Outdoor electric energy metering box monitoring system and method based on intelligent sensor

Abstract

The invention relates to the technical field of electric energy metering boxes, and discloses an outdoor electric energy metering box monitoring system and method based on an intelligent sensor. A magnetic variable detection module; the electromagnetic analysis module is configured to judge whether the to-be-monitored electric energy metering box has an abnormal power utilization behavior or not based on a correlation calculation result between the electric variable change trend and the magnetic variable change trend; the abnormal type determination module is configured to determine the abnormal type of the abnormal power consumption behavior based on the trend deviation degree; the abnormal alarm value determining module is configured to determine an abnormal alarm value based on the abnormal type and the historical electromagnetic variable time sequence; and the alarm module is configured to determine the alarm level of the abnormal power consumption behavior based on the abnormal alarm value and give an alarm in real time. According to the invention, through cooperative work of multiple modules, the outdoor electric energy metering box can be comprehensively and accurately monitored.

Description

Technical Field

[0001] This invention relates to the field of electricity metering box technology, and more specifically, to an outdoor electricity metering box monitoring system and method based on intelligent sensors. Background Technology

[0002] In power systems, outdoor electricity metering boxes are crucial equipment used to accurately measure users' electricity consumption. Traditional monitoring methods for these boxes have several shortcomings. Firstly, the lack of comprehensive real-time monitoring of the metering box's operational status makes it impossible to promptly detect abnormal electricity consumption behaviors such as electricity theft and equipment malfunctions, leading to economic losses for power companies. Secondly, traditional monitoring methods struggle to accurately analyze electrical and magnetic variables, failing to accurately determine the type and severity of abnormal electricity consumption behaviors, resulting in low efficiency in troubleshooting and handling.

[0003] Therefore, it is necessary to design an outdoor power metering box monitoring system and method based on intelligent sensors to solve the problems existing in the current technology. Summary of the Invention

[0004] In view of this, the present invention proposes an outdoor power metering box monitoring system and method based on intelligent sensors, aiming to solve the above problems.

[0005] In one aspect, the present invention proposes an outdoor power metering box monitoring system based on intelligent sensors, comprising: The electrical variable detection module is configured to collect real-time electrical variable output data of the power supply circuit of the power metering box to be monitored; The magnetic variable detection module is configured to detect the real-time magnetic variable output data within the monitoring range of the power supply wire of the power metering box to be monitored. The electromagnetic analysis module, connected to the electrical variable detection module and the magnetic variable detection module, is configured to preprocess the real-time electrical variable output data and the real-time magnetic variable output data, and extract the trends of electrical variable change and magnetic variable change respectively; it is also configured to determine whether the monitored power metering box has abnormal power consumption behavior based on the correlation calculation results between the trends of electrical variable change and magnetic variable change. An anomaly type determination module is configured to, when an abnormal electricity consumption behavior is determined, obtain the trend deviation between the trend of change of the electrical variable and the trend of change of the magnetic variable, and determine the anomaly type of the abnormal electricity consumption behavior based on the trend deviation. The abnormal alarm value determination module is configured to collect historical electrical variable output data of the power supply circuit of the power metering box to be monitored and real-time magnetic variable output data within the monitoring range of the power supply wire of the power metering box to be monitored at the past n time points, and construct a historical electromagnetic variable time series, and determine the abnormal alarm value based on the abnormal type and the historical electromagnetic variable time series; The alarm module is configured to determine the alarm level of the abnormal power consumption behavior based on the abnormal alarm value and to issue an alarm in real time.

[0006] Furthermore, the real-time electrical variable output data includes real-time voltage, current, and power parameters; the real-time magnetic variable output data includes magnetic field strength, magnetic flux density, and magnetic flux change rate.

[0007] Furthermore, the monitoring range includes an effective magnetic field sensing area formed by extending along the circumference and axial direction of each of the power supply conductors with their respective axes as the center, and the radial distance and axial length of the effective magnetic field sensing area are determined based on the magnetic field distribution characteristics of the power supply conductors under rated current conditions. The power supply conductors include incoming conductors and outgoing conductors.

[0008] Further, when determining the radial distance and axial length of the effective magnetic field induction region based on the magnetic field distribution characteristics of the power supply conductor under rated current conditions, the following steps are included: The magnetic field distribution characteristic value is compared with the first magnetic field distribution characteristic value and the second magnetic field distribution characteristic value, and the radial distance and axial length of the effective magnetic field sensing area are determined based on the comparison result; wherein, the first magnetic field distribution characteristic value is smaller than the second magnetic field distribution characteristic value; When the magnetic field distribution characteristic value is less than or equal to the first magnetic field distribution characteristic value, the radial distance and axial length are determined to be the first radial distance and the first axial length, respectively. When the magnetic field distribution characteristic value is greater than the first magnetic field distribution characteristic value and less than or equal to the second magnetic field distribution characteristic value, the radial distance and axial length are determined to be the second radial distance and the second axial length, respectively. When the magnetic field distribution characteristic value is greater than the second magnetic field distribution characteristic value, the radial distance and axial length are determined to be the third radial distance and the third axial length, respectively.

[0009] Furthermore, based on the correlation calculation results between the trends of electrical and magnetic variables, when determining whether the monitored electricity metering box exhibits abnormal electricity consumption behavior, the following steps are taken: Calculate the correlation coefficient between the trends of change of the electrical variable and the trends of change of the magnetic variable; The correlation coefficient is compared with the correlation coefficient threshold, and the comparison result is used to determine whether the monitored electricity meter box has abnormal electricity consumption behavior. If the correlation coefficient is greater than the correlation coefficient threshold, it is determined that the monitored electricity meter box does not have abnormal electricity consumption behavior. If the correlation coefficient is less than or equal to the correlation coefficient threshold, it is determined that the monitored electricity metering box has abnormal electricity consumption behavior.

[0010] Furthermore, when determining the abnormal type of the abnormal electricity consumption behavior based on the trend deviation, the method includes: The abnormality types include current abnormality, voltage abnormality, electromagnetic interference abnormality, and combined abnormality. When the contribution of the trend deviation to the trend of current-related electrical and magnetic variables exceeds a preset threshold, the anomaly type is determined to be a current anomaly. When the contribution of the trend deviation to the trend of voltage-related electrical and magnetic variables exceeds a preset threshold, the anomaly type is determined to be a voltage anomaly. When the contribution of the trend deviation to the abnormal trend of electrical and magnetic variables caused by external electromagnetic interference exceeds a preset threshold, the abnormality type is determined to be electromagnetic interference type. When the contribution of the trend deviation to the trends of electrical and magnetic variables such as current, voltage, and electromagnetic interference all exceed a preset threshold, the anomaly type is determined to be a comprehensive anomaly.

[0011] Furthermore, when determining the abnormal alarm value based on the abnormality type and the historical electromagnetic variable time series, the process includes: Determine the alarm baseline value for the abnormal electricity consumption behavior based on the abnormality type; The electromagnetic coupling anomaly index of the power metering box to be monitored is determined based on the historical electromagnetic variable time series. The alarm baseline value is adjusted based on the electromagnetic coupling anomaly index to obtain the anomaly alarm value; When determining the alarm baseline value for the abnormal electricity consumption behavior based on the abnormality type, the following are included: When the anomaly type is current anomaly, the alarm base value is determined to be the first alarm base value; When the anomaly type is voltage anomaly, the alarm base value is determined to be the second alarm base value; When the anomaly type is electromagnetic interference, the alarm base value is determined to be the third alarm base value; When the anomaly type is a comprehensive anomaly, the alarm base value is determined to be the fourth alarm base value.

[0012] Further, when adjusting the alarm baseline value based on the electromagnetic coupling anomaly index and obtaining the abnormal alarm value, the process includes: The electromagnetic coupling anomaly index is compared with the first electromagnetic coupling anomaly index and the second electromagnetic coupling anomaly index, and the adjustment coefficient of the alarm base value is determined based on the comparison result; wherein, the first electromagnetic coupling anomaly index is less than the second electromagnetic coupling anomaly index. When the electromagnetic coupling anomaly index is less than or equal to the first electromagnetic coupling anomaly index, the adjustment coefficient is determined to be the first adjustment coefficient; When the electromagnetic coupling anomaly index is greater than the first electromagnetic coupling anomaly index and less than or equal to the second electromagnetic coupling anomaly index, the adjustment coefficient is determined to be the second adjustment coefficient. When the electromagnetic coupling anomaly index is greater than the second electromagnetic coupling anomaly index, the adjustment coefficient is determined to be the third adjustment coefficient; The abnormal alarm value is obtained by multiplying the alarm base value by the adjustment coefficient.

[0013] Furthermore, when determining the alarm level of the abnormal electricity consumption behavior based on the abnormal alarm value and issuing a real-time alarm, the process includes: The abnormal alarm value is compared with the warning threshold, and the alarm level is determined based on the comparison result; The warning thresholds include a first warning threshold, a second warning threshold, and a third warning threshold; When the abnormal alarm value is within the first warning threshold, the alarm level is determined to be mild, and a real-time alarm is issued using an audio prompt. When the abnormal alarm value is within the second warning threshold, the alarm level is determined to be moderate, and relevant management personnel are notified via SMS or system message. When the abnormal alarm value is within the third warning threshold, the alarm level is determined to be severe, and an emergency alarm is immediately triggered.

[0014] Compared with existing technologies, the beneficial effects of this invention are as follows: The outdoor power metering box monitoring system based on intelligent sensors provided by this invention can comprehensively and accurately monitor outdoor power metering boxes through the collaborative work of multiple modules. The electrical variable detection module and the magnetic variable detection module collect electrical and magnetic variable data respectively, providing a basis for subsequent analysis. The electromagnetic analysis module processes and analyzes this data, effectively determining whether abnormal power consumption behavior exists. The anomaly type determination module can accurately identify the specific type of abnormal power consumption behavior, helping to take targeted measures. The anomaly alarm value determination module combines historical data and anomaly type to determine a reasonable alarm value, making the alarm more scientific and accurate. The alarm module determines the alarm level based on the alarm value and alarms in real time, promptly reminding relevant personnel to handle abnormal situations. This system can greatly improve the monitoring efficiency and accuracy of outdoor power metering boxes, promptly detect and handle abnormal power consumption behaviors such as electricity theft and leakage, reduce the waste and loss of power resources, and ensure the safe and stable operation of the power system. At the same time, the system's intelligent analysis and alarm functions also reduce the workload and cost of manual inspections, improving management efficiency.

[0015] In another aspect, the present invention also proposes a monitoring method for outdoor power metering boxes based on intelligent sensors, comprising the following steps: Collect real-time electrical variable output data of the power supply circuit of the power metering box to be monitored; Real-time detection of the real-time magnetic variable output data within the monitoring range of the power supply wire of the power metering box to be monitored; The real-time electrical variable output data and real-time magnetic variable output data are preprocessed, and the trends of electrical variable change and magnetic variable change are extracted respectively; it is also configured to determine whether there is abnormal electricity consumption behavior in the monitored electricity metering box based on the correlation calculation results between the trends of electrical variable change and magnetic variable change. When abnormal electricity consumption behavior is determined, the trend deviation between the trend of the electrical variable and the trend of the magnetic variable is obtained, and the abnormality type of the abnormal electricity consumption behavior is determined based on the trend deviation. Historical electrical variable output data of the power supply circuit of the power meter box to be monitored and real-time magnetic variable output data within the monitoring range of the power supply wire of the power meter box to be monitored are collected at n time points in the past. A historical electromagnetic variable time series is constructed, and an abnormal alarm value is determined based on the abnormal type and the historical electromagnetic variable time series. The alarm level of the abnormal electricity consumption behavior is determined based on the abnormal alarm value, and a real-time alarm is triggered. It is understood that the above-described outdoor power metering box monitoring system and method based on intelligent sensors have the same beneficial effects, and will not be elaborated further here. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a structural block diagram of an outdoor power metering box monitoring system based on intelligent sensors provided in an embodiment of the present invention; Figure 2 A flowchart illustrating the outdoor power metering box monitoring method based on intelligent sensors provided in this embodiment of the invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] See Figure 1 As shown in some embodiments of this application, this embodiment provides an outdoor power metering box monitoring system based on intelligent sensors, including: The electrical variable detection module is configured to collect real-time electrical variable output data of the power supply circuit of the power metering box to be monitored; The magnetic variable detection module is configured to detect the real-time magnetic variable output data within the monitoring range of the power supply wire of the power metering box to be monitored. The electromagnetic analysis module, connected to the electrical variable detection module and the magnetic variable detection module, is configured to preprocess the real-time electrical variable output data and the real-time magnetic variable output data, and extract the trends of electrical variable change and magnetic variable change respectively; it is also configured to determine whether the monitored power metering box has abnormal power consumption behavior based on the correlation calculation results between the trends of electrical variable change and magnetic variable change. An anomaly type determination module is configured to, when an abnormal electricity consumption behavior is determined, obtain the trend deviation between the trend of change of the electrical variable and the trend of change of the magnetic variable, and determine the anomaly type of the abnormal electricity consumption behavior based on the trend deviation. The abnormal alarm value determination module is configured to collect historical electrical variable output data of the power supply circuit of the power metering box to be monitored and real-time magnetic variable output data within the monitoring range of the power supply wire of the power metering box to be monitored at the past n time points, and construct a historical electromagnetic variable time series, and determine the abnormal alarm value based on the abnormal type and the historical electromagnetic variable time series; The alarm module is configured to determine the alarm level of the abnormal power consumption behavior based on the abnormal alarm value and to issue an alarm in real time.

[0021] It is understood that the outdoor electricity metering box monitoring system based on intelligent sensors provided in this embodiment can comprehensively and accurately monitor outdoor electricity metering boxes through the collaborative work of multiple modules. The electrical variable detection module and the magnetic variable detection module collect electrical and magnetic variable data respectively, providing a basis for subsequent analysis. The electromagnetic analysis module processes and analyzes this data, effectively determining whether abnormal electricity consumption behavior exists. The anomaly type determination module can accurately identify the specific type of abnormal electricity consumption behavior, helping to take targeted measures. The anomaly alarm value determination module combines historical data and anomaly type to determine a reasonable alarm value, making the alarm more scientific and accurate. The alarm module determines the alarm level based on the alarm value and issues an alarm in real time, promptly reminding relevant personnel to handle abnormal situations. This system can greatly improve the monitoring efficiency and accuracy of outdoor electricity metering boxes, promptly detect and handle abnormal electricity consumption behaviors such as electricity theft and leakage, reduce the waste and loss of power resources, and ensure the safe and stable operation of the power system. At the same time, the system's intelligent analysis and alarm functions also reduce the workload and cost of manual inspections, improving management efficiency.

[0022] Specifically, the real-time electrical variable output data includes real-time voltage, current, and power parameters; the real-time magnetic variable output data includes magnetic field strength, magnetic flux density, and magnetic flux change rate.

[0023] Understandably, these real-time electrical and magnetic variable output data cover key physical quantities during the operation of the electricity metering box, providing a comprehensive and detailed basis for accurately judging the operating status of the electricity metering box. Real-time voltage, current, and power parameters directly reflect the electrical characteristics of the power supply circuit. The stability of voltage, the magnitude of current, and the changes in power can all reflect the use and transmission of electrical energy from different perspectives. Meanwhile, magnetic field strength, magnetic flux density, and rate of change of magnetic flux reflect the electromagnetic environment around the power supply conductor from the perspective of the magnetic field. When the electricity metering box is operating normally, these electrical and magnetic variables will exhibit certain patterns and characteristics. Once abnormal electricity consumption occurs, such as electricity theft or leakage, the trends of these data will change significantly.

[0024] Specifically, the monitoring range includes an effective magnetic field sensing area formed by extending along the circumference and axial direction of each of the power supply conductors with their respective axes as the center, and the radial distance and axial length of the effective magnetic field sensing area are determined based on the magnetic field distribution characteristics of the power supply conductors under rated current conditions. The power supply conductors include incoming conductors and outgoing conductors.

[0025] Understandably, clearly defining the monitoring range helps improve the accuracy and effectiveness of magnetic variable detection. The effective magnetic field sensing area, centered on the power supply conductor axis, can accurately capture changes in the magnetic field around the conductor. The radial distance and axial length of this area are determined based on the magnetic field distribution characteristics of the power supply conductor under rated current conditions, fully considering the actual magnetic field distribution during normal operation. Both incoming and outgoing conductors play crucial roles in power transmission; effectively monitoring the magnetic field around them allows for a more comprehensive understanding of the operating status of the power metering box.

[0026] Specifically, when determining the radial distance and axial length of the effective magnetic field induction region based on the magnetic field distribution characteristics of the power supply conductor under rated current conditions, the following steps are included: The magnetic field distribution characteristic value is compared with the first magnetic field distribution characteristic value and the second magnetic field distribution characteristic value, and the radial distance and axial length of the effective magnetic field sensing area are determined based on the comparison result; wherein, the first magnetic field distribution characteristic value is smaller than the second magnetic field distribution characteristic value; When the magnetic field distribution characteristic value is less than or equal to the first magnetic field distribution characteristic value, the radial distance and axial length are determined to be the first radial distance and the first axial length, respectively. When the magnetic field distribution characteristic value is greater than the first magnetic field distribution characteristic value and less than or equal to the second magnetic field distribution characteristic value, the radial distance and axial length are determined to be the second radial distance and the second axial length, respectively. When the magnetic field distribution characteristic value is greater than the second magnetic field distribution characteristic value, the radial distance and axial length are determined to be the third radial distance and the third axial length, respectively.

[0027] It is understandable that the preferred characteristic value for magnetic field distribution is magnetic field strength.

[0028] Understandably, the preferred first radial distance is 5 cm, and the preferred first axial length is 10 cm. When the magnetic field distribution characteristic value is less than or equal to the first magnetic field distribution characteristic value, the magnetic field around the power supply conductor is weak and concentrated. A radial distance of 5 cm can effectively capture magnetic field changes, avoiding interference and signal loss. The preferred second radial distance is 10 cm, and the preferred second axial length is 20 cm. When the magnetic field distribution characteristic value is between the first and second magnetic field distribution characteristic values, the magnetic field strength is enhanced and the range is expanded. A radial distance of 10 cm expands the applicable range, and an axial length of 20 cm accurately senses magnetic field changes, avoiding information omission. The preferred third radial distance is 15 cm, and the preferred third axial length is 30 cm. When the magnetic field distribution characteristic value is greater than the second magnetic field distribution characteristic value, the magnetic field strength is stronger and the range is wider. A radial distance of 15 cm and an axial length of 30 cm meet the monitoring requirements, ensuring the acquisition of complete magnetic field data. This method of dynamically adjusting the effective sensing area of ​​the magnetic field based on the magnetic field distribution characteristic value improves the accuracy and effectiveness of magnetic variable detection, providing a reliable basis for judging abnormal electricity consumption behavior of outdoor power metering boxes. By setting a precise monitoring range, the system can keenly capture subtle changes in the magnetic field, detect anomalies in a timely manner, provide accurate data for subsequent modules, and ensure the safe and stable operation of the power system.

[0029] Specifically, when determining whether the monitored electricity metering box exhibits abnormal electricity consumption behavior based on the correlation calculation results between the trends of electrical and magnetic variables, the following methods are used: Calculate the correlation coefficient between the trends of change of the electrical variable and the trends of change of the magnetic variable; The correlation coefficient is compared with the correlation coefficient threshold, and the comparison result is used to determine whether the monitored electricity meter box has abnormal electricity consumption behavior. If the correlation coefficient is greater than the correlation coefficient threshold, it is determined that the monitored electricity meter box does not have abnormal electricity consumption behavior. If the correlation coefficient is less than or equal to the correlation coefficient threshold, it is determined that the monitored electricity metering box has abnormal electricity consumption behavior.

[0030] Understandably, the correlation coefficient is obtained by calculating the ratio of the covariance between the trends of electrical and magnetic variables to the product of their standard deviations. Assume that at a certain moment, the data sequence of the electrical variable trend is [10, 12, 14, 16, 18], and the data sequence of the magnetic variable trend is [5, 6, 7, 8, 9]. First, calculate the mean of the electrical variable trend as (10+12+14+16+18) / 5=14, and the mean of the magnetic variable trend as (5+6+7+8+9) / 5=7. Next, calculate the covariance. The formula for covariance is: for two variables X and Y, covariance Cov(X,Y)=Σ[(Xi-X mean)×(Yi-Y mean)] / (n-1), where n is the number of data points. For both electrical and magnetic variables, the covariance Cov=5. Then, calculate the standard deviation of the electrical variable trend σX≈1.58. The standard deviation of the magnetic variable's trend is σY≈0.79. Therefore, the correlation coefficient r=Cov / (σX·σY)≈1.

[0031] Specifically, when determining the abnormal type of the abnormal electricity consumption behavior based on the trend deviation, it includes: The abnormality types include current abnormality, voltage abnormality, electromagnetic interference abnormality, and combined abnormality. When the contribution of the trend deviation to the trend of current-related electrical and magnetic variables exceeds a preset threshold, the anomaly type is determined to be a current anomaly. When the contribution of the trend deviation to the trend of voltage-related electrical and magnetic variables exceeds a preset threshold, the anomaly type is determined to be a voltage anomaly. When the contribution of the trend deviation to the abnormal trend of electrical and magnetic variables caused by external electromagnetic interference exceeds a preset threshold, the abnormality type is determined to be electromagnetic interference type. When the contribution of the trend deviation to the trends of electrical and magnetic variables such as current, voltage, and electromagnetic interference all exceed a preset threshold, the anomaly type is determined to be a comprehensive anomaly.

[0032] Understandably, clearly identifying different types of anomalies helps the system respond effectively. Current anomalies may be caused by short circuits or appliance malfunctions leading to sudden current changes. The system can check the circuit and appliance status to troubleshoot and prevent accidents. Voltage anomalies may be caused by power supply system problems or unauthorized connections affecting voltage stability. The system can notify the power company to adjust the voltage and troubleshoot the lines to ensure normal power supply. Electromagnetic interference (EMI) from large electrical equipment, communication base stations, etc., can affect the system's detection accuracy. The system can add shielding devices and optimize sensor anti-interference capabilities to reduce the impact. Comprehensive anomalies are complex and may be caused by multiple factors, such as short circuits causing current anomalies and generating EMI that affects voltage stability. In this case, the system needs a comprehensive inspection and analysis, checking the circuit, detecting the EMI source, and monitoring the power supply system, which may require collaboration among multiple departments. Through accurate judgment and targeted handling, the system can ensure the safe and stable operation of outdoor electricity metering boxes and reduce the risk of losses.

[0033] Understandably, the trend deviation is obtained by quantitatively calculating the degree of deviation between the changing trends of electrical and magnetic variables within the same time period. Specifically, the trend sequences of the electrical and magnetic variables are first normalized to eliminate dimensional differences. Then, the differences in the values ​​of the electrical and magnetic variables at each time point are calculated. The accumulated or averaged values ​​yield a comprehensive deviation index, i.e., the trend deviation, which reflects the overall consistency or degree of deviation between the two trends. Assume that for five consecutive time points, the trend sequence of the electrical variable is [20, 22, 24, 26, 28], and the trend sequence of the magnetic variable is [10, 11, 13, 14, 16]. After normalization, the electrical variable sequence is normalized to [0, 0.25, 0.5, 0.75, 1], and the magnetic variable sequence is normalized to [0, 0.167, 0.5, 0.667, 1]. Next, the differences at each time point are calculated, which are 0, 0.083, 0, 0.083, and 0, respectively. Finally, the differences are accumulated, and the trend deviation is 0.166.

[0034] Specifically, determining the abnormal alarm value based on the abnormality type and the historical electromagnetic variable time series includes: Determine the alarm baseline value for the abnormal electricity consumption behavior based on the abnormality type; The electromagnetic coupling anomaly index of the power metering box to be monitored is determined based on the historical electromagnetic variable time series. The alarm baseline value is adjusted based on the electromagnetic coupling anomaly index to obtain the anomaly alarm value; When determining the alarm baseline value for the abnormal electricity consumption behavior based on the abnormality type, the following are included: When the anomaly type is current anomaly, the alarm base value is determined to be the first alarm base value; When the anomaly type is voltage anomaly, the alarm base value is determined to be the second alarm base value; When the anomaly type is electromagnetic interference, the alarm base value is determined to be the third alarm base value; When the anomaly type is a comprehensive anomaly, the alarm base value is determined to be the fourth alarm base value.

[0035] Understandably, the preferred baseline value for the first alarm is set to 30; the preferred baseline value for the second alarm is set to 40; the preferred baseline value for the third alarm is set to 50; and the preferred baseline value for the fourth alarm is set to 60. This is because different types of anomalies have varying degrees of impact on outdoor power metering boxes. Current anomalies may be localized equipment problems with relatively minor impacts, hence the lower baseline value for the first alarm. Voltage anomalies involve the stability of the power supply system and have a wider impact, so the baseline value for the second alarm is appropriately increased. Electromagnetic interference can interfere with the system's detection accuracy and significantly affect the normal operation of the entire monitoring system, therefore the baseline value for the third alarm is further increased. Comprehensive anomalies involve multiple factors working together, making the situation the most complex and the impact the most severe, hence the highest baseline value for the fourth alarm.

[0036] Specifically, when adjusting the alarm baseline value based on the electromagnetic coupling anomaly index to obtain the abnormal alarm value, the process includes: The electromagnetic coupling anomaly index is compared with the first electromagnetic coupling anomaly index and the second electromagnetic coupling anomaly index, and the adjustment coefficient of the alarm base value is determined based on the comparison result; wherein, the first electromagnetic coupling anomaly index is less than the second electromagnetic coupling anomaly index. When the electromagnetic coupling anomaly index is less than or equal to the first electromagnetic coupling anomaly index, the adjustment coefficient is determined to be the first adjustment coefficient; When the electromagnetic coupling anomaly index is greater than the first electromagnetic coupling anomaly index and less than or equal to the second electromagnetic coupling anomaly index, the adjustment coefficient is determined to be the second adjustment coefficient. When the electromagnetic coupling anomaly index is greater than the second electromagnetic coupling anomaly index, the adjustment coefficient is determined to be the third adjustment coefficient; The abnormal alarm value is obtained by multiplying the alarm base value by the adjustment coefficient.

[0037] It is understandable that the electromagnetic coupling anomaly index is: Ec = α × (1 - r) + β × D; Where Ec represents the electromagnetic coupling anomaly index; r represents the correlation coefficient; D represents the trend deviation degree; α and β are weighting coefficients, which can be adjusted according to the actual system (e.g., α+β=1).

[0038] Understandably, the adjustment coefficients should be in the following order: First adjustment coefficient < Second adjustment coefficient < Third adjustment coefficient. When the electromagnetic coupling anomaly index is small, it indicates a high correlation between electrical and magnetic variables and a small deviation in their trends. In this case, the anomaly is relatively minor, so a smaller first adjustment coefficient is used to adjust the alarm baseline value, resulting in a relatively low alarm value and enabling timely response to potential anomalies. When the electromagnetic coupling anomaly index is in the middle range, the anomaly is more severe. A moderate second adjustment coefficient is used to appropriately increase the alarm value to balance sensitivity to anomalies and false alarm rate. When the electromagnetic coupling anomaly index is large, it indicates a very low correlation between electrical and magnetic variables and a large deviation in their trends, indicating a more serious anomaly. A larger third adjustment coefficient is used to significantly increase the alarm value, ensuring the system can issue timely alerts when serious anomalies occur.

[0039] Specifically, when determining the alarm level of the abnormal electricity consumption behavior based on the abnormal alarm value and issuing a real-time alarm, the process includes: The abnormal alarm value is compared with the warning threshold, and the alarm level is determined based on the comparison result; The warning thresholds include a first warning threshold, a second warning threshold, and a third warning threshold; When the abnormal alarm value is within the first warning threshold, the alarm level is determined to be mild, and a real-time alarm is issued using an audio prompt. When the abnormal alarm value is within the second warning threshold, the alarm level is determined to be moderate, and relevant management personnel are notified via SMS or system message. When the abnormal alarm value is within the third warning threshold, the alarm level is determined to be severe, and an emergency alarm is immediately triggered.

[0040] Understandably, the first, second, and third warning thresholds are scientifically set based on long-term system operation data and actual application scenarios. The first warning threshold is in the lower range; anomalies in this range are due to accidental factors or minor system fluctuations, hence it's set as a mild alarm, with only an audible alert, both reminding personnel to pay attention to the problem and avoiding excessive interference. The second warning threshold is in the middle range; abnormal alarm values ​​falling into this range indicate that the anomaly has exceeded accidental fluctuations and may affect the operation of outdoor electricity metering boxes. In this case, management personnel are notified via SMS or system message so they can be informed of the situation and take timely measures to prevent the problem from escalating. The third warning threshold is in the higher range; abnormal alarm values ​​reaching this range indicate a serious anomaly that may threaten the safe and stable operation of the electricity metering box, immediately triggering an emergency alarm to prompt personnel to take immediate emergency measures to ensure system operation and stable power supply. Simultaneously, the system records and stores alarm information for subsequent analysis and processing. By analyzing alarm information, the characteristics and patterns of abnormal electricity consumption behavior can be summarized, warning thresholds and alarm mechanisms can be optimized, and the efficiency and accuracy of system monitoring can be improved.

[0041] See Figure 2 As shown in some embodiments of this application, this embodiment provides a monitoring method for outdoor power metering boxes based on smart sensors, including the following steps: S100: Collects real-time electrical variable output data of the power supply circuit of the power metering box to be monitored; S200: Real-time detection of the real-time magnetic variable output data within the monitoring range of the power supply wire of the power metering box to be monitored; S300: The real-time electrical variable output data and the real-time magnetic variable output data are preprocessed, and the trends of change of electrical variables and magnetic variables are extracted respectively; it is also configured to determine whether there is abnormal electricity consumption behavior of the monitored electricity metering box based on the correlation calculation results between the trends of change of electrical variables and the trends of change of magnetic variables. S400: When abnormal electricity consumption behavior is determined, the trend deviation between the trend of the electrical variable and the trend of the magnetic variable is obtained, and the abnormal type of the abnormal electricity consumption behavior is determined based on the trend deviation. S500: Collect historical electrical variable output data of the power supply circuit of the power meter box to be monitored and real-time magnetic variable output data within the monitoring range of the power supply conductor of the power meter box to be monitored at the past n time points, and construct a historical electromagnetic variable time series. Determine the abnormal alarm value based on the abnormal type and the historical electromagnetic variable time series. S600: Determine the alarm level of the abnormal power consumption behavior based on the abnormal alarm value, and issue a real-time alarm.

[0042] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "up", "down", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this invention, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0043] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. An intelligent sensor based outdoor electrical energy metering kiosk monitoring system characterized by, The method comprises the following steps: An electric variable detection module is configured to collect real-time electric variable output data of a power supply loop of an electric energy metering box to be monitored; A magnetic variable detection module is configured to detect real-time magnetic variable output data within a monitoring range of a power supply conductor of the electric energy metering box to be monitored; An electromagnetic analysis module is connected with the electric variable detection module and the magnetic variable detection module, and is configured to pre-process the real-time electric variable output data and the real-time magnetic variable output data, and extract electric variable change trend and magnetic variable change trend respectively; and is further configured to determine whether there is abnormal power consumption behavior of the electric energy metering box to be monitored based on a correlation calculation result between the electric variable change trend and the magnetic variable change trend; An abnormal type determination module is configured to, when it is determined that there is abnormal power consumption behavior, obtain a trend deviation degree between the electric variable change trend and the magnetic variable change trend, and determine an abnormal type of the abnormal power consumption behavior based on the trend deviation degree; An abnormal alarm value determination module is configured to collect historical electric variable output data of the power supply loop of the electric energy metering box to be monitored at n past time points, real-time magnetic variable output data within the monitoring range of the power supply conductor of the electric energy metering box to be monitored, and construct a historical electromagnetic variable time sequence, and determine an abnormal alarm value based on the abnormal type and the historical electromagnetic variable time sequence; An alarm module is configured to determine an alarm level of the abnormal power consumption behavior based on the abnormal alarm value, and perform real-time alarm.

2. The smart sensor based outdoor electrical energy metering kiosk monitoring system as claimed in claim 1 wherein, The real-time electric variable output data includes real-time voltage, current and power parameters; and the real-time magnetic variable output data includes magnetic field strength, magnetic flux density and magnetic flux change rate.

3. The smart sensor based outdoor electrical energy metering kiosk monitoring system as claimed in claim 2, wherein, The monitoring range includes a magnetic field effective induction area formed along the circumferential direction and the axial direction of each power supply conductor with the axis of each power supply conductor as the center, and the radial distance and the axial length of the magnetic field effective induction area are determined according to the magnetic field distribution characteristic value of the power supply conductor under the rated current condition; The power supply conductor includes incoming line conductors and outgoing line conductors.

4. The smart sensor based outdoor electrical energy metering kiosk monitoring system as claimed in claim 3, wherein, When the radial distance and the axial length of the magnetic field effective induction area are determined according to the magnetic field distribution characteristic value of the power supply conductor under the rated current condition, the following steps are included: The magnetic field distribution characteristic value is compared with a first magnetic field distribution characteristic value and a second magnetic field distribution characteristic value, and the radial distance and the axial length of the magnetic field effective induction area are determined according to the comparison result; wherein the first magnetic field distribution characteristic value is smaller than the second magnetic field distribution characteristic value; When the magnetic field distribution characteristic value is smaller than or equal to the first magnetic field distribution characteristic value, the radial distance and the axial length are determined as a first radial distance and a first axial length respectively; When the magnetic field distribution characteristic value is greater than the first magnetic field distribution characteristic value and smaller than or equal to the second magnetic field distribution characteristic value, the radial distance and the axial length are determined as a second radial distance and a second axial length respectively; When the magnetic field distribution characteristic value is greater than the second magnetic field distribution characteristic value, the radial distance and the axial length are determined as a third radial distance and a third axial length respectively.

5. The smart sensor based outdoor electrical energy metering kiosk monitoring system as claimed in claim 4, wherein, The method comprises the following steps: calculating a correlation coefficient between the change trend of the electric variable and the change trend of the magnetic variable; comparing the correlation coefficient with a correlation coefficient threshold value, and determining whether the abnormal power consumption behavior exists in the to-be-monitored electric energy metering box according to the comparison result; if the correlation coefficient is greater than the correlation coefficient threshold value, it is determined that the abnormal power consumption behavior does not exist in the to-be-monitored electric energy metering box; if the correlation coefficient is less than or equal to the correlation coefficient threshold value, it is determined that the abnormal power consumption behavior exists in the to-be-monitored electric energy metering box.

6. The smart sensor based outdoor electrical energy metering kiosk monitoring system as claimed in claim 5, wherein, The method comprises the following steps: the abnormal type includes a current abnormal type, a voltage abnormal type, an electromagnetic interference type and a comprehensive abnormal type; when the contribution value of the trend deviation degree on the electric variable and magnetic variable trends related to the current exceeds a preset threshold value, it is determined that the abnormal type is the current abnormal type; when the contribution value of the trend deviation degree on the electric variable and magnetic variable trends related to the voltage exceeds a preset threshold value, it is determined that the abnormal type is the voltage abnormal type; when the contribution value of the trend deviation degree on the electric variable and magnetic variable trend anomalies caused by external electromagnetic interference exceeds a preset threshold value, it is determined that the abnormal type is the electromagnetic interference type; when the contribution values of the trend deviation degree on the electric variable and magnetic variable trends in aspects such as current, voltage and electromagnetic interference all exceed preset threshold values, it is determined that the abnormal type is the comprehensive abnormal type.

7. The smart sensor based outdoor electrical energy metering kiosk monitoring system as claimed in claim 6, wherein, The method comprises the following steps: determining an alarm base value of the abnormal power consumption behavior according to the abnormal type; determining an electromagnetic coupling abnormality index of the to-be-monitored electric energy metering box according to the historical electromagnetic variable time sequence; adjusting the alarm base value according to the electromagnetic coupling abnormality index, and obtaining the abnormal alarm value; wherein, when the abnormal type is the current abnormal type, the alarm base value is determined as a first alarm base value; when the abnormal type is the voltage abnormal type, the alarm base value is determined as a second alarm base value; when the abnormal type is the electromagnetic interference type, the alarm base value is determined as a third alarm base value; when the abnormal type is the comprehensive abnormal type, the alarm base value is determined as a fourth alarm base value. The method comprises the following steps:

8. The smart sensor based outdoor electrical energy metering kiosk monitoring system as claimed in claim 7, wherein, comparing the electromagnetic coupling abnormality index with a first electromagnetic coupling abnormality index and a second electromagnetic coupling abnormality index, and determining an adjustment coefficient of the alarm base value according to the comparison result; wherein the first electromagnetic coupling abnormality index is less than the second electromagnetic coupling abnormality index; when the electromagnetic coupling abnormality index is less than or equal to the first electromagnetic coupling abnormality index, the adjustment coefficient is determined as a first adjustment coefficient; ​ determining the adjustment coefficient as a second adjustment coefficient when the electromagnetic coupling anomaly index is greater than the first electromagnetic coupling anomaly index and less than or equal to the second electromagnetic coupling anomaly index; determining the adjustment coefficient as a third adjustment coefficient when the electromagnetic coupling anomaly index is greater than the second electromagnetic coupling anomaly index; multiplying the alarm base value by the adjustment coefficient to obtain the abnormal alarm value.

9. The smart sensor based outdoor electrical energy metering kiosk monitoring system as claimed in claim 8, wherein, When determining the alarm level of the abnormal power consumption behavior based on the abnormal alarm value and performing real-time alarm, the method comprises: comparing the abnormal alarm value with a warning threshold value, and determining the alarm level according to the comparison result; wherein the warning threshold value comprises a first warning threshold value, a second warning threshold value and a third warning threshold value; when the abnormal alarm value is within the first warning threshold value, determining that the alarm level is mild, and performing real-time alarm in the form of sound prompt; when the abnormal alarm value is within the second warning threshold value, determining that the alarm level is moderate, and notifying the relevant management personnel through the form of short message or system message; when the abnormal alarm value is within the third warning threshold value, determining that the alarm level is severe, and immediately triggering an emergency alarm.

10. The intelligent sensor-based outdoor electric energy metering box monitoring method applied to the intelligent sensor-based outdoor electric energy metering box monitoring system of any one of claims 1-9, characterized in that, The method comprises: collecting real-time electric variable output data of a power supply circuit of a to-be-monitored electric energy metering box; performing real-time detection on real-time magnetic variable output data within a monitoring range of a power supply wire of the to-be-monitored electric energy metering box; preprocessing the real-time electric variable output data and the real-time magnetic variable output data, and extracting electric variable change trend and magnetic variable change trend respectively; and the method is further configured to determine whether the to-be-monitored electric energy metering box has an abnormal power consumption behavior based on a correlation calculation result between the electric variable change trend and the magnetic variable change trend; when it is determined that there is an abnormal power consumption behavior, obtaining a trend deviation degree between the electric variable change trend and the magnetic variable change trend, and determining an abnormal type of the abnormal power consumption behavior based on the trend deviation degree; collecting historical electric variable output data of the power supply circuit of the to-be-monitored electric energy metering box and real-time magnetic variable output data within the monitoring range of the power supply wire of the to-be-monitored electric energy metering box at the past n time points, and constructing a historical electromagnetic variable time sequence, determining an abnormal alarm value based on the abnormal type and the historical electromagnetic variable time sequence; determining the alarm level of the abnormal power consumption behavior based on the abnormal alarm value and performing real-time alarm.

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