Anti-electric shock warning and protection method and device for power box

Abstract

The present application relates to the field of anti-electric shock technology, and specifically to an anti-electric shock warning and protection method and device for a power supply box, the method comprising: collecting leakage current data, box temperature data, box humidity data, and box humidity data for each time period of the power supply box; analyzing the correlation between the box temperature data and the box humidity data and the leakage current data, and combining the data change trend to obtain the first influence coefficient of the leakage current and the second influence coefficient of the leakage network, and obtain the comprehensive influence coefficient of the leakage current; according to the change trend and data distribution of the humidity data outside the box in each time period, obtain the risk of electric shock outside the box, and obtain the electric shock risk coefficient for each time period; according to the electric shock risk coefficient of continuous time periods, combined with a neural network model, obtain the anti-electric shock safety distance and determine whether to issue an alarm. The present application aims to obtain the anti-electric shock safety distance of the power supply box and improve the accuracy and reliability of the anti-electric shock warning.

Description

Technical Field

[0001] The present application relates to the field of electric shock prevention technology, and specifically to an electric shock prevention warning and protection method and device for a power box. Background Art

[0002] The power box is an important part of the power grid, and it is very important to provide anti-electric shock warning and protection for the power box. Electric shock accidents may cause serious personal injury or even death. By implementing anti-electric shock warning and protection measures, potential electric shock risks can be detected and responded to in a timely manner to protect the safety of operators and surrounding people.

[0003] Traditional power box anti-electric shock warning and protection methods mainly rely on sensors to monitor the distance between people and power boxes and compare it with the preset safety distance to issue a warning. However, the preset safety distance in this method is usually a fixed value set based on the voltage level of the distribution network. In fact, under different environmental conditions, even if the distance between people and the power box is the same, the risk of electric shock will vary. Therefore, the fixed safety distance may not be able to fully adapt to all situations, resulting in the accuracy and reliability of the anti-electric shock warning being affected. Summary of the Invention

[0004] In view of the above, it is necessary to provide an anti-electric shock warning and protection method and device for a power box to solve the above problems.

[0005] According to one aspect of the present application, a method for preventing electric shock and protecting a power supply box is provided, the method comprising:

[0006] Collect leakage current data, internal environment monitoring data, and external humidity data of the power box at each time period; the internal environment monitoring data includes internal temperature data and internal humidity data;

[0007] Analyze the correlation between the in-box environment monitoring data and the leakage current data in the preset historical time period, and combine the change trend of the in-box environment monitoring data in each time period to obtain the first influence coefficient of the leakage current in each time period;

[0008] The second influence coefficient of leakage current in each time period is obtained by combining the in-box environmental monitoring data of each time period with the preset number of time periods before it with the preset environmental threshold, and the comprehensive influence coefficient of leakage current in each time period is obtained;

[0009] According to the changing trend and data distribution of the external environment monitoring data in each time period, the risk of electric shock outside the power box in each time period is obtained; the distribution of leakage current data in each time period, the leakage current comprehensive impact index and the risk of electric shock outside the box are combined to obtain the electric shock risk coefficient in each time period;

[0010] According to the electric shock risk coefficient of continuous time periods, combined with the neural network model, the anti-electric shock safety distance of each time period is obtained;

[0011] Determine whether to issue an alarm based on the distance between the person and the power box and the safety distance against electric shock in the corresponding time period.

[0012] The first influence coefficient of the leakage current in each time period is obtained as follows:

[0013] Extract the temperature data of the box in the preset historical time period and record it as the historical box temperature sequence; adopt the same method as the historical box temperature sequence to obtain the historical box humidity sequence and the historical leakage current sequence based on the box humidity data and the leakage current data in the preset historical time period;

[0014] The correlation degree of the historical temperature series and the historical leakage current series in the box is analyzed to obtain the correlation degree W1 of the temperature data in the box to the leakage current data; the correlation degree of the historical humidity series and the historical leakage current series in the box is analyzed to obtain the correlation degree W2 of the humidity data in the box to the leakage current data;

[0015] All the temperature data in the box in each time period are subjected to trend decomposition to obtain the trend item sequence; the elements in the first-order difference sequence of the trend item sequence and their position numbers are combined to obtain the adjacent temperature trends of each time period. ; Using the same acquisition method as the adjacent temperature trend, according to all the humidity data in the box in each time period, the adjacent humidity trend L3 of each time period is obtained;

[0016] The first impact coefficient of leakage current in each time period is recorded as S1, and its formula is as follows: .

[0017] The obtained adjacent temperature trends in each time period are specifically the result of fusing the normalized values ​​of the position numbers of all elements in the first-order difference sequence with the corresponding elements.

[0018] The step of obtaining the second influence coefficient of the leakage current in each time period is as follows:

[0019] Record the sequence of all the in-box temperature data of each time period and a preset number of previous time periods as the recent in-box temperature sequence; obtain the negative correlation mapping between the difference between each data point in the recent in-box temperature sequence and the preset temperature threshold to obtain the temperature suitability sequence;

[0020] The same method as that for obtaining the suitable temperature sequence is used to obtain the suitable humidity sequence based on all humidity data in each time period and a preset number of time periods before;

[0021] According to the distribution of the mean values ​​of the elements at the same position in the temperature suitable sequence and the humidity suitable sequence, the second influence coefficient of the leakage current in each time period is obtained.

[0022] The comprehensive influence coefficient of the leakage current in each time period is specifically the average of the normalized value of the first influence coefficient of the leakage current and the normalized value of the second influence coefficient of the leakage current.

[0023] The process of obtaining the risk of electric shock outside the power box in each time period includes:

[0024] Adopting the same acquisition method as the adjacent temperature trend, according to all the humidity data outside the box in each time period, the adjacent humidity trend outside the box in each time period is obtained;

[0025] According to the humidity trends outside the adjacent boxes in each time period and the distribution of all humidity data outside the box in each time period, the risk of electric shock outside the box in each time period is obtained.

[0026] The electric shock risk coefficient for each time period is obtained as follows:

[0027] The electric shock risk factor of the power box in the tth time period is recorded as F(t), and its formula is: , where a1 represents the maximum value of all leakage current data in the t-th time period; a represents the preset leakage current safety threshold; S(t) represents the comprehensive impact coefficient of leakage current of the power box in the t-th time period; f(t) represents the risk of electric shock outside the power box in the t-th time period; exp() represents the exponential function with a natural constant as the base.

[0028] The step of obtaining the electric shock prevention safety distance in each time period includes:

[0029] The electric shock risk coefficients of the tth time period and the previous n consecutive time periods are combined into the electric shock risk coefficient sequence of the tth time period; the electric shock safety distances from the tn-1th time period to the t-1th time period are combined into the electric shock safety distance prediction sequence of the tth time period; the electric shock risk coefficient sequence of the tth time period and the electric shock safety distance prediction sequence are used as inputs of the long-short-term memory neural network to obtain the electric shock safety distance of the tth time period; where n is a preset value.

[0030] The determination of whether to issue an alarm is specifically as follows: issuing an alarm when it is detected that the distance between the person and the power box is less than or equal to the anti-electric shock safety distance of the corresponding time period.

[0031] According to another aspect of the present application, there is provided an anti-electric shock warning and protection device for a power supply box, comprising:

[0032] The data acquisition module is used to collect leakage current data of the power box in each time period, internal environment monitoring data, and external humidity data of the box; wherein the internal environment monitoring data includes internal temperature data and internal humidity data;

[0033] A data processing unit is used to analyze the correlation between the in-box environmental monitoring data and the leakage current data in a preset historical time period, and obtain the first leakage current influence coefficient of each time period in combination with the change trend of the in-box environmental monitoring data in each time period; combine each time period with the in-box environmental monitoring data of a preset number of time periods before it, and combine it with a preset environmental threshold to obtain the second leakage current influence coefficient of each time period, and obtain the comprehensive leakage current influence coefficient of each time period; according to the change trend and data distribution of the external environmental monitoring data in each time period, obtain the external electric shock risk of the power box in each time period; comprehensively analyze the distribution of the leakage current data in each time period, the comprehensive leakage current influence index and the external electric shock risk, to obtain the electric shock risk coefficient of each time period; according to the electric shock risk coefficient of continuous time periods, combined with a neural network model, obtain the anti-electric shock safety distance of each time period;

[0034] The early warning module is used to determine whether to issue an alarm based on the distance between the person and the power box and the anti-electric shock safety distance in the corresponding time period.

[0035] This application has at least the following beneficial effects:

[0036] 1. This application constructs a first influence coefficient of leakage current by analyzing the changing trends of temperature and humidity in the power box, reflecting the influence of changes in the insulation performance of the insulating materials of the power equipment in the power box caused by temperature and humidity on the leakage current; constructs a second influence coefficient of leakage current, and obtains a comprehensive influence coefficient of leakage current. The beneficial effect is that it takes into account the influence of the temperature and humidity in the power box and the changes in the insulation performance of the air in the power box and the power equipment caused by mold on the leakage current, and can more accurately evaluate the changing trend of the leakage current of the power box.

[0037] 2. This application constructs the risk of electric shock outside the box by analyzing the impact of the ambient humidity outside the power box on human electric shock, and constructs the electric shock risk coefficient by combining the comprehensive impact coefficient of leakage current and the risk of electric shock outside the box. The beneficial effect is that it takes into account the impact of the ambient humidity outside the power box on human electric shock, and can more accurately evaluate the possibility of electric shock to the human body.

[0038] 3. This application calculates the anti-electric shock safety distance based on the constructed electric shock risk coefficient, and completes the anti-electric shock safety warning and protection of the power box based on the calculated anti-electric shock safety distance. Its beneficial effect is that it takes into account the impact of environmental factors inside and outside the power box on the electric shock to the human body caused by leakage current, and adjusts the setting of the anti-electric shock safety distance of the power box accordingly, thereby improving the accuracy and reliability of the anti-electric shock warning, and thus being able to better provide anti-electric shock warning and protection for people near the power box. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flowchart of the steps of the anti-electric shock warning and protection method for the power box provided in this application;

[0040] Figure 2 Schematic diagram for obtaining the first influence coefficient of leakage current provided by this application;

[0041] Figure 3 Schematic diagram for obtaining the second influence coefficient of leakage current provided by this application;

[0042] Figure 4 This is a schematic diagram for obtaining the electric shock risk factor provided in this application. DETAILED DESCRIPTION

[0043] In the description of the embodiments of this application, words such as "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "or," and "for example" is intended to present the relevant concepts in a concrete manner.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the art of this application. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0045] It should also be noted that the terms "first" and "second" in this application and the accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The methods disclosed in the embodiments of this application or the methods shown in the flowcharts include one or more steps for implementing the methods. Without departing from the scope of this application, the order of executing multiple steps can be interchanged with each other, and some steps can also be deleted.

[0046] See also Figure 1 , which shows a flowchart of the steps of the anti-electric shock warning and protection method of the power box provided by an embodiment of the present application, the method includes the following steps:

[0047] Step 1: Collect leakage current data, in-box environment monitoring data, and outside-box humidity data of the power box at each time period; wherein the in-box environment monitoring data includes in-box temperature data and in-box humidity data.

[0048] The anti-electric shock warning and protection device in this application includes a data acquisition module, a data processing unit and a warning module; wherein the data acquisition module includes a leakage current acquisition module, an environmental data acquisition module, and a human body perception and ranging module.

[0049] In the leakage current acquisition module, a leakage current detector is used to collect leakage current data of the circuit where the power box is located, and the collected data is transmitted to the data processing unit; in the environmental data acquisition module, a temperature and humidity sensor is used to collect the internal environment monitoring data of the power box and the external environment monitoring data, and the collected data is transmitted to the data processing unit. In this embodiment, the internal environment monitoring data includes the internal temperature data and the internal humidity data, and the external environment monitoring data includes the external humidity data; in the human perception and ranging module, a human infrared sensor is used to monitor people entering the monitoring range, and a laser rangefinder is used to obtain the real-time distance value between the person and the power box, and the obtained real-time distance value is transmitted to the early warning module; the data processing unit is responsible for calculating the anti-electric shock safety distance of the power box and transmitting the calculated safety distance to the early warning module; the early warning module is responsible for issuing warnings and displaying early warning information. Among them, the data collection time interval of the leakage current, the internal temperature, the internal humidity, the external humidity and the distance value are all set to 30 seconds, and the calculation time interval of the anti-electric shock safety distance is 1 hour. The implementer can adjust the time interval of data collection and the time interval of calculation according to actual conditions, and this application does not impose any restrictions on this.

[0050] This application takes the t-th calculation of the anti-electric shock safety distance as an example, that is, the t-th time period corresponding to the anti-electric shock safety distance, hereinafter referred to as the t-th time period, and the leakage current data, the temperature data inside the box, the humidity data inside the box, and the humidity data outside the box of this time period, all the data obtained in each type of data are arranged in ascending order according to the data acquisition time, and the leakage current sequence A1, the temperature sequence A2 inside the box, the humidity sequence A3 inside the box, and the humidity sequence A4 outside the box of the t-th time period are obtained respectively, which are used to characterize the data changes of the leakage current, temperature, and humidity inside and outside the power box in the t-th time period over time.

[0051] Step 2: Analyze the correlation between the in-box environment monitoring data and the leakage current data in the preset historical time period, and combine the change trend of the in-box environment monitoring data in each time period to obtain the first influence coefficient of the leakage current in each time period.

[0052] Typically, many parts of the electrical equipment in a power supply box are insulated by air gaps. However, in high humidity environments, the number of water molecules in the air increases. These water molecules can act as a conductive medium, reducing the insulation performance of the air, thereby increasing the risk of current leakage from the electrical equipment in the power supply box. Furthermore, the magnitude of leakage current is generally temperature-dependent, with higher temperatures increasing the leakage current. This is because rising temperature intensifies the polarization of the insulating medium in the electrical equipment, increasing conductivity and reducing the insulation resistance of the equipment, which in turn causes an increase in leakage current.

[0053] Based on the above analysis, the power box's internal temperature data, internal humidity data, and leakage current data are collected for a preset historical time period. The historical time period is one month long, and the historical data collection interval is one hour. The historical time period and data collection interval can be set by the implementer. All the historical temperature data collected are sorted by the data collection time to form a historical internal temperature sequence. The historical humidity and leakage current data are extracted using the same method as the historical internal temperature sequence, respectively, to obtain the historical internal humidity sequence and historical leakage current sequence.

[0054] The historical temperature sequence inside the box, the historical humidity sequence inside the box, and the historical leakage current sequence are used as the input of the grey correlation analysis algorithm, and the correlation degree W1 of the temperature inside the box to the leakage current and the correlation degree W2 of the humidity inside the box to the leakage current are output, which are used to characterize the degree of influence of the temperature and humidity inside the power box on its leakage current. The grey correlation analysis algorithm is a well-known technology and the specific process will not be repeated here.

[0055] Taking the temperature sequence A2 in the box as an example, the seasonal trend decomposition using LOESS (STL) decomposition algorithm is used to obtain the trend item sequence B2 of the temperature sequence A2 in the box, so as to reduce the impact of noise data in the collected humidity data on the subsequent analysis of the change trend of humidity over time. The STL decomposition algorithm is a well-known technology, and the specific process will not be repeated here.

[0056] Perform first-order difference processing on the trend item sequence B2 to obtain the first-order difference sequence , which is used to characterize the instantaneous change rate of the data points in the box temperature sequence A2. The first-order difference processing is a well-known technology and the specific process will not be repeated here.

[0057] In a short period of time, changes in temperature and humidity often have strong inertia. Therefore, the temperature change trend of the current time period can be used to characterize the temperature change trend of the next time period. The adjacent temperature trend of the t-th time period is recorded as L2(t). It is used to characterize the trend of the influence of the change of the internal temperature of the power box in the t-th time period on its air insulation performance. The formula is: , where represents the first-order difference sequence The i-th data point in the equation can represent the instantaneous rate of change of the temperature in the box; I represents the first-order difference sequence The number of data points in b2(i) represents the first-order difference sequence The position number of the i-th data point in ; softmax() represents the Softmax function, which is used to normalize the data.

[0058] First-order difference sequence The later the data collection time of the box temperature data corresponding to the i-th data point in , that is, the larger b2(i), the more the instantaneous change rate of the box temperature corresponding to the data point B2(i) can reflect the temperature change trend in the t+1-th time period, and the greater the weight of the instantaneous change rate should be, that is, The larger it is, and thus the larger L2(t), the greater the temperature change in the power box in adjacent time periods, and the greater the possibility that the insulation performance of the air in the power box will show a downward trend in the time period.

[0059] Using the same method as for the adjacent temperature trend L2(t), replace the box temperature sequence A2 with the box humidity sequence A3 to obtain the adjacent humidity trend L3(t). This indicates that changes in humidity within the power box during the tth time period may affect the insulation resistance of the power equipment, and thus the air insulation performance of the power box. If the humidity change trend L3(t) within the adjacent time period is large, it indicates that the humidity within the power box has changed significantly, and the insulation resistance of the power equipment may be decreasing.

[0060] Furthermore, the first influence coefficient S1(t) of the leakage current of the power box in the tth time period is obtained, which is used to characterize the influence of the changes in temperature and humidity in the power box on the insulation performance of the air in the power box and the insulation resistance of the power equipment in the tth time period, thereby affecting the leakage current in the t+1th time period. The formula is: , where W2(t) and W3(t) represent the correlation between the temperature and humidity in the box and the leakage current in the t-th time period, respectively; L2(t) and L3(t) represent the adjacent temperature trend and adjacent humidity trend in the t-th time period, respectively.

[0061] Among them, the schematic diagram of obtaining the first influence coefficient of leakage current is as follows: Figure 2 shown.

[0062] If the temperature and humidity in the power box are more likely to show an increasing trend in the t+1 time period, that is, the larger L2(t) and L3(t) are, the more likely the insulation performance of the air in the power box and the insulation resistance of the power equipment are to show a decreasing trend in the t+1 time period, and the more likely it is that the leakage current of the power box will show an increasing trend in the t+1 time period.

[0063] Step 3: Combine the in-box environmental monitoring data of each time period with the preset number of time periods before it, and the preset environmental threshold to obtain the second influence coefficient of the leakage current in each time period, and obtain the comprehensive influence coefficient of the leakage current in each time period.

[0064] Since a humid environment is conducive to the growth of mold, when the temperature is 25 30 degrees, relative humidity 75% When the humidity is 95%, it is a good condition for mold growth. The acidic substances secreted by mold during metabolism, such as acetic acid and citric acid, will interact with the insulating materials of the power equipment in the power box, causing changes in the chemical structure and physical properties of the insulating materials, thereby reducing its insulation performance, and further increasing the risk of current leakage of the power equipment in the power box.

[0065] Based on the above analysis, the sequence composed of the temperature sequences in the box for the tth time period and a preset number of time periods before is recorded as the recent temperature sequence in the box, and the same method is used to obtain the recent humidity sequence in the box, which is used to reflect the changes in temperature and humidity in the power box in the tth time period and the previous time periods; in this embodiment, the preset number is 24; the implementer can adjust it according to actual conditions, and this application does not impose any restrictions on this.

[0066] The difference between each data point in the recent in-box temperature sequence and a preset temperature threshold is obtained to obtain a temperature suitability sequence. In this embodiment, the reciprocal of the absolute value of the difference between each data point in the recent in-box temperature sequence and the temperature threshold is calculated, and all the obtained reciprocals are arranged in ascending order according to the sequence number of the corresponding data point to obtain a temperature suitability sequence, which is used to characterize the degree to which the temperature corresponding to each data point in the recent in-box temperature sequence is suitable for mold growth. The temperature threshold is set to 27.5, which is the quantile of the optimal growth temperature range of mold. In addition, to prevent the denominator from being zero, a constant greater than zero needs to be added to the denominator. In this embodiment, the value of this constant is 0.01.

[0067] Using the same acquisition method as the temperature suitability sequence, the recent in-box temperature sequence and temperature threshold are replaced with the recent in-box humidity sequence and humidity threshold, respectively. In this embodiment, the humidity threshold is set to 85, which is the quantile of the optimal humidity range for mold growth. The humidity suitability sequence is obtained to characterize the degree to which the humidity corresponding to each data point in the recent in-box humidity sequence is suitable for mold growth.

[0068] The sequence composed of the means of the elements in corresponding positions from left to right in the temperature suitability sequence and the humidity suitability sequence is recorded as the mold growth suitability sequence of the power box, which is used to characterize the degree to which the temperature and humidity data in the power box at the corresponding moments in the t-th time period and the previous time periods are suitable for mold growth. The larger the value of the data point in the mold growth suitability sequence, the more suitable the moment corresponding to the data point is for mold growth.

[0069] The mean of all data points in the mold growth suitability sequence is recorded as the second influence coefficient S2(t) of the leakage current of the power box, which is used to characterize the influence of mold in the power box on the change in the insulation performance of the insulating materials of the power equipment in the t-th time period, and the effect of this change on the leakage current in the t+1-th time period. The larger the second influence coefficient of the leakage current, the more suitable the environment in the power box is for mold growth, the more likely it is to cause the insulation performance of the insulating materials of the power equipment in the power box to decrease, and the greater the possibility of an increase in the leakage current in the t+1-th time period.

[0070] Among them, the schematic diagram for obtaining the second influence coefficient of leakage current is as follows Figure 3 shown.

[0071] Furthermore, the first influence coefficient S1(t) of the leakage current in the t-th time period and the second influence coefficient S2(t) of the leakage current in the t-th time period are respectively subjected to Min-Max normalization processing, and the average of the two normalized results is recorded as the comprehensive influence coefficient S(t) of the leakage current of the power box, which is used to characterize the influence of changes in the temperature, humidity and insulation performance of the air in the power box and the power equipment caused by mold in the t-th time period on the leakage current in the t+1-th time period. The larger the comprehensive influence coefficient of the leakage current, the greater the possibility of an increase in the leakage current in the t+1-th time period.

[0072] Step 4: According to the changing trend and data distribution of the external environment monitoring data in each time period, the risk of electric shock outside the power box in each time period is obtained; the distribution of leakage current data in each time period, the leakage current comprehensive impact index and the risk of electric shock outside the box are combined to obtain the electric shock risk coefficient for each time period.

[0073] The higher the humidity outside the power box, the more likely it is to cause electric shock to the human body. This is because in a high humidity environment, the moisture in the air will increase the conductivity of the human body surface, causing the human body resistance to decrease, which not only increases the possibility of current passing through the human body, but also increases the magnitude of the current passing through the human body, thereby increasing the risk of electric shock to the human body.

[0074] Using the same acquisition method as the adjacent temperature trend L2(t), the temperature sequence A2 inside the box is replaced by the humidity sequence A4 outside the box to obtain the adjacent humidity trend L4(t) outside the box, which is used to characterize the changing trend of the ambient humidity outside the power box in the t-th time period. The larger the adjacent humidity trend L4(t) outside the box is, the greater the possibility of an increase in the ambient humidity in the t+1-th time period.

[0075] Calculate the product of the adjacent humidity trend L4(t) outside the box plus 1 and the mean of the humidity sequence A4 outside the box, and record the Min-Max normalized result of the product as the risk of electric shock outside the power box in the t-th time period f(t), which is used to characterize the possibility of the external environmental humidity of the power box increasing the risk of electric shock to the human body in the t+1-th time period.

[0076] Furthermore, the electric shock risk factor F(t) of the power box in the tth time period is obtained, which is used to characterize the possibility of electric shock to the human body caused by the leakage current of the power box in the environment outside the box in the t+1th time period. The formula is: , where a1 represents the maximum value of the leakage current sequence A1 in the t-th time period, that is, the maximum value of all leakage current data in the t-th time period; a represents the leakage current safety threshold of the preset leakage current, which is 50 mA in this embodiment; S(t) represents the comprehensive impact coefficient of the leakage current of the power box in the t-th time period; f(t) represents the risk of electric shock outside the power box in the t-th time period; exp() represents an exponential function with a natural constant as the base.

[0077] Among them, the schematic diagram of obtaining the electric shock risk factor is as follows: Figure 4 shown.

[0078] The greater the leakage current value of the power box in the tth time period is, the greater it is than the leakage current safety threshold, that is, the greater exp(a1-a); and the greater the comprehensive impact index of the leakage current in the tth time period, that is, the temperature and humidity inside the power box and the weakening of the insulation performance of the air and power equipment in the power box caused by mold, the greater the possibility of increased leakage current; the greater the electric shock risk coefficient in the tth time period, that is, the greater the possibility that the humidity of the environment outside the power box will increase the risk of electric shock to the human body, and thus the greater the possibility that the leakage current of the power box in the environment outside the box will cause electric shock to the human body, that is, the greater the electric shock risk coefficient F(t).

[0079] Step 5: Based on the electric shock risk coefficient of continuous time periods, combined with the neural network model, the anti-electric shock safety distance of each time period is obtained.

[0080] Get the initial safety distance d for preventing electric shock and set it according to the voltage level of the line where the power box is located. In this application, the value of d is 9 meters, which is used for the 500kV voltage level.

[0081] Obtain the electric shock risk coefficients for the t-th time period and the previous n consecutive time periods. In this embodiment, n is 10. Arrange all the obtained electric shock risk coefficients in ascending order according to chronological order to obtain the electric shock risk coefficient sequence FN(t) corresponding to the t-th time period. If tn 0, the missing data before the electric shock risk coefficient sequence are padded with 0 until the length of the electric shock risk coefficient sequence is equal to n.

[0082] In the early warning module, the calculated safety distances against electric shock are obtained from the tn-1th time period to the t-1th time period. All the obtained safety distances against electric shock are arranged in ascending order according to the time sequence to obtain the electric shock safety distance prediction sequence DN(t) for the tth time period. If tn 0, then the first data point of the obtained electric shock risk coefficient sequence is supplemented, and the supplemented value is the initial anti-electric shock safety distance d, until the length of the electric shock safety distance prediction sequence is equal to n.

[0083] The electric shock risk coefficient sequence of each time period is taken as a sample, and the electric shock safety distance prediction sequence of each time period is taken as the label sequence; and the length of all samples and the length of the corresponding label sequence are ensured to be equal. The purpose of this is to enable the model to extract the deep features of the electric shock safety distance and time, so that the model output is more in line with the actual situation.

[0084] Secondly, the samples encoded with the label sequence are used as input to train the long short-term memory neural network model, wherein the root mean square loss function is used as the loss function of the neural network model, the gradient descent method is used as the optimization algorithm of the neural network model, and the trained model is used as a prediction model for the safe distance of electric shock. The training of the long short-term memory neural network model is a well-known technology, and the specific process will not be repeated here.

[0085] The electric shock risk coefficient sequence FN(t) and the electric shock safety distance prediction sequence DN(t) are used as the input of the electric shock safety distance prediction model, and the predicted electric shock safety distance is output as the electric shock safety distance in the t-th time period, denoted as d(t), which is transmitted to the early warning module.

[0086] Step 6: Determine whether to issue an alarm based on the distance between the person and the power box and the safe distance against electric shock during the corresponding time period.

[0087] In the t+1th time period, if the human perception and ranging module senses the presence of a person and the distance between the person and the power box is less than or equal to d(t), an alarm is triggered, and the real-time distance value of the person and the current anti-electric shock safety distance d(t) are displayed in real time in the early warning module, completing the anti-electric shock safety warning and protection of the power box.

[0088] Based on the same concept as the embodiment of the method of the present application, a device for preventing electric shock and protecting the power box is provided, including:

[0089] The data acquisition module is used to collect leakage current data of the power box in each time period, internal environment monitoring data, and external humidity data of the box; wherein the internal environment monitoring data includes internal temperature data and internal humidity data;

[0090] A data processing unit is used to analyze the correlation between the in-box environmental monitoring data and the leakage current data in a preset historical time period, and obtain the first leakage current influence coefficient of each time period in combination with the change trend of the in-box environmental monitoring data in each time period; combine each time period with the in-box environmental monitoring data of a preset number of time periods before it, and combine it with a preset environmental threshold to obtain the second leakage current influence coefficient of each time period, and obtain the comprehensive leakage current influence coefficient of each time period; according to the change trend and data distribution of the external environmental monitoring data in each time period, obtain the external electric shock risk of the power box in each time period; comprehensively analyze the distribution of the leakage current data in each time period, the comprehensive leakage current influence index and the external electric shock risk, to obtain the electric shock risk coefficient of each time period; according to the electric shock risk coefficient of continuous time periods, combined with a neural network model, obtain the anti-electric shock safety distance of each time period;

[0091] The early warning module is used to determine whether to issue an alarm based on the distance between the person and the power box and the anti-electric shock safety distance in the corresponding time period.

[0092] It should be noted that the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the systems, methods and computer program products according to the embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of the code, and the module, program segment or part of the code contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

[0093] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. The anti-electric shock warning and protection method of the power box is characterized by: The method comprises the following steps: Collect leakage current data, internal environment monitoring data, and external humidity data of the power box at each time period; the internal environment monitoring data includes internal temperature data and internal humidity data; Analyze the correlation between the in-box environment monitoring data and the leakage current data in the preset historical time period, and combine the change trend of the in-box environment monitoring data in each time period to obtain the first influence coefficient of the leakage current in each time period; The in-box environmental monitoring data of each time period and the previous preset number of time periods are combined with the preset environmental threshold to obtain the second influence coefficient of the leakage current of each time period, and obtain the comprehensive influence coefficient of the leakage current of each time period; the obtaining of the second influence coefficient of the leakage current of each time period is specifically as follows: the sequence composed of all the in-box temperature data of each time period and the previous preset number of time periods is recorded as the recent in-box temperature sequence; the negative correlation mapping of the difference between each data point in the recent in-box temperature sequence and the preset temperature threshold is obtained to obtain a suitable temperature sequence; the same acquisition method as that of the suitable temperature sequence is adopted to obtain a suitable humidity sequence based on all the in-box humidity data of each time period and the previous preset number of time periods; the second influence coefficient of the leakage current of each time period is determined based on the distribution of the mean of the elements at the same position in the suitable temperature sequence and the suitable humidity sequence; According to the changing trend and data distribution of the external environment monitoring data in each time period, the risk of electric shock outside the power box in each time period is obtained; the distribution of leakage current data in each time period, the leakage current comprehensive impact index and the risk of electric shock outside the box are combined to obtain the electric shock risk coefficient in each time period; According to the electric shock risk coefficient of continuous time periods, combined with the neural network model, the anti-electric shock safety distance of each time period is obtained; Determine whether to issue an alarm based on the distance between the person and the power box and the safety distance against electric shock in the corresponding time period.

2. The anti-electric shock warning and protection method for a power box according to claim 1, characterized in that: The first influence coefficient of the leakage current in each time period is obtained as follows: Extract the temperature data of the box in the preset historical time period and record it as the historical box temperature sequence; adopt the same method as the historical box temperature sequence to obtain the historical box humidity sequence and the historical leakage current sequence based on the box humidity data and the leakage current data in the preset historical time period; The correlation degree of the historical temperature series and the historical leakage current series in the box is analyzed to obtain the correlation degree W1 of the temperature data in the box to the leakage current data; the correlation degree of the historical humidity series and the historical leakage current series in the box is analyzed to obtain the correlation degree W2 of the humidity data in the box to the leakage current data; All the temperature data in the box in each time period are subjected to trend decomposition to obtain the trend item sequence; the elements in the first-order difference sequence of the trend item sequence and their position numbers are combined to obtain the adjacent temperature trends of each time period. ; Using the same acquisition method as the adjacent temperature trend, according to all the humidity data in the box in each time period, the adjacent humidity trend L3 of each time period is obtained; The first impact coefficient of leakage current in each time period is recorded as S1, and its formula is as follows: .

3. The anti-electric shock warning and protection method for a power box according to claim 2, characterized in that: The obtained adjacent temperature trends in each time period are specifically the result of fusing the normalized values ​​of the position numbers of all elements in the first-order difference sequence with the corresponding elements.

4. The anti-electric shock warning and protection method for a power box according to claim 1, characterized in that: The comprehensive influence coefficient of the leakage current in each time period is specifically an average of a normalized value of the first influence coefficient of the leakage current and a normalized value of the second influence coefficient of the leakage current.

5. The anti-electric shock warning and protection method for a power box according to claim 3, characterized in that: The process of obtaining the risk of electric shock outside the power box in each time period includes: Adopting the same acquisition method as the adjacent temperature trend, according to all the humidity data outside the box in each time period, the adjacent humidity trend outside the box in each time period is obtained; According to the humidity trends outside the adjacent boxes in each time period and the distribution of all humidity data outside the box in each time period, the risk of electric shock outside the box in each time period is obtained.

6. The anti-electric shock warning and protection method for a power box according to claim 1, characterized in that: The electric shock risk coefficient for each time period is obtained as follows: The electric shock risk factor of the power box in the tth time period is recorded as F(t), and its formula is: , where a1 represents the maximum value of all leakage current data in the t-th time period; a represents the preset leakage current safety threshold; S(t) represents the comprehensive impact coefficient of leakage current of the power box in the t-th time period; f(t) represents the risk of electric shock outside the power box in the t-th time period; exp() represents the exponential function with a natural constant as the base.

7. The anti-electric shock warning and protection method for a power box according to claim 1, characterized in that: The obtaining of the electric shock prevention safety distance in each time period includes: The electric shock risk coefficients of the tth time period and the previous n consecutive time periods are combined into the electric shock risk coefficient sequence of the tth time period; the electric shock safety distances from the tn-1th time period to the t-1th time period are combined into the electric shock safety distance prediction sequence of the tth time period; the electric shock risk coefficient sequence of the tth time period and the electric shock safety distance prediction sequence are used as inputs of the long-short-term memory neural network to obtain the electric shock safety distance of the tth time period; where n is a preset value.

8. The anti-electric shock warning and protection method for a power box according to claim 1, characterized in that: The determination of whether to issue an alarm is specifically as follows: issuing an alarm when it is detected that the distance between the person and the power box is less than or equal to the anti-electric shock safety distance of the corresponding time period.

9. The anti-electric shock warning and protection device of the power box is characterized by: include: The data acquisition module is used to collect leakage current data of the power box in each time period, internal environment monitoring data, and external humidity data of the box; wherein the internal environment monitoring data includes internal temperature data and internal humidity data; The data processing unit is used to analyze the correlation between the in-box environmental monitoring data and the leakage current data in the preset historical time period, and obtain the first leakage current influence coefficient of each time period in combination with the change trend of the in-box environmental monitoring data in each time period; combine each time period with the in-box environmental monitoring data of a preset number of time periods before it, and combine it with the preset environmental threshold to obtain the second leakage current influence coefficient of each time period, and obtain the comprehensive leakage current influence coefficient of each time period; obtain the risk of electric shock outside the power box in each time period according to the change trend and data distribution of the external environmental monitoring data in each time period; obtain the electric shock risk coefficient of each time period by combining the distribution of the leakage current data in each time period, the comprehensive leakage current influence index and the risk of electric shock outside the box; according to the continuous The electric shock risk coefficient of each time period is combined with a neural network model to obtain the anti-electric shock safety distance of each time period; the second influence coefficient of the leakage current in each time period is obtained by: recording the sequence composed of all the temperature data in the box for each time period and a preset number of time periods before as the recent box temperature sequence; obtaining the negative correlation mapping of the difference between each data point in the recent box temperature sequence and the preset temperature threshold to obtain a suitable temperature sequence; using the same acquisition method as the suitable temperature sequence, according to all the humidity data in the box for each time period and a preset number of time periods before, a suitable humidity sequence is obtained; according to the distribution of the mean of the elements at the same position in the suitable temperature sequence and the suitable humidity sequence, the second influence coefficient of the leakage current in each time period is determined; The early warning module is used to determine whether to issue an alarm based on the distance between the person and the power box and the anti-electric shock safety distance in the corresponding time period.

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