortening response time. Wireless communication facilitates integration with remote monitoring systems, achieving wider monitoring coverage. This facilitates timely action to address meter safety issues, reducing potential risks and losses. This enhances the timeliness and effectiveness of meter safety monitoring alarm information transmission, improving the performance of the safety monitoring system.

[0127] Example 8:

[0128] Based on Example 1, the safety monitoring method for the electric energy meter further includes:

[0129] All types of operation monitoring data and all abnormal operation monitoring data, all risk assessment results and all alarm information obtained in real time are stored in the monitoring background, among which the risk assessment results include the risk level of the electricity meter.

[0130] In this embodiment, the monitoring backend is a system or platform used to centrally store and manage all types of operational monitoring data, all abnormal operational monitoring data, all risk assessment results, and all alarm information acquired by electric energy meters in real time. For example, the monitoring backend can be a server-side database system capable of storing, querying, and analyzing large amounts of electric energy meter-related data. For example, staff can use the monitoring backend to view the operating status and risk assessment results of all electric energy meters in a specific area during a certain time period, so as to promptly identify problems and take appropriate measures.

[0131] The benefits of these technologies include: All relevant data, assessment results, and alarm information are stored in the monitoring backend, enabling centralized data management and storage. This facilitates subsequent review and analysis of meter operation, providing data support for optimizing monitoring methods and improving meter performance. This helps identify potential patterns and issues, providing a reference for preventing similar risks. It also provides comprehensive historical records for regulatory authorities and relevant personnel, facilitating audits and oversight. This improves the meter safety monitoring system, enhancing monitoring traceability and management capabilities.

[0132] Example 9:

[0133] The present invention provides a safety monitoring device for an electric energy meter, configured to execute the safety monitoring method for an electric energy meter according to any one of Embodiments 1 to 8, comprising:

[0134] The data acquisition and processing module is used to collect each type of operation monitoring data during the operation of the electric energy meter, and pre-process each type of operation monitoring data to obtain each type of effective operation monitoring data;

[0135] An abnormal data extraction module is used to extract all abnormal operation monitoring data in the corresponding class of valid operation monitoring data when it is determined based on the abnormal detection model that there is abnormal operation monitoring data in each class of valid operation monitoring data;

[0136] A safety risk assessment module is used to assess the safety risk of the electric energy meter based on all abnormal operation monitoring data in all types of effective operation monitoring data and risk assessment fuzzy logic to determine the risk level of the electric energy meter;

[0137] The alarm module is used to issue an alarm message when the risk level of the electric energy meter reaches the risk level threshold.

[0138] The beneficial effects of the above technology include: by collecting and preprocessing operation monitoring data, accurate and valid data is obtained, providing a reliable foundation for subsequent analysis. The anomaly detection model can promptly detect anomalies in various types of operation monitoring data. Abnormal operation monitoring data is extracted and combined with risk assessment fuzzy logic to assess the safety risk of the electricity meter and determine the risk level, making the assessment more scientific and comprehensive. When the risk level reaches the threshold, an alarm is issued, prompting relevant personnel to take measures to prevent risks. This achieves effective monitoring of electricity meter operation and improves the safety and stability of electricity meter use.

[0139] Example 10:

[0140] The present invention provides a smart electric energy meter connected to the safety monitoring device of Example 9.

[0141] The beneficial effects of the above technology include: by connecting the safety monitoring device of Example 9, the operating status of the electricity meter can be monitored in real time, abnormal situations can be discovered and handled in a timely manner, and the safety and stability of the electricity meter operation can be improved. It provides comprehensive safety protection for smart electricity meters and reduces the losses and risks caused by failures or abnormalities. It helps to improve the reliability and service life of smart electricity meters and enhance users' trust in electricity meters. It enables smart electricity meters to have more powerful self-monitoring and protection capabilities and adapt to complex power consumption environments. It provides strong support for the stable operation of the power system and ensures the accurate measurement and supply of electricity. It improves the performance and value of smart electricity meters and promotes technological development and progress in the field of power metering.

[0142] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A safety monitoring method for an electric energy meter, characterized in that: include: S1: Collect each type of operation monitoring data during the operation of the electric energy meter, and pre-process each type of operation monitoring data to obtain each type of effective operation monitoring data; S2: When it is determined based on the anomaly detection model that there is abnormal operation monitoring data in each category of valid operation monitoring data, all abnormal operation monitoring data in the corresponding category of valid operation monitoring data are extracted; S3: Evaluate the safety risk of the electric energy meter based on all abnormal operation monitoring data in all types of effective operation monitoring data and risk assessment fuzzy logic to determine the risk level of the electric energy meter, including: Extract the performance features of all abnormal operation monitoring data from all types of valid operation monitoring data, and perform feature quantization and normalization on the performance features of all abnormal operation monitoring data to obtain the standard quantified values ​​of the performance features of all abnormal operation monitoring data; Define all fuzzy sets and corresponding membership functions and all fuzzy rules of risk assessment fuzzy logic; Based on the performance characteristic standard quantitative values ​​of all abnormal operation monitoring data in all types of effective operation monitoring data, all fuzzy sets of risk assessment fuzzy logic and corresponding membership functions, the membership of each performance characteristic standard quantitative value to each fuzzy set is determined; Based on the membership of each performance characteristic standard quantization value to each fuzzy set and the premise parts of all fuzzy rules, all fuzzy rules activated by each performance characteristic standard quantization value are determined, and the membership of each performance characteristic standard quantization value to all fuzzy sets contained in the premise parts of each corresponding activated fuzzy rule is regarded as all activation intensities of the corresponding fuzzy rules; Obtaining the membership distribution of the fuzzy sets of the conclusion parts of all fuzzy rules activated by all the quantitative values ​​of the performance characteristic standards, taking each activation intensity corresponding to each fuzzy rule activated by all the quantitative values ​​of the performance characteristic standards as a membership adjustment multiple, performing membership adjustment on the membership distribution of the fuzzy sets of the conclusion parts of the corresponding fuzzy rules, and obtaining the adjusted membership distribution of the fuzzy sets of the conclusion parts of the corresponding fuzzy rules under each corresponding activation intensity; The adjusted membership distributions of the conclusion parts of all fuzzy rules with the same corresponding conclusion parts in all fuzzy rules activated by the quantitative values ​​of all performance characteristic standards are summarized under all corresponding activation intensities to obtain all the adjusted membership distributions of each conclusion; Based on all the adjusted membership distributions of all the conclusions, combined reasoning and defuzzification are performed to obtain the fuzzy logic decision value; Retrieve the preset risk level classification standard based on the fuzzy logic decision value to determine the risk level of the electric energy meter; S4: When the risk level of the electric energy meter reaches the risk level threshold, an alarm message is issued.

2. The safety monitoring method of an electric energy meter according to claim 1, characterized in that: S2: When it is determined based on the anomaly detection model that there is abnormal operation monitoring data in each category of valid operation monitoring data, all abnormal operation monitoring data in the corresponding category of valid operation monitoring data are extracted, including: Obtaining a preset number of manual judgment instances of electric energy meter monitoring data, and extracting abnormal operation monitoring data and complete operation monitoring data contained in each manual judgment instance of electric energy meter monitoring data; A neural network model is trained based on abnormal operation monitoring data and complete operation monitoring data contained in manual judgment instances of electricity meter monitoring data to obtain an abnormality detection model for the electricity meter; Input each type of valid operation monitoring data obtained in real time into the anomaly detection model to determine whether there is abnormal operation monitoring data in each type of valid operation monitoring data; If so, all abnormal operation monitoring data in the corresponding class of valid operation monitoring data are obtained based on the output result of the anomaly detection model; Otherwise, keep the corresponding model output results.

3. The safety monitoring method of an electric energy meter according to claim 1, characterized in that: Based on the combined reasoning and defuzzification of all adjusted membership distributions of all conclusions, fuzzy logic decision values ​​are obtained, including: Where R u is the fuzzy logic decision value, max is the maximum independent variable value in the membership distribution function, n is the total number of conclusions, m i is the total number of all adjusted membership distributions of the i-th conclusion, θ ij (x) is the function representation of the jth adjusted membership distribution of the i-th conclusion, x is the independent variable of the adjusted membership distribution function, α ij is the preset weight of the fuzzy rule corresponding to the jth adjusted membership distribution of the i-th conclusion, From 0 to max Perform an integration operation with the integral variable x, From 0 to max Performs an integration operation with the integral variable x.

4. The safety monitoring method of an electric energy meter according to claim 1, characterized in that: The alarm information is sent through the wireless communication module.

5. The safety monitoring method of an electric energy meter according to claim 1, characterized in that: Also includes: All types of operation monitoring data and all abnormal operation monitoring data, all risk assessment results and all alarm information obtained in real time are stored in the monitoring background, among which the risk assessment results include the risk level of the electricity meter.

6. A safety monitoring device for an electric energy meter, characterized in that: A method for implementing the safety monitoring method of an electric energy meter according to any one of claims 1 to 5, comprising: The data acquisition and processing module is used to collect each type of operation monitoring data during the operation of the electric energy meter, and pre-process each type of operation monitoring data to obtain each type of effective operation monitoring data; An abnormal data extraction module is used to extract all abnormal operation monitoring data in the corresponding class of valid operation monitoring data when it is determined based on the abnormal detection model that there is abnormal operation monitoring data in each class of valid operation monitoring data; A safety risk assessment module is used to assess the safety risk of the electric energy meter based on all abnormal operation monitoring data in all types of effective operation monitoring data and risk assessment fuzzy logic to determine the risk level of the electric energy meter; The alarm module is used to issue an alarm message when the risk level of the electric energy meter reaches the risk level threshold.

7. A smart electric energy meter, characterized in that: The safety monitoring device according to claim 6 is connected.

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