An early warning management system for electric energy metering boxes based on the Internet of Things

Abstract

The present invention relates to the technical field of electric energy meter box management, and specifically discloses an electric energy meter box early warning management system based on the Internet of Things, comprising a data analysis module, which obtains a corresponding real-time discharge state coefficient according to a discharge parameter at each time point, and obtains a corrected temperature of a transformer after eliminating the influence of room temperature according to a combined transformer temperature and a room temperature, and then analyzes the corrected temperature and the real-time discharge state coefficient to obtain a predicted discharge state coefficient. The present invention obtains the real-time discharge state coefficient by weighted summing a plurality of parameters for evaluating the discharge state in safe operation with different weight values, and can monitor the situation where a plurality of real-time discharge state coefficients are close to a safety threshold, thereby solving the problem that a single discharge parameter is not comprehensive enough during monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy meter box management, and in particular to an electric energy meter box early warning management system based on the Internet of Things. Background Art

[0002] The power meter box is a device used for electric energy metering in 3-35kv, including a combined mutual inductor (collecting three-phase current and voltage signals), a box, an electric meter, a lightning arrester, a vacuum circuit breaker (prepaid type), etc. At present, the monitoring and early warning of the power meter box is mainly carried out through the temperature change of the power meter box, that is, an alarm is issued when the temperature is abnormal. For example, in the Chinese invention with the announcement number CN 112103811 B, a temperature acquisition module is provided, which includes a temperature acquisition unit. The temperature acquisition unit transmits the collected temperature value to the database server through the monitoring terminal. The collected temperature value can be used to determine whether the temperature of the combined mutual inductor in the high-voltage power meter box is too high, a primary power acquisition module, by comparing the primary current and the rated current in the primary power, it can be determined whether the high-voltage power meter box is overloaded, and a partial discharge monitoring module, by collecting the partial discharge state signal, it can be determined whether the combined mutual inductor in the high-voltage power meter box is in a partial discharge state.

[0003] In other words, the current monitoring is based on evaluating independent projects separately to obtain evaluation results to complete the monitoring, but this monitoring method is not comprehensive enough. It cannot detect that the equipment is in a dangerous state in the early stage of equipment danger. It is necessary to wait until the equipment reaches a certain temperature or the discharge signal reaches a certain threshold before making a judgment.

[0004] In view of this, the present invention provides an Internet of Things-based electric energy meter box early warning management system, which can comprehensively evaluate the operating status of the electric energy meter box, give early warning of the dangers of the electric energy meter box, and give management personnel sufficient supervision time. Summary of the invention

[0005] The purpose of the present invention is to provide an electric energy meter box early warning management system based on the Internet of Things to solve the following technical problems:

[0006] How to comprehensively evaluate the operating status of the electric energy meter box and provide early warning of dangers to the electric energy meter box.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An electric energy meter box early warning management system based on the Internet of Things, comprising:

[0009] Monitoring module, data storage module, data analysis module and early warning module: the monitoring module is used to obtain temperature parameters and discharge parameters related to the electric energy metering box, the temperature parameters include the temperature of the combined transformer and the room temperature, the discharge parameters include the apparent discharge amount and the number of short pulses per unit time, and upload the temperature parameters and discharge parameters to the data storage module;

[0010] The data analysis module obtains the corresponding real-time discharge state coefficient according to the discharge parameters at each time point, and obtains the transformer correction temperature after eliminating the influence of room temperature according to the combined transformer temperature and room temperature, and then analyzes the correction temperature and the real-time discharge state coefficient to obtain the predicted discharge state coefficient;

[0011] The early warning module compares the predicted discharge state coefficient with a preset safety interval and issues an early warning based on the comparison result.

[0012] As a further technical solution of the present invention: the process of obtaining the real-time discharge state coefficient includes:

[0013] Divide the monitored electric energy meter box into a number of continuous unit time periods during the operating time;

[0014] Then, during the normal operation of the electric energy meter box, the formula is:

[0015]

[0016] Get the discharge state coefficient Con, where n is the total number of discharge parameters taken into consideration, i∈1, 2, ..., n, M i is the amount of discharge parameter i generated per unit time, is the standard quantity of the amount of the i-th discharge parameter generated in the current unit time obtained from historical data, σ i is the weight coefficient of the i-th discharge parameter, which is set based on empirical data

[0017] Through the above technical solution: a process for obtaining a real-time discharge state coefficient is provided. The present invention obtains the real-time discharge state coefficient by weighted summing up multiple parameters for evaluating the discharge state in safe operation with different weight values. In this way, the situation where multiple real-time discharge state coefficients are close to the safety threshold can be monitored, solving the problem that a single discharge parameter is not comprehensive enough during monitoring.

[0018] As a further technical solution of the present invention: the process of obtaining the corrected temperature of the transformer after eliminating the influence of room temperature includes:

[0019] Obtain all first temperature values ​​of the combined mutual inductor temperature reaching stability after the external temperature changes during the normal operation of the electric energy meter box, and obtain the critical temperature, wherein the external temperature change is to adjust the ambient temperature according to a fixed temperature change amplitude under laboratory conditions, and the critical temperature is the external temperature value at which the direction of the combined mutual inductor temperature change amplitude changes during the adjustment of the external temperature;

[0020] Selecting several reference items from the discharge parameters and setting corresponding reference intervals, and then selecting several first temperature values ​​whose values ​​of at least two reference items are both within the reference interval from all the first temperature values ​​as reference temperature values;

[0021] The corrected temperature is obtained based on the temperature difference between the ambient temperature and the critical temperature.

[0022] As a further technical solution of the present invention: the process of obtaining the corrected temperature based on the temperature difference between the ambient temperature and the critical temperature includes:

[0023]

[0024] Get the corrected temperature T S , where T1 is the reference temperature value of the combined mutual inductor at the current time, T2 is the ambient temperature, T0 is the critical temperature, R1 and R2 are both preset influence conversion functions with the temperature difference between the ambient temperature and the critical temperature as the independent variable. It is worth noting that when T2-T0=0, R1(T2-T0)=0.

[0025] Through the above technical scheme: a step method for obtaining the corrected temperature is provided. The corrected temperature obtained by the present invention needs to eliminate the influence of the ambient temperature on the self-heating of the combined transformer. The corrected temperature is obtained from the correspondence between the remaining reference temperature value and the ambient temperature under the condition of limiting the discharge parameters. The corrected temperature represents the temperature of the transformer after excluding the influence of the room temperature, that is, the ambient temperature is included in the consideration range of temperature changes, thereby avoiding false alarms caused by excessive changes in ambient temperature in the electric energy meter box.

[0026] As a further technical solution of the present invention: the process of obtaining the predicted discharge state coefficient includes:

[0027] Step 100: Divide the value range of the corrected temperature into several temperature intervals, obtain the real-time discharge state coefficient corresponding to each temperature interval to establish a comparison relationship, and the real-time discharge state coefficients of several unit time periods closest to the current unit time period;

[0028] Step 200, performing data analysis on a plurality of real-time discharge state coefficients in step 100 and obtaining a triple exponential smoothing average line, and obtaining a first prediction value based on the triple exponential smoothing average line;

[0029] Step 300: Substitute the first predicted value into the control relationship to obtain a plurality of corresponding temperature intervals;

[0030] Step 400, obtaining a change curve of the corrected temperature with a period of one day from historical data, obtaining a temperature prediction value based on the change curve, analyzing the temperature prediction value and a plurality of corresponding temperature intervals obtained in step 300, and obtaining a predicted discharge state coefficient based on the analysis result.

[0031] As a further technical solution of the present invention: the process of obtaining the analysis result includes:

[0032] Determine whether the temperature prediction value falls within the multiple corresponding temperature intervals obtained in step 300;

[0033] If yes, the first predicted value is used as the predicted discharge state coefficient;

[0034] If not, the first prediction value is adjusted based on the difference between the temperature prediction value and the center points of the multiple corresponding temperature intervals obtained in step 300 to obtain the predicted discharge state coefficient.

[0035] Through the above technical solution: a step of obtaining a predicted discharge state coefficient is provided. The present invention first obtains a first predicted value of the real-time discharge state coefficient, and then adjusts the first predicted value based on the first predicted value and the temperature predicted value, and then based on the relationship between the temperature predicted value and a plurality of corresponding temperature intervals. That is, the discharge parameter is linked to the temperature change by a comparative verification method, so that the factor of temperature change is added in the process of obtaining the first predicted value based on the discharge parameter. Compared with simply determining the state of the electric energy meter box by electric power parameters, it is more comprehensive, and the electric energy meter box can be more effectively monitored based on the Internet of Things.

[0036] As a further technical solution of the present invention: the process of adjusting the first prediction value includes:

[0037] By formula:

[0038]

[0039] Get the predicted discharge state coefficient Pr, where φ is an adjustment coefficient with a value range of (0, 1), h is the number of unit times between the first predicted value and the current time, and P Con is the first prediction value. IF(X) is a judgment function based on the independent variable X. The value range of the independent variable X is 0 or 1. It is determined based on the positional relationship between the temperature prediction value and the closest corresponding temperature interval. When the temperature prediction value is on the right side of the closest corresponding temperature interval, the independent variable X takes 1, otherwise it takes 0. The output value of IF(X) has a positive or negative sign. jis the difference between the temperature prediction value and the center point of the jth corresponding temperature interval, m is the total number of the multiple corresponding temperature intervals obtained in step 300, j∈1,2,…,n, G is the preset The adjustment range conversion function is preferably a lookup table function.

[0040] Through the above technical scheme: a process for adjusting the first predicted value is provided. The present invention adjusts the first predicted value based on the secondary verification result of the temperature, that is, the first predicted value whose temperature change is in line with the expectation is not adjusted, and the first predicted value that does not meet the expectation is adjusted, so that the temperature change corresponding to the adjusted first predicted value can be more in line with the expected temperature change, and the prediction result is made more accurate through comparative verification.

[0041] As a further technical solution of the present invention: the process of comparing the predicted discharge state coefficient with the preset safety interval includes:

[0042] Compare the predicted discharge state coefficient Pr with the preset safety interval (Pr1, Pr2);

[0043] If the predicted discharge state coefficient Pr is not within the preset safety interval (Pr1, Pr2), an early warning is issued. When the predicted discharge state coefficient Pr exceeds the preset safety interval (Pr1, Pr2), the early warning content is abnormal temperature change. If the predicted discharge state coefficient Pr is lower than the preset safety interval (Pr1, Pr2), the early warning content is abnormal discharge parameters.

[0044] If the predicted discharge state coefficient Pr falls into the preset safety interval (Pr1, Pr2), no warning is issued.

[0045] Beneficial effects of the present invention:

[0046] (1) The present invention obtains a real-time discharge state coefficient by weighted summing multiple parameters for evaluating the discharge state during safe operation with different weight values, so as to monitor the situation where multiple real-time discharge state coefficients are close to the safety threshold, thereby solving the problem that a single discharge parameter is not comprehensive enough during monitoring.

[0047] (2) The corrected temperature obtained by the present invention needs to eliminate the influence of the ambient temperature on the heating of the combined transformer itself. The corrected temperature is obtained from the correspondence between the remaining reference temperature value and the ambient temperature under the condition of limiting the discharge parameters. The corrected temperature represents the temperature of the transformer after excluding the influence of the room temperature, that is, the ambient temperature is included in the consideration range of temperature change, thereby avoiding the false alarm phenomenon caused by excessive changes in the ambient temperature of the electric energy meter box.

[0048] (3) The present invention first obtains a first predicted value of the real-time discharge state coefficient, and then adjusts the first predicted value based on the first predicted value and the temperature predicted value, and then based on the relationship between the temperature predicted value and a plurality of corresponding temperature intervals. That is, the discharge parameter is linked to the temperature change by a comparative verification method, so that the factor of temperature change is added in the process of obtaining the first predicted value based on the discharge parameter. Compared with simply determining the state of the electric energy meter box by electric power parameters, the present invention is more comprehensive, and the electric energy meter box can be more effectively monitored based on the Internet of Things.

[0049] (4) The present invention adjusts the first predicted value based on the secondary verification result of the temperature, that is, the first predicted value whose temperature change is in line with the expectation is not adjusted, and the first predicted value that is not in line with the expectation is adjusted, so that the temperature change corresponding to the adjusted first predicted value can be more consistent with the expected temperature change, and the prediction result is made more accurate through the comparative verification method. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention will be further described below in conjunction with the accompanying drawings.

[0051] Figure 1 It is a schematic diagram of the module relationship of the present invention;

[0052] Figure 2 It is a flow chart of the steps of obtaining the predicted discharge state coefficient of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] See also Figure 1-Figure 2As shown, in one embodiment, an Internet of Things-based electric energy meter box early warning management system is provided, including: a monitoring module, a data storage module, a data analysis module and an early warning module: the monitoring module is used to obtain temperature parameters and discharge parameters related to the electric energy meter box, the temperature parameters include the temperature of the combined transformer and the room temperature, the discharge parameters include the apparent discharge amount and the number of short pulses per unit time, and the temperature parameters and the discharge parameters are uploaded to the data storage module; the data analysis module obtains the corresponding real-time discharge state coefficient according to the discharge parameters at each time point, and obtains the transformer correction temperature after excluding the influence of the room temperature according to the combined transformer temperature and the room temperature, and then analyzes the corrected temperature and the real-time discharge state coefficient to obtain the predicted discharge state coefficient; the early warning module compares the predicted discharge state coefficient with the preset safety interval, and issues an early warning based on the comparison result.

[0055] The process of obtaining the real-time discharge state coefficient includes:

[0056] The monitored electric energy meter box is divided into several continuous unit time periods during the operation time; then, during the normal operation of the electric energy meter box, the formula is used:

[0057]

[0058] Get the discharge state coefficient Con, where n is the total number of discharge parameters taken into consideration, i∈1, 2, ..., n, M i is the amount of discharge parameter i generated per unit time, is the standard quantity of the amount of the i-th discharge parameter generated in the current unit time obtained from historical data, σ i is the weight coefficient of the ith discharge parameter, which is set based on empirical data.

[0059] In this embodiment, a process for obtaining a real-time discharge state coefficient is provided. The present invention obtains the real-time discharge state coefficient by weighted summing multiple parameters for evaluating the discharge state during safe operation with different weight values. In this way, the situation where multiple real-time discharge state coefficients are close to the safety threshold can be monitored, solving the problem that a single discharge parameter is not comprehensive enough during monitoring.

[0060] The process of obtaining the corrected temperature of the transformer after eliminating the influence of room temperature includes:

[0061] Obtain all first temperature values ​​of the combined mutual inductor temperature reaching stability after the external temperature changes during the normal operation of the electric energy meter box, and obtain the critical temperature, wherein the external temperature change is to adjust the ambient temperature according to a fixed temperature change amplitude under laboratory conditions, and the critical temperature is the external temperature value at which the direction of the combined mutual inductor temperature change amplitude changes during the adjustment of the external temperature;

[0062] Selecting several reference items from the discharge parameters and setting corresponding reference intervals, and then selecting several first temperature values ​​whose values ​​of at least two reference items are both within the reference interval from all the first temperature values ​​as reference temperature values;

[0063] The corrected temperature is obtained based on the temperature difference between the ambient temperature and the critical temperature.

[0064] As an example, in this embodiment, the process of obtaining the corrected temperature based on the temperature difference between the ambient temperature and the critical temperature includes:

[0065]

[0066] Get the corrected temperature T S , where T1 is the reference temperature value of the combined mutual inductor at the current time, T2 is the ambient temperature, T0 is the critical temperature, R1 and R2 are both preset influence conversion functions with the temperature difference between the ambient temperature and the critical temperature as the independent variable. It is worth noting that when T2-T0=0, R1(T2-T0)=0.

[0067] In this embodiment, a method for obtaining a corrected temperature is provided. The corrected temperature obtained by the present invention needs to eliminate the influence of the ambient temperature on the heating of the combined transformer itself. The corrected temperature is obtained from the correspondence between the remaining reference temperature value and the ambient temperature under the condition of limiting the discharge parameters. The corrected temperature represents the temperature of the transformer after excluding the influence of the room temperature, that is, the ambient temperature is included in the consideration range of temperature changes, thereby avoiding false alarms caused by excessive changes in ambient temperature in the electric energy meter box.

[0068] refer to Figure 2 , the process of obtaining the predicted discharge state coefficient includes:

[0069] Step 100: Divide the value range of the corrected temperature into several temperature intervals, obtain the real-time discharge state coefficient corresponding to each temperature interval to establish a comparison relationship, and the real-time discharge state coefficients of several unit time periods closest to the current unit time period;

[0070] Step 200, performing data analysis on a plurality of real-time discharge state coefficients in step 100 and obtaining a triple exponential smoothing average line, and obtaining a first prediction value based on the triple exponential smoothing average line;

[0071] Step 300: Substitute the first predicted value into the control relationship to obtain a plurality of corresponding temperature intervals;

[0072] Step 400, obtaining a change curve of the corrected temperature with a period of one day from historical data, obtaining a temperature prediction value based on the change curve, analyzing the temperature prediction value and a plurality of corresponding temperature intervals obtained in step 300, and obtaining a predicted discharge state coefficient based on the analysis result.

[0073] The process of obtaining analysis results includes:

[0074] Determine whether the temperature prediction value falls within the multiple corresponding temperature intervals obtained in step 300;

[0075] If yes, the first predicted value is used as the predicted discharge state coefficient;

[0076] If not, the first prediction value is adjusted based on the difference between the temperature prediction value and the center points of the multiple corresponding temperature intervals obtained in step 300 to obtain the predicted discharge state coefficient.

[0077] In this embodiment, a step of obtaining a predicted discharge state coefficient is provided. The present invention first obtains a first predicted value of the real-time discharge state coefficient, and then adjusts the first predicted value based on the first predicted value and the temperature predicted value, and then based on the relationship between the temperature predicted value and a plurality of corresponding temperature intervals. That is, the discharge parameter is linked to the temperature change by a comparative verification method, so that the factor of temperature change is added in the process of obtaining the first predicted value based on the discharge parameter. Compared with simply determining the state of the electric energy meter box by electric power parameters, it is more comprehensive, and the electric energy meter box can be more effectively monitored based on the Internet of Things.

[0078] The process of adjusting the first prediction value includes:

[0079] By formula:

[0080]

[0081] Get the predicted discharge state coefficient Pr, where: is an adjustment coefficient with a value range of (0, 1), h is the number of unit times between the first predicted value and the current time, P Con is the first prediction value. IF(X) is a judgment function based on the independent variable X. The value range of the independent variable X is 0 or 1. It is determined based on the positional relationship between the temperature prediction value and the closest corresponding temperature interval. When the temperature prediction value is on the right side of the closest corresponding temperature interval, the independent variable X takes 1, otherwise it takes 0. The output value of IF(X) has a positive or negative sign. j is the difference between the temperature prediction value and the center point of the jth corresponding temperature interval, m is the total number of the multiple corresponding temperature intervals obtained in step 300, j∈1,2,…,n, G is the preset The adjustment range conversion function is preferably a lookup table function.

[0082] In this embodiment, a process for adjusting the first predicted value is provided. The present invention adjusts the first predicted value based on the secondary verification result of the temperature. That is, the first predicted value whose temperature change is in line with expectations is not adjusted, and the first predicted value that is not in line with expectations is adjusted, so that the temperature change corresponding to the adjusted first predicted value can be more in line with the expected temperature change, and the prediction result is made more accurate through comparative verification.

[0083] The process of comparing the predicted discharge state coefficient with the preset safety interval includes:

[0084] Compare the predicted discharge state coefficient Pr with the preset safety interval (Pr1, Pr2);

[0085] If the predicted discharge state coefficient Pr is not within the preset safety interval (Pr1, Pr2), an early warning is issued. When the predicted discharge state coefficient Pr exceeds the preset safety interval (Pr1, Pr2), the early warning content is abnormal temperature change. If the predicted discharge state coefficient Pr is lower than the preset safety interval (Pr1, Pr2), the early warning content is abnormal discharge parameters.

[0086] If the predicted discharge state coefficient Pr falls into the preset safety interval (Pr1, Pr2), no warning is issued.

[0087] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An electric energy meter box early warning management system based on the Internet of Things, including a monitoring module, a data storage module, a data analysis module and an early warning module, characterized in that: The monitoring module is used to obtain temperature parameters and discharge parameters related to the electric energy metering box, the temperature parameters include the temperature of the combined mutual inductor and the room temperature, and the discharge parameters include the apparent discharge amount and the number of short pulses per unit time, and upload the temperature parameters and discharge parameters to the data storage module; The data analysis module obtains the corresponding real-time discharge state coefficient according to the discharge parameters at each time point, and obtains the transformer correction temperature after eliminating the influence of room temperature according to the combined transformer temperature and room temperature, and then analyzes the correction temperature and the real-time discharge state coefficient to obtain the predicted discharge state coefficient; The early warning module compares the predicted discharge state coefficient with the preset safety interval and issues an early warning based on the comparison result; The process of obtaining the real-time discharge state coefficient includes: Divide the monitored electric energy meter box into a number of continuous unit time periods during the operating time; Then, during the normal operation of the electric energy meter box, the formula is: Get the discharge state coefficient Con, where n is the total number of discharge parameters, i∈1, 2, ..., n, M i is the amount of discharge parameter i generated per unit time, is the standard quantity of the amount of the i-th discharge parameter generated in the current unit time obtained from historical data, σ i is the weight coefficient of the i-th discharge parameter, which is set based on empirical data; The process of obtaining the predicted discharge state coefficient includes: Step 100: Divide the value range of the corrected temperature into several temperature intervals, obtain the real-time discharge state coefficient corresponding to each temperature interval to establish a comparison relationship, and the real-time discharge state coefficients of several unit time periods closest to the current unit time period; Step 200, performing data analysis on a plurality of real-time discharge state coefficients in step 100 and obtaining a triple exponential smoothing average line, and obtaining a first prediction value based on the triple exponential smoothing average line; Step 300: Substitute the first predicted value into the control relationship to obtain a plurality of corresponding temperature intervals; Step 400, obtaining a change curve of the corrected temperature with a period of one day from historical data, obtaining a temperature prediction value according to the change curve, analyzing the temperature prediction value and a plurality of corresponding temperature intervals obtained in step 300, and obtaining a predicted discharge state coefficient according to the analysis result; The process of obtaining analysis results includes: Determine whether the temperature prediction value falls within the multiple corresponding temperature intervals obtained in step 300; If yes, the first predicted value is used as the predicted discharge state coefficient; If not, adjusting the first prediction value based on the difference between the temperature prediction value and the center points of the multiple corresponding temperature intervals obtained in step 300 to obtain a predicted discharge state coefficient; The process of adjusting the first predicted value includes: By formula: Get the predicted discharge state coefficient Pr, where: is the adjustment coefficient, h is the number of unit times between the first prediction value and the current time, P Con is the first prediction value, IF(X) is a judgment function based on the independent variable X, the value range of the independent variable X is 0 or 1, and it is determined based on the positional relationship between the temperature prediction value and the closest corresponding temperature interval, d j is the difference between the temperature prediction value and the center point of the jth corresponding temperature interval, m is the total number of the multiple corresponding temperature intervals obtained in step 300, j∈1,2,…,m, G is the preset The adjustment amplitude conversion function.

2. According to the Internet of Things-based electric energy meter box early warning management system of claim 1, it is characterized in that: The process of obtaining the corrected temperature of the transformer after eliminating the influence of room temperature includes: Obtain all first temperature values ​​at which the temperature of the combined mutual inductor reaches stability after the external temperature changes during the normal operation of the electric energy metering box, and obtain the critical temperature; Selecting several reference items from the discharge parameters and setting corresponding reference intervals, and then selecting several first temperature values ​​whose values ​​of at least two reference items are both within the reference interval from all the first temperature values ​​as reference temperature values; The corrected temperature is obtained based on the temperature difference between the ambient temperature and the critical temperature.

3. According to the Internet of Things-based electric energy meter box early warning management system of claim 2, it is characterized in that: The process of obtaining the corrected temperature based on the temperature difference between the ambient temperature and the critical temperature includes the following formula: Get the corrected temperature T S , where T1 is the reference temperature value of the combined mutual inductor at the current time, T2 is the ambient temperature, T0 is the critical temperature, and R1 and R2 are both preset influence conversion functions with the temperature difference between the ambient temperature and the critical temperature as the independent variable.

4. According to the Internet of Things-based electric energy meter box early warning management system of claim 1, it is characterized in that: The process of comparing the predicted discharge state coefficient with the preset safety interval includes: Compare the predicted discharge state coefficient Pr with the preset safety interval (Pr1, Pr2); If the predicted discharge state coefficient Pr is not within the preset safety interval (Pr1, Pr2), an early warning is issued. When the predicted discharge state coefficient Pr exceeds the preset safety interval (Pr1, Pr2), the early warning content is abnormal temperature change. If the predicted discharge state coefficient Pr is lower than the preset safety interval (Pr1, Pr2), the early warning content is abnormal discharge parameters. If the predicted discharge state coefficient Pr falls into the preset safety interval (Pr1, Pr2), no warning is issued.

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