Collecting ring real-time monitoring method driven by edge calculation

Abstract

The invention discloses a collecting ring real-time monitoring method driven by edge calculation, and relates to the technical field of collecting ring temperature monitoring, and the method comprises the following steps: synchronously collecting the temperatures of all carbon brushes when a collecting ring operates normally, and obtaining the temperature sample data of the carbon brushes; performing sample division, calculating the difference degree of the temperature of the carbon brush at different temperature levels, and performing sample screening to obtain temperature difference sample data of the carbon brush; normal difference analysis is carried out to obtain the normal difference range of the carbon brush temperature at different temperature levels, and normal difference reference data is obtained; the temperature of the carbon brush is monitored and analyzed according to the normal difference reference data; the invention is used for solving the problems that different temperature difference thresholds cannot be set at different temperature levels and the temperature difference cannot be reliably monitored according to the temperature difference of the carbon brushes under the normal operation of the collector ring when the temperature difference among a plurality of carbon brushes is monitored by the existing collector ring temperature monitoring technology.

Description

Technical Field

[0001] This invention relates to the field of slip ring temperature monitoring technology, specifically to a slip ring real-time monitoring method driven by edge computing. Background Technology

[0002] Slip ring temperature monitoring technology is a technology for monitoring the operating status of key heat-generating components in slip ring systems. Its core is to use a full-process approach of sensor acquisition, data transmission, and algorithm analysis to detect the temperature parameters of key heat-generating parts such as the contact interface between the slip ring and the carbon brush, the carbon brush body, the surface of the conductive ring, and the insulating support in real time or periodically. It also evaluates the temperature distribution characteristics and fluctuation patterns to ensure the stable operation of the slip ring.

[0003] Existing slip ring temperature monitoring technologies, when monitoring temperature differences between multiple carbon brushes, often simultaneously collect the temperature of each carbon brush, calculate the corresponding temperature range, and then compare it with a manually set fixed threshold. If the threshold is exceeded, the carbon brush temperature is judged to be abnormal. However, under normal operating conditions, the temperature difference between the slip ring and carbon brushes will show a reasonable slight increase as the overall temperature level rises. The heat generation of carbon brushes is mainly driven by contact loss and friction loss. When the motor load increases, the overall temperature level of the carbon brushes rises, and the current flowing through the carbon brushes increases. Even if the contact resistance of each carbon brush remains unchanged, the difference in contact loss will increase significantly due to the amplification effect of the current, ultimately resulting in a natural increase in the temperature difference between the carbon brushes. Furthermore, in high-temperature environments, the resistivity of carbon brushes changes nonlinearly with increasing temperature, amplifying even minor material uniformity differences and further exacerbating fluctuations in normal temperature variations. Fixed threshold values ​​completely ignore the impact of carbon brush temperature levels on temperature differences. Using a single threshold to judge temperature differences at different temperature levels easily leads to high false alarm rates in temperature monitoring and low fault identification, failing to meet the precise requirements of certain scenarios. Therefore, existing slip ring temperature monitoring technology, when monitoring temperature differences between multiple carbon brushes, cannot reliably monitor temperature differences by setting different temperature difference thresholds at different temperature levels based on the temperature differences of the carbon brushes under normal slip ring operation. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art. It obtains carbon brush temperature sample data by synchronously collecting the temperature of all carbon brushes during normal operation of the slip ring; dividing the samples and calculating the degree of temperature difference at different temperature levels; filtering the samples to obtain carbon brush temperature difference sample data; performing normal difference analysis to obtain the normal difference range of carbon brush temperature at different temperature levels, thus obtaining normal difference reference data; and monitoring and analyzing the carbon brush temperature based on the normal difference reference data. This addresses the problem that existing slip ring temperature monitoring technologies, when monitoring temperature differences among multiple carbon brushes, cannot reliably monitor temperature differences by setting different temperature difference thresholds at different temperature levels based on the temperature differences of the carbon brushes under normal slip ring operation.

[0005] To achieve the above objectives, this application provides a real-time monitoring method for collector rings driven by edge computing, comprising the following steps: The temperature of all carbon brushes is collected synchronously during normal operation of the slip ring to obtain carbon brush temperature sample data. The carbon brush temperature sample data was divided into samples, and the degree of difference in carbon brush temperature under different temperature levels was calculated. The samples were then screened to obtain carbon brush temperature difference sample data. Based on the sample data of carbon brush temperature differences, a normal difference analysis was performed to obtain the normal difference range of carbon brush temperature under different temperature levels, and normal difference reference data was obtained. The temperature of all carbon brushes is collected during normal operation of the slip ring, and the temperature of the carbon brushes is monitored and analyzed based on the normal difference reference data.

[0006] Furthermore, the temperature of all carbon brushes is synchronously collected during normal operation of the slip ring to obtain carbon brush temperature sample data, including the following sub-steps: Any collector ring with no less than n carbon brushes is designated as the first collector ring, and the carbon brushes of the first collector ring are designated as carbon brush 1 to carbon brush n in sequence. Select a position from the same location of carbon brush 1 to carbon brush n for measuring carbon brush temperature, and record them as temperature measurement position 1 to temperature measurement position n respectively; obtain the temperature range of carbon brush when the first collector ring is running normally, and record it as the normal temperature range of carbon brush [AT, BT].

[0007] Furthermore, the process of synchronously collecting the temperature of all carbon brushes during normal operation of the slip ring to obtain carbon brush temperature sample data includes the following sub-steps: During normal operation of the collector ring, the temperature of carbon brush 1 to carbon brush n is synchronously collected from temperature measurement position 1 to temperature measurement position n and recorded as carbon brush temperature information. Any carbon brush temperature information is recorded as the first temperature information. Obtain the median of the first temperature information and record it as the carbon brush temperature level corresponding to the first temperature information; repeatedly collect multiple carbon brush temperature information and obtain the corresponding carbon brush temperature levels to obtain carbon brush temperature sample data.

[0008] Furthermore, the carbon brush temperature sample data is divided into samples, and the degree of difference in carbon brush temperature at different temperature levels is calculated. Sample selection is then performed to obtain the carbon brush temperature difference sample data, including the following sub-steps: Divide the normal temperature range of the carbon brush [AT, BT] into multiple temperature intervals with a length of k1, denoted as temperature level intervals, and denot any one of the temperature level intervals as the first temperature interval, where k1 is the set interval length; For carbon brush temperature sample data, all carbon brush temperature information is divided according to the temperature level range in which the corresponding carbon brush temperature level is located; carbon brush temperature information located in the first temperature range is recorded as the first sample information.

[0009] Furthermore, the carbon brush temperature sample data is divided into samples, and the degree of difference in carbon brush temperature at different temperature levels is calculated. Sample selection is then performed to obtain the carbon brush temperature difference sample data, which includes the following sub-steps: Record any carbon brush temperature information in the first sample information as the second temperature information, calculate the range and standard deviation of the second temperature information, and repeat the calculation of the range and standard deviation of all carbon brush temperature information in the first sample information to obtain the temperature range set and temperature standard deviation set of the first temperature range respectively. The range and standard deviation of the temperature range set and the temperature standard deviation set are matched one-to-one according to the carbon brush temperature information they belong to, and combined into data pairs, denoted as D={(AR1, AB1), (AR2, AB2), ..., (ARm, ABm)}, where m is the total number of data pairs; and any data pair is denoted as (ARi, ABi), i∈[1,m]; Calculate the mean AR0 of the set of temperature ranges and the mean AB0 of the set of temperature standard deviations, and denote (AR0, AB0) as the initial core point; calculate the standard deviation RB0 of the set of temperature ranges and the standard deviation BB0 of the set of temperature standard deviations.

[0010] Furthermore, the carbon brush temperature sample data is divided into samples, and the degree of difference in carbon brush temperature at different temperature levels is calculated. Sample selection is then performed to obtain the carbon brush temperature difference sample data, which includes the following sub-steps: calculate Let (ARi, ABi) be the standard distance from the initial core point; repeat the calculation of the standard distance from all data pairs to the initial core point, and calculate the average of all standard distances, DP. Data pairs whose standard distance to the initial core point is greater than 2×DP are denoted as suspected data pairs; otherwise, they are denoted as valid data pairs. Based on all valid data pairs, recalculate the mean of the range YR0 and the mean of the standard deviation YB0; denote (YR0, YB0) as the valid core point; and recalculate the standard deviation of the range RB1 and the standard deviation of the standard deviation BB1. Calculate the standard distance from each valid data pair to the valid core point based on RB1 and BB1, and calculate the average DY and standard deviation DB of all standard distances to the valid core point; denote DY+k2×DB as the normal distance threshold DT, where k2 is the set scaling factor.

[0011] Furthermore, the carbon brush temperature sample data is divided into samples, and the degree of difference in carbon brush temperature at different temperature levels is calculated. Sample selection is then performed to obtain the carbon brush temperature difference sample data, which includes the following sub-steps: If (ARi, ABi) is a valid data pair, calculate ARi / (ABi+e0), denoted as the effective proportion of (ARi, ABi). Repeat the calculation of the effective proportion of all valid data pairs, and calculate the mean RP and standard deviation RB of the effective proportion, where e0 is a set minimum constant. For any data pair (ARi, ABi), calculate the standard distance ADi to the effective core point based on RB1 and BB1; if ADi is greater than DT and ABi / (ABi+e0) is not located in [RP-k3×RB, RP+k3×RB], then ABi and ABi are determined to be outliers and removed from the corresponding temperature range set and temperature standard deviation set, where k3 is the set scaling factor; Repeatedly acquire all outliers in the temperature range set and temperature standard deviation set, and remove them. After completion, the standard range set and standard standard deviation set of the first temperature range are obtained, which are recorded as the difference sample data of the first temperature range. Repeatedly acquire the difference sample data of all temperature level ranges to obtain the carbon brush temperature difference sample data.

[0012] Furthermore, based on the carbon brush temperature difference sample data, a normal difference analysis is performed to obtain the normal difference range of carbon brush temperature at different temperature levels, and the normal difference reference data is obtained, including the following sub-steps: Based on the set of standard ranges and standard deviations for the first temperature range, obtain all corresponding data pairs, denoted as standard data pairs. Plot the data on a two-dimensional plane with the range as the x-axis and the standard deviation as the y-axis, and denoted as a data scatter plot. Calculate the mean PV of the set of standard ranges and the mean PU of the set of standard deviations, and denote (PV, PU) as the standard core point; and calculate the slope of each standard data pair relative to the origin of the scatter plot, and denote it as the individual slope. Calculate the mean KP and standard deviation KB of the slope for all individuals. Denote [KP-e1×KB, KP+e1×KB] as the allowable range of slope, where e1 is the set proportional coefficient.

[0013] Furthermore, based on the carbon brush temperature difference sample data, a normal difference analysis is performed to obtain the normal difference range of carbon brush temperature at different temperature levels, and the normal difference reference data is obtained, including the following sub-steps: Calculate the Euclidean distance from each point to the standard core point in the scatter plot of the data, arrange all Euclidean distances from smallest to largest, and take the k4 percentile as the Euclidean distance threshold, where k4 is the set percentile. Based on the fact that the slope of a point relative to the origin is within the allowable slope range and the Euclidean distance from the point to the standard core point is not greater than the Euclidean distance threshold, a sector area is delineated in the data scatter plot and denoted as the normal difference area. Obtain the maximum range MC and the maximum standard deviation MB of the standard data pairs located in the normal difference region, and denote MC and MB as the range threshold and standard deviation threshold of the first temperature range, respectively. Repeatedly obtain the range threshold and standard deviation threshold for all temperature level ranges to obtain normal difference reference data.

[0014] Furthermore, the temperature of all carbon brushes is collected during normal operation of the slip ring, and the temperature of the carbon brushes is monitored and analyzed based on the normal difference reference data, including the following sub-steps: When the first collector ring is running, the temperature of all carbon brushes is synchronously collected at temperature measurement positions 1 to n at a first time interval, and the currently collected temperature is recorded as the current temperature information, where the first time interval is t1. Obtain the median of the current temperature information, denoted as the current temperature level, and calculate the range GC and standard deviation GB of the current temperature information; Based on the current temperature level range, obtain the corresponding range threshold and standard deviation threshold. If GC is greater than the corresponding range threshold or GB is greater than the corresponding standard deviation threshold, the carbon brush temperature difference of the first collector ring is judged to be abnormal; otherwise, the carbon brush temperature difference of the first collector ring is judged to be normal. Repeat the monitoring of carbon brush temperature.

[0015] The beneficial effects of this invention are as follows: This invention obtains carbon brush temperature sample data by synchronously collecting the temperature of all carbon brushes during normal operation of the slip ring; it then divides the carbon brush temperature sample data into samples, calculates the degree of difference in carbon brush temperature at different temperature levels, and filters the samples to obtain carbon brush temperature difference sample data; based on the carbon brush temperature difference sample data, it performs normal difference analysis to obtain the normal difference range of carbon brush temperature at different temperature levels, thus obtaining normal difference reference data; it collects the temperature of all carbon brushes during normal operation of the slip ring and monitors and analyzes the carbon brush temperature based on the normal difference reference data; when monitoring the temperature difference between multiple carbon brushes, different temperature difference thresholds can be set at different temperature levels according to the temperature difference of the carbon brushes under normal operation of the slip ring, thereby improving the reliability and stability of temperature monitoring. This invention first calculates the initial core point and standard deviation, then identifies suspected data pairs based on the standard distance. For valid data pairs, it recalculates the valid core point and obtains a new standard deviation, then calculates the normal distance threshold. The ratio of range to standard deviation is used for dual judgment and anomaly removal. This accurately distinguishes between normal and erroneous data, maximizing the retention of normal values, improving the accuracy of anomaly removal, and reducing the contamination of threshold statistics by anomalies, thus obtaining more reliable difference sample data. Scattered points are plotted on a plane with the range as the y-axis and the standard deviation as the x-axis, and a sector-shaped normal zone is defined by the slope range and the Euclidean distance threshold to the core point. This preserves the relationship between the two dimensions while limiting the distance to the core point, ensuring that the obtained threshold is not a result of single-dimensional statistics but a threshold constrained by two-dimensional correlation. Considering both relative morphology and absolute deviation, this improves the accuracy and reliability of temperature difference monitoring. Attached Figure Description

[0016] Figure 1 This is a flowchart of the steps of the method of the present invention; Figure 2 This is a flowchart illustrating the process of obtaining the range threshold and standard deviation threshold in this invention. Figure 3 This is a schematic diagram of the normal difference region in the present invention; Figure 4 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1 As shown, this application provides a real-time monitoring method for collector rings driven by edge computing, including the following steps: Step S1 involves synchronously collecting the temperature of all carbon brushes while the slip ring is running normally, obtaining carbon brush temperature sample data. Step S1 includes the following sub-steps: Step S101: Any slip ring with no less than n carbon brushes is designated as the first slip ring, and the carbon brushes of the first slip ring are designated as carbon brush 1 to carbon brush n. n is generally 4, meaning that the number of carbon brushes in the slip ring is required to be no less than 4. If the number of carbon brushes in the slip ring is too small, the corresponding standard deviation cannot be calculated, affecting subsequent analysis and processing. The slip ring is an electrical contact sliding connection device that establishes a reliable electrical path between the rotating structure and the stationary structure through the sliding or rolling contact, electrostatic coupling or electromagnetic coupling of conductive parts, so as to realize the continuous transmission of electrical energy and electrical signals. Step S101: Select a position for measuring the carbon brush temperature from the same position of carbon brush 1 to carbon brush n, and record them as temperature measurement position 1 to temperature measurement position n respectively; obtain the temperature range of the carbon brush when the first collector ring is running normally, and record it as the normal temperature range of the carbon brush [AT, BT], that is, the temperature range that the carbon brush may reach during the operation of the collector ring. Step S101: When the collector ring is running normally, the temperature of carbon brush 1 to carbon brush n is synchronously collected from temperature measurement position 1 to temperature measurement position n and recorded as carbon brush temperature information. Any carbon brush temperature information is recorded as the first temperature information. Step S104: Obtain the median of the first temperature information, and record it as the carbon brush temperature level corresponding to the first temperature information; repeatedly collect multiple carbon brush temperature information and obtain the corresponding carbon brush temperature levels to obtain carbon brush temperature sample data; the median is more robust to single or a few outliers and is more resistant to outliers than the arithmetic mean. For example, if a carbon brush experiences a sudden failure and abnormally high temperature, the median can more stably represent the overall temperature level of the carbon brush. In the specific implementation process, the temperature measurement positions of carbon brush 1 to carbon brush n should be in the same geometric position on each carbon brush, such as the midpoint of the side of the carbon brush or the outer edge of the contact area. Otherwise, different temperature measurement positions will introduce deviations. For example, if the temperature measurement points on different brushes are inconsistent, the range and standard deviation obtained later will be mixed with position differences instead of reflecting temperature differences.

[0019] Step S2 involves dividing the carbon brush temperature sample data into sub-samples, calculating the degree of difference in carbon brush temperature at different temperature levels, and filtering the samples to obtain carbon brush temperature difference sample data. Step S2 includes the following sub-steps: Step S201: Divide the normal temperature range of the carbon brush [AT, BT] into multiple temperature intervals with a length of k1, denoted as temperature level intervals, and designate any one of the temperature level intervals as the first temperature interval, where k1 is the set interval length; in this embodiment, k1 = 5℃, that is, every 5℃ is a temperature level interval, for example, (25, 30], (30, 35], the unit is ℃, k1 can be flexibly set, generally [3℃, 10℃]; the absolute difference between carbon brushes will amplify as the overall temperature increases, therefore, a difference distribution should be established for different temperature intervals; Step S202: For the carbon brush temperature sample data, divide all carbon brush temperature information according to the temperature level range in which the corresponding carbon brush temperature level is located; the carbon brush temperature information located in the first temperature range is recorded as the first sample information.

[0020] Step S203: Record any carbon brush temperature information in the first sample information as the second temperature information, calculate the range and standard deviation of the second temperature information, and repeat the calculation of the range and standard deviation of all carbon brush temperature information in the first sample information to obtain the temperature range set and temperature standard deviation set of the first temperature range respectively; the range reflects the maximum temperature difference between carbon brushes, and the standard deviation reflects the overall temperature fluctuation between carbon brushes; the two together can distinguish between different fault types such as single-point jump and overall fluctuation increase. Step S204: The range and standard deviation of the temperature range set and the temperature standard deviation set are matched one by one according to the carbon brush temperature information to which they belong, and combined into data pairs, denoted as D={(AR1, AB1), (AR2, AB2), ..., (ARm, ABm)}, where m is the total number of data pairs; and any data pair is denoted as (ARi, ABi), i∈[1,m]; Under normal operating conditions, the range and standard deviation of carbon brush temperature are strongly positively correlated. The larger the range, the greater the temperature difference between carbon brushes, and the larger the corresponding standard deviation will be. The ratio between the two is stable. The two originally independent one-dimensional indicators are transformed into two-dimensional coordinate points, i.e. data pairs. Subsequently, outliers can be identified through two-dimensional constraints to avoid misjudgment in traditional single-indicator analysis. Step S205: Calculate the mean AR0 of the temperature range set and the mean AB0 of the temperature standard deviation set, and denote (AR0, AB0) as the initial core point; calculate the standard deviation RB0 of the temperature range set and the standard deviation BB0 of the temperature standard deviation set; the initial core point is the average position of all data pairs, representing the initial correlation center of the range and standard deviation, and providing a benchmark for subsequent distance calculation.

[0021] Step S206, calculate Let (ARi, ABi) be the standard distance from the initial core point; dividing by their respective standard deviations is to eliminate dimensional differences; repeat the calculation of the standard distance from all data pairs to the initial core point, and calculate the average of all standard distances, DP. Step S207: Data pairs whose standard distance to the initial core point is greater than 2×DP are recorded as suspected data pairs, otherwise they are recorded as valid data pairs. The standard distance of extreme outliers will be much greater than the average distance of normal data. Using 2×DP as a threshold can quickly screen out such points and avoid them interfering with the subsequent calculation of stable cores. 2 is used as a proportional coefficient, which can also be flexibly adjusted according to the actual application scenario.

[0022] Step S208: Based on all valid data pairs, recalculate the mean YR0 of the range and the mean YB0 of the standard deviation; denote (YR0, YB0) as the valid core point; and recalculate the standard deviation RB1 of the range and the standard deviation BB1 of the standard deviation; the valid core point is calculated based on valid data pairs that have already removed extreme outliers, and can represent the true average position of the range and standard deviation under normal operating conditions. Step S209: Calculate the standard distance from each valid data pair to the valid core point based on RB1 and BB1, and calculate the average DY and standard deviation DB of all standard distances to the valid core point; denote DY+k2×DB as the normal distance threshold DT, where k2 is the set scaling factor; in this embodiment, k2=2, which can be flexibly set according to the actual application scenario, generally [1.5, 2.5]; DT considers both typical deviations and the range of variation, thereby distinguishing slight deviations from abnormal points.

[0023] Step S210: If (ARi, ABi) is a valid data pair, calculate ARi / (ABi+e0), denoted as the effective proportion of (ARi, ABi). Repeat the calculation of the effective proportion of all valid data pairs, and calculate the mean RP and standard deviation RB of the effective proportion, where e0 is a set minimum constant. Step S211: For any data pair (ARi, ABi), calculate the standard distance ADi to the effective core point based on RB1 and BB1; if ADi is greater than DT, and ARi / (ABi+e0) is not located in [RP-k3×RB, RP+k3×RB], then ARi and ABi are determined to be outliers and removed from the corresponding temperature range set and temperature standard deviation set, where k3 is a set proportional coefficient; in this embodiment, k3 is 1.5, which can be flexibly set, generally [1, 2]; under normal operating conditions, the ratio of range and standard deviation is stable, but the ratio will be unbalanced during faults, and the dual threshold can improve the accuracy of anomaly judgment; Step S212: Repeatedly acquire all outliers in the temperature range set and temperature standard deviation set, and remove them. After completion, the standard range set and standard standard deviation set of the first temperature range are obtained, which are recorded as the difference sample data of the first temperature range. Repeatedly acquire the difference sample data of all temperature level ranges to obtain the carbon brush temperature difference sample data.

[0024] For example, if D = {(2.1, 0.7), (2.3, 0.8), (1.8, 0.6), (9.0, 2.5), (2.5, 0.9), (2.0, 0.7), (2.2, 0.8), (1.9, 0.6), (2.4, 0.9), (2.2, 0.8)}, then AR0 = 2.84, AB0 = 0.93, and the initial core point is (2.84, 0.93); RB0 = 2.13, BB0 = 0.54; then the standard distances to the initial core point are {0.55, 0.48, 0.72, 4.10, 0.31, 0.62, 0.45, 0.68, 0.28, 0.45}, then DP = 0.864, 2 × DP = 1.728, then (9.0, 2.5) is a suspected data pair; After removing the core points, YR0 = 2.16 and YB0 = 0.76 are recalculated, so the effective core point is (2.16, 0.76); RB1 = 0.25 and BB1 = 0.13; then the standard distance to the effective core point is recalculated, and DY = 0.32 and DB = 0.15; therefore, DT = 0.62. The effective proportions of the valid data pairs are calculated as {3.0, 2.88, 3.0, 2.78, 2.86, 2.75, 3.17, 2.67, 2.75}, then RP = 2.88, RB = 0.15; [RP - k3 × RB, RP + k3 × RB] is [2.66, 3.11]; for (9.0, 2.5), the standard distance to the valid core point is 30.5, 9.0 / 2.5 = 3.6, so it is an outlier; In practice, points that are far from the center but still within the normal range may be rare but proportional samples. Only points that are far from the center and have an abnormal proportion will be truly removed to preserve sample data to the greatest extent.

[0025] Step S3 involves performing a normal difference analysis based on the carbon brush temperature difference sample data to obtain the normal difference range of carbon brush temperature at different temperature levels, thus obtaining normal difference reference data. Step S3 includes the following sub-steps: For step S301, please refer to... Figure 2 As shown, based on the set of standard ranges and standard deviations of the first temperature range, all corresponding data pairs are obtained and denoted as standard data pairs. The range is plotted on a two-dimensional plane with the standard deviation as the x-axis and the standard deviation as the y-axis, and the result is denoted as a data scatter plot. Step S302: Calculate the average value PV of the standard range set and the average value PU of the standard standard deviation set, and denote (PV, PU) as the standard core point; calculate the slope of each standard data pair relative to the origin of the data scatter plot, and denote it as the individual slope, which is the ratio of the standard deviation to the range of each standard data; the standard core point is the average position of all standard data pairs, representing the core correlation state between the range and the standard deviation under normal operating conditions of the collector ring; Step S303: Calculate the mean KP and standard deviation KB of the slopes of all individuals. Record [KP-e1×KB, KP+e1×KB] as the allowable range of the slope, where e1 is the set proportional coefficient. In this embodiment, e1=1.5, which can be flexibly set according to the actual application scenario, generally [1, 2]. Under normal working conditions, the range and standard deviation of carbon brush temperature are strongly positively correlated. The larger the temperature difference, the higher the dispersion of the temperature distribution. The ratio of the two is relatively stable. KP is the concentration value of the normal proportion, and the standard deviation KB is the normal fluctuation range of the proportion. Through KP and KB, some abnormal points that have been missed can be eliminated to avoid affecting the calculation of the subsequent threshold.

[0026] Step S304: Calculate the Euclidean distance from each point to the standard core point in the scatter plot of the data, arrange all Euclidean distances from smallest to largest, and take the k4 percentile as the Euclidean distance threshold, where k4 is the set percentile; in this embodiment, k4=95, generally 90 to 98, which can be flexibly set; the Euclidean distance to the standard core point is the straight-line distance from the point in the two-dimensional plane to the standard core point, which comprehensively reflects the degree to which the range and standard deviation of the point deviate from the normal mean. The larger the distance, the more the temperature difference characteristics of the sample deviate from the normal working conditions; taking the 95th percentile as the threshold can adapt to data with different distribution characteristics and avoid the bias of a fixed threshold; For step S305, please refer to [link / reference]. Figure 3 As shown, based on the fact that the slope of a point relative to the origin is within the allowable slope range and the Euclidean distance from the point to the standard core point is not greater than the Euclidean distance threshold, a sector-shaped region is defined in the data scatter plot, denoted as the normal difference region; that is, the region formed by the two slope boundaries overlaps with the circular region with the standard core point as the circle and the Euclidean distance threshold as the radius; the normal difference region is the geometric manifestation of the proportional constraint and the distance constraint. Only points that simultaneously satisfy both constraints are normal data with reasonable proportions and small deviations. Step S306: Obtain the maximum value of the range MC and the maximum value of the standard deviation MB of the standard data pair located in the normal difference region, and record MC and MB as the range threshold and standard deviation threshold of the first temperature range, respectively. Step S307: Repeatedly obtain the range threshold and standard deviation threshold for all temperature level ranges to obtain normal difference reference data.

[0027] For example, in the first temperature range, the standard data pairs are {(1.8, 0.6), (2.0, 0.7), (2.1, 0.7), (1.9, 0.6), (2.3, 0.8), (2.2, 0.7), (1.7, 0.5), (2.4, 0.8), (2.0, 0.7), (2.1, 0.7), (1.9, 0.6), (2.2, 0.8)}, then PV=2.05, PU=0.68, and the standard nucleus... The center point is (2.05, 0.68); the slopes of the standard data pairs relative to the origin are {0.333, 0.350, 0.333, 0.316, 0.348, 0.318, 0.294, 0.333, 0.350, 0.333, 0.316, 0.364}; then KP=0.332, KB=0.018; then [KP-e1×KB, KP+e1×KB] is [0.305, 0.359]; Calculate the distances of all standard data pairs to the standard core point and arrange them in descending order to obtain {0.030, 0.051, 0.072, 0.080, 0.101, 0.102, 0.120, 0.150, 0.160, 0.180, 0.220, 0.262}. The Euclidean distance threshold is 0.262, and a normal difference region is constructed. The standard data pairs (1.7, 0.5) and (2.2, 0.8) have slopes that are not in the range [0.305, 0.359], so they are excluded from the statistics. From the remaining standard data pairs, we get MC=2.4 and MB=0.8. In practice, the range and standard deviation, as statistical quantities describing the degree of temperature dispersion, cannot be negative in a physical sense. Therefore, the upper limit of the range and standard deviation in the normal difference range can be taken as the threshold.

[0028] Step S4 involves collecting the temperature of all carbon brushes during normal operation of the slip ring, and monitoring and analyzing the carbon brush temperature based on the normal difference reference data. Step S4 includes the following sub-steps: Step S401: When the first collector ring is running, the temperature of all carbon brushes is collected synchronously at temperature measurement positions 1 to n at a first time interval, and the currently collected temperature is recorded as the current temperature information. The first time interval is t1. In this embodiment, t1 = 1 second, that is, it is monitored once per second, which can be flexibly set. Step S402: Obtain the median of the current temperature information, denoted as the current temperature level, and calculate the range GC and standard deviation GB of the current temperature information; Step S403: Based on the temperature range in which the current temperature level is located, obtain the corresponding range threshold and standard deviation threshold. If GC is greater than the corresponding range threshold or GB is greater than the corresponding standard deviation threshold, then the carbon brush temperature difference of the first collector ring is determined to be abnormal, that is, the temperature difference or temperature dispersion between the carbon brushes exceeds the reasonable range under normal operating conditions; otherwise, the carbon brush temperature difference of the first collector ring is determined to be normal; repeat the monitoring of carbon brush temperature. In practical implementation, the judgment of carbon brush temperature differences only relies on lightweight statistical operations such as median, range, and standard deviation. The computational load is small and the memory usage is low. It can be completed in real time and with low latency on the acquisition module or industrial edge gateway. Complex threshold construction can be placed in the cloud, and edge computing can be driven by periodically issuing and updating the thresholds for each temperature level range.

[0029] Example 2, please refer to Figure 4 As shown, Figure 4 A schematic diagram of an electronic device is provided, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor can call these instructions. When the processor executes a computer-readable instruction, it performs steps such as those in an edge computing-driven real-time monitoring method for a slip ring to achieve the following functions: synchronously acquiring the temperature of all carbon brushes during normal operation of the slip ring, obtaining carbon brush temperature sample data; dividing the carbon brush temperature sample data into samples, calculating the degree of difference in carbon brush temperature at different temperature levels, and filtering the samples to obtain carbon brush temperature difference sample data; performing normal difference analysis based on the carbon brush temperature difference sample data to obtain the normal difference range of carbon brush temperature at different temperature levels, obtaining normal difference reference data; and acquiring the temperature of all carbon brushes during normal operation of the slip ring and monitoring and analyzing the carbon brush temperature based on the normal difference reference data.

[0030] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0031] Example 3: This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program performs steps such as those in the edge computing-driven real-time monitoring method for a slip ring to achieve the following functions: synchronously collecting the temperature of all carbon brushes during normal operation of the slip ring to obtain carbon brush temperature sample data; dividing the carbon brush temperature sample data into samples, calculating the degree of difference in carbon brush temperature at different temperature levels, and filtering the samples to obtain carbon brush temperature difference sample data; performing normal difference analysis based on the carbon brush temperature difference sample data to obtain the normal difference range of carbon brush temperature at different temperature levels, and obtaining normal difference reference data; collecting the temperature of all carbon brushes during normal operation of the slip ring, and monitoring and analyzing the carbon brush temperature based on the normal difference reference data.

[0032] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0033] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0034] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. Edge computing driven power collection loop real-time monitoring method, characterized in that, The method comprises the following steps: Synchronously collecting the temperature of all carbon brushes when the collector ring is in normal operation to obtain carbon brush temperature sample data; Dividing the carbon brush temperature sample data into samples, calculating the difference degree of the carbon brush temperature at different temperature levels, and performing sample screening to obtain carbon brush temperature difference sample data; Performing normal difference analysis according to the carbon brush temperature difference sample data to obtain the normal difference range of the carbon brush temperature at different temperature levels and obtain normal difference reference data; Collecting the temperature of all carbon brushes when the collector ring is in normal operation and monitoring and analyzing the temperature of the carbon brushes according to the normal difference reference data. 2.The edge computing driven current collector ring real-time monitoring method according to claim 1, wherein, The step of synchronously collecting the temperature of all carbon brushes when the collector ring is in normal operation to obtain carbon brush temperature sample data comprises the following sub-steps: Any collector ring with no less than n carbon brushes is denoted as a first collector ring, and the carbon brushes of the first collector ring are denoted as carbon brush 1 to carbon brush n in turn; A position for measuring the carbon brush temperature is selected in the same position of carbon brush 1 to carbon brush n to be denoted as temperature measuring position 1 to temperature measuring position n in turn, and the temperature range of the carbon brushes of the first collector ring when the collector ring is in normal operation is obtained and denoted as carbon brush normal temperature range [AT, BT].

3. The edge computing driven slip ring real-time monitoring method according to claim 2, characterized in that, The step of synchronously collecting the temperature of all carbon brushes when the collector ring is in normal operation to obtain carbon brush temperature sample data further comprises the following sub-steps: When the collector ring is in normal operation, the temperature of carbon brush 1 to carbon brush n is synchronously collected from temperature measuring position 1 to temperature measuring position n to be denoted as carbon brush temperature information, and any piece of carbon brush temperature information is denoted as first temperature information; The median of the first temperature information is obtained and denoted as the carbon brush temperature level corresponding to the first temperature information, a plurality of pieces of carbon brush temperature information are repeatedly collected, the corresponding carbon brush temperature levels are obtained, and carbon brush temperature sample data is obtained.

4. The edge computing driven slip ring real-time monitoring method according to claim 3, characterized in that, The step of dividing the carbon brush temperature sample data into samples, calculating the difference degree of the carbon brush temperature at different temperature levels, and performing sample screening to obtain carbon brush temperature difference sample data comprises the following sub-steps: The carbon brush normal temperature range [AT, BT] is divided into a plurality of temperature intervals with interval length k1 and denoted as temperature level intervals, and any temperature level interval is denoted as a first temperature interval, wherein k1 is the set interval length; For the carbon brush temperature sample data, all carbon brush temperature information is divided according to the temperature level interval in which the corresponding carbon brush temperature level is located, and carbon brush temperature information located in the first temperature interval is denoted as first sample information.

5. The edge computing driven slip ring real-time monitoring method according to claim 4, characterized in that, The step of dividing the carbon brush temperature sample data into samples, calculating the difference degree of the carbon brush temperature at different temperature levels, and performing sample screening to obtain carbon brush temperature difference sample data further comprises the following sub-steps: Any piece of carbon brush temperature information in the first sample information is denoted as second temperature information, the range and standard deviation of the second temperature information are calculated, the range and standard deviation of all carbon brush temperature information in the first sample information are repeatedly calculated, and the temperature range collection and temperature standard deviation collection of the first temperature interval are obtained, respectively. The range and the standard deviation in the temperature range set and the temperature standard deviation set are one-to-one corresponding to the carbon brush temperature information belonging to, and are combined into data pairs, denoted as D={ (AR1, AB1), (AR2, AB2), …, (ARm, ABm)}, where m is the total number of data pairs; and any data pair is denoted as (ARi, ABi), i∈[1, m]; The average value AR0 of the temperature range set and the average value AB0 of the temperature standard deviation set are calculated respectively, and (AR0, AB0) is recorded as the initial core point; The standard deviation RB0 of the temperature range set and the standard deviation BB0 of the temperature standard deviation set are calculated respectively.

6. The edge computing driven slip ring real-time monitoring method according to claim 5, characterized in that, The sample division is performed on the carbon brush temperature sample data, the difference degree of the carbon brush temperature at different temperature levels is calculated, and the sample screening is performed to obtain the carbon brush temperature difference sample data, which further includes the following sub-steps: Computing the standard distance of all data pairs to the initial core point and computing the average value DP of all standard distances; The data pair with the standard distance to the initial core point greater than 2×DP is recorded as a suspected data pair, otherwise it is recorded as an effective data pair; Based on all the effective data pairs, the average value YR0 of the range and the average value YB0 of the standard deviation are calculated again; (YR0, YB0) is recorded as the effective core point; and the standard deviation RB1 of the range and the standard deviation BB1 of the standard deviation are calculated again; The standard distance of each effective data pair to the effective core point is calculated according to RB1 and BB1, and the average value DY and the standard deviation DB of the standard distances to the effective core point are calculated; DY+k2×DB is recorded as the normal distance threshold DT, where k2 is a set proportion coefficient.

7. The edge computing driven slip ring real-time monitoring method according to claim 6, characterized in that, The sample division is performed on the carbon brush temperature sample data, the difference degree of the carbon brush temperature at different temperature levels is calculated, and the sample screening is performed to obtain the carbon brush temperature difference sample data, which further includes the following sub-steps: If (ARi, ABi) is an effective data pair, ARi / (ABi+e0) is calculated, which is recorded as the effective proportion of (ARi, ABi); the effective proportions of all effective data pairs are repeatedly calculated, and the average value RP and the standard deviation RB of the effective proportions are calculated, where e0 is a set minimum constant; For any data pair (ARi, ABi), the standard distance ADi to the effective core point is calculated according to RB1 and BB1; if ADi is greater than DT, and ARi / (ABi+e0) is not located in [RP-k3×RB, RP+k3×RB], it is determined that ARi and ABi are outliers, and are removed from the corresponding temperature range set and the temperature standard deviation set, where k3 is a set proportion coefficient; All the outliers in the temperature range set and the temperature standard deviation set are repeatedly obtained and removed, and after completion, the standard range set and the standard standard deviation set of the first temperature interval are obtained, which are recorded as the difference sample data of the first temperature interval; the difference sample data of all temperature level intervals are repeatedly obtained to obtain the carbon brush temperature difference sample data.

8. The edge computing driven slip ring real-time monitoring method according to claim 7, characterized in that, The normal difference analysis is performed according to the carbon brush temperature difference sample data, the normal difference range of the carbon brush temperature at different temperature levels is obtained, and the normal difference reference data includes the following sub-steps: According to the standard range set and the standard deviation set of the first temperature interval, all corresponding data pairs are obtained, denoted as standard data pairs, and a two-dimensional plane is plotted with the range as the x-axis and the standard deviation as the y-axis, denoted as a data scatter plot; The average value PV of the standard range set and the average value PU of the standard deviation set are calculated respectively, and (PV, PU) is denoted as a standard core point; and the slope of each standard data pair relative to the origin of the data scatter plot is calculated, denoted as an individual slope; The average value KP of all individual slopes and the standard deviation KB are calculated, and [KP-e1×KB, KP+e1×KB] is denoted as a slope allowable range, wherein e1 is a set proportion coefficient.

9. The edge computing driven slip ring real-time monitoring method according to claim 8, characterized in that, According to the normal difference analysis of the carbon brush temperature difference sample data, the normal difference range of the carbon brush temperature under different temperature levels is obtained, and the normal difference reference data includes the following sub-steps: The Euclidean distance of each point in the data scatter plot to the standard core point is calculated, and all Euclidean distances are arranged from small to large, and the k4 percentile is taken, denoted as an Euclidean distance threshold, wherein k4 is a set percentile; According to the slope of the point relative to the origin being located in the slope allowable range and the Euclidean distance of the point to the standard core point being not greater than the Euclidean distance threshold, a sector region is demarcated in the data scatter plot, denoted as a normal difference region; The maximum value MC of the range and the maximum value MB of the standard deviation of the standard data pairs located in the normal difference region are obtained, and MC and MB are denoted as the range threshold and the deviation threshold of the first temperature interval respectively; The range threshold and the deviation threshold of all temperature level intervals are repeatedly obtained, and the normal difference reference data is obtained.

10. The edge computing driven slip ring real-time monitoring method according to claim 9, characterized in that, The temperatures of all carbon brushes are collected when the collector ring is normally running, and the carbon brush temperature is monitored and analyzed according to the normal difference reference data, including the following sub-steps: When the first collector ring is running, the temperatures of all carbon brushes are collected at the temperature measurement positions 1 to n at a first time interval, and the current collection is denoted as current temperature information, wherein the first time interval is t1; The median of the current temperature information is obtained, denoted as a current temperature level, and the range GC and the standard deviation GB of the current temperature information are calculated; According to the temperature level interval where the current temperature level is located, the corresponding range threshold and deviation threshold are obtained, if GC is greater than the corresponding range threshold or GB is greater than the corresponding deviation threshold, it is judged that the carbon brush temperature difference of the first collector ring is abnormal, otherwise it is judged that the carbon brush temperature difference of the first collector ring is normal; the carbon brush temperature is repeatedly monitored.

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