Method and system for on-line monitoring of electric energy metering box based on cover opening electricity stealing

Abstract

The application relates to an online monitoring method and system for electricity metering boxes based on cover opening electricity stealing, and relates to the technical field of electricity metering box monitoring, which comprises the following steps: acquiring a fault trigger signal of a monitoring sensor; acquiring a fault environment data set and a sensor use time length based on the fault trigger signal; analyzing the fault environment data set and the sensor use time length to determine mechanical damage energy and electromagnetic damage energy; analyzing the mechanical damage energy and the electromagnetic damage energy to determine a comprehensive artificial damage confidence; and searching for corresponding hierarchical alarm prompt information in a preset hierarchical alarm mapping relationship according to the comprehensive artificial damage confidence and outputting the prompt. The application has the effect of improving the accuracy of detecting the cover opening electricity stealing behavior of the electricity metering box.

Description

Technical Field

[0001] This application relates to the technical field of monitoring electricity meter boxes, and in particular to an online monitoring method and system for electricity meter boxes based on theft of electricity by opening the cover. Background Technology

[0002] Electricity theft by opening the meter box is a common method of electricity theft. Electricity thieves illegally open the electricity meter box and perform operations such as short-circuiting, reconnecting, or disconnecting voltage / current circuits in the internal wiring, causing the meter to under-count or not count electricity consumption.

[0003] In related technologies, sensors such as door magnets and microswitches are usually installed on the door of the electricity metering box to detect the opening behavior. The terminal records the time, number of times and duration of the opening. The opening data is then sent to the control center through a set communication method. The control center compares the opening plan with the actual opening data to determine whether someone has illegally opened the electricity metering box, and thus whether electricity theft has occurred.

[0004] Regarding the aforementioned technologies, the sensor for detecting opening behavior is installed on the door of the electricity metering box. If an electricity thief first damages the sensor for detecting opening behavior and then opens the door of the electricity metering box to steal electricity, the terminal will only record the sensor malfunction and will not record the opening data, resulting in the inability to accurately detect whether there is opening behavior for electricity theft. There is still room for improvement. Summary of the Invention

[0005] To improve the accuracy of detecting electricity theft by opening the cover of an electricity meter box, this application provides an online monitoring method and system for electricity meter boxes based on electricity theft by opening the cover.

[0006] Firstly, this application provides an online monitoring method for electricity metering boxes based on theft by opening the cover, employing the following technical solution: Online monitoring methods for electricity metering boxes based on theft by opening the cover include: Acquire fault trigger signals from monitoring sensors; Based on the fault trigger signal, obtain the fault environment dataset and sensor usage time; The fault environment dataset and sensor usage time were analyzed to determine the mechanical damage energy and electromagnetic damage energy. The mechanical damage energy and electromagnetic damage energy were analyzed to determine the overall confidence level of human-caused damage. Based on the comprehensive confidence level of human-caused damage, the corresponding graded alarm prompt information is found in the preset graded alarm mapping relationship and the prompt is output.

[0007] Optionally, the steps of analyzing the fault environment dataset and sensor usage time to determine mechanical damage energy and electromagnetic damage energy include: The duration of sensor use was analyzed to determine the cumulative mechanical aging damage energy and the cumulative electromagnetic aging damage energy. Based on the fault environment dataset, environmental impact dataset and emergency impact dataset are extracted; The environmental impact dataset was analyzed to determine the damage energy caused by the mechanical environment and the damage energy caused by the electromagnetic environment. The dataset of sudden impacts was analyzed to determine the equivalent damage energy of mechanical sudden impacts and electromagnetic sudden impacts. The mechanical damage energy and electromagnetic damage energy are generated by correlating the mechanical cumulative aging damage energy, mechanical environmental damage energy, and mechanical sudden equivalent damage energy, as well as the electromagnetic cumulative aging damage energy, electromagnetic environmental damage energy, and electromagnetic sudden equivalent damage energy.

[0008] Optionally, the steps of analyzing sensor usage time to determine mechanical cumulative aging damage energy and electromagnetic cumulative aging damage energy include: The cumulative hazard rate is determined based on the sensor's usage time. Calculate the product of the cumulative risk rate and the preset total aging characteristic energy to generate the cumulative aging damage energy; The cumulative aging damage energy is calculated by multiplying it by a preset mechanical aging distribution coefficient to generate the cumulative mechanical aging damage energy. The cumulative aging damage energy is calculated by multiplying it by a preset electromagnetic aging distribution coefficient to generate the electromagnetic cumulative aging damage energy.

[0009] Optionally, the steps of analyzing the environmental impact dataset to determine the mechanical and electromagnetic damage energies include: Based on the environmental impact dataset, determine the mechanical stress impact data and the electromagnetic stress impact data; Based on a preset mechanical stress threshold, a deviation analysis is performed on the mechanical stress influence data to generate a mechanical stress exceedance coefficient. Based on a preset electromagnetic stress threshold, a deviation analysis is performed on the electromagnetic stress influence data to generate an electromagnetic stress exceedance coefficient. The mechanical stress excess coefficient is weighted and summed according to the preset mechanical stress weighting coefficient, and the electromagnetic stress excess coefficient is weighted and summed according to the preset electromagnetic stress weighting coefficient, respectively, to generate the comprehensive mechanical stress excess coefficient and the comprehensive electromagnetic stress excess coefficient. The product of the preset mechanical stress characteristic energy and the comprehensive mechanical stress exceedance coefficient, and the product of the preset electromagnetic stress characteristic energy and the comprehensive electromagnetic stress exceedance coefficient are calculated respectively to generate the mechanical environment damage energy and the electromagnetic environment damage energy.

[0010] Optionally, the steps of analyzing the sudden impact dataset to determine the equivalent damage energy of mechanical sudden incidents and electromagnetic sudden incidents include: Based on the sudden impact dataset, determine the sudden impact velocity data and sudden magnetic field increment data; The sudden impact velocity data and the preset equivalent mass of the sensor structure are analyzed and accumulated to generate the mechanical sudden equivalent damage energy. The sudden magnetic field increment data, the preset vacuum permeability, and the preset equivalent action volume are analyzed to generate the electromagnetic sudden equivalent damage energy.

[0011] Optionally, the steps of analyzing mechanical damage energy and electromagnetic damage energy to determine the overall confidence level of human-caused damage include: Obtain the dynamic thresholds for mechanical failure and electromagnetic failure; The mechanical damage energy and dynamic threshold of mechanical failure are analyzed to determine the confidence level of human-caused damage in the mechanical dimension. The electromagnetic damage energy and electromagnetic failure dynamic threshold are analyzed to determine the confidence level of human-caused damage in the electromagnetic dimension. The confidence levels of human-caused damage in the mechanical dimension and the electromagnetic dimension are fused to generate a comprehensive confidence level of human-caused damage.

[0012] Optionally, the steps of analyzing mechanical damage energy and dynamic threshold of mechanical failure to determine the confidence level of human-caused damage in the mechanical dimension include: Based on the mechanical damage energy, determine the cumulative aging damage energy of machinery, the environmental damage energy of machinery, and the equivalent damage energy of sudden mechanical damage. The energy of natural mechanical loss is determined based on the cumulative aging damage energy and the environmental damage energy. The mechanical loss energy gap and the baseline confidence level of human-caused damage are determined based on the mechanical natural loss energy and the dynamic threshold of mechanical failure. A comparative analysis of the equivalent damage energy and mechanical loss energy gap during a mechanical emergency is conducted to determine the mechanical emergency energy matching coefficient. The baseline confidence level of human-caused damage is corrected based on the mechanical sudden energy matching coefficient to generate the confidence level of human-caused damage in the mechanical dimension.

[0013] Optionally, the steps for determining the energy lost naturally by the machine based on the accumulated aging damage energy and the environmental damage energy include: The energy of mechanical cumulative aging damage, the energy of mechanical environmental damage, the preset mechanical failure benchmark threshold, and the preset mechanical dimension damage interaction coupling coefficient are analyzed to generate the mechanical environmental collaborative loss energy. The sum of the accumulated aging damage energy of the machinery, the damage energy caused by the mechanical environment, and the energy lost in tandem with the mechanical environment is calculated to generate the natural loss energy of the machinery.

[0014] Secondly, this application provides an online monitoring system for electricity metering boxes based on theft by opening the cover, and adopts the following technical solution: An online monitoring system for electricity metering boxes that are used for electricity theft by opening the cover includes: The acquisition module is used to acquire fault trigger signals, fault environment datasets, and sensor usage time. A memory for storing a program for an online monitoring method of an electricity metering box based on opening the cover to steal electricity, as described in any of the above-mentioned methods; The processor and the program in the memory can be loaded and executed by the processor to implement the online monitoring method for electricity metering boxes based on opening the cover to steal electricity, as described in any of the above items.

[0015] In summary, this application includes at least one of the following beneficial technical effects: 1. Upon receiving a fault trigger signal, the mechanical damage energy and electromagnetic damage energy are obtained by analyzing the fault environment dataset and sensor usage time. Based on the mechanical damage energy and electromagnetic damage energy, the comprehensive confidence level of human-caused damage is determined. Then, based on the comprehensive confidence level of human-caused damage, the hierarchical alarm prompt information is found in the hierarchical alarm mapping relationship and an alarm is output. When the sensor fails, it can accurately distinguish between natural damage or human-caused damage to the sensor. When the sensor is deliberately damaged, the intent of the person to open the cover and steal electricity is clear, thereby improving the accuracy of detecting the behavior of opening the cover of the electricity metering box to steal electricity. 2. By comparing the mechanical sudden equivalent damage energy and the mechanical loss energy gap, the mechanical sudden energy matching coefficient is determined. Then, the baseline human-caused damage confidence level is corrected based on the mechanical sudden energy matching coefficient to obtain the mechanical dimension human-caused damage confidence level. When the mechanical sudden equivalent damage energy better meets the mechanical loss energy gap, it indicates that more loss energy is artificially supplemented and the probability of human-caused damage is greater. Therefore, the higher the mechanical dimension human-caused damage confidence level, the lower the probability, thereby improving the accuracy of the mechanical dimension human-caused damage confidence level. 3. By analyzing the cumulative aging damage energy of machinery, the damage energy caused by the mechanical environment, the mechanical failure benchmark threshold, and the mechanical dimension damage interaction coupling coefficient, the mechanical environment synergistic loss energy is obtained, thus taking into account the synergistic effect of natural aging and environmental stress, thereby improving the accuracy of the mechanical natural loss energy. Attached Figure Description

[0016] Figure 1 This is a flowchart of an online monitoring method for electricity metering boxes based on opening the cover to steal electricity, as described in this application.

[0017] Figure 2 This is a flowchart of the steps in this application embodiment to analyze the fault environment dataset and sensor usage time to determine the mechanical damage energy and electromagnetic damage energy.

[0018] Figure 3 This is a flowchart of the steps in this application embodiment to analyze the sensor usage time to determine the mechanical cumulative aging damage energy and the electromagnetic cumulative aging damage energy.

[0019] Figure 4 This is a flowchart illustrating the steps in this application embodiment to analyze the environmental impact dataset to determine the mechanical environment damage energy and the electromagnetic environment damage energy.

[0020] Figure 5 This is a flowchart of the steps in this application embodiment to analyze the dataset of sudden impacts to determine the equivalent damage energy of mechanical sudden impacts and the equivalent damage energy of electromagnetic sudden impacts.

[0021] Figure 6 This is a flowchart illustrating the steps in this application embodiment to analyze mechanical damage energy and electromagnetic damage energy to determine the overall confidence level of human-caused damage.

[0022] Figure 7 This is a flowchart of the steps in this application embodiment to analyze mechanical damage energy and dynamic threshold of mechanical failure in order to determine the confidence level of human-caused damage in the mechanical dimension.

[0023] Figure 8 This is a flowchart illustrating the steps in this application to determine the energy lost naturally by the machine based on the accumulated aging damage energy and the environmental damage energy. Detailed Implementation

[0024] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figures 1 to 8 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0025] Reference Figure 1 This application discloses an online monitoring method for electricity metering boxes based on the theft of electricity by opening the cover, including the following steps: Step S100: Obtain the fault trigger signal of the monitoring sensor.

[0026] The fault trigger signal is a fault alarm signal from the sensor used to monitor the open status of the electricity metering box door. When the processing terminal receives the fault trigger signal, it determines that the sensor monitoring the opening of the box for electricity theft is faulty. Since it's impossible to determine whether the fault is due to environmental factors or human sabotage, a fault mode identification process is initiated. If the fault is determined to be due to human sabotage, an early warning for electricity theft is issued, ensuring that the act of intentionally damaging the sensor is not misjudged as a sensor malfunction. In this embodiment, the monitoring sensor can be a Hall effect door magnetic sensor or similar sensor used to monitor the open status. The fault trigger signal is obtained by real-time monitoring and analysis of the sensor's output signal. Specifically, the fault trigger signal is output under the following conditions: First, when the output signal is consistently invalid, such as an output voltage of 0V or exceeding the rated range; second, when the signal-to-noise ratio of the sensor's output signal is below a threshold; third, when the sensor's operating voltage or current exceeds the rated operating range.

[0027] Step S101: Obtain the fault environment dataset and sensor usage time based on the fault trigger signal.

[0028] When the processing terminal receives a fault trigger signal, it queries the sensor number field in the fault trigger signal. Based on the mapping relationship between the sensor number field and the environmental sensor, it determines to start the environmental sensor, controls the environmental sensor to detect the fault environment dataset and the sensor usage time, and provides data support for subsequent determination of environmental and human damage contributions to the monitoring sensor.

[0029] The fault environment dataset is a collection of data related to environmental stress and human-caused damage within the range of the power metering box where the fault monitoring sensor is located. When a fault trigger signal is detected, the processing terminal controls the corresponding environmental sensor to obtain data within a time window before and after the fault period. The time window can be selected as 30 seconds to ensure that the environmental sensor can detect environmental data related to human-caused damage without introducing too much irrelevant noise data.

[0030] In this embodiment, the environmental sensors include a temperature sensor, a humidity sensor, a triaxial vibration acceleration sensor, and a triaxial magnetic field sensor, which detect corresponding environmental data. All environmental sensors are installed inside the power metering box. After detecting the output, the data is uploaded to the processing terminal for preprocessing and noise reduction to obtain the fault environment dataset.

[0031] The sensor usage time is the cumulative operating time of the monitoring sensor from the start of operation to the time of failure. The sensor usage time is used to quantify the contribution of natural aging of the monitoring sensor to the damage of the monitoring sensor, and is obtained by timing the timer integrated into the processing terminal.

[0032] Step S102: Analyze the fault environment dataset and sensor usage time to determine the mechanical damage energy and electromagnetic damage energy.

[0033] Among them, mechanical damage energy is the equivalent damage energy of the monitoring sensor in the mechanical dimension caused by natural and human factors, including mechanical cumulative aging damage energy, mechanical environmental damage energy, and mechanical sudden equivalent damage energy.

[0034] Mechanical cumulative aging damage energy refers to the equivalent damage energy of the monitoring sensor in the mechanical dimension caused by aging factors. Mechanical environmental damage energy refers to the short-term increase in energy of the monitoring sensor's mechanical dimension due to environmental stress (such as temperature, humidity, etc.) through mechanisms such as thermal expansion and contraction and corrosion within the failure time window. Mechanical sudden equivalent damage energy refers to the equivalent impact damage energy of the monitoring sensor's mechanical dimension caused by human mechanical damage (such as knocking) within the failure time window.

[0035] Electromagnetic damage energy is the equivalent damage energy of monitoring sensors in the electromagnetic dimension caused by natural and human factors, including electromagnetic cumulative aging damage energy, electromagnetic environmental damage energy, and electromagnetic sudden equivalent damage energy.

[0036] Electromagnetic cumulative aging damage energy refers to the equivalent damage energy of the monitoring sensor in the electromagnetic dimension caused by aging factors. Electromagnetic environment-induced damage energy refers to the short-term increase in damage energy of the monitoring sensor in the electromagnetic dimension within the fault time window due to environmental stress (such as voltage fluctuations) through the inductive interference mechanism. Electromagnetic burst equivalent damage energy refers to the equivalent electromagnetic damage energy of the monitoring sensor in the electromagnetic dimension within the fault time window caused by human-induced electromagnetic destructive actions (such as applying strong magnets) injected into the magnetically sensitive area of ​​the sensor.

[0037] Mechanical damage energy and electromagnetic damage energy were obtained by analyzing fault environment datasets and sensor usage time; the specific method is described in [reference needed]. Figure 2 The steps.

[0038] Step S103: Analyze the mechanical damage energy and electromagnetic damage energy to determine the overall confidence level of human-caused damage.

[0039] The comprehensive confidence level for human-caused damage is a probability value determined by combining the mechanical and electromagnetic dimensions of the monitoring sensor. The value ranges from 0 to 1; a higher value indicates a greater likelihood of human-caused damage to the sensor and a higher probability of individuals attempting to steal electricity by opening the cover. This is obtained by analyzing the mechanical and electromagnetic damage energy at the processing terminal. Specific methods are described in [reference needed]. Figure 6 The steps.

[0040] Step S104: Based on the comprehensive confidence level of human-caused damage, find the corresponding graded alarm prompt information in the preset graded alarm mapping relationship and output the prompt.

[0041] The tiered alarm mapping relationship corresponds to different comprehensive human-caused damage confidence levels and tiered alarm prompts. In this embodiment, there are three levels of alarms: Level 1 is a Level 1 alarm prompt with a comprehensive human-caused damage confidence level of not less than 0.8. This alarm prompt includes the location of the electricity metering box and an electricity theft warning sign, requiring maintenance personnel to investigate immediately; Level 2 is a Level 2 alarm prompt with a comprehensive human-caused damage confidence level between 0.5 and 0.8. This alarm prompt includes the location of the electricity metering box and a sign indicating that it needs to be checked, requiring maintenance personnel to investigate during routine inspections; Level 3 is a Level 3 alarm prompt with a comprehensive human-caused damage confidence level not greater than 0.5. This alarm prompt includes the location of the electricity metering box and a sensor fault warning sign, requiring maintenance personnel to immediately replace or repair the monitoring sensor on the electricity metering box.

[0042] The tiered alarm message is an alarm message indicating the cause of the current sensor failure. It is obtained by the processing terminal using the comprehensive confidence level of human-caused damage as the query key and accurately matching it in the tiered alarm mapping relationship. After determining the tiered alarm message, the processing terminal uploads the tiered alarm message to the operation and maintenance monitoring platform and pushes it to the terminal of the on-duty personnel.

[0043] Reference Figure 2 The steps for analyzing fault environment datasets and sensor usage time to determine mechanical and electromagnetic damage energy include: Step S200: Analyze the sensor usage time to determine the mechanical cumulative aging damage energy and the electromagnetic cumulative aging damage energy.

[0044] In this step, the mechanical cumulative aging damage energy and electromagnetic cumulative aging damage energy are consistent with the definitions disclosed in step S102. They are obtained by the processing terminal after analyzing the sensor's usage time. The mechanical cumulative aging damage energy and electromagnetic cumulative aging damage energy are used to quantify the basic energy of natural aging damage received by the monitoring sensor from operation to failure, providing a benchmark value for subsequent calculation of the energy gap. The specific method is described in [reference needed]. Figure 3 The steps.

[0045] Step S201: Extract the environmental impact dataset and the emergency impact dataset from the fault environment dataset.

[0046] The environmental impact dataset is a subset of the fault environment dataset that represents slow, continuous environmental stress, including slowly changing parameters such as temperature and humidity. The sudden impact dataset is a subset of the fault environment dataset that represents instantaneous, abrupt external disturbances, including vibration and shock pulses and sudden magnetic field changes.

[0047] The environmental impact dataset and the emergency impact dataset are classified according to the type of sensor and the sampling frequency. Among them, temperature data and humidity data are classified into the environmental impact dataset because of their slow rate of change; triaxial vibration acceleration data and triaxial magnetic field data are classified into the emergency impact dataset.

[0048] Step S202: Analyze the environmental impact dataset to determine the mechanical and electromagnetic environmental damage energy.

[0049] The mechanical and electromagnetic environment damage energies in this step are consistent with those disclosed in step S102, and are obtained by the processing terminal after analyzing the environmental impact dataset. These energies are used to quantify the additional environmental damage energy experienced by the monitoring sensor within the fault time window. The specific method is described in [reference needed]. Figure 4 The steps.

[0050] Step S203: Analyze the sudden impact dataset to determine the equivalent damage energy of mechanical sudden impact and the equivalent damage energy of electromagnetic sudden impact.

[0051] In this step, the equivalent mechanical and electromagnetic damage energies are consistent with those disclosed in step S102. These energies are obtained by the processing terminal after analyzing the sudden impact dataset. The equivalent mechanical and electromagnetic damage energies are used to quantify the disturbance energy caused by human-caused damage. After comparing this energy with the energy loss gap, it is determined whether the sensor was damaged by human intervention. The specific method is described in [reference needed]. Figure 5 The steps.

[0052] Step S204: Correlate the mechanical cumulative aging damage energy, mechanical environmental damage energy, and mechanical sudden equivalent damage energy, as well as the electromagnetic cumulative aging damage energy, electromagnetic environmental damage energy, and electromagnetic sudden equivalent damage energy, to generate mechanical damage energy and electromagnetic damage energy.

[0053] In this step, the mechanical damage energy and electromagnetic damage energy are the same as those in step S102. The processing terminal organizes the mechanical cumulative aging damage energy, mechanical environmental damage energy, and mechanical sudden equivalent damage energy into a data set according to the data order to obtain the mechanical damage energy. It also organizes the electromagnetic cumulative aging damage energy, electromagnetic environmental damage energy, and electromagnetic sudden equivalent damage energy into a data set to obtain the electromagnetic damage energy.

[0054] Reference Figure 3 The steps for analyzing sensor usage time to determine mechanical cumulative aging damage energy and electromagnetic cumulative aging damage energy include: Step S300: Determine the cumulative hazard rate based on the sensor usage time.

[0055] The cumulative hazard rate is a dimensionless quantified value of the total aging damage accumulated by the monitoring sensor from operation to failure. A higher cumulative hazard rate indicates a higher degree of aging of the monitoring sensor. The cumulative hazard rate is used to convert aging information in the time dimension into aging damage energy. The cumulative hazard rate is calculated based on the Weibull distribution reliability model. .

[0056] In the formula, To accumulate risk rate, For sensor usage time, For the Weibull scale parameter, These are Weibull shape parameters.

[0057] The instantaneous hazard rate function is the conditional probability density of the monitoring sensor failing in the next unit of time. It is obtained by calculating the quotient of the probability density work function and the reliability function of the Weibull distribution. Then, the instantaneous hazard rate function is integrated from 0 to the sensor's usage time to obtain the cumulative hazard rate function, which has the physical meaning of the total amount of aging damage that the monitoring sensor has accumulated during operation.

[0058] The Weibull scale parameters and Weibull shape parameters were obtained through experimental calibration. Specifically, multiple identical monitoring sensors were placed in a constant temperature and humidity aging chamber, and their rated operating voltage was applied to ensure continuous operation. When a sensor failure was determined, the usage time of each sensor was recorded. The maximum likelihood estimation method was used to fit a Weibull distribution to the usage time, and the likelihood function was... ,in, To monitor the total number of sensors, Let be the probability density function of the Weibull distribution. Taking the logarithm of the likelihood function, we then... and Find the partial derivative and set it to zero, then solve using the Newton-Raphson iterative method to obtain the solution. and The maximum likelihood estimate.

[0059] Step S301: Calculate the product of the cumulative hazard rate and the preset total aging characteristic energy to generate the cumulative aging damage energy.

[0060] The total aging characteristic energy is a proportionality constant used to convert the cumulative hazard rate into aging damage energy, measured in joules, reflecting the damage energy corresponding to a unit of cumulative hazard rate. The total aging characteristic energy is obtained through experimental calibration. The specific process is as follows: multiple monitoring sensors of the same type are grouped, and each group is operated under different accelerated stress levels (e.g., different temperatures, but all other conditions are the same). At fixed intervals, the monitoring sensors are removed for functional testing, and key performance parameters, such as the degradation curve of Hall output voltage or mechanical bonding force, are recorded. When the degradation exceeds 20% of the initial value, a standard drop hammer impact is applied to the group of monitoring sensors, and the minimum impact energy that causes the monitoring sensor to fail is recorded. This is the equivalent damage energy corresponding to that degree of aging. A linear relationship between the cumulative hazard rate and the equivalent loss energy is obtained by fitting multiple sets of data; the slope is the total aging characteristic energy.

[0061] The cumulative aging damage energy is the equivalent energy loss suffered by the monitoring sensor during operation, which is obtained by the processing terminal by multiplying the cumulative danger rate and the total aging characteristic energy.

[0062] Step S302: Calculate the product of the cumulative aging damage energy and the preset mechanical aging distribution coefficient to generate the mechanical cumulative aging damage energy.

[0063] Among them, the mechanical aging allocation coefficient is the proportion of mechanical aging in the natural aging damage energy of the monitoring sensor. The mechanical aging allocation coefficient extracts the damage energy belonging to the mechanical aging dimension from the total aging damage energy. In the experiment in step S301, the number of monitoring sensors that failed due to mechanical performance degradation and the number of monitoring sensors that failed due to electromagnetic performance degradation are recorded. The mechanical aging allocation coefficient and the electromagnetic aging allocation coefficient are obtained by quotient between the number of sensors in the two dimensions and the total number of failed sensors.

[0064] The mechanical cumulative aging damage energy in this step is the same as the mechanical cumulative aging damage energy in step S200, and is obtained by the processing terminal by multiplying the cumulative aging damage energy with the mechanical aging distribution coefficient.

[0065] Step S303: Calculate the product of the cumulative aging damage energy and the preset electromagnetic aging distribution coefficient to generate the electromagnetic cumulative aging damage energy.

[0066] Among them, the electromagnetic aging allocation coefficient is the proportion of electromagnetic aging in the natural aging damage energy of the monitoring sensor. The electromagnetic aging allocation coefficient extracts the damage energy belonging to the electromagnetic aging dimension from the total aging damage energy. The specific determination method will not be elaborated here.

[0067] The electromagnetic cumulative aging damage energy in this step and the electromagnetic cumulative aging damage energy in step S200 are obtained by the processing terminal by multiplying the cumulative aging damage energy and the electromagnetic aging distribution coefficient.

[0068] Reference Figure 4 The steps for analyzing environmental impact datasets to determine the damage energy caused by mechanical and electromagnetic environments include: Step S400: Determine the mechanical stress impact data and electromagnetic stress impact data based on the environmental impact dataset.

[0069] Among them, the mechanical stress impact data is the environmental data in the environmental impact dataset that affects the monitoring sensor through mechanisms such as thermal expansion and contraction, corrosion, and structural deformation. This includes temperature and humidity. Temperature affects the thermal expansion and contraction of the monitoring sensor's fixed structure and shell, causing the solder joints to bear alternating thermal stress, which is a mechanical dimension of damage. This data is extracted from the environmental impact dataset by the processing terminal.

[0070] Electromagnetic stress impact data is environmental data that affects the monitoring sensor through mechanisms such as electromagnetic induction, magnetic saturation, and circuit parameter deviation in the environmental impact dataset. This includes power supply voltage fluctuations, which cause changes in the bias current of the Hall element, resulting in output signal deviation and overvoltage damage to the internal circuit. These are electromagnetic dimension damages and are extracted from the environmental impact dataset by the processing terminal.

[0071] Step S401: Perform deviation analysis on the mechanical stress influence data according to the preset mechanical stress threshold to generate the mechanical stress exceedance coefficient.

[0072] Among them, the mechanical stress threshold is the boundary value of the normal working environment conditions specified in the sensor specification, and is used to determine whether the mechanical stress exceeds the standard.

[0073] The mechanical stress exceedance coefficient indicates the degree to which environmental mechanical stress exceeds the rated operating range. The larger the mechanical stress exceedance coefficient, the greater the damage energy caused by environmental mechanical stress to the monitoring sensor. For upper limit type environmental stress data, such as excessively high temperature or humidity leading to exceedance, the degree of mechanical stress exceedance is determined by calculating the difference between the mechanical stress impact data and the corresponding mechanical stress threshold, and then dividing by the mechanical stress threshold. If the result is negative, the mechanical stress exceedance coefficient is set to 0. For lower limit type environmental stress data, the degree of mechanical stress exceedance is determined by calculating the difference between the corresponding mechanical stress threshold and the mechanical stress impact data, and then dividing by the mechanical stress threshold. If the result is negative, the mechanical stress exceedance coefficient is set to 0.

[0074] Step S402: Perform deviation analysis on the electromagnetic stress influence data according to the preset electromagnetic stress threshold to generate the electromagnetic stress exceedance coefficient.

[0075] Among them, the electromagnetic stress threshold is the normal operating voltage fluctuation range specified in the sensor's datasheet, used to determine whether the environmental electromagnetic stress exceeds the normal range.

[0076] The electromagnetic stress exceedance coefficient represents the degree to which environmental electromagnetic stress exceeds the rated operating range. It is obtained by performing upper and lower limit deviation analysis on the electromagnetic stress influence data through electromagnetic stress threshold. The specific logic is consistent with the logic for determining the mechanical stress exceedance coefficient, and will not be elaborated here.

[0077] Step S403: The mechanical stress excess coefficient is weighted and summed according to the preset mechanical stress weighting coefficient, and the electromagnetic stress excess coefficient is weighted and summed according to the preset electromagnetic stress weighting coefficient, so as to generate the comprehensive mechanical stress excess coefficient and the comprehensive electromagnetic stress excess coefficient.

[0078] The mechanical stress weighting coefficient represents the proportion of importance of different mechanical environmental stresses in the exceedance assessment, reflecting the degree of influence of different types of mechanical environmental stresses on the damage of monitoring sensors. The greater the influence, the greater the weight. By collecting historical failure case data, the exceedance coefficient of each environmental stress in each case is extracted. After normalizing the exceedance coefficient, the information entropy of each stress index is calculated. The difference between 1 and the information entropy is calculated to obtain the difference coefficient. The larger the difference coefficient, the higher the distinguishability of the stress among different cases. After normalizing the difference coefficient, the weighting coefficient is obtained. The electromagnetic stress weighting coefficient represents the proportion of importance of different electromagnetic environmental stresses in the exceedance assessment, reflecting the degree of influence of different types of electromagnetic environmental stresses on the damage of monitoring sensors. The greater the influence, the greater the weight. The method for determining this is not elaborated here.

[0079] The comprehensive mechanical stress exceedance coefficient represents the combined degree to which various environmental mechanical stresses exceed the rated operating range. It is obtained by weighted summation of the mechanical stress exceedance coefficients based on the mechanical stress weighting coefficients at the processing terminal.

[0080] The electromagnetic stress comprehensive exceedance coefficient is the comprehensive degree to which electromagnetic stress from various environments exceeds the rated operating range. It is obtained by the processing terminal by weighting and summing the electromagnetic stress exceedance coefficients according to the electromagnetic stress weighting coefficient.

[0081] Step S404: Calculate the product of the preset mechanical stress characteristic energy and the mechanical stress comprehensive excess coefficient, and the product of the preset electromagnetic stress characteristic energy and the electromagnetic stress comprehensive excess coefficient, respectively, to generate the mechanical environment damage energy and the electromagnetic environment damage energy.

[0082] Among them, the mechanical stress characteristic energy is the mechanical damage energy corresponding to the unit comprehensive exceedance coefficient, used to convert the comprehensive exceedance coefficient of mechanical stress into damage energy, and the unit is joules. By placing multiple monitoring sensors of the same type in a high and low temperature alternating test chamber and conducting temperature cycling tests, after the cycle is completed, the degradation of the bonding force of the monitoring sensor pins is measured, and the number of cycles when the degradation is lower than 20% of the initial value and the corresponding cumulative thermal stress energy are determined. The mechanical stress characteristic energy is calculated based on the temperature change range, the thermal expansion coefficient of the material, and the elastic modulus, and the average value is taken multiple times.

[0083] The electromagnetic stress characteristic energy is the electromagnetic damage energy corresponding to a unit comprehensive exceedance coefficient. It is used to convert the comprehensive exceedance coefficient of electromagnetic stress into damage energy, with the unit being joules. By applying gradient voltage fluctuations to multiple monitoring sensors of the same type, the cumulative overvoltage energy required to cause the Hall output voltage to deviate from the initial value by 20% is recorded, and the average value is taken multiple times to obtain the electromagnetic stress characteristic energy.

[0084] The mechanical and electromagnetic environment damage energies in this step are consistent with those in step S202. They are obtained by calculating the product of the mechanical stress characteristic energy and the comprehensive mechanical stress exceedance coefficient, as well as the product of the electromagnetic stress characteristic energy and the comprehensive electromagnetic stress exceedance coefficient, using the processing terminal. Based on the stress-intensity interference theory, the comprehensive reliability of the monitoring sensor under environmental stress can be expressed as follows: ,in, For overall reliability, To determine the overall exceedance coefficient, the mapping relationship between energy loss and reliability is as follows: ,Will After substituting, we get .

[0085] Reference Figure 5The steps for analyzing the dataset of sudden impacts to determine the equivalent damage energy of mechanical and electromagnetic sudden impacts include: Step S500: Determine the sudden impact velocity data and sudden magnetic field increment data based on the sudden impact dataset.

[0086] Among them, the sudden impact velocity data is the peak sequence of impact velocities received by the monitoring sensor within the fault time window, which provides data support for subsequent calculation of the equivalent damage energy of mechanical sudden impacts. The processing terminal extracts three-dimensional acceleration data from the sudden impact dataset, calculates the resultant acceleration, and determines the effective impact pulse by detecting the peak value: if the resultant acceleration exceeds the baseline mean plus 3 times the baseline standard deviation, and the pulse width is between 3 and 50 sampling points, it is determined to be an effective impact pulse. Then, the resultant acceleration within the effective impact pulse is integrated by trapezoidal numerical integration to obtain the velocity peak value of the pulse.

[0087] The sudden magnetic field increment data is the maximum deviation of the combined magnetic field strength of the monitoring sensor from the normal baseline within the fault time window. It provides data support for subsequent calculation of the equivalent damage energy of electromagnetic sudden events. The processing terminal extracts the three-dimensional magnetic field data from the sudden impact dataset, calculates the combined magnetic field strength, identifies the maximum magnetic field strength, and subtracts the normal geomagnetic field strength to obtain the maximum magnetic field anomaly increment.

[0088] Step S501: After analyzing the sudden impact velocity data and the preset equivalent mass of the sensor structure, the data is accumulated to generate the mechanical sudden equivalent damage energy.

[0089] Among them, the equivalent mass of the sensor structure is the equivalent inertial mass when the monitoring sensor is installed on the door of the box. By installing the same type of monitoring sensor on the same type of power metering box door, using a standard impact hammer to impact the sensor body at a known height, and detecting the peak velocity of the sensor, after changing the impact energy, and conducting multiple tests, the impact energy and velocity are substituted into the formulas of kinetic energy, mass and velocity to solve for the equivalent mass of the sensor structure.

[0090] The mechanical burst equivalent damage energy in this step is the same as the mechanical burst equivalent damage energy in step S203. It is obtained by multiplying one-half of the energy by the equivalent mass of the sensor structure and the square of the burst impact velocity data by the processing terminal. During multiple impacts, the energy of each pulse is accumulated to obtain the mechanical burst equivalent damage energy.

[0091] Step S502: Analyze the sudden magnetic field increment data, the preset vacuum permeability, and the preset equivalent action volume to generate electromagnetic sudden equivalent damage energy.

[0092] The equivalent action volume is the effective magnetic field volume of the magnetic sensing element inside the monitoring sensor, reflecting the spatial range of the actual damage effect of the external magnetic field on the monitoring sensor. By placing a monitoring sensor of the same type in a standard Helmholtz coil, applying a gradient magnetic field, and recording the critical magnetic field that causes the Hall output voltage to exceed the initial value by 20%, a magnetic circuit model of the sensor's internal circuit is established using finite element simulation software. The actual magnetic field distribution of the magnetic sensing element under the critical magnetic field condition is calculated, and the volume of the region where the magnetic field strength exceeds 0.5 times the critical magnetic field is extracted as the equivalent action volume.

[0093] The equivalent damage energy of the electromagnetic burst in this step is the same as that in step S203. The processing terminal calculates the quotient of the square of the burst magnetic field increment data and twice the vacuum permeability, and then multiplies it by the equivalent action volume to obtain the equivalent damage energy of the electromagnetic burst. According to electromagnetic field theory, the magnetic field energy density is the quotient of the square of the magnetic field strength and the vacuum permeability, which represents the magnetic field energy stored in a unit volume of space. The magnetic field energy density is integrated over the magnetic field action space volume to obtain the total magnetic field energy, which is the quotient of the square of the burst magnetic field increment data and twice the vacuum permeability, and then multiplied by the equivalent action volume.

[0094] Reference Figure 6 The steps for analyzing mechanical and electromagnetic damage energy to determine the overall confidence level of human-caused damage include: Step S600: Obtain the dynamic threshold for mechanical failure and the dynamic threshold for electromagnetic failure.

[0095] Among them, the dynamic threshold for mechanical failure is the minimum external impact energy required for the monitoring sensor to undergo mechanical functional failure at its current aging level.

[0096] The electromagnetic failure dynamic threshold is the minimum external magnetic field energy required for a monitoring sensor to fail in its magnetic sensing function at its current aging level.

[0097] After conducting standard drop hammer impact tests and gradient magnetic field tests on similar monitoring sensors, the baseline mechanical failure threshold and baseline electromagnetic failure threshold are determined. Then, the adjustment coefficient is determined based on the reciprocal of the exponential function of the sensor's usage time. The product of the adjustment coefficient and the baseline mechanical failure threshold and the baseline electromagnetic failure threshold is calculated to obtain the dynamic threshold for mechanical failure and the dynamic threshold for electromagnetic failure.

[0098] Step S601: Analyze the mechanical damage energy and the dynamic threshold of mechanical failure to determine the confidence level of human-caused damage in the mechanical dimension.

[0099] Among them, the confidence level of human-caused damage in the mechanical dimension is the probability of judging human-caused damage to the monitoring sensor from the mechanical dimension, which is obtained by the processing terminal after analyzing the mechanical damage energy and the dynamic threshold of mechanical failure.

[0100] Step S602: Analyze the electromagnetic damage energy and electromagnetic failure dynamic threshold to determine the confidence level of human-caused damage in the electromagnetic dimension.

[0101] Among them, the confidence level of human-caused damage in the electromagnetic dimension is the probability of judging human-caused damage to the monitoring sensor from the electromagnetic dimension. It is obtained by the processing terminal after analyzing the electromagnetic damage energy and the electromagnetic failure dynamic threshold. The determination process of the confidence level of human-caused damage in the electromagnetic dimension is the same as that of the determination process of human-caused damage in the mechanical dimension, and will not be elaborated here.

[0102] Step S603: Fuse the confidence scores for human-caused damage in the mechanical dimension and the confidence scores for human-caused damage in the electromagnetic dimension to generate a comprehensive confidence score for human-caused damage.

[0103] In this step, the comprehensive confidence level of human-caused damage is consistent with that in step S103. The processing terminal calculates the difference between 1 and the confidence level of human-caused damage in the mechanical dimension, and the difference between 1 and the confidence level of human-caused damage in the electromagnetic dimension. After calculating the product of the two differences, the difference between 1 and the product is calculated to obtain the comprehensive confidence level of human-caused damage. Based on probability theory, the mechanical dimension and the electromagnetic dimension are independent events. The probability that at least one of the two independent events occurs is 1 minus the probability that neither event occurs. The difference between 1 and the confidence level of human-caused damage in the mechanical dimension, and the difference between 1 and the confidence level of human-caused damage in the electromagnetic dimension are the probabilities of non-human damage in the mechanical dimension and non-human damage in the electromagnetic dimension, respectively. Therefore, the product of the two is the probability that both dimensions are non-human damage. The difference between 1 and the probability that both dimensions are non-human damage is calculated to obtain the probability that at least one dimension is human-caused damage.

[0104] Reference Figure 7 The steps for analyzing mechanical damage energy and dynamic threshold of mechanical failure to determine the confidence level of human-caused damage in the mechanical dimension include: Step S700: Determine the cumulative mechanical aging damage energy, the mechanical environmental damage energy, and the mechanical sudden equivalent damage energy based on the mechanical damage energy.

[0105] The processing terminal retrieves the accumulated aging damage energy, environmental damage energy, and sudden equivalent damage energy from the mechanical damage energy according to usage requirements, providing data support for subsequently determining the confidence level of human-caused damage in the mechanical dimension.

[0106] Step S701: Determine the energy of natural mechanical loss based on the energy of accumulated mechanical aging damage and the energy of mechanical environmental damage.

[0107] Among them, the mechanical natural loss energy refers to the energy loss caused by all natural factors in the mechanical dimension of the monitoring sensor. It is obtained by the processing terminal after analyzing the accumulated mechanical aging damage energy and the mechanical environmental damage energy. The specific method is described in [reference]. Figure 8 The steps.

[0108] Step S702: Determine the mechanical loss energy gap and the baseline human-caused damage confidence level based on the mechanical natural loss energy and the mechanical failure dynamic threshold.

[0109] Among them, the mechanical loss energy gap is the minimum energy that needs to be applied to cause the monitoring sensor to fail due to mechanical dimension. The processing terminal calculates the difference between the dynamic threshold of mechanical failure and the natural mechanical loss energy. If the difference is negative, the mechanical loss energy gap is determined to be 0.

[0110] The baseline confidence level for human-caused damage is the probability of a controlled human-caused damage to the monitoring sensor without considering external interference. The processing terminal calculates the quotient of the mechanical natural loss energy and the dynamic threshold of mechanical failure to determine the proportion of natural damage energy. If the mechanical natural loss energy is less than the quotient of the dynamic threshold of mechanical failure, the proportion is less than 1, and it is still possible that it is human-caused damage. Therefore, the difference between 1 and the proportion of natural damage energy is calculated to obtain the baseline confidence level for human-caused damage. Conversely, if it is greater than 1, then natural factors are sufficient to cause damage to the monitoring sensor, and the baseline confidence level for human-caused damage is 0.

[0111] Step S703: Compare and analyze the mechanical sudden equivalent damage energy and mechanical loss energy gap to determine the mechanical sudden energy matching coefficient.

[0112] Among them, the mechanical sudden energy matching coefficient is the degree of matching between the mechanical sudden equivalent damage energy and the mechanical loss energy gap. The value ranges from 0 to 1. The larger the value, the better the mechanical sudden equivalent damage energy matches the energy gap, and the more complete the evidence chain of human-caused damage to the monitoring sensor. The processing terminal compares the mechanical sudden equivalent damage energy and the mechanical loss energy gap. If the mechanical sudden equivalent damage energy is greater than the mechanical loss energy gap, the mechanical sudden energy matching coefficient is 1. If the mechanical sudden equivalent damage energy is less than the mechanical loss energy gap, the mechanical sudden energy matching coefficient is obtained by calculating the quotient of the mechanical sudden equivalent damage energy and the mechanical loss energy gap.

[0113] Step S704: Correct the baseline human-caused damage confidence level based on the mechanical sudden energy matching coefficient to generate the mechanical dimension human-caused damage confidence level.

[0114] In this step, the confidence level of human-caused damage in the mechanical dimension is consistent with the confidence level of human-caused damage in the mechanical dimension in step S601. It is obtained by the processing terminal calculating the product of the mechanical burst energy matching coefficient and the baseline confidence level of human-caused damage.

[0115] Reference Figure 8 The steps for determining the energy of natural mechanical loss based on the cumulative aging damage energy and the environmental damage energy include: Step S800: Analyze the cumulative mechanical aging damage energy, the mechanical environment-induced damage energy, the preset mechanical failure benchmark threshold, and the preset mechanical dimension damage interaction coupling coefficient to generate the mechanical environment collaborative loss energy.

[0116] The mechanical failure baseline threshold is the energy that causes the new monitoring sensor to fail in the mechanical dimension. The determination process is consistent with the determination process of the quasi-mechanical failure threshold disclosed in step S600.

[0117] The mechanical dimension damage interaction coupling coefficient is a dimensionless coefficient reflecting the intensity of the synergistic enhancement effect between mechanical dimension aging loss and environmental stress. Multiple monitoring sensors of the same type were divided into two groups. The control group underwent accelerated aging experiments under standard conditions, and the mechanical failure thresholds at different aging stages were recorded. The experimental group underwent the same accelerated aging experiments under high temperature and high humidity conditions, and the mechanical failure thresholds at different aging stages were recorded. The difference in thresholds between the two groups under the same degree of aging was compared. The wider drop in threshold in the experimental group was due to the synergistic effect. The product of natural aging energy and environmental loss energy and the quotient of the mechanical failure threshold were used as the horizontal axis, and the difference in thresholds between the two groups was used as the vertical axis for linear fitting. The slope obtained is the mechanical dimension damage interaction coupling coefficient.

[0118] The mechanical-environmental collaborative loss energy is the additional damage energy generated by the interaction of natural aging and environmental stress. It is obtained by multiplying the product of the accumulated mechanical aging damage energy and the mechanical-environmental damage energy calculated by the processing terminal with the mechanical failure benchmark threshold, and then multiplying it with the mechanical dimension damage interaction coupling coefficient.

[0119] Step S801: Calculate the sum of mechanical cumulative aging damage energy, mechanical environmental damage energy, and mechanical environmental synergistic loss energy to generate mechanical natural loss energy.

[0120] In this step, the mechanical natural loss energy is consistent with the mechanical natural loss energy disclosed in step S701, and is obtained by the processing terminal by calculating the sum of the mechanical cumulative aging damage energy, the mechanical environmental damage energy, and the mechanical environmental synergistic loss energy.

[0121] Based on the same inventive concept, embodiments of this application provide an online monitoring system for electricity meter boxes that are used for electricity theft by opening the cover, including: The acquisition module is used to acquire fault trigger signals, fault environment datasets, sensor usage time, dynamic thresholds for mechanical failure, and dynamic thresholds for electromagnetic failure. A memory for storing programs for online monitoring methods of electricity metering boxes based on opening the cover to steal electricity; The processor and memory programs can be loaded and executed by the processor to implement an online monitoring method for electricity metering boxes based on opening the cover to steal electricity.

[0122] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0123] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as an online monitoring method for electricity metering boxes based on opening the cover to steal electricity.

[0124] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0125] Based on the same inventive concept, this application provides a smart terminal, including a memory and a processor. The memory stores a computer program that can be loaded and executed by the processor to perform an online monitoring method for electricity metering boxes based on opening the cover to steal electricity.

[0126] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0127] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for online monitoring of electricity metering boxes used for electricity theft by opening the cover, characterized in that, include: Acquire fault trigger signals from monitoring sensors; Based on the fault trigger signal, obtain the fault environment dataset and sensor usage time; The fault environment dataset and sensor usage time were analyzed to determine the mechanical damage energy and electromagnetic damage energy. The mechanical damage energy and electromagnetic damage energy were analyzed to determine the overall confidence level of human-caused damage. Based on the comprehensive confidence level of human-caused damage, the corresponding graded alarm prompt information is found in the preset graded alarm mapping relationship and the prompt is output.

2. The online monitoring method for electricity metering boxes based on uncovered electricity theft according to claim 1, characterized in that, The steps for analyzing fault environment datasets and sensor usage durations to determine mechanical and electromagnetic damage energies include: The duration of sensor use was analyzed to determine the cumulative mechanical aging damage energy and the cumulative electromagnetic aging damage energy. Based on the fault environment dataset, environmental impact dataset and emergency impact dataset are extracted; The environmental impact dataset was analyzed to determine the damage energy caused by the mechanical environment and the damage energy caused by the electromagnetic environment. The dataset of sudden impacts was analyzed to determine the equivalent damage energy of mechanical sudden impacts and electromagnetic sudden impacts. Mechanical damage energy and electromagnetic damage energy are generated by associating mechanical cumulative aging damage energy, mechanical environmental damage energy, and mechanical sudden equivalent damage energy, as well as electromagnetic cumulative aging damage energy, electromagnetic environmental damage energy, and electromagnetic sudden equivalent damage energy.

3. The online monitoring method for electricity metering boxes based on uncovered electricity theft according to claim 2, characterized in that, The steps for analyzing sensor usage time to determine mechanical cumulative aging damage energy and electromagnetic cumulative aging damage energy include: The cumulative hazard rate is determined based on the sensor's usage time. Calculate the product of the cumulative risk rate and the preset total aging characteristic energy to generate the cumulative aging damage energy; The cumulative aging damage energy is calculated by multiplying it by a preset mechanical aging distribution coefficient to generate the cumulative mechanical aging damage energy. The cumulative aging damage energy is calculated by multiplying it by a preset electromagnetic aging distribution coefficient to generate the electromagnetic cumulative aging damage energy.

4. The online monitoring method for electricity metering boxes based on uncovered electricity theft according to claim 2, characterized in that, The steps for analyzing environmental impact datasets to determine the damaging energies of the mechanical and electromagnetic environments include: Based on the environmental impact dataset, determine the mechanical stress impact data and the electromagnetic stress impact data; Based on a preset mechanical stress threshold, a deviation analysis is performed on the mechanical stress influence data to generate a mechanical stress exceedance coefficient. Based on a preset electromagnetic stress threshold, a deviation analysis is performed on the electromagnetic stress influence data to generate an electromagnetic stress exceedance coefficient. The mechanical stress excess coefficient is weighted and summed according to the preset mechanical stress weighting coefficient, and the electromagnetic stress excess coefficient is weighted and summed according to the preset electromagnetic stress weighting coefficient, respectively, to generate the comprehensive mechanical stress excess coefficient and the comprehensive electromagnetic stress excess coefficient. The product of the preset mechanical stress characteristic energy and the comprehensive mechanical stress exceedance coefficient, and the product of the preset electromagnetic stress characteristic energy and the comprehensive electromagnetic stress exceedance coefficient are calculated respectively to generate the mechanical environment damage energy and the electromagnetic environment damage energy.

5. The online monitoring method for electricity metering boxes based on uncovered electricity theft according to claim 2, characterized in that, The steps for analyzing the dataset of sudden impacts to determine the equivalent damage energy of mechanical and electromagnetic sudden impacts include: Based on the sudden impact dataset, determine the sudden impact velocity data and sudden magnetic field increment data; The sudden impact velocity data and the preset equivalent mass of the sensor structure are analyzed and accumulated to generate the mechanical sudden equivalent damage energy. The sudden magnetic field increment data, the preset vacuum permeability, and the preset equivalent action volume are analyzed to generate the electromagnetic sudden equivalent damage energy.

6. The online monitoring method for electricity metering boxes based on uncovered electricity theft according to claim 1, characterized in that, The steps for analyzing mechanical and electromagnetic damage energies to determine the overall confidence level of human-caused damage include: Obtain the dynamic thresholds for mechanical failure and electromagnetic failure; The mechanical damage energy and dynamic threshold of mechanical failure are analyzed to determine the confidence level of human-caused damage in the mechanical dimension. The electromagnetic damage energy and electromagnetic failure dynamic threshold are analyzed to determine the confidence level of human-caused damage in the electromagnetic dimension. The confidence levels of human-caused damage in the mechanical dimension and the electromagnetic dimension are fused to generate a comprehensive confidence level of human-caused damage.

7. The online monitoring method for electricity metering boxes based on uncovered electricity theft according to claim 6, characterized in that, The steps for analyzing mechanical damage energy and dynamic threshold of mechanical failure to determine the confidence level of human-caused damage in the mechanical dimension include: Based on the mechanical damage energy, determine the cumulative aging damage energy of machinery, the environmental damage energy of machinery, and the equivalent damage energy of sudden mechanical damage. The energy of natural mechanical loss is determined based on the cumulative aging damage energy and the environmental damage energy. The mechanical loss energy gap and the baseline confidence level of human-caused damage are determined based on the mechanical natural loss energy and the dynamic threshold of mechanical failure. A comparative analysis of the equivalent damage energy and mechanical loss energy gap during a mechanical emergency is conducted to determine the mechanical emergency energy matching coefficient. The baseline confidence level of human-caused damage is corrected based on the mechanical sudden energy matching coefficient to generate the confidence level of human-caused damage in the mechanical dimension.

8. The online monitoring method for electricity metering boxes based on uncovered electricity theft according to claim 7, characterized in that, The steps for determining the energy of natural mechanical loss based on the cumulative aging damage energy and the environmental damage energy include: The mechanical cumulative aging damage energy, mechanical environmental damage energy, preset mechanical failure benchmark threshold, and preset mechanical dimension damage interaction coupling coefficient are analyzed to generate mechanical environmental collaborative loss energy. The sum of the accumulated aging damage energy of the machinery, the damage energy caused by the mechanical environment, and the energy lost in tandem with the mechanical environment is calculated to generate the natural loss energy of the machinery.

9. An online monitoring system for electricity metering boxes used for electricity theft by opening the cover, characterized in that, include: The acquisition module is used to acquire fault trigger signals, fault environment datasets, and sensor usage time. A memory for storing the program of the online monitoring method for electricity metering boxes based on opening the cover to steal electricity as described in any one of claims 1 to 8; The processor and the program in the memory can be loaded and executed by the processor to implement the online monitoring method for electricity metering boxes based on opening the cover to steal electricity as described in any one of claims 1 to 8.

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