Electric meter box fault monitoring system based on multi-source data fusion

Abstract

The invention relates to the technical field of electric meter box fault monitoring, in particular to an electric meter box fault monitoring system based on multi-source data fusion. The system comprises a data acquisition module used for acquiring internal and external temperature data of an electric meter box, total load power of the electric meter box and heat dissipation power of a heat dissipation system of the electric meter box; the temperature analysis module is used for setting a sliding window to synchronously slide on the internal temperature data, the external temperature data, the heat dissipation power and the total load power respectively to obtain a temperature drop coefficient and a heat dissipation effect correction value; the power analysis module is used for calculating a heat dissipation power characteristic value based on the heat dissipation power in the current sliding window, obtaining a working efficiency conversion coefficient of the heat dissipation system, and then obtaining a corrected working efficiency conversion coefficient; the early warning analysis module is used for acquiring an early warning index based on the correction working efficiency conversion coefficient corresponding to each sliding window; and carrying out early warning on the electric meter box fault according to the early warning index. The fault monitoring accuracy of the electric meter box can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric meter box fault monitoring, and in particular to an electric meter box fault monitoring system based on multi-source data fusion. Background Art

[0002] With the rapid development of urban power grids and the continued growth of residential electricity loads, the operating status of electricity meter boxes, as key nodes in low-voltage distribution systems, is directly related to user safety and the stability of the power supply system. In actual operation, electricity meter boxes are susceptible to various factors, such as environmental changes, equipment aging, and power anomalies, which can lead to equipment failures such as overload, excessive temperature rise, and burnout. Because meter box failures can be caused by multiple factors or a combination of them, the use of multi-source data fusion to monitor meter box failures is crucial for providing timely and accurate warnings of meter box failures.

[0003] Existing fault monitoring for electric meter boxes often relies on a single data source for analysis, often overlooking the combined impact of multiple data sources, such as environmental factors, voltage, and current, on the device's operating status. For example, when monitoring a fault in an electric meter box's cooling system, factors such as voltage fluctuations, current load, and ambient temperature can significantly impact the cooling system's performance. Relying solely on a single parameter can easily lead to distorted monitoring results or inaccurate fault identification, making it difficult to achieve comprehensive fault analysis and accurate early warning. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide an electric meter box fault monitoring system based on multi-source data fusion, and the technical solutions adopted are as follows:

[0005] One embodiment of the present invention provides an electric meter box fault monitoring system based on multi-source data fusion, the system comprising:

[0006] A data acquisition module is used to obtain internal temperature data of the meter box, external temperature data, total load power of the meter box, and heat dissipation power of its heat dissipation system;

[0007] The temperature analysis module is used to set a sliding window to slide synchronously on the internal temperature data, external temperature data, heat dissipation power, and total load power; obtain the temperature drop coefficient based on the internal temperature data in the current sliding window; and use the external temperature data in the current sliding window to correct the temperature drop coefficient to obtain a heat dissipation effect correction value;

[0008] The power analysis module is used to calculate a heat dissipation power characteristic value based on the heat dissipation power in the current sliding window; compare the heat dissipation effect correction value with the heat dissipation power characteristic value to obtain a working efficiency conversion coefficient of the heat dissipation system; calculate a correction coefficient based on the total load power in the current sliding window, and correct the working efficiency conversion coefficient to obtain a corrected working efficiency conversion coefficient;

[0009] The early warning analysis module is used to obtain early warning indicators based on the modified work efficiency conversion coefficients corresponding to each sliding window; and to issue early warnings for meter box failures based on the early warning indicators.

[0010] Preferably, obtaining the temperature drop coefficient according to the internal temperature data in the current sliding window includes:

[0011] Obtain the difference between the first internal temperature data and the second internal temperature data of two adjacent internal temperature data in the current sliding window, and use the exponential function with the natural constant as the base to map the difference to obtain the temperature change characteristic values corresponding to the two adjacent internal temperature data; calculate the average of the temperature change characteristic values corresponding to every two adjacent internal temperature data to obtain the temperature drop coefficient corresponding to the current sliding window.

[0012] Preferably, the temperature drop coefficient is corrected using the external temperature data in the current sliding window to obtain a heat dissipation effect correction value, including:

[0013] A temperature drop coefficient corresponding to the external temperature is obtained using the external temperature data in the current sliding window; the temperature drop coefficient obtained using the internal temperature data in the current sliding window is compared with the temperature drop coefficient corresponding to the external temperature to obtain a first ratio; the last external temperature data in the current sliding window is compared with the last internal temperature data to obtain a second ratio; the first external temperature data in the current sliding window is compared with the first internal temperature data to obtain a third ratio; the second ratio is compared with the third ratio and multiplied by the first ratio to obtain a heat dissipation effect correction value.

[0014] Preferably, calculating the heat dissipation power characteristic value based on the heat dissipation power in the current sliding window includes:

[0015] The temperature drop coefficient is calculated based on the heat dissipation power in the current sliding window by using the method of obtaining the temperature drop coefficient; the heat dissipation power characteristic value is obtained by multiplying the first heat dissipation power in the current sliding window by the inverse of the drop coefficient corresponding to the heat dissipation power.

[0016] Preferably, the correction coefficient is calculated according to the total load power in the current sliding window, including:

[0017] The total load power characteristic value is calculated based on the total load power in the current sliding window using the method for calculating the heat dissipation power characteristic value; the standard deviation of all the total load powers in the current sliding window after first-order difference is obtained, and the standard deviation is mapped using an exponential function with a natural constant as the base to obtain a fluctuation characteristic index; the total load power characteristic value is multiplied by the fluctuation characteristic index to obtain a correction coefficient.

[0018] Preferably, correcting the work efficiency conversion coefficient to obtain a corrected work efficiency conversion coefficient includes:

[0019] The preset value is added to the correction coefficient and then multiplied by the work efficiency conversion coefficient to obtain the corrected work efficiency conversion coefficient.

[0020] Preferably, obtaining the early warning indicator based on the modified work efficiency conversion coefficient corresponding to each sliding window includes:

[0021] The corrected work efficiency conversion coefficients corresponding to each sliding window are formed into a first sequence, wherein the last corrected work efficiency conversion coefficient in the first sequence is the corrected work efficiency conversion coefficient corresponding to the current sliding window; the first corrected work efficiency conversion coefficient in the first sequence is divided by the sum of the corrected work efficiency conversion coefficient corresponding to the current sliding window and the hyperparameter to obtain a first indicator; the method for obtaining the temperature drop coefficient is used to calculate the decrease coefficient of the corrected work efficiency conversion coefficient corresponding to the current sliding window based on the corrected work efficiency conversion coefficient corresponding to the current sliding window and its previous corrected work efficiency conversion coefficient; the method for obtaining the temperature drop coefficient is used to calculate the decrease coefficients corresponding to two adjacent corrected work efficiency conversion coefficients based on two adjacent corrected work efficiency conversion coefficients; the average of the decrease coefficients corresponding to every two adjacent corrected work efficiency conversion coefficients in the first sequence except the corrected work efficiency conversion coefficient corresponding to the current sliding window is calculated, and recorded as the decrease coefficient mean; the decrease coefficient of the corrected work efficiency conversion coefficient corresponding to the current sliding window is divided by the sum of the decrease coefficient mean and the hyperparameter to obtain a second indicator; the sum of the first indicator and the second indicator is mapped using the hyperbolic tangent function to obtain an early warning indicator.

[0022] Preferably, the early warning of the meter box failure is performed according to the early warning indicator, including:

[0023] If the warning indicator is greater than the reference threshold, a fault warning is issued for the meter box.

[0024] The embodiments of the present invention have at least the following beneficial effects: the present application collects multi-source data related to the heat dissipation of the meter box, including the internal temperature data of the meter box, the external temperature data, the total load power of the meter box and the heat dissipation power of its heat dissipation system, considers the influence of various factors on the heat dissipation of the meter box, improves the amount of information, and increases the accuracy of the heat dissipation fault monitoring of the meter box; then uses a sliding window to synchronously slide on the internal temperature data, the external temperature data, the heat dissipation power and the total load power respectively, analyzes the internal temperature data in the current sliding window and the external temperature data in the current sliding window, and obtains the heat dissipation effect correction value; then analyzes the working power (heat dissipation power) of the heat dissipation system. The heat dissipation power characteristic value is obtained by analysis, and it is combined with the heat dissipation effect correction value to obtain the working efficiency conversion coefficient of the heat dissipation system. Taking into account the overall total load power of the meter box and the influence of the fluctuation of the total load power on the working efficiency conversion coefficient of the heat dissipation system, the total load power in the current sliding window is further analyzed to obtain the correction coefficient, and the working efficiency conversion coefficient is corrected to obtain the corrected working efficiency conversion coefficient; finally, based on the corrected working efficiency conversion coefficient corresponding to each sliding window, the early warning index is obtained, and then the meter box fault early warning is carried out. Taking into account the relationship between internal and external temperature, heat dissipation power and total power, the heat dissipation fault of the meter box can be monitored and warned more accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 A system block diagram of an electric meter box fault monitoring system based on multi-source data fusion provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] To further illustrate the technical means and effectiveness of the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of an electric meter box fault monitoring system based on multi-source data fusion proposed in accordance with the present invention. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0028] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0029] The following describes in detail a specific solution of an electric meter box fault monitoring system based on multi-source data fusion provided by the present invention with reference to the accompanying drawings.

[0030] Example:

[0031] The main application scenario of the present invention is to analyze the internal temperature of the meter box, the external ambient temperature, the working power of the heat dissipation system and the total load power of the meter box, thereby preventing problems with the heat dissipation of the meter box and ensuring the normal operation of the meter box.

[0032] See also Figure 1 , which shows a system block diagram of a meter box fault monitoring system based on multi-source data fusion provided by an embodiment of the present invention. The system includes the following modules:

[0033] The data acquisition module is used to obtain the internal temperature data of the meter box, the external temperature data, the total load power of the meter box and the heat dissipation power of its heat dissipation system.

[0034] This application solution primarily monitors meter box faults, requiring the installation of voltage, current, and temperature sensors inside the meter box, as well as an external temperature sensor. This allows for the collection of both internal and external temperature data. The external temperature data refers to the external ambient temperature, as the internal and external temperature differences affect heat dissipation.

[0035] Furthermore, voltage and current sensors are used to collect voltage and current data from the meter box's cooling system during operation, thereby determining the cooling power of the cooling system. Simultaneously, the total current and total voltage of all devices within the meter box are collected during operation (sensors can be deployed at bus access points for collection), thereby determining the total load power of the meter box. It should be noted that the frequency of data collection (which can be adjusted by the implementer based on actual conditions) is the same and is collected in real time, with the most recent collection time being the current moment. This allows the acquisition of internal and external temperature data, the total load power of the meter box, and the cooling power of its cooling system.

[0036] The temperature analysis module is used to set the sliding window to slide synchronously on the internal temperature data, external temperature data, heat dissipation power and total load power; obtain the temperature drop coefficient based on the internal temperature data in the current sliding window; and use the external temperature data in the current sliding window to correct the temperature drop coefficient to obtain a heat dissipation effect correction value.

[0037] The meter box's cooling system is primarily responsible for regulating and controlling the temperature inside the box, ensuring the meter and its supporting electrical components operate stably within a safe temperature range while also reducing the accelerated aging of internal equipment and circuitry caused by prolonged exposure to high temperatures. Therefore, fault monitoring of the meter box's cooling system is particularly important.

[0038] Since the temperature change inside the meter box is mainly determined by the heat generated by the internal equipment circuits of the meter box, the heat dissipated by the heat dissipation system of the meter box, and the ambient temperature. In order to be able to combine the above-mentioned multiple data sources to more accurately monitor the heat dissipation failure of the meter system. This application establishes a sliding window to synchronously slide on each type of data, and then conducts analysis. The length of the sliding window is the set time length, with a reference value of 10 minutes, and then the internal temperature data, external temperature data, the total load power of the meter box and the heat dissipation power of its heat dissipation system are synchronously slid with the set sliding step length (reference value is 8 minutes) as the step length; the setting of the sliding step length is determined according to the monitoring accuracy requirements. The shorter the step length, the more accurate the monitoring. When the sliding window slides to the current moment, it is recorded as the current sliding window, that is, the last moment in the sliding window is the current moment.

[0039] During any sliding window, the cooling system of the meter box may remove some of the heat from the meter box when it is operating, causing the internal temperature to drop. Therefore, in order to obtain the cooling capacity performance of the cooling system during operation, it is necessary to accurately monitor subsequent cooling failures.

[0040] First, take the internal temperature of the meter box monitored by the heat dissipation system in the current sliding window as an example for analysis to obtain the speed of temperature reduction during the heat dissipation process. Obtain the temperature drop coefficient based on the internal temperature data in the current sliding window. Specifically, obtain the difference between the first internal temperature data and the second internal temperature data of two adjacent internal temperature data in the current sliding window, and use an exponential function with a natural constant as the base to map the difference to obtain the temperature change characteristic value corresponding to the two adjacent internal temperature data; calculate the average of the temperature change characteristic values corresponding to each two adjacent internal temperature data to obtain the temperature drop coefficient corresponding to the current sliding window.

[0041] The specific calculation model of the temperature drop coefficient is:

[0042]

[0043] Among them, Q(T S ) represents the temperature drop coefficient corresponding to the current sliding window, T S represents the sequence of internal temperature data in the current sliding window, m represents the number of internal temperature data in the current sliding window, e represents the natural constant, T i 、Ti+1 Respectively represent the i-th and i+1-th internal temperature data in the meter box within the current sliding window, Indicates the temperature change characteristic value corresponding to the adjacent i-th and i+1-th internal temperature data; It represents the temperature difference in the meter box before and after the current sliding window, which is used as the independent variable of the exponential function with the natural constant as the base. Then, the average of all the exponential function values with the natural constant as the base is summed up to make the final value positive, which is convenient for subsequent analysis and description. The larger the value, the faster the temperature in the sliding window drops.

[0044] During the operation of the meter box cooling system, changes in ambient temperature have a significant impact on its cooling efficiency. Typically, the meter box transfers internal heat to the external environment through natural heat dissipation or active heat dissipation through the cooling system. However, when the external ambient temperature rises, the temperature difference between the meter box and the environment decreases, and the heat transfer rate decreases, resulting in reduced cooling efficiency. Conversely, when the external ambient temperature is low, a larger temperature difference forms between the box and the outside, facilitating rapid heat release and improving the cooling system's efficiency. Therefore, changes in ambient temperature not only affect the heat exchange capacity of the cooling system but also directly impact the safe operation of the electrical equipment within the meter box. Ambient temperature is a key factor that must be fully considered when designing fault monitoring and thermal management strategies for the meter box cooling system. To eliminate the influence of environmental factors, the outdoor ambient temperature is combined to determine the cooling efficiency value of the cooling system within any sliding window.

[0045] The temperature drop coefficient is corrected using the external temperature data within the current sliding window to obtain a heat dissipation effect correction value. Specifically, the temperature drop coefficient corresponding to the external temperature is obtained using the external temperature data within the current sliding window; the temperature drop coefficient obtained using the internal temperature data within the current sliding window is compared with the temperature drop coefficient corresponding to the external temperature to obtain a first ratio; the last external temperature data within the current sliding window is compared with the last internal temperature data to obtain a second ratio; the first external temperature data within the current sliding window is compared with the first internal temperature data to obtain a third ratio; the second ratio is compared with the third ratio and multiplied by the first ratio to obtain the heat dissipation effect correction value.

[0046] The calculation model of the heat dissipation effect correction value is:

[0047]

[0048] Among them, M represents the cooling effect correction value corresponding to the current sliding window; Q(T S ) represents the temperature drop coefficient obtained using the internal temperature data in the current sliding window, Q(T E) represents the temperature drop coefficient corresponding to the external temperature obtained using the external temperature data in the current sliding window, and the acquisition method is the same. It represents the first ratio. The larger the value is, the better the heat dissipation effect of the heat dissipation system in the current sliding window under analysis is under the influence of the environment. and Respectively represent the last external temperature data and the last internal temperature data in the current sliding window, represents the second ratio; and Respectively represent the initial external temperature data and internal temperature data in the current sliding window, that is, the first external temperature data and the first internal temperature data in the current sliding window, Represents the third ratio. The larger the value, the better the cooling effect of the cooling system in the current sliding window combined with the environment. It represents the ratio of the external ambient temperature to the internal temperature of the meter box at the end of the current sliding window divided by the ratio of the external ambient temperature to the internal temperature of the meter box at the initial moment of the current sliding window. The larger the value, the better the active heat dissipation effect of the meter box in the current sliding window may be. Because the larger the ratio, it means that during this time period, the internal temperature of the meter box has decreased relative to the external ambient temperature or the decrease is large, and the initial external temperature is lower than the internal temperature, and the heat exchange effect is good, indicating that the active heat dissipation effect of the meter box is better in the tail external environment.

[0049] The power analysis module is used to calculate the heat dissipation power characteristic value based on the heat dissipation power in the current sliding window; compare the heat dissipation effect correction value with the heat dissipation power characteristic value to obtain the work efficiency conversion coefficient of the heat dissipation system; calculate the correction coefficient according to the total load power in the current sliding window, and correct the work efficiency conversion coefficient to obtain the corrected work efficiency conversion coefficient.

[0050] Through the above, the heat dissipation effect correction value of the heat dissipation system in any sliding window can be obtained. However, since the heat dissipation effect correction value of the heat dissipation system in different sliding windows has a direct positive correlation with the working power of the heat dissipation system, for example, when the heat dissipation power of the heat dissipation system is increased, the heat dissipation effect may be better, and vice versa.

[0051] Therefore, further analysis is needed based on the heat dissipation power value of the cooling system. The heat dissipation power characteristic value is calculated based on the heat dissipation power within the current sliding window. Specifically, the corresponding heat dissipation power reduction coefficient is calculated based on the heat dissipation power within the current sliding window using the method for obtaining the temperature drop coefficient. The heat dissipation power characteristic value is obtained by multiplying the first heat dissipation power within the current sliding window by the inverse of the corresponding heat dissipation power reduction coefficient.

[0052] The calculation model of the heat dissipation power characteristic value is:

[0053]

[0054] Among them, R S Indicates the heat dissipation power characteristic value corresponding to the current sliding window, Indicates the first heat dissipation power in the current sliding window, that is, the heat dissipation power of the heat dissipation system at the initial moment in the period corresponding to the current sliding window. The larger its value is, the greater the working power characteristic value of the system during heat dissipation. S It represents the sequence of heat dissipation power at each moment in the current sliding window, Q(P S ) represents the decrease coefficient of the sequence of heat dissipation power of the heat dissipation system in the current sliding window. The method of obtaining it is the same as the method of obtaining the temperature drop coefficient based on the internal temperature data in the current sliding window. The smaller the value, the The larger the value, the greater the increase in heat dissipation power during this period, and the larger its characteristic value.

[0055] When the meter box and its cooling system are operating normally, the cooling efficiency tends to be stable. Therefore, for any sliding window, the ratio of the cooling system's cooling effect correction value to the cooling system's cooling power characteristic value can be expressed as the cooling system's operating efficiency conversion coefficient within that sliding window. In other words, when the heat source is constant and the cooling system is operating normally, theoretically, the cooling system's performance and cooling system power exhibit a stable positive correlation. In other words, the cooling effect correction value (ignoring the influence of environmental conditions at this time) and the cooling system's cooling power characteristic value maintain a relatively constant relationship.

[0056] The working efficiency conversion coefficient of the cooling system within the current sliding window is calculated as follows: X represents the working efficiency conversion coefficient of the cooling system in the current sliding window, M represents the cooling effect correction value corresponding to the current sliding window, R S Indicates the heat dissipation power characteristic value corresponding to the current sliding window.

[0057] However, the meter box may also generate a continuous amount of heat during the heat dissipation process. This is because the equipment within the meter box generates heat during operation due to the flow of current, and this heat generation is generally proportional to the power load. When the total power of the equipment is high, the heat generated also increases. Furthermore, greater fluctuations in the total load power can lead to unstable instantaneous energy consumption, causing current fluctuations and, in turn, increasing the conversion of electrical energy into thermal energy, leading to abnormally high heat levels. Therefore, even if the cooling system itself has high heat dissipation efficiency, the continuous heat generation during the heat dissipation process may lead to a decrease in the cooling system's efficiency coefficient during this period. Therefore, when evaluating the actual operating efficiency of the meter box cooling system, it is necessary to further integrate the equipment's load status and power fluctuations into a comprehensive analysis to accurately determine whether the temperature rise is caused by a fault such as a deterioration in the cooling system's performance, thereby improving the accuracy of cooling system fault monitoring.

[0058] The correction coefficient is calculated based on the total load power within the current sliding window. Specifically, the total load power characteristic value is calculated based on the total load power within the current sliding window using the method for calculating the heat dissipation power characteristic value; the standard deviation of all total load powers within the current sliding window after first-order difference is obtained, and the standard deviation is mapped using an exponential function with a natural constant as the base to obtain a fluctuation characteristic index; the total load power characteristic value is multiplied by the fluctuation characteristic index to obtain the correction coefficient.

[0059] The calculation model of the correction coefficient is:

[0060]

[0061] Among them, G represents the correction coefficient of the working efficiency conversion coefficient of the meter box cooling system in the current sliding window, R C It indicates the total load power characteristic value calculated based on the total load power of all devices in the meter box within the current sliding window using the above-mentioned method of calculating the heat dissipation power characteristic value. The larger the value, the greater the heat generation may be during this period; e represents the natural constant; ΔP C represents the sequence formed by taking the first-order difference of all the total load powers in the current sliding window; f(ΔP C ) represents the standard deviation of the elements in the sequence formed by taking the first-order difference of all the total load powers in the current sliding window. The larger the value, the greater the power fluctuation of the total load of all devices in the meter box during the period, and the greater the heat generated in the meter box.

[0062] The correction coefficient of the work efficiency conversion coefficient corresponding to the current sliding window can be obtained through the above, and then the work efficiency conversion coefficient is corrected using the correction coefficient. Specifically, the preset value is added to the correction coefficient and then multiplied by the work efficiency conversion coefficient to obtain the corrected work efficiency conversion coefficient.

[0063] The specific calculation model is:

[0064] X′=X×(1+G),

[0065] X' represents the corrected efficiency conversion coefficient of the current sliding window cooling system. A larger value for G indicates a greater amount of heat generated by the meter during this period, which can result in a smaller cooling system efficiency conversion coefficient and a greater degree of correction.

[0066] From this, the corrected work efficiency conversion coefficient can be obtained, which is also the corrected work efficiency conversion coefficient.

[0067] The early warning analysis module is used to obtain early warning indicators based on the modified work efficiency conversion coefficients corresponding to each sliding window; and to issue early warnings for meter box failures based on the early warning indicators.

[0068] The above method can be used to obtain the corrected efficiency conversion coefficient of the cooling system within any sliding window. Based on historical data, a sequence of corrected efficiency conversion coefficients for the current cooling system can then be obtained. If the corrected efficiency conversion coefficient of the cooling system within the current sliding window is smaller than the corrected efficiency conversion coefficient corresponding to the initial sliding window, then the probability of a cooling system failure is greater at the current moment. Alternatively, if the corrected efficiency conversion coefficient of the cooling system decreases significantly compared to historical data, then the probability of a failure is also greater.

[0069] A warning indicator is obtained based on the modified work efficiency conversion coefficient corresponding to each sliding window. Specifically, the modified work efficiency conversion coefficients corresponding to each sliding window are formed into a first sequence, wherein the last modified work efficiency conversion coefficient in the first sequence is the modified work efficiency conversion coefficient corresponding to the current sliding window; the first modified work efficiency conversion coefficient in the first sequence is divided by the sum of the modified work efficiency conversion coefficient corresponding to the current sliding window and the hyperparameter to obtain a first indicator; a method for obtaining a temperature drop coefficient is used to calculate a decrease coefficient of the modified work efficiency conversion coefficient corresponding to the current sliding window based on the modified work efficiency conversion coefficient corresponding to the current sliding window and its previous modified work efficiency conversion coefficient in the first sequence; a method for obtaining a temperature drop coefficient is used to calculate decrease coefficients corresponding to two adjacent modified work efficiency conversion coefficients based on two adjacent modified work efficiency conversion coefficients; the average decrease coefficients corresponding to every two adjacent modified work efficiency conversion coefficients in the first sequence except the modified work efficiency conversion coefficient corresponding to the current sliding window are calculated, and recorded as the decrease coefficient mean; the decrease coefficient of the modified work efficiency conversion coefficient corresponding to the current sliding window is divided by the sum of the decrease coefficient mean and the hyperparameter to obtain a second indicator; and a hyperbolic tangent function is used to map the sum of the first indicator and the second indicator to obtain a warning indicator.

[0070] The specific calculation model of the early warning indicator is:

[0071]

[0072] Y n Represents the warning indicator corresponding to the current sliding window, that is, the warning indicator corresponding to the current moment; tanh represents the hyperbolic tangent function, which is used for normalization operation; c represents a hyperparameter, which prevents the denominator from being 0, and the reference value is 0.001; X'0 represents the first corrected work efficiency conversion coefficient in the first sequence, that is, the corrected work efficiency conversion coefficient of the cooling system in the initial sliding window. Because as the cooling system works, its cooling efficiency may decrease with the accumulation of dust in the cooling system in the meter box and the aging of the lines, and the probability of cooling failure increases gradually. Therefore, the initial corresponding cooling system corrected work efficiency conversion coefficient is selected here, which means its optimal state when it starts working, that is, it is used as the reference value; X' n Indicates the modified working efficiency conversion coefficient of the cooling system within the current sliding window;

[0073] Q(X' n ) represents a decrease coefficient of the corrected work efficiency conversion coefficient corresponding to the current sliding window obtained by using the method for obtaining the temperature drop coefficient based on the corrected work efficiency conversion coefficient corresponding to the current sliding window in the first sequence and the previous corrected work efficiency conversion coefficient; It represents the average of the decrease coefficients corresponding to every two adjacent modified work efficiency conversion coefficients in the first sequence except the modified work efficiency conversion coefficient corresponding to the current sliding window, that is, the overall decrease coefficient average, indicating the overall decrease in the heat dissipation performance;

[0074] Indicates the ratio between the corrected working efficiency conversion coefficient corresponding to the current sliding window of the heat dissipation system of the meter box and the reference value of the corrected working efficiency conversion coefficient of the heat dissipation system of the meter box. The larger the value, the worse the heat dissipation efficiency at the current moment, and the greater the possibility that it is caused by a heat dissipation system failure or abnormal heating inside the meter box; It represents the ratio of the decrease coefficient of the corrected work efficiency conversion coefficient corresponding to the current sliding window to the average decrease coefficient of the corrected work efficiency conversion coefficient in history. The larger the value, the greater the degree of sudden change in the work efficiency of the cooling system at the current moment, that is, the more unstable the cooling system is, and the greater the probability of sudden failure.

[0075] Through the above, we get the early warning index of the heat dissipation system of the electric meter box corresponding to the current sliding window (current moment), set the reference threshold T, which is set to 0.2 (experience value) here. The implementer can adjust it according to the actual situation. n >T, it means that the cooling system may have insufficient heat dissipation or serious heating failure. At this time, timely warning is needed to remind the operation and maintenance personnel to conduct further inspection and maintenance of the fault.

[0076] In summary, this application considers that the failure of the heat dissipation system of the meter box may be affected by multiple factors, and therefore improves the accuracy of the judgment of the failure of the heat dissipation system of the meter box by combining multi-source data for comprehensive analysis.

[0077] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0078] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0079] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A meter box fault monitoring system based on multi-source data fusion, characterized in that: The system includes: A data acquisition module is used to obtain internal temperature data of the meter box, external temperature data, total load power of the meter box, and heat dissipation power of its heat dissipation system; The temperature analysis module is used to set the sliding window to slide synchronously on the internal temperature data, external temperature data, heat dissipation power and total load power respectively; obtain the temperature drop coefficient based on the internal temperature data in the current sliding window; and use the external temperature data in the current sliding window to correct the temperature drop coefficient to obtain the heat dissipation effect correction value; The power analysis module is used to calculate a heat dissipation power characteristic value based on the heat dissipation power in the current sliding window; compare the heat dissipation effect correction value with the heat dissipation power characteristic value to obtain a working efficiency conversion coefficient of the heat dissipation system; calculate a correction coefficient based on the total load power in the current sliding window, and correct the working efficiency conversion coefficient to obtain a corrected working efficiency conversion coefficient; The early warning analysis module is used to obtain early warning indicators based on the modified work efficiency conversion coefficients corresponding to each sliding window; and to issue early warnings for meter box failures based on the early warning indicators.

2. The electric meter box fault monitoring system based on multi-source data fusion according to claim 1 is characterized in that: The obtaining of the temperature drop coefficient according to the internal temperature data in the current sliding window includes: Obtain the difference between the first internal temperature data and the second internal temperature data of two adjacent internal temperature data in the current sliding window, and use the exponential function with the natural constant as the base to map the difference to obtain the temperature change characteristic values corresponding to the two adjacent internal temperature data; calculate the average of the temperature change characteristic values corresponding to every two adjacent internal temperature data to obtain the temperature drop coefficient corresponding to the current sliding window.

3. The electric meter box fault monitoring system based on multi-source data fusion according to claim 1 is characterized in that: The method of correcting the temperature drop coefficient by using the external temperature data in the current sliding window to obtain a heat dissipation effect correction value includes: A temperature drop coefficient corresponding to the external temperature is obtained using the external temperature data in the current sliding window; the temperature drop coefficient obtained using the internal temperature data in the current sliding window is compared with the temperature drop coefficient corresponding to the external temperature to obtain a first ratio; the last external temperature data in the current sliding window is compared with the last internal temperature data to obtain a second ratio; the first external temperature data in the current sliding window is compared with the first internal temperature data to obtain a third ratio; the second ratio is compared with the third ratio and multiplied by the first ratio to obtain a heat dissipation effect correction value.

4. The electric meter box fault monitoring system based on multi-source data fusion according to claim 1 is characterized in that: The calculating of the heat dissipation power characteristic value based on the heat dissipation power in the current sliding window includes: The temperature drop coefficient is calculated based on the heat dissipation power in the current sliding window by using the method of obtaining the temperature drop coefficient; the heat dissipation power characteristic value is obtained by multiplying the first heat dissipation power in the current sliding window by the inverse of the drop coefficient corresponding to the heat dissipation power.

5. The electric meter box fault monitoring system based on multi-source data fusion according to claim 1 is characterized in that: Calculating the correction coefficient according to the total load power in the current sliding window includes: The total load power characteristic value is calculated based on the total load power in the current sliding window using the method for calculating the heat dissipation power characteristic value; the standard deviation of all the total load powers in the current sliding window after first-order difference is obtained, and the standard deviation is mapped using an exponential function with a natural constant as the base to obtain a fluctuation characteristic index; the total load power characteristic value is multiplied by the fluctuation characteristic index to obtain a correction coefficient.

6. The electric meter box fault monitoring system based on multi-source data fusion according to claim 1 is characterized in that: The correcting the work efficiency conversion coefficient to obtain the corrected work efficiency conversion coefficient includes: The preset value is added to the correction coefficient and then multiplied by the work efficiency conversion coefficient to obtain the corrected work efficiency conversion coefficient.

7. The electric meter box fault monitoring system based on multi-source data fusion according to claim 1 is characterized in that: The obtaining of the early warning indicator based on the modified work efficiency conversion coefficient corresponding to each sliding window includes: The corrected work efficiency conversion coefficients corresponding to each sliding window are formed into a first sequence, wherein the last corrected work efficiency conversion coefficient in the first sequence is the corrected work efficiency conversion coefficient corresponding to the current sliding window; the first corrected work efficiency conversion coefficient in the first sequence is divided by the sum of the corrected work efficiency conversion coefficient corresponding to the current sliding window and the hyperparameter to obtain a first indicator; the method for obtaining the temperature drop coefficient is used to calculate the decrease coefficient of the corrected work efficiency conversion coefficient corresponding to the current sliding window based on the corrected work efficiency conversion coefficient corresponding to the current sliding window and its previous corrected work efficiency conversion coefficient; the method for obtaining the temperature drop coefficient is used to calculate the decrease coefficients corresponding to two adjacent corrected work efficiency conversion coefficients based on two adjacent corrected work efficiency conversion coefficients; the average of the decrease coefficients corresponding to every two adjacent corrected work efficiency conversion coefficients in the first sequence except the corrected work efficiency conversion coefficient corresponding to the current sliding window is calculated, and recorded as the decrease coefficient mean; the decrease coefficient of the corrected work efficiency conversion coefficient corresponding to the current sliding window is divided by the sum of the decrease coefficient mean and the hyperparameter to obtain a second indicator; the sum of the first indicator and the second indicator is mapped using the hyperbolic tangent function to obtain an early warning indicator.

8. The electric meter box fault monitoring system based on multi-source data fusion according to claim 1 is characterized in that: The method of providing an early warning for an electric meter box failure based on early warning indicators includes: If the warning indicator is greater than the reference threshold, a fault warning is issued for the meter box.

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