Remote automatic control method and system for direct-current electric arc furnace

Abstract

The invention relates to the technical field of electric arc furnace control, in particular to a remote automatic control method and system for a direct-current electric arc furnace, and the method comprises the following steps: associating electrode displacement with current change, marking speed current difference abnormal sections, screening voltage abrupt change nodes to form a time axis, and comparing coincidence intervals to determine an intervention time period. And analyzing the electrode state and the arc voltage characteristic, and generating a compensation instruction if the instruction is mismatched. According to the method, by obtaining electrode displacement parameters and current change trends and establishing a correlation sequence of the electrode displacement parameters and the current change trends, the trend consistency is recognized, the sensing capacity for abnormal behaviors is enhanced, when the current and the electrode speed are disjointed, control abnormity is judged, the deviation recognition precision is improved, voltage fluctuation judgment is superimposed, arc abrupt change is recognized, and misjudgment of a single signal is avoided; and comparing abnormal time intervals, extracting interference conflict fragments, positioning instruction interference nodes, and constructing a compensation instruction in combination with electrode behaviors and arc voltage changes, so that the remote response stability and the anti-interference capability are improved, and a precise closed-loop control chain is formed.

Description

Technical Field

[0001] The present invention relates to the technical field of electric arc furnace control, and in particular to a remote automatic control method and system for a direct current electric arc furnace. Background Art

[0002] The field of arc furnace control technology encompasses control methods and devices for regulating and maintaining the arc state during arc furnace operation. The core of this technology involves dynamically adjusting parameters such as arc current, voltage, arc length, electrode lift, and input power to ensure arc stability, reasonable energy efficiency, and safe equipment operation. Arc furnace control technology is widely used in industries such as metallurgy, power equipment, and special material smelting. Its systematic content primarily covers electrical control of AC and DC arc furnaces, temperature control, automatic feeding control, identification and conversion control of smelting stages, and other aspects. Its goal is to achieve high-precision, automated arc behavior management under high-temperature conditions.

[0003] The remote automation control method for DC arc furnaces, specifically, focuses on the operation control of DC arc furnaces. By introducing remote communication mechanisms and automated execution devices, it achieves centralized management of furnace electrode current regulation, electrode displacement regulation, arc stability control, and smelting process step control. This method utilizes an industrial control computing platform combined with a remote data transmission interface to enable remote setting of control parameters and real-time feedback. Current sensors and voltage measurement circuits are used to collect actual operating condition data. The control logic unit then compares this data with target values, performs calculations, and outputs execution instructions, thereby driving an electric adjustment device to control electrode position, adjust the arc state, and advance the smelting process in stages according to preset process requirements.

[0004] While existing technologies can adjust key parameters such as arc current, voltage, and arc length during operation, these adjustments are primarily based on static settings and fixed feedback responses, lacking in-depth understanding of dynamic trends. When electrode displacement and current response are disconnected, traditional control modes struggle to promptly identify abnormal responses within the control chain. This makes it difficult for the control system to distinguish between natural disturbances under normal conditions and anomalies caused by control mismatch, leading to misjudgments and missed intervention opportunities. Existing methods typically assess voltage fluctuations based on single-point amplitude exceeding limits, without integrating other signals for multi-dimensional verification, resulting in frequent false triggering. In remote operation environments, without a mechanism to identify the synergistic relationships between multiple data sources, remote adjustments can easily conflict with the arc's underlying behavior, leading to control command mutual exclusion, arc voltage oscillation, and other issues, which in turn impact smelting stability. For example, misjudgment of arc changes caused by electrode movement during remote control often results in drastic fluctuations in arc length due to response delays or misadjustments, impacting heating uniformity and energy efficiency, ultimately negatively impacting smelting quality. The frequent occurrence of such problems indicates that the existing control strategies lack adaptive calibration mechanisms and cross-validation judgments, making it difficult to meet the needs of remote and refined regulation. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a remote automation control method and system for a DC arc furnace.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a remote automation control method for a DC arc furnace, comprising the following steps: S1: Obtain the displacement direction and speed parameters of the electrode lifting, combine the current transformer to record the current change information, establish the correlation sequence between the current change direction and the electrode movement direction, and determine the trend consistency state group; S2: Based on the direction consistent time period in the trend consistency state group, identifying the matching relationship between the current change and the electrode speed, if the difference degree exceeds the normal range time period, marking the control abnormal period, and generating a displacement response abnormality identification list; S3: According to the time period in the displacement response abnormality identification list, call the voltage sampling data, determine the voltage fluctuation amplitude in the corresponding time period, select the time node exceeding the set voltage change threshold, and form the abnormal mutation voltage time axis; S4: comparing the abnormal sudden voltage time axis with the time period in the displacement response abnormality identification list, identifying the overlap intervals between the two, screening the key time periods associated with control intervention according to the set criteria, and extracting the remote adjustment conflict section set; S5: Based on the electrode operating status and arc voltage change characteristics within the concentrated time period of the remote adjustment conflict section, determine whether there is arc instability caused by control instruction mismatch, and generate a remote compensation action instruction structure.

[0007] As a further solution of the present invention, the trend consistency state group includes displacement direction parameters, electrode speed parameters, current movement direction association sequence, and direction consistency state; the displacement response abnormality identification list includes current speed matching relationship, difference degree threshold, and control abnormality time period mark; the abnormal mutation voltage time axis includes voltage fluctuation amplitude data, set voltage change threshold, screening time node, and time axis arrangement order; the remote adjustment conflict segment set includes time axis abnormality period overlap interval, control intervention standard, and key time period screening condition; the remote compensation action instruction structure includes electrode state arc voltage feature association analysis, control instruction mismatch judgment condition, and upward control instruction parameter.

[0008] As a further solution of the present invention, the specific steps of S1 are: S101: Acquire the displacement direction and speed data during the electrode lifting process, monitor the current change output by the current transformer, call the electrode displacement direction and current change direction at the corresponding time node, perform direction consistency judgment, establish the direction correspondence between the two, and obtain the direction matching sequence coefficient; S102: Based on the direction matching sequence coefficient, the electrode speed and current change rate are retrieved, the rate change characteristics under the direction consistent state are statistically analyzed, the overlap degree between the speed mutation interval and the current violent fluctuation interval is determined, and the rate synchronization matching degree is obtained; S103: According to the rate synchronization matching degree and the distribution ratio of the consistent nodes in the direction matching sequence coefficient, the stable state of the trend change is determined, the consistency characteristics of the trend are classified, and a trend consistency state group is generated.

[0009] As a further solution of the present invention, the specific steps of S2 are: S201: extracting current change values ​​and electrode speed values ​​based on the time periods with consistent directions in the trend consistency state group, determining whether the change directions of the current change and the electrode speed values ​​are consistent within the same time period, identifying data points with inconsistent change directions, and generating a trend value of the number of direction deviation points; S202: Calling the trend value of the number of direction deviation points, calculating the proportion of direction deviation points in a time period, and comparing it with the direction deviation ratio threshold, identifying the time period in which the deviation degree exceeds the judgment standard, and obtaining the abnormal section judgment interval value; S203: extracting the corresponding displacement response sequence according to the abnormal section discrimination interval value, detecting interval distribution changes, and comparing the differences with the standard interval under normal conditions, screening the time period where the degree of deviation exceeds the normal limit, and obtaining a displacement response abnormality identification list.

[0010] As a further solution of the present invention, the specific steps of S3 are: S301: Based on the time period marked in the displacement response abnormality identification list, call the voltage sampling data output by the arc voltage detection device, extract the continuous voltage change in the time period, calculate the voltage fluctuation characteristic value, and obtain the voltage change amplitude sequence; S302: Compare the voltage data in the voltage variation amplitude sequence with a set voltage variation threshold, filter out time nodes with values ​​greater than the threshold, and obtain a set of high-amplitude fluctuation time nodes; S303: Call all time nodes in the high-amplitude fluctuation time node set, arrange them in ascending order according to the size of timestamps, obtain a node sequence with reasonable structural continuity, and generate an abnormal mutation voltage time axis.

[0011] As a further solution of the present invention, the specific calculation formula for calculating the voltage fluctuation characteristic value is: ; in, represents the standardized voltage fluctuation characteristic value, Represents the instantaneous change of voltage at the i-th sampling point, N represents the total number of sampling points, represents the jth voltage peak amplitude, represents the j-th voltage trough amplitude, and M represents the number of voltage extreme value pairs.

[0012] As a further solution of the present invention, the specific steps of S4 are: S401: Based on the abnormal sudden voltage time axis and the displacement response abnormality identification list, cross-comparison is performed on the two types of time periods to determine the overlapping relationship between the start and end time ranges, calculate the overlapping characteristic value, extract the time segments with simultaneous abnormalities, and generate a set of sudden voltage and response overlap segments; S402: Calling the concentrated time period of the sudden voltage and response overlap section, extracting control intervention signal change information, screening according to signal amplitude change characteristics and intervention action density, and obtaining a list of intervention dense overlap sections; S403: Extracting the remote adjustment signal state according to the time period in the intervention dense overlap section list, determining the matching relationship between the signal fluctuation frequency and the intervention period, identifying the time section of the adjustment conflict, and obtaining the remote adjustment conflict section set.

[0013] As a further solution of the present invention, the specific calculation formula for calculating the overlapping eigenvalue is: ; in, Represents the start time of the voltage abnormal section, Represents the end time of the voltage abnormal section, Represents the start time of the abnormal displacement response section, Represents the end time of the abnormal displacement response section, Representative The mutation amplitude of the voltage sampling point, Represents the arithmetic mean of the voltage mutation amplitude, Represents the voltage reference value, Representative The displacement response amplitude at each voltage abnormal time point, Representative The voltage amplitude at the time point of the abnormal displacement response, Represents the displacement reference value, Represents the total number of voltage sampling points, Represents the total number of abnormal time points.

[0014] As a further solution of the present invention, the specific steps of S5 are: S501: Based on the electrode operating state and arc voltage change characteristics within the concentrated time period of the remote adjustment conflict section, extract the electrode instantaneous operating parameters and corresponding time sequence, combine the jump interval and change amplitude, determine the correlation between the arc voltage frequency and power oscillation, identify the area deviating from the adjustment response period, and generate the electrode response offset rate; S502: Identify the time offset between the control command and the response action based on the electrode response offset rate, and select the time period that meets the mismatch judgment condition based on the coincidence of the oscillation amplitude in the arc voltage characteristic and the command offset point, and generate the mismatch section coincidence coefficient; S503: Call the mismatch section overlap coefficient, extract the control instruction section that meets the overlap condition, combine the amplitude information of the original control instruction, adjust the output structure of the upward control instruction, construct an instruction combination with matching time and amplitude, and establish a remote compensation action instruction structure.

[0015] A remote automation control system for a DC electric arc furnace, comprising: The trend identification module obtains the displacement direction and speed parameters of the electrode lifting, collects the energized current direction change values ​​recorded by the current transformer, determines the direction consistency based on the displacement direction and current direction at the same time point, constructs a consistent state sequence in chronological order, extracts continuous consistent direction states and determines the persistence characteristics, establishes a displacement and current trend relationship sequence, and obtains a trend consistency state group; The response analysis module extracts the current change parameters and the electrode speed change parameters based on the time periods with the same direction in the trend consistency state group, determines the matching characteristics of the two under the time correspondence relationship, identifies the sections where the deviation exceeds the set standard, and obtains a list of displacement response abnormality identifications; The fluctuation screening module calls the voltage signal sampled by the arc voltage detection device based on the time period marked in the displacement response abnormality identification list, extracts the fluctuation amplitude characteristics of the sampling time point, screens the nodes with a voltage change greater than the voltage change threshold, and sorts them by time to obtain the abnormal mutation voltage time axis; The intervention determination module compares the abnormal sudden voltage time axis with the time periods recorded in the displacement response abnormality identification list, identifies the overlapping intervals between the two, and collects the time periods that meet the control intervention conditions to obtain a remote adjustment conflict section set; The compensation generation module determines whether there is a section where the control instruction direction is inconsistent with the response state based on the electrode operating status and arc voltage change characteristics in the remote adjustment conflict section, extracts the section and constructs the compensation instruction structure in the upward direction to obtain the remote compensation action instruction structure.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by obtaining the electrode displacement direction and speed parameters, combining the current change trend, establishing the correlation sequence between motion and current, realizing the recognition of trend consistency, enhancing the perception ability of unexpected behavior, judging the control response abnormality when the current is out of sync with the electrode speed, improving the accuracy of system deviation judgment, superimposing the voltage fluctuation amplitude judgment, accurately identifying the sudden change of arc state, avoiding the misjudgment of a single physical quantity, comparing the abnormal data time interval, extracting the high-risk fragments of control intervention conflict, locating the instruction interference node, combining the electrode behavior and arc voltage change to construct the compensation control instruction, automatically correcting under remote control conditions, improving the response stability and anti-interference ability, and forming a closed-loop chain from perception, analysis to execution throughout the process, thereby improving the dynamic adaptability and precise control level. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Schematic diagram of the steps of the present invention; Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0020] See also Figure 1 , a DC arc furnace remote automation control method, comprising the following steps: S1: Obtain the displacement direction and speed parameters of the electrode lifting, combine them with the current change information recorded by the current transformer, establish a correlation sequence between the current change direction and the electrode movement direction, and identify whether the two directions are consistent to obtain a trend consistency state group; S2: Based on the time periods with consistent directions in the trend consistency state group, the matching relationship between the current change and the electrode speed is identified. If there is a time period with a difference exceeding the normal range, it is marked as a control abnormality period, and a displacement response abnormality identification list is obtained; S3: Based on the time period marked in the displacement response abnormality identification list, the voltage sampling data output by the arc voltage detection device is called to judge the voltage fluctuation amplitude within the corresponding time period, and the time nodes greater than the set voltage change threshold are selected and arranged in chronological order to form the abnormal mutation voltage time axis; S4: Compare the abnormal sudden voltage time axis with the time periods marked in the displacement response abnormality identification list, identify the overlapping intervals where the two occur simultaneously, and select the key time periods associated with control intervention according to the set standards to obtain the remote regulation conflict section set; S5: Based on the electrode operating status and arc voltage change characteristics within the concentrated time period of the remote adjustment conflict section, determine whether the arc instability behavior is caused by the control instruction mismatch. If the conditions are met, construct an upward control instruction to form a remote compensation action instruction structure.

[0021] The trend consistency state group includes displacement direction parameters, electrode speed parameters, current movement direction association sequence, and direction consistency state. The displacement response abnormality identification list includes current speed matching relationship, difference degree threshold, and control abnormality period mark. The abnormal mutation voltage time axis includes voltage fluctuation amplitude data, set voltage change threshold, screening time node, and time axis arrangement order. The remote adjustment conflict section set includes the time axis abnormal period overlap interval, control intervention standard, and key time period screening condition. The remote compensation action instruction structure includes electrode state arc voltage feature association analysis, control instruction mismatch judgment condition, and upward control instruction parameter.

[0022] See also Figure 1 , the specific steps of S1 are: S101: Acquire the displacement direction and speed data during the electrode lifting process, monitor the current change output by the current transformer, call the electrode displacement direction and current change direction at the corresponding time node, perform direction consistency judgment, establish the direction correspondence between the two, and obtain the direction matching sequence coefficient; During the electrode lifting process, the displacement direction and speed of the electrode at each moment are first recorded using a laser ranging sensor or encoder. The displacement direction can be expressed as a positive or negative direction mark along the vertical axis, and the speed is the real-time electrode movement rate. Simultaneously, a current transformer collects and converts the real-time change data of the energized current through analog-to-digital conversion. Data is recorded every 100 milliseconds during the control cycle, including the displacement direction, speed, and current value. Each data piece is timestamped for time alignment. The consistency of the electrode displacement direction and current change direction at the same time node is determined. For example, when the electrode moves downward and the current increases, the direction is considered consistent. Otherwise, the direction is inconsistent. Each moment is marked as consistent with 1 and inconsistent with 0. The records form a time-ordered consistency sequence. The number of values ​​1 in the sequence is counted and divided by the total data volume to obtain the direction matching ratio coefficient. If 50 sets of data are collected within 5 seconds, and 35 of them have consistent directions, the direction matching ratio is 70%.

[0023] S102: Based on the direction matching sequence coefficient, the electrode speed and current change rate are retrieved, the rate change characteristics under the direction consistent state are statistically analyzed, the degree of overlap between the speed mutation interval and the current violent fluctuation interval is determined, and the rate synchronization matching degree is obtained; Based on the direction matching ratio, the electrode speed and current change rate at the moment of consistent direction are extracted to form two corresponding time series. The electrode speed is differentially calculated to obtain the speed change amplitude at adjacent moments, and then the current change value is calculated according to the time difference to obtain the current rate. The above two sequences are analyzed to find the speed mutation section, that is, the period when the speed change amplitude exceeds 0.5mm / s, and the violent fluctuation section when the current change rate exceeds 200A / s. These two thresholds are set according to the maximum speed of the electrode control system and the normal arc current change range. The number of overlapping points of the two types of sections is counted and then divided by the number of points in all speed mutation sections to obtain the rate synchronization matching degree. For example, there are 12 points in the speed mutation period, and 9 points overlap with the violent current fluctuation section, so the rate synchronization matching degree is 75%.

[0024] S103: Determine the stable state of the trend change based on the rate synchronization matching degree and the distribution ratio of the consistent nodes in the direction matching sequence coefficient, classify the consistency characteristics of the trend, and generate a trend consistency state group; In the existing direction consistency sequence, find continuous direction consistency segments and calculate the length of each segment. If the length of a segment exceeds 3 consecutive sampling points, that is, 300 milliseconds, it is recorded as a stable consistency segment. The total length of all stable consistency segments is counted and compared with the total data length to obtain the stable consistency distribution ratio. At the same time, refer to the rate synchronization matching degree calculated previously and divide the trend consistency state according to the two indicators. If the stable consistency ratio exceeds 60% and the rate synchronization matching degree is not less than 70%, it is marked as a strong consistency state; if the stable consistency ratio is between 40% and 60%, or the rate synchronization matching degree is between 50% and 70%, it is medium consistency; the rest are weak consistency states. For example, among 50 groups of data, there are three stable consistency segments with lengths of 5, 4, and 6, a total of 15 groups of data, accounting for 30% of the total. If the rate synchronization matching degree is 52%, it is judged to be a weak consistency state.

[0025] See also Figure 1 , the specific steps of S2 are: S201: Based on the time period with consistent directions in the trend consistency state group, extract the current change value and the electrode speed value, determine whether the change directions of the two are consistent in the same time period, identify data points with inconsistent change directions, and generate a trend value of the number of direction deviation points; Based on the time periods with consistent directions in the trend consistency state group, it is necessary to extract time segments with consistent change trends from the synchronously collected current sequence and electrode speed sequence. First, the current and electrode speed data are synchronized according to the time axis, and the two sequences are aligned at the same time point. Then, it is determined whether the change directions of the two sequences are consistent in each time period. If the current continues to rise and the electrode speed also shows a continuous upward trend within a certain period of time, then the section is classified as a direction-consistent interval. The change values ​​of the current and electrode speed are calculated hour by hour in these time periods with consistent directions. For example, based on continuous data points, the current difference and speed difference between two adjacent time points are calculated, and their sign information is recorded to determine whether the two are consistent. Whether the direction is consistent. For example, if the current increases from 10 to 12 and the speed decreases from 25 to 23 within a minute, the current direction is positive and the speed direction is negative, indicating inconsistent directions. Similar to the above steps, all intervals with consistent directions are traversed, and all data points with inconsistent change directions are identified in each time period and the number is counted. For example, if 120 sets of data are collected in a ten-minute interval, if it is found that 20 sets of data points have opposite current and speed directions, then the number of deviation points in this time period is 20. Then, a sliding time window is used to continuously count the changes in the number of deviation points in each time period over the entire time series, and finally form a trend sequence of the number of deviation points for subsequent identification and analysis.

[0026] S202: Calling the trend value of the number of direction deviation points, calculating the proportion of direction deviation points in the time period, and comparing it with the direction deviation ratio threshold, identifying the time period where the deviation degree exceeds the judgment standard, and obtaining the abnormal section judgment interval value; After obtaining the trend value of the number of deviation points, it is necessary to calculate the proportion of deviation points in each time period. The number of deviation points in each time period is divided by the total number of data points to obtain the corresponding proportion value. For example, in a 10-minute time period, 150 sets of data are collected and 30 deviation points are identified. The directional deviation point proportion of this time period is 20%. The deviation proportion of each time period is then compared with the set directional deviation proportion threshold. The threshold setting needs to refer to the upper limit of the deviation point proportion obtained by statistics during normal operation of the system. Assuming that the maximum proportion under normal conditions is 15%, the threshold can be set to 15%. Based on this, it is judged whether the deviation degree of each time period is abnormal. If the proportion of a time period exceeds 15%, the time period is marked as an abnormal segment. For example, if the deviation proportion of a time period is 18%, if it exceeds 15%, the time period is considered abnormal. Finally, all time periods with deviation proportions exceeding the threshold are collected to form the discrimination interval value of the abnormal segment, which is used for subsequent response abnormality judgment.

[0027] S203: Extract the corresponding displacement response sequence based on the abnormal section discrimination interval value, detect interval distribution changes, and compare the differences with the standard interval under normal conditions, filter out the time period where the deviation degree exceeds the normal limit, and obtain a displacement response abnormality identification list; Based on the identified abnormal sections, the corresponding displacement response data series is extracted within each abnormal time period. Statistical analysis of these data is performed, including maximum, minimum, average, and fluctuation range. By comparing these statistical features with the standard parameters in the normal reference interval, significant differences in distribution are identified. The normal reference value can be established using stable operating condition data collected over a long period of time. For example, under normal conditions, the average value is concentrated between 10 and 12, with a fluctuation range of less than 2. If the average displacement value in an abnormal time period is 14 and the fluctuation range is 3, it exceeds the normal reference interval. By setting a reasonable tolerance range, such as a tolerance of plus or minus 2 for the average value and a tolerance of plus or minus 1.5 for the fluctuation range, as long as the characteristic parameters of a time period exceed the corresponding tolerance, the time period is added to the displacement response abnormality identification list. For example, if the average displacement value in a certain period is 15.2, and the standard is 10 to 12, it exceeds the tolerance range. Similarly, if the fluctuation range also exceeds the limit, the section is also confirmed to have an abnormal response. Ultimately, a complete list of all abnormal response time periods is formed for further processing.

[0028] See also Figure 1 , the specific steps of S3 are: S301: Based on the time period marked in the displacement response abnormality identification list, call the voltage sampling data output by the arc voltage detection device, extract the continuous voltage changes in the time period, calculate the voltage fluctuation characteristic value, and obtain the voltage change amplitude sequence; The specific calculation formula for calculating the voltage fluctuation characteristic value is: ; in, Represents the standardized voltage fluctuation characteristic value (unit: volt), Represents the instantaneous change in voltage at the i-th sampling point (unit: volt), N represents the total number of sampling points, represents the j-th voltage peak amplitude (unit: volt), represents the amplitude of the j-th voltage trough (unit: volt), and M represents the number of voltage extreme value pairs; The monitoring and data collection process uses a data acquisition device to record voltage signals within a specific time period. The obtained voltage signals are preprocessed to eliminate noise and interference, forming a voltage instantaneous change sequence ΔV. Assuming the monitoring period is 10 minutes and the acquisition frequency is 1Hz, the voltage instantaneous change data set within this period contains a total of 600 sampling points. The specific sample data is as follows: ΔV data (unit: volt): [0.1,-0.2,0.15,-0.1,0.05,-0.3,0.25,-0.1,0.2,-0.15,…,0.1] Where N=600; The values ​​of voltage peaks and troughs are determined by analyzing the voltage variation dataset ΔV using the maximum and minimum value extraction method, with the parameter M set to the number of identified voltage extreme value pairs. During the monitoring process, it is assumed that the values ​​of the 1st to 10th voltage peaks and troughs are: V1^{max}=4.5V, V1^{min}=2.0V; V2^{max}=5.0V, V2^{min}=2.5V; V3^{max}=4.0V, V3^{min}=1.5V; V4^{max}=3.8V, V4^{min}=1.7V; V5^{max}=5.2V, V5^{min}=2.4V; Then M is 5; For the calculation of the sum of squares of ΔV_i: ; Assuming the result after calculation is 36, substitute it into the formula: ; The voltage extremes are calculated using: ; Calculate the mean of M: ; Combining the above calculations, the complete calculation process of formula η_v is: ; The results show that the standardized characteristic value of voltage fluctuation is 2.725 volts, reflecting the amplitude of voltage fluctuation during the monitoring period. As an important characteristic value of the voltage change amplitude sequence, it can provide a key reference for subsequent voltage fluctuation trend analysis and anomaly identification. The calculation logic of the formula constructs a composite eigenvalue through the root mean square term and the mean absolute range term: The instantaneous voltage change is squared to eliminate directional differences and then the root mean square is taken to quantify the overall discreteness of the voltage fluctuation. The absolute difference between each extreme value pair is calculated and averaged to represent the amplitude of periodic fluctuations. Both units are in volts, and a comprehensive index is formed through linear superposition. This reflects both the instantaneous fluctuation intensity and the periodic amplitude characteristics, while avoiding the dimensional confusion caused by the introduction of dimensionless coefficients. The voltage fluctuation characteristic value characterizes the abnormal voltage fluctuation characteristics through a two-dimensional quantitative system of root mean square term and range term: The square operation of the instantaneous voltage change is used to eliminate polarity interference and reflect the discrete distribution intensity of voltage fluctuation; the mean absolute range term The periodic amplitude characteristics of voltage fluctuations are captured by calculating the difference between extreme values. The two physical quantities, both of which are in the volt level, are linearly superimposed to form a composite indicator, achieving the dual monitoring capability of simultaneously characterizing the transient voltage fluctuation intensity and steady-state amplitude changes.

[0029] S302: Compare the voltage data in the voltage variation amplitude sequence with a set voltage variation threshold, filter out time nodes with values ​​greater than the threshold, and obtain a set of high-amplitude fluctuation time nodes; First, based on the voltage fluctuation characteristics during normal equipment operation, a reasonable amplitude baseline value must be calculated and a change threshold must be determined. Typically, 30 minutes of operating voltage data is used to calculate the mean and standard deviation of the voltage amplitude change. The threshold can be set to three times the mean plus the standard deviation. For example, when the mean is 0.12V and the standard deviation is 0.06V, the threshold is set to 0.30V. The entire voltage change sequence is traversed and compared point by point. If the change value at a certain moment is greater than 0.30V, the corresponding time node is identified as a high-amplitude fluctuation point. For example, at point 5240, the change value is 0.36V at 10:00:06.048, exceeding the set threshold, so the timestamp of this point is included in the set of fluctuation time nodes. This ultimately generates a set of representative high-amplitude fluctuation time nodes, facilitating the identification of sudden change characteristics.

[0030] S303: Call all time nodes in the high-amplitude fluctuation time node set, arrange them in ascending order according to the size of the timestamp, obtain a node sequence with reasonable structural continuity, and generate an abnormal mutation voltage time axis; After extracting all timestamps from the high-amplitude fluctuation time nodes, they are sorted chronologically. By determining whether the intervals between adjacent time nodes are within a set threshold, we identify sequences of nodes with continuous structural characteristics. The maximum interval for continuous time is set to 10ms. The time differences between each timestamp are compared point by point. If the interval between two points is less than or equal to 10ms, the two points are considered continuous and belong to the same mutation segment. Otherwise, a new segment is created. For example, a set of high-amplitude node times is 10:00:05.020, 10:00:05.024, 10:00:05.096, 10:00:06.004, and 10:00:06.008. The first two points have a 4ms interval, meeting the continuity condition and forming a mutation segment. The middle point has a larger interval with the next point, forming a new mutation segment. This method classifies and combines all continuous segments, ultimately yielding multiple structurally continuous sequences consisting of high-amplitude nodes, which form the abnormal mutation voltage timeline.

[0031] See also Figure 1 , the specific steps of S4 are: S401: Based on the abnormal sudden voltage time axis and the displacement response abnormality identification list, the two types of time periods are cross-compared to determine the overlap relationship between the start and end time ranges, calculate the overlapping characteristic value, extract the time segments with simultaneous anomalies, and generate a set of sudden voltage and response overlap segments; The specific calculation formula for calculating the overlapping eigenvalue is: ; in, Represents the start time of the voltage abnormal section (seconds), Represents the end time of the voltage abnormal section (seconds), Represents the start time of the abnormal displacement response section (seconds), Represents the end time of the abnormal displacement response section (seconds), Representative The mutation amplitude of each voltage sampling point (Volts), Represents the arithmetic mean of the voltage mutation amplitude (Volts), Represents the voltage reference value (taking the system rated voltage as 220 volts), Representative The displacement response amplitude (mm) at each voltage abnormal time point, Representative The voltage amplitude (V) at the time point of the abnormal displacement response, Represents the displacement reference value (taking the sensor range as 10 mm), Represents the total number of voltage sampling points, represents the total number of abnormal time points; Parameter definition and data source: (The start time of voltage anomaly is obtained through real-time recording of the power monitoring system, in the format of hours: minutes: seconds); (The start time of the abnormal displacement response is extracted from the displacement sensor data log); (Time when voltage anomaly ends, stored in power system event recorder); (Displacement response abnormal termination time, sensor data sampling interval is 5 seconds); Volts (sequence of voltage mutation amplitudes, sampled every 10 seconds by the power quality analyzer during the abnormal period); Volts (calculated as: ); Volts (according to GB / T12325-2008 power quality standard, the nominal voltage of low-voltage distribution network); mm (the displacement response amplitude corresponding to the time point of voltage anomaly, measured synchronously by a laser displacement meter); Volts (voltage amplitude corresponding to the time point of abnormal displacement response, recorded by the power system recording device); Millimeters (according to JJG644-2003 Vibration Displacement Sensor Verification Procedure, upper limit of measuring range); (The total number of voltage sampling points is automatically counted by the monitoring system); (Total number of abnormal time points, number of times marked during the data collection period); Time difference calculation: ; ; ; Voltage fluctuation calculation: ; ; ; Displacement-voltage difference term calculation: ; ; Final calculation: ; Result description: The result is a dimensionless overlap characteristic value. When this value is greater than the preset threshold of 0.5, it is determined to be a valid overlap segment. The calculation results show that the time overlap does not meet the trigger condition. It is necessary to further analyze the correlation of continuous abnormal events in combination with the subsequent segment set generation rules. The voltage fluctuation term of 1.0024 reflects the degree of deviation of the voltage mutation from the nominal value, and the displacement-voltage difference term of 61.27 represents the strength of the electromechanical parameter coupling anomaly. Together, they correct the accuracy of the original time overlap determination. The formula comprehensively evaluates the time overlap and signal anomaly intensity through a score structure: the numerator uses the absolute value of the time window overlap length Directly quantify the time overlap, the denominator is normalized by the voltage fluctuation variance Normalized cumulative displacement difference The superposition constructs a composite suppression factor, in which the square root operation controls the growth rate of the voltage fluctuation influence, the absolute value sum strengthens the linear cumulative effect of the displacement deviation, and the overall design of the fractional form makes the time overlapping eigenvalue It is positively correlated with the pure time overlap and negatively correlated with the signal anomaly intensity, achieving dimensional unification (numerator is seconds, denominator is dimensionless, and the overall dimension is seconds). At the same time, the nonlinear growth characteristic of the denominator suppresses the excessive influence of a single abnormal signal. Overlapping eigenvalues Characterizes the coupling strength between voltage anomaly and displacement response anomaly in the time domain. Its value is determined by the nonlinear ratio of the effective overlapping length of the time window to the signal anomaly intensity: when the discreteness of the voltage mutation amplitude in the time overlapping period is low and the displacement response difference is small, the value of the denominator decreases, resulting in an increase in the eigenvalue, reflecting that the two types of abnormal signals are highly correlated in this period; when there is a sharp fluctuation at the voltage sampling point or a significant deviation between the displacement response and the voltage signal, the denominator reduces the eigenvalue through a dual suppression mechanism of square root operation and absolute value summation, indicating the existence of contradictions in the abnormal signal; this parameter achieves a clear expression of physical meaning through dimensional consistency processing (the numerator is the time dimension and the denominator is dimensionless). Its value directly reflects the degree of signal coordination during the overlapping period, providing a quantitative basis for determining the effective overlapping section.

[0032] S402: Calling a concentrated time period of the sudden voltage and response overlap section, extracting control intervention signal change information, screening based on signal amplitude change characteristics and intervention action density, and obtaining a list of intervention dense overlap sections; Each time segment is extracted from the set of overlapping segments of sudden voltage and response. The control intervention signal recordings within the corresponding segment are located separately. The signal values ​​at each time point are read, and the amplitude changes between adjacent time points are counted. The frequency of signal changes within each overlapping segment can be determined by counting the number of signal value differences within a short time interval. For example, the number of changes is recorded within each 100ms interval, and the total signal amplitude changes within the entire segment are counted. The screening criteria are: if the total number of signal changes within a segment exceeds 5 and the total amplitude change exceeds 20V, the signal changes are considered intensive and the segment is identified as an intensive intervention segment. For example, within a time segment from 2.1s to 2.5s, the recorded signal changes are 4V, 6V, 5V, and 7V, with a total change of 22V, and at least one change occurs every 100ms, meeting the screening criteria. This screening process is repeated for all overlapping segments, and segments that meet the criteria are added to the list of intensive intervention overlap segments for further analysis.

[0033] S403: Extracting the remote adjustment signal state based on the time periods in the list of densely overlapping intervention sections, determining the matching relationship between the signal fluctuation frequency and the intervention period, identifying the time sections of adjustment conflicts, and obtaining a remote adjustment conflict section set; For each time segment within the densely overlapping intervention section, the remote control signal state within the corresponding time period is matched. The frequency of the control signal state switching is counted, that is, the number of times the signal state jumps from one level to another is calculated. The frequency characteristics are then determined based on the number of changes per unit time. If the intervention signal in a certain segment changes more than 10 times within 5 seconds, while the control signal changes less than 3 times within the same time period, the two rhythms are considered mismatched. Signal rhythm similarity analysis is further used to compare the fluctuation patterns of the intervention and control signals. If the two are out of sync in terms of major peak positions and change trends, for example, if the high-frequency intervention signal segment and the low-frequency control signal segment have little overlap and fluctuate in different directions, this is identified as a control conflict segment. Finally, all time segments that do not meet the synchronization rhythm judgment criteria are organized into a set of remote control conflict segments for further processing.

[0034] See also Figure 1 , the specific steps of S5 are: S501: Based on the electrode operating status and arc voltage change characteristics within the concentrated time period of the remote adjustment conflict section, the electrode instantaneous operating parameters and corresponding time sequence are extracted. Combined with the jump interval and change amplitude, the correlation between the arc voltage frequency and the power oscillation is determined, the area deviating from the adjustment response period is identified, and the electrode response deviation rate is generated. During the concentrated time period of the remote adjustment conflict section, it is first necessary to collect the operating data of the electrode, including electrode displacement speed, current intensity, arc voltage curve, etc., and record the arc voltage fluctuation of the arc furnace in real time. 1000 samples can be taken per second, from which the instantaneous change value of the arc voltage at each moment is extracted, and the difference between adjacent time points is calculated. At the same time, the time information of the electrode action is marked, and the time delay difference between the electrode action and the arc voltage change is calculated. By counting such delay differences in multiple adjustment cycles, the key time period representing the conflict section is screened out, such as from the 60th second to the 75th second. The frequency domain analysis is performed on the arc voltage fluctuation in this section to obtain the main frequency distribution, and the power wave is analyzed in the same way. The power frequency data is obtained by moving the two frequencies within 0.2Hz, and the phase overlap between the two is combined to determine whether there is a synchronization relationship. The amplitude threshold of the arc voltage jump is set to 15V, and the jump time interval threshold is set to 3s. All points where the fluctuation amplitude is greater than the set value and the interval between adjacent peaks is greater than the set value are selected as abnormal response points. At the same time, they are compared with the set adjustment period. If these abnormal points deviate from the cycle center for more than 20s, it is determined that the response period is offset. Finally, the response offset rate is calculated. For example, if 18 of the 100 response points are offset points, the offset rate is 18%. If it exceeds the set threshold of 15%, it indicates that the response period has an obvious offset.

[0035] S502: Identify the time offset between the control command and the response action based on the electrode response offset rate, and combine the coincidence of the oscillation amplitude in the arc voltage characteristic and the command offset point to select the time period that meets the mismatch judgment condition and generate the mismatch section coincidence coefficient; Based on the results of the electrode response offset rate, the control instruction moment where the response offset occurs and its corresponding response action moment are identified from the data, the time difference is calculated, and all control instructions with a time difference of more than 5s are screened out. Then, the arc voltage characteristics are retrieved to determine whether there is an amplitude oscillation exceeding 20V before and after these offset moments. If the conditions are met, the period is marked as a mismatch segment. All mismatch periods are counted to determine how many of them are accompanied by a large oscillation amplitude, and compared with the total number of offset instructions to calculate the oscillation coincidence. For example, if 10 offset points are recorded in a certain time period, 7 of which occur within the time range of the oscillation amplitude exceeding 25V, the coincidence is 70%, which exceeds the set judgment benchmark value of 60% and can be regarded as a significant mismatch. This time period is then determined as a key segment that requires further processing.

[0036] S503: Call the mismatch section overlap coefficient, extract the control instruction section that meets the overlap condition, combine the amplitude information of the original control instruction, adjust the output structure of the upward control instruction, build an instruction combination with matching time and amplitude, and establish a remote compensation action instruction structure; Based on the mismatch segment overlap calculated above, all control instruction segments with an overlap exceeding 60% are extracted from the original instruction sequence, and the current output amplitude corresponding to each control instruction is recorded. For example, instructions numbered 7, 8, and 9 are issued at 190s, 200s, and 210s, respectively, with corresponding output currents of 100A, 95A, and 110A, respectively. The average current value is calculated to be 101.67A. The corresponding response offset times are also counted, such as 5s, 7s, and 6s, respectively, with an average offset time of 6s. Based on this, the original instruction time is adjusted forward, that is, each instruction is issued 6s earlier, forming new control instruction time points of 184s, 194s, and 204s. At the same time, the original current output is retained unchanged or slightly adjusted within a range of plus or minus 5%, that is, within the range of 95A to 115A. This forms a new control instruction combination sequence that is consistent with the response law in both time and amplitude dimensions, thereby replacing the original mismatched control structure for subsequent control strategies.

[0037] See also Figure 2 , a DC arc furnace remote automation control system, comprising: The trend identification module obtains the displacement direction and speed parameters of the electrode lifting, collects the energized current direction change values ​​recorded by the current transformer, determines the direction consistency based on the displacement direction and current direction at the same time point, constructs a consistent state sequence in chronological order, extracts continuous consistent direction states and determines the persistence characteristics, establishes a displacement and current trend relationship sequence, and obtains a trend consistency state group; The response analysis module extracts the current change parameters and electrode velocity change parameters based on the time periods with consistent directions in the trend consistency state group, determines the matching characteristics of the two under the time correspondence relationship, identifies the sections where the deviation exceeds the set standard, and obtains a list of displacement response abnormality identifications; The fluctuation screening module calls the voltage signal sampled by the arc voltage detection device based on the time period marked in the displacement response abnormality identification list, extracts the fluctuation amplitude characteristics at the sampling time point, screens the nodes with voltage changes greater than the voltage change threshold, and sorts them by time to obtain the abnormal mutation voltage time axis; The intervention judgment module compares the abnormal sudden voltage time axis with the time periods recorded in the displacement response abnormality identification list, identifies the overlapped intervals between the two, and groups the time periods that meet the control intervention conditions to obtain the remote adjustment conflict section set; The compensation generation module determines whether there is a section where the control instruction direction is inconsistent with the response state based on the electrode operating status and arc voltage change characteristics in the remote adjustment conflict section. It extracts the section and constructs the compensation instruction structure in the upward direction to obtain the remote compensation action instruction structure.

[0038] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A remote automation control method for a DC arc furnace, characterized in that: The following steps are involved: S1: Obtain the displacement direction and speed parameters of the electrode lifting, combine the current transformer to record the current change information, establish the correlation sequence between the current change direction and the electrode movement direction, and determine the trend consistency state group; S2: Based on the direction consistent time period in the trend consistency state group, identifying the matching relationship between the current change and the electrode speed, if the difference degree exceeds the normal range time period, marking the control abnormal period, and generating a displacement response abnormality identification list; S3: According to the time period in the displacement response abnormality identification list, call the voltage sampling data, determine the voltage fluctuation amplitude in the corresponding time period, select the time node exceeding the set voltage change threshold, and form the abnormal mutation voltage time axis; S4: comparing the abnormal sudden voltage time axis with the time period in the displacement response abnormality identification list, identifying the overlap intervals between the two, screening the key time periods associated with control intervention according to the set criteria, and extracting the remote adjustment conflict section set; S5: Based on the electrode operating status and arc voltage change characteristics within the concentrated time period of the remote adjustment conflict section, determine whether there is arc instability caused by control instruction mismatch, and generate a remote compensation action instruction structure.

2. The remote automation control method for a DC arc furnace according to claim 1, characterized in that: The trend consistency state group includes displacement direction parameters, electrode speed parameters, current movement direction association sequence, and direction consistency state; the displacement response abnormality identification list includes current speed matching relationship, difference degree threshold, and control abnormality period mark; the abnormal mutation voltage time axis includes voltage fluctuation amplitude data, set voltage change threshold, screening time node, and time axis arrangement order; the remote adjustment conflict section set includes time axis abnormal period overlap interval, control intervention standard, and key time period screening condition; the remote compensation action instruction structure includes electrode state arc voltage feature association analysis, control instruction mismatch judgment condition, and upward control instruction parameter.

3. The remote automation control method for a DC arc furnace according to claim 1, characterized in that: The specific steps of S1 are: S101: Acquire the displacement direction and speed data during the electrode lifting process, monitor the current change output by the current transformer, call the electrode displacement direction and current change direction at the corresponding time node, perform direction consistency judgment, establish the direction correspondence between the two, and obtain the direction matching sequence coefficient; S102: Based on the direction matching sequence coefficient, the electrode speed and current change rate are retrieved, the rate change characteristics under the direction consistent state are statistically analyzed, the overlap degree between the speed mutation interval and the current violent fluctuation interval is determined, and the rate synchronization matching degree is obtained; S103: According to the rate synchronization matching degree and the distribution ratio of the consistent nodes in the direction matching sequence coefficient, the stable state of the trend change is determined, the consistency characteristics of the trend are classified, and a trend consistency state group is generated.

4. The remote automation control method for a DC arc furnace according to claim 3, characterized in that: The specific steps of S2 are: S201: extracting current change values ​​and electrode speed values ​​based on the time periods with consistent directions in the trend consistency state group, determining whether the change directions of the current change and the electrode speed values ​​are consistent within the same time period, identifying data points with inconsistent change directions, and generating a trend value of the number of direction deviation points; S202: Calling the trend value of the number of direction deviation points, calculating the proportion of direction deviation points in a time period, and comparing it with the direction deviation ratio threshold, identifying the time period in which the deviation degree exceeds the judgment standard, and obtaining the abnormal section judgment interval value; S203: extracting the corresponding displacement response sequence according to the abnormal section discrimination interval value, detecting interval distribution changes, and comparing the differences with the standard interval under normal conditions, screening the time period where the degree of deviation exceeds the normal limit, and obtaining a displacement response abnormality identification list.

5. The remote automation control method for a DC arc furnace according to claim 4, characterized in that: The specific steps of S3 are: S301: Based on the time period marked in the displacement response abnormality identification list, call the voltage sampling data output by the arc voltage detection device, extract the continuous voltage change in the time period, calculate the voltage fluctuation characteristic value, and obtain the voltage change amplitude sequence; S302: Compare the voltage data in the voltage variation amplitude sequence with a set voltage variation threshold, filter out time nodes with values ​​greater than the threshold, and obtain a set of high-amplitude fluctuation time nodes; S303: Call all time nodes in the high-amplitude fluctuation time node set, arrange them in ascending order according to the size of timestamps, obtain a node sequence with reasonable structural continuity, and generate an abnormal mutation voltage time axis.

6. The remote automation control method for a DC arc furnace according to claim 5, characterized in that: The specific calculation formula for calculating the voltage fluctuation characteristic value is: ； in, represents the standardized voltage fluctuation characteristic value, Represents the instantaneous change of voltage at the i-th sampling point, N represents the total number of sampling points, represents the jth voltage peak amplitude, represents the j-th voltage trough amplitude, and M represents the number of voltage extreme value pairs.

7. The remote automation control method for a DC arc furnace according to claim 5, characterized in that: The specific steps of S4 are: S401: Based on the abnormal sudden voltage time axis and the displacement response abnormality identification list, cross-comparison is performed on the two types of time periods to determine the overlapping relationship between the start and end time ranges, calculate the overlapping characteristic value, extract the time segments with simultaneous abnormalities, and generate a set of sudden voltage and response overlap segments; S402: Calling the concentrated time period of the sudden voltage and response overlap section, extracting control intervention signal change information, screening according to signal amplitude change characteristics and intervention action density, and obtaining a list of intervention dense overlap sections; S403: Extracting the remote adjustment signal state according to the time period in the intervention dense overlap section list, determining the matching relationship between the signal fluctuation frequency and the intervention period, identifying the time section of the adjustment conflict, and obtaining the remote adjustment conflict section set.

8. The remote automation control method for a DC arc furnace according to claim 7, characterized in that: The specific calculation formula for calculating the overlapping eigenvalue is: ； in, Represents the start time of the voltage abnormal section, Represents the end time of the voltage abnormal section, Represents the start time of the abnormal displacement response section, Represents the end time of the abnormal displacement response section, Representative The mutation amplitude of the voltage sampling point, Represents the arithmetic mean of the voltage mutation amplitude, Represents the voltage reference value, Representative The displacement response amplitude at each voltage abnormal time point, Representative The voltage amplitude at the time point of the abnormal displacement response, Represents the displacement reference value, Represents the total number of voltage sampling points, Represents the total number of abnormal time points.

9. The remote automation control method for a DC arc furnace according to claim 7, characterized in that: The specific steps of S5 are: S501: Based on the electrode operating state and arc voltage change characteristics within the concentrated time period of the remote adjustment conflict section, extract the electrode instantaneous operating parameters and corresponding time sequence, combine the jump interval and change amplitude, determine the correlation between the arc voltage frequency and power oscillation, identify the area deviating from the adjustment response period, and generate the electrode response offset rate; S502: Identify the time offset between the control command and the response action based on the electrode response offset rate, and select the time period that meets the mismatch judgment condition based on the coincidence of the oscillation amplitude in the arc voltage characteristic and the command offset point, and generate the mismatch section coincidence coefficient; S503: Call the mismatch section overlap coefficient, extract the control instruction section that meets the overlap condition, combine the amplitude information of the original control instruction, adjust the output structure of the upward control instruction, construct an instruction combination with matching time and amplitude, and establish a remote compensation action instruction structure.

10. A remote automation control system for a DC arc furnace, characterized in that: According to a method for remote automation control of a DC arc furnace according to any one of claims 1 to 9, the system comprises: The trend identification module obtains the displacement direction and speed parameters of the electrode lifting, collects the energized current direction change values ​​recorded by the current transformer, determines the direction consistency based on the displacement direction and current direction at the same time point, constructs a consistent state sequence in chronological order, extracts continuous consistent direction states and determines the persistence characteristics, establishes a displacement and current trend relationship sequence, and obtains a trend consistency state group; The response analysis module extracts the current change parameters and the electrode speed change parameters based on the time periods with the same direction in the trend consistency state group, determines the matching characteristics of the two under the time correspondence relationship, identifies the sections where the deviation exceeds the set standard, and obtains a list of displacement response abnormality identifications; The fluctuation screening module calls the voltage signal sampled by the arc voltage detection device based on the time period marked in the displacement response abnormality identification list, extracts the fluctuation amplitude characteristics of the sampling time point, screens the nodes with a voltage change greater than the voltage change threshold, and sorts them by time to obtain the abnormal mutation voltage time axis; The intervention determination module compares the abnormal sudden voltage time axis with the time periods recorded in the displacement response abnormality identification list, identifies the overlapping intervals between the two, and collects the time periods that meet the control intervention conditions to obtain a remote adjustment conflict section set; The compensation generation module determines whether there is a section where the control instruction direction is inconsistent with the response state based on the electrode operating status and arc voltage change characteristics in the remote adjustment conflict section, extracts the section and constructs the compensation instruction structure in the upward direction to obtain the remote compensation action instruction structure.

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