A circuit breaker operation state full life cycle detection method based on remote monitoring

Abstract

The application discloses a circuit breaker operation state full life cycle detection method based on remote monitoring, and relates to the technical field of power equipment monitoring, which comprises the following steps: acquiring control instruction data and multi-channel state monitoring data recorded by a circuit breaker control device, wherein the multi-channel state monitoring data comprises current channel data, voltage channel data and vibration channel data; constructing a sliding alignment window based on a time stamp in the control instruction data, and intercepting the multi-channel state monitoring data through the sliding alignment window to generate a channel response segment; and constructing cross-channel response time difference data based on response starting time points of each channel in the channel response segment; the application realizes causal coupling analysis and abnormal evolution tracking of circuit breaker control actions and multi-channel responses by fusing control instruction data and multi-channel state monitoring data to construct an operation response mapping chain, so that accurate detection of the full life cycle of the circuit breaker operation state is completed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power equipment monitoring, in particular to a circuit breaker operating state full life cycle detection method based on remote monitoring. BACKGROUND

[0002] In the power distribution network operation scene, as a key switching device, the change of the operating state of the circuit breaker directly affects the safety and stability of the power grid. In order to ensure its long-term stable operation, the existing method generally periodically samples and monitors the operating state of the circuit breaker by installing a single type of sensor, and combines the periodic maintenance records to make an empirical evaluation of the operating state. This method can assist in judging the fault risk when the performance of the circuit breaker deteriorates to a large extent, but it is difficult to cover the multi-channel dynamic response behavior triggered by the control action in the running process, resulting in the inability to realize the synchronous perception and joint analysis of the multi-source state of the circuit breaker in the early stage of operation.

[0003] At the same time, the existing method usually takes single-channel monitoring data as an independent sample for threshold judgment or trend fitting analysis, which can give an alarm prompt when the abnormality is high, but lacks a causal coupling verification mechanism between the control action and the physical response. It is difficult to accurately determine the correspondence between the operation action and the state change of the circuit breaker in the early stage of abnormality, which limits the causal determination ability of the operating state;

[0004] In addition, the existing method relies on historical maintenance records to speculate the life stage of the circuit breaker, which can provide a phased risk assessment after long-term operation of the device, but cannot continuously track abnormal interruptions in the operation response process during actual operation. It lacks the ability to dynamically record the closed state of the causal link between the control action and the channel response, making it difficult to judge the trend change of the state evolution of the circuit breaker, so there is an urgent need for a detection method that can integrate control instruction data and multi-channel state monitoring data and realize causal verification and abnormal evolution identification to realize dynamic detection and state stage determination of the full life cycle of the circuit breaker operating state. SUMMARY

[0005] In order to solve the above technical problems, the present application provides a circuit breaker operating state full life cycle detection method based on remote monitoring.

[0006] A circuit breaker operating state full life cycle detection method based on remote monitoring, the method comprising:

[0007] S11, obtaining control instruction data and multi-channel state monitoring data recorded by a circuit breaker control device, the multi-channel state monitoring data comprising current channel data, voltage channel data and vibration channel data;

[0008] S12, constructing a sliding alignment window based on the time stamp in the control instruction data, and cutting the multi-channel state monitoring data through the sliding alignment window to generate a channel response segment;

[0009] S13, constructing cross-channel response time difference data based on the response starting time point of each channel in the channel response segment, and inputting the response time difference data into a delay correction model to generate a delay alignment label;

[0010] S14, filtering the channel response segment that meets the response synchronization through the delay alignment label, and calculating a matching value based on the control instruction data and the channel response segment to generate a cause-effect verification label;

[0011] S15, constructing an operation response mapping chain based on the cause-effect verification label arranged in time sequence, and generating state evolution marker data according to the continuous non-closed loop events in the mapping chain.

[0012] Further, the step of obtaining the multi-channel state monitoring data is:

[0013] S111, obtaining current channel data through a current sensor on the main circuit of the circuit breaker according to a preset sampling time interval;

[0014] S112, obtaining voltage channel data through voltage sensors on the incoming line side and the outgoing line side of the circuit breaker according to a preset sampling time interval;

[0015] S113, obtaining vibration channel data through a three-axis vibration acceleration sensor installed on the housing of the circuit breaker according to a preset sampling time interval;

[0016] S114, performing time series integration processing according to the sampling time stamps of the current channel data, the voltage channel data and the vibration channel data to generate the multi-channel state monitoring data.

[0017] Further, the step of integrating the current channel data, the voltage channel data and the vibration channel data is:

[0018] S114.1, extracting the sampling time stamp field of the current channel data, the voltage channel data and the vibration channel data;

[0019] S114.2, constructing a time index list according to all the sampling time stamp fields;

[0020] S114.3, performing synchronous resampling on the current channel data, the voltage channel data and the vibration channel data based on the time index list;

[0021] S114.4, outputting the synchronous resampling result as the multi-channel state monitoring data.

[0022] Further, the step of S12 is:

[0023] S121, calculating adjacent time stamp intervals according to time stamp fields in the control instruction data;

[0024] S122, generating sliding alignment window boundaries based on the adjacent time stamp intervals and a set sliding step;

[0025] S123, intercepting multi-channel state monitoring data through the sliding alignment window boundaries to obtain channel response segments.

[0026] Further, the step of generating the channel response segments is:

[0027] S123.1, extracting a start time and an end time of each sliding alignment window;

[0028] S123.2, intercepting multi-channel state monitoring data within the window range according to the start time and the end time;

[0029] S123.3, arranging the intercepted multi-channel state monitoring data in ascending order of time stamps;

[0030] S123.4, outputting the multi-channel state monitoring data arranged in ascending order of time stamps as the channel response segments.

[0031] Further, the step of constructing the cross-channel response time difference data is:

[0032] S131, extracting response start time points of current channel data, voltage channel data and vibration channel data in the channel response segments;

[0033] S132, calculating response start time difference values between each pair of channels;

[0034] S133, combining the response start time difference values between all channel pairs into cross-channel response time difference data;

[0035] S134, inputting the cross-channel response time difference data into a delay correction model to generate a delay alignment label.

[0036] Further, the step of generating the cross-channel response time difference data is:

[0037] S133.1, performing difference calculation on the response start time points of the current channel data and the voltage channel data to obtain current-voltage start time difference values;

[0038] S133.2, performing difference calculation on the response start time points of the current channel data and the vibration channel data to obtain current-vibration start time difference values;

[0039] S133.3, difference calculation is performed on the response starting time points of the voltage channel data and the vibration channel data, to obtain a voltage vibration starting time difference value;

[0040] S133.4, the current voltage starting time difference value, the current vibration starting time difference value, and the voltage vibration starting time difference value are summarized as cross-channel response time difference data.

[0041] Further, the step of generating the cause-effect verification label is:

[0042] S141, based on the delay alignment label, filtering out channel response segments with a response starting time difference value below a set synchronization threshold;

[0043] S142, extracting control instruction data corresponding to the synchronized channel response segment;

[0044] S143, performing matching value calculation based on the time interval and amplitude change of the control instruction data and the channel response segment;

[0045] S144, marking the channel response segment with a matching value exceeding a set threshold as a cause-effect verification label.

[0046] Further, the step of calculating the matching value is:

[0047] S143.1, extracting the action type field and the timestamp field in the control instruction data;

[0048] S143.2, extracting the response amplitude and the response starting time point in the channel response segment;

[0049] S143.3, difference calculation is performed on the timestamp field of the control instruction data and the response starting time point of the channel response segment, to obtain a time interval value;

[0050] S143.4, taking the time interval value and the response amplitude as joint inputs to calculate the matching value.

[0051] Further, the step of generating state evolution label data according to the continuous non-closed loop events in the mapping chain is:

[0052] S151, adjacent pairing connection is performed on the cause-effect verification labels arranged in time sequence to generate mapping chain units;

[0053] S152, performing time sequence splicing processing based on all mapping chain units to construct an operation response mapping chain;

[0054] S153, detecting whether there is a mapping chain unit with an open end node in the operation response mapping chain;

[0055] S154, marking the continuous non-closed loop mapping chain units as state evolution label data;

[0056] The logic of marking the state evolution marking data is:

[0057] S154.1, extracting the starting node and the ending node of all mapping chain units in the operation response mapping chain;

[0058] S154.2, screening out the mapping chain units whose ending nodes do not appear in any starting node;

[0059] S154.3, performing aggregation index processing on the adjacent time-continuous unclosed loop mapping chain units;

[0060] S154.4, outputting the unclosed loop mapping chain units after the aggregation index processing as the state evolution marking data.

[0061] Compared with the prior art, the present application has the beneficial effects that:

[0062] The present application can realize joint analysis on the synchronism and response characteristics of each physical channel after the circuit breaker executes the action under the remote monitoring environment by constructing the channel response segment based on the control instruction data and the multi-channel state monitoring data, so as to obtain the real response process caused by the control action in different channels, and further improve the multi-source state perception ability of the circuit breaker in the running process, thereby expanding the collection coverage of the running state.

[0063] In addition, the present application can identify the causal coupling degree between the control action and the channel response in two dimensions of time and amplitude by screening out the channel response segment with higher response synchronism according to the cross-channel response time difference data calculated from the channel response segment and the delay alignment label, and then calculating the matching value from the corresponding control instruction data and marking it as a causal verification label, thereby improving the analysis accuracy of the corresponding relationship between the circuit breaker operation action and the state change, and thereby enhancing the causal judgment ability of the running state.

[0064] Further, the present application can identify the concentration degree and evolution trend of abnormal events according to the distribution characteristics of the state evolution marking data in time by constructing the operation response mapping chain based on the causal verification label and extracting the continuous unclosed mapping chain units as the state evolution marking data, and then judge the state evolution stage of the circuit breaker from commissioning, stable operation to degradation failure, thereby improving the evolution detection ability of the whole life cycle of the circuit breaker running state.

[0065] In summary, the present application can realize causal coupling analysis and abnormal evolution tracking of the circuit breaker control action and multi-channel response by fusing the control instruction data and the multi-channel state monitoring data to construct the operation response mapping chain, thereby completing the accurate detection of the whole life cycle of the circuit breaker running state. BRIEF DESCRIPTION OF DRAWINGS

[0066] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0067] Figure 1 A flow chart of a circuit breaker operating state full life cycle detection method based on remote monitoring provided for Embodiment 1 of the present application. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application. EMBODIMENT

[0069] Please refer to Figure 1 As shown in the drawings, the present embodiment discloses a circuit breaker operating state full life cycle detection method based on remote monitoring, which comprises:

[0070] S11, acquiring control instruction data recorded by a circuit breaker control device and multi-channel state monitoring data, wherein the multi-channel state monitoring data comprises current channel data, voltage channel data and vibration channel data;

[0071] Specifically, the step of acquiring the multi-channel state monitoring data comprises:

[0072] S111, acquiring current channel data through a current sensor on a main circuit of the circuit breaker according to a preset sampling time interval;

[0073] In a specific embodiment, a Hall current sensor is installed on the main circuit of the circuit breaker, and a sampling time interval is set.

[0074] It should be noted that the current sensor collects instantaneous current amplitude at each sampling time interval and synchronously records a sampling time stamp, and the record of all sampling time stamps and corresponding current amplitudes is taken as the current channel data.

[0075] S112, acquiring voltage channel data through voltage sensors on the incoming line side and the outgoing line side of the circuit breaker according to a preset sampling time interval;

[0076] In one specific embodiment, one voltage sensor is arranged at each of the incoming line side and the outgoing line side of the circuit breaker, and a sampling time interval is set;

[0077] It should be noted that the voltage sensor synchronously collects the incoming line voltage value and the outgoing line voltage value at each sampling time interval, and records the corresponding sampling time stamp. The record composed of the incoming line voltage value, the outgoing line voltage value and the sampling time stamp is taken as voltage channel data;

[0078] S113, acquiring vibration channel data by a three-axis vibration acceleration sensor installed on the circuit breaker housing according to a preset sampling time interval;

[0079] In one specific embodiment, a three-axis vibration acceleration sensor is fixedly installed on the outer surface of the circuit breaker housing, and a sampling time interval is set;

[0080] It should be noted that the vibration acceleration sensor collects the instantaneous acceleration vector in three-axis direction at each sampling time interval, and records the corresponding sampling time stamp. The record composed of the three-axis acceleration vector and the sampling time stamp is taken as vibration channel data;

[0081] S114, performing time sequence integration processing according to the sampling time stamps of the current channel data, the voltage channel data and the vibration channel data, to generate multi-channel state monitoring data.

[0082] In one specific embodiment, the three types of channel data are integrated to ensure time synchronization;

[0083] It should be noted that the integration processing constructs a unified time index by extracting the sampling time stamps of the three types of channel data, and performs resampling on each type of channel data according to the time index. The current, voltage and vibration data at the same time point are combined into one synchronous record to form multi-channel state monitoring data;

[0084] Specifically, the steps of integrating the current channel data, the voltage channel data and the vibration channel data are as follows:

[0085] S114.1, extracting the sampling time stamp fields of the current channel data, the voltage channel data and the vibration channel data;

[0086] In one specific embodiment, the time stamps recorded in the three types of channel data are read one by one;

[0087] It should be noted that the extraction result is a time stamp field set containing all time stamps.

[0088] S114.2, constructing a time index list according to all the sampling time stamp fields;

[0089] In one specific implementation, all timestamps in the timestamp field set are sorted in ascending order by time;

[0090] It should be noted that the ascending order results are used for subsequent data alignment, and the time points in the time index list are divided at equal intervals.

[0091] S114.3, Perform synchronous resampling of current channel data, voltage channel data and vibration channel data based on time index list;

[0092] For each index time point in the time index list, find the record with the closest time in the three types of channel data;

[0093] It should be noted that when the indexed time point does not appear in the original record, linear interpolation is used to fill in the corresponding channel values ​​to avoid missing data.

[0094] S114.4 outputs the synchronous resampling results as multi-channel status monitoring data.

[0095] The current channel data, voltage channel data, and vibration channel data matched at each index time point are jointly encapsulated into a unified structure record;

[0096] It should be noted that the set of all output structure records constitutes the multi-channel status monitoring data.

[0097] S12, construct a sliding alignment window based on the timestamp in the control command data, and extract multi-channel status monitoring data through the sliding alignment window to generate channel response segments;

[0098] Specifically, the steps in S12 are as follows:

[0099] S121, Calculate the adjacent timestamp interval based on the timestamp field in the control command data;

[0100] In one specific embodiment, all timestamp fields arranged in chronological order are extracted from the control instruction data;

[0101] It should be noted that the calculation method is to subtract the timestamp of the previous control command data from the timestamp of the subsequent control command data to obtain the interval between adjacent timestamps, which is used for subsequent window boundary settings.

[0102] S122, Generate sliding alignment window boundaries based on adjacent timestamp intervals and set sliding step size;

[0103] In one specific embodiment, the timestamp of the earliest control instruction data is set as the starting point of the initial window, and the window start and end times are generated by sequentially shifting backward according to the set sliding step size.

[0104] It should be noted that the sliding step size is a preset parameter, which is set according to the historical instruction interval statistics. It is used to control the movement distance of the window start and end time. Each window boundary includes the window start time and end time, which serve as the sliding alignment window boundary.

[0105] S123, multi-channel status monitoring data is captured by sliding the alignment window boundaries to obtain channel response segments;

[0106] In one specific embodiment, based on the start time and end time range defined by the boundary of each sliding alignment window, all records within the corresponding time period are extracted from the multi-channel status monitoring data;

[0107] It should be noted that the extracted records will be reassembled into channel response fragments;

[0108] Specifically, the steps for generating a channel response fragment are as follows:

[0109] S123.1, Extract the start and end times of each sliding alignment window;

[0110] In one specific embodiment, the time range field defined in the sliding alignment window boundary is read;

[0111] It should be noted that each sliding alignment window contains a set of start and end times to limit the truncation range;

[0112] S123.2, extract multi-channel status monitoring data within the window range based on the start time and end time;

[0113] In one specific embodiment, records in the multi-channel status monitoring data whose timestamps fall between the start and end times of the window are filtered out.

[0114] It should be noted that the extracted records are the collection of channel monitoring records within this window.

[0115] S123.3, sort the captured multi-channel status monitoring data in ascending order by timestamp;

[0116] In one specific embodiment, all records in the channel monitoring record set captured in the previous step are rearranged in ascending order by the timestamp field;

[0117] It should be noted that ascending order is used to ensure the consistency of the time sequence of channel monitoring records.

[0118] S123.4 outputs the multi-channel status monitoring data arranged in ascending order of timestamps as channel response segments.

[0119] S13, construct cross-channel response time difference data based on the response start time point of each channel in the channel response segment, and input the response time difference data into the delay correction model to generate delay alignment labels;

[0120] Specifically, the steps for constructing cross-channel response time difference data are as follows:

[0121] S131, extract the response start time points of current channel data, voltage channel data and vibration channel data in the channel response segment;

[0122] In a specific embodiment, the response start time points of current channel data, voltage channel data and vibration channel data are respectively located from the channel response segments;

[0123] It should be noted that the response start time is not the first timestamp of the channel sampling record, but the time point when the instantaneous change rate of the current channel data, voltage channel data and vibration channel data corresponding to that channel first exceeds the corresponding preset noise threshold.

[0124] The noise threshold is obtained by statistically calculating the natural fluctuation range of current channel data, voltage channel data and vibration channel data at a fixed sampling time interval when the circuit breaker is in steady-state operation. It is used to distinguish between the natural noise fluctuation of the monitoring data and the actual response change.

[0125] Specifically, for each type of channel data, continuous monitoring records under no control command input state are extracted, and the mean and standard deviation of their instantaneous change rate are calculated. The change rate value corresponding to "mean plus three times the standard deviation" is set as the noise threshold for that type of channel data. When the instantaneous change rate of any type of channel data exceeds the corresponding preset noise threshold for the first time, that moment is determined as the response start time point of that channel.

[0126] In a specific embodiment, the formula for determining the response start time point is expressed as:

[0127] In the formula, For channel The response start time point, ∈{current channel, voltage channel, vibration channel}, For channel In time The sampled values, The predetermined response threshold; To satisfy the conditions Select the earliest time point from all the time points.

[0128] S132, calculate the response start time difference between each pair of channels;

[0129] In one specific embodiment, the response start time points of the three types of channels are paired up in pairs, and the time difference is calculated.

[0130] It should be noted that the time difference is calculated by subtracting the response start time of the previous channel from the response start time of the subsequent channel, thus obtaining the response start time difference value of the channel pair.

[0131] S133, combine the response start time differences between all channel pairs into cross-channel response time difference data;

[0132] In one specific embodiment, the calculation results of the three types of channels are aggregated and encapsulated by pairing them together;

[0133] It should be noted that the collected and encapsulated results are cross-channel response time difference data, which includes all initial response difference information between the three types of channels.

[0134] Specifically, the steps for generating cross-channel response time difference data are as follows:

[0135] S133.1, Perform difference calculation on the response start time points of the current channel data and the voltage channel data to obtain the current and voltage start time difference;

[0136] In one specific embodiment, the voltage channel data response start time is subtracted from the current channel data response start time.

[0137] It should be noted that the results are recorded as the initial time difference between current and voltage.

[0138] S133.2, Perform difference calculation on the response start time points of the current channel data and the vibration channel data to obtain the current vibration start time difference value;

[0139] In one specific embodiment, the start time of the current channel data response is subtracted from the start time of the vibration channel data response.

[0140] It should be noted that the results are recorded as the time difference at the start of the current oscillation.

[0141] S133.3, calculate the difference between the response start time points of the voltage channel data and the vibration channel data to obtain the voltage vibration start time difference value;

[0142] In one specific embodiment, the start time of the vibration channel data response is subtracted from the start time of the voltage channel data response.

[0143] It should be noted that the results are recorded as the time difference at the start of voltage oscillation.

[0144] S133.4, summarize the current and voltage start time difference, current vibration start time difference, and voltage vibration start time difference into cross-channel response time difference data.

[0145] In one specific embodiment, the three types of time difference values ​​are uniformly encapsulated into a structured record;

[0146] It should be noted that the output record is the cross-channel response time difference data.

[0147] S134, input cross-channel response time difference data into the delay correction model to generate delay alignment labels.

[0148] In one specific embodiment, the cross-channel response time difference data is used as input one by one and fed into a preset delay correction model to calculate the delay alignment label;

[0149] The training steps for the delay correction model are as follows:

[0150] Acquire historical operation record data and divide the historical operation record data into a delay correction training set and a delay correction test set. The historical operation record data includes cross-channel response time difference data and its corresponding delay alignment labels.

[0151] It should be noted that the historical operation record data comes from control command data and multi-channel status monitoring data collected by the circuit breaker under different operating states, including three typical operating conditions: normal closing, action lag, and fault response.

[0152] The cross-channel response time difference data consists of three types of data: current-voltage start time difference, current vibration start time difference, and voltage vibration start time difference. It is calculated by pairing the response start time points of the current channel data, voltage channel data, and vibration channel data.

[0153] The delayed alignment labels include "synchronous alignment labels", "lagging alignment labels", and "abnormal alignment labels";

[0154] Configure the initial classifier by taking the cross-channel response time difference data in the delay correction training set as the input data of the initial classifier and the delay alignment label corresponding to the delay correction training set as the output data of the initial classifier, and train the initial classifier to obtain the initial delay correction network.

[0155] It should be noted that the initial classifier includes, but is not limited to, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, or Random Forest;

[0156] The initial delay correction network is validated using a delay correction test set. The output of the initial delay correction network with an accuracy greater than or equal to the preset test accuracy is used as the pre-built delay correction model.

[0157] Preferably, the preset test accuracy is set to 90% or higher.

[0158] S141, based on the delay alignment label, filter out channel response segments whose response start time difference is lower than the set synchronization threshold;

[0159] In one specific embodiment, the response start time difference of all delayed alignment tag records is extracted;

[0160] It should be noted that: the difference in the start time of each response is compared with a preset synchronization threshold, and channel response segments that are lower than the threshold are marked as synchronous channel response segments;

[0161] The synchronization threshold is a time difference limit value used to determine the synchronization of channel response segments. It is determined based on the statistical results of cross-channel response time difference data collected by the circuit breaker under normal operating conditions. Specifically, during the operation phase without abnormal events, the historical records of the initial time difference of current and voltage, the initial time difference of current vibration, and the initial time difference of voltage vibration are extracted. The mean and standard deviation of each type of cross-channel response time difference data are calculated, and the value corresponding to "mean plus twice the standard deviation" is set as the synchronization threshold of that type of cross-channel response time difference data. The synchronization threshold is a one-sided judgment threshold. When the response start time difference of any channel pair is less than the corresponding synchronization threshold, the channel response segment is judged to meet the synchronization requirements.

[0162] The delay alignment label is used to mark the synchronization status of channel response segments. The "synchronization alignment label" category indicates that the cross-channel response time difference data of the channel response segment is within the normal synchronization range determined by the delay correction model. The "hysteresis alignment label" and "abnormal alignment label" categories indicate the existence of response delay and unexpected response status, respectively.

[0163] By filtering based on delay alignment labels, channel response segments that meet synchronization requirements can be dynamically selected without relying on fixed synchronization thresholds, providing a data foundation for subsequent matching value calculation and causal verification.

[0164] S142, Extract the control command data corresponding to the synchronous channel response segment;

[0165] In one specific embodiment, records within the same time range are retrieved from the control command data based on the timestamp range in the synchronization channel response segment;

[0166] It should be noted that the extracted records are control command data that correspond one-to-one with the response segments of the synchronization channel.

[0167] S143, perform matching value calculation based on the time interval and amplitude change between control command data and channel response segments;

[0168] In one specific embodiment, the timestamp field of the control command data is aligned with the response start time of the channel response segment, and the degree of matching between the two is calculated in combination with the response amplitude change of the channel response segment.

[0169] It should be noted that the result of the matching degree calculation is a matching value, which is used to characterize the strength of the causal relationship between the instruction and the response.

[0170] Specifically, the steps for calculating the matching value are as follows:

[0171] S143.1 Extract the action type field and timestamp field from the control instruction data;

[0172] S143.2, Extract the response amplitude and response start time point from the channel response segment;

[0173] In one specific embodiment, the response start time of the first record of the channel response segment and the maximum response amplitude within the entire record segment are read;

[0174] It should be noted that the response amplitude is used to measure the intensity of the action, and the response start time is used to align with the command time.

[0175] S143.3, perform difference calculation between the timestamp field of the control command data and the response start time of the channel response segment to obtain the time interval value;

[0176] In one specific embodiment, the timestamp field of the control command data is subtracted from the response start time of the channel response segment;

[0177] It should be noted that the difference results are recorded as time interval values.

[0178] S143.4 uses the time interval value and the response amplitude as joint inputs to calculate the matching value.

[0179] In a specific embodiment, the logical formula for calculating the matching value is:

[0180] In the formula, For the matching value, The normalized value of the time interval. In response to the normalized amplitude value, , Normalized value of time interval Normalized response amplitude The corresponding weighting coefficients, + =1, > Preferably, =0.7, β=0.3;

[0181] Among them, the time interval normalized value The calculation formula is: ;middle, This is the time interval value. This is the average response delay time calculated based on historical response delay times under normal response conditions;

[0182] Normalized response amplitude The calculation formula is: ;in, In response to amplitude, This is the average response amplitude calculated based on historical response amplitudes under normal response conditions;

[0183] It should be noted that: matching value This is a dimensionless score, with a value ranging from [0,1]. Matching value The closer the value is to 1, the more synchronized the time response between the control command data and the channel response segment, and the better the match of the action intensity.

[0184] S144, mark channel response fragments with matching values ​​exceeding a set threshold as causal verification labels.

[0185] In one specific embodiment, when the matching value The value is greater than the preset matching threshold. When the corresponding channel response segment is determined to have a causal relationship with the control command data, a causal verification label is generated.

[0186] The matching value threshold The generation logic is as follows:

[0187] By collecting a large number of historical samples under normal operating conditions of the circuit breaker, the matching value of each sample is calculated to form a set of matching values, and the mean of the set of matching values ​​is calculated. and standard deviation And based on the set confidence coefficient, calculate the threshold for generating matching values. ;

[0188] Represented as: , The confidence coefficient is the preferred one. The value is 95%;

[0189] It should be noted that: This is the lower limit of the matching value that 95% of historical samples can exceed under normal conditions; based on this, when a certain test... > Only when the response is normal is it considered to be normal; otherwise, it is considered that there is an abnormality in the command-response link or a synchronization deviation.

[0190] S15, construct an operation response mapping chain based on causal verification labels arranged in chronological order, and generate state evolution label data based on continuous non-closed-loop events in the mapping chain.

[0191] Specifically, the steps for generating state evolution label data based on consecutive non-closed-loop events in the mapping chain are as follows:

[0192] S151, pair and connect adjacent causal verification tags arranged in chronological order to generate mapping chain units;

[0193] In one specific embodiment, the causal verification tags are sorted in ascending order according to their timestamp fields, and adjacent causal verification tags are paired end-to-end.

[0194] It should be noted that each pair of beginning and end matching results constitutes a mapping chain unit, which includes a start node and an end node.

[0195] S152, perform time series splicing processing based on all mapping chain units to construct the operation response mapping chain;

[0196] In one specific embodiment, all mapping chain units are spliced ​​together end to end in chronological order, and the end node of the previous unit is aligned with the start node of the next unit.

[0197] It should be noted that the complete structure obtained after splicing is an operation-response mapping chain, which reflects the causal evolution path of operation-response arranged in time.

[0198] S153, Detect whether there is a mapping chain unit in the operation response mapping chain with an unclosed end node;

[0199] In one specific embodiment, it is checked whether the end node of each mapping chain unit in the operation response mapping chain appears simultaneously in the start node of any other mapping chain unit;

[0200] It should be noted that: the sequential concatenation of mapping chain units only indicates the order in which events occur, but does not guarantee the logical continuity of the operation-response causal chain;

[0201] This can be understood as follows: the temporal concatenation of mapping chain units only reflects the order in which operation command data and channel response segments occur, and does not necessarily mean that the causal link between these events is continuous. In actual operation, there may be situations where control command data does not trigger a channel response segment, or a channel response segment does not trigger subsequent control command data. In this case, the link has a logical break and cannot form a closed operation-response causal chain. Therefore, relying solely on temporal sequence concatenation cannot identify such breaks. It is necessary to detect whether the end node reappears in the start node of the subsequent mapping chain unit to determine whether there is an unclosed loop unit.

[0202] The generation of unclosed-loop units originates from logical breaks in the control chain, typically caused by contact aging, drive mechanism lag, or control mismatch. The purpose of detecting unclosed-loop units is to identify abnormal interruptions in the operational response chain and to provide a basis for generating subsequent state evolution marker data.

[0203] S154, mark the continuous non-closed-loop mapping chain units as state evolution label data;

[0204] In one specific embodiment, multiple temporally adjacent unclosed-loop mapping chain units are aggregated and marked;

[0205] It should be noted that the aggregated labeling results are state evolution labeling data, used to represent unclosed abnormal evolution states in the operation chain.

[0206] Specifically, the logic for the evolution of labeled state labeled data is as follows:

[0207] S154.1 Extract the start and end nodes of all mapping chain units in the operation response mapping chain;

[0208] In one specific embodiment, the start node and end node of each mapping chain unit in the operation response mapping chain are read, and all start nodes are combined into a start node list, and all end nodes are combined into an end node list.

[0209] S154.2, filter out mapping chain units whose end node does not appear in any starting node;

[0210] In one specific embodiment, the list of ending nodes is compared with the list of starting nodes to filter out all ending nodes that do not appear in the list of starting nodes, and the corresponding mapping chain units are assigned to the set of unclosed-loop units. The set of unclosed-loop units reflects the location where the link between control command data and channel response segments breaks.

[0211] It should be noted that the mapping chain units containing these end nodes are the unclosed loop units.

[0212] S154.3, perform aggregated index processing on consecutive unclosed loop mapping chain units in adjacent time intervals;

[0213] In one specific embodiment, the set of unclosed loop units is arranged in ascending order by timestamp, and an aggregation index is performed on unclosed loop units with adjacent time intervals less than a preset clustering threshold, merging them into a continuous abnormal evolution record.

[0214] It should be noted that the clustering threshold is determined based on the statistical distribution of time intervals of unclosed events under historical normal operating conditions.

[0215] S154.4 outputs the unclosed-loop mapping chain units after aggregated index processing as state evolution marker data.

[0216] In one specific embodiment, the aggregated continuous anomalous evolution records are encapsulated as state evolution marker data and arranged in ascending order by timestamp;

[0217] It should be noted that the encapsulated record is the state evolution marker data, which is used to characterize the abnormal evolution process of the operation-response causal chain throughout the entire life cycle of the circuit breaker's operating state.

[0218] All state evolution marker data are arranged in ascending order by timestamp. The concentration of abnormal events in time is determined based on the time interval between adjacent records. When the time interval gradually shortens, it indicates that the frequency of abnormal events is increasing, and it can be determined that the circuit breaker is in the state deterioration stage. When the time interval remains stable or extends, it indicates that the circuit breaker is in the stable operation stage.

[0219] By counting the number of state evolution marker data that appear within the same time range, when a large number of state evolution marker data appear consecutively in a short period of time, it indicates that the operation-response link is interrupted in large numbers, and it can be determined that the circuit breaker is close to the fault shutdown stage.

[0220] By comparing the temporal distribution of state evolution marker data with the circuit breaker commissioning records, the scattered state evolution marker data that appeared in the early stage of commissioning represent the early commissioning stage, while the high-frequency state evolution marker data that appeared after long-term operation represent the aging and degradation stage.

[0221] The above analysis enables us to deduce the state evolution trajectory of a circuit breaker from commissioning and stable operation to deterioration and failure based on state evolution marker data, thus achieving full life cycle detection of the circuit breaker's operating status.

[0222] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired or wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0223] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0224] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0225] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0226] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0227] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for detecting the full lifecycle operating status of circuit breakers based on remote monitoring, characterized in that, The method includes: S11, acquire control command data and multi-channel status monitoring data recorded by the circuit breaker control device, wherein the multi-channel status monitoring data includes current channel data, voltage channel data and vibration channel data; S12, construct a sliding alignment window based on the timestamp in the control command data, and extract multi-channel status monitoring data through the sliding alignment window to generate channel response segments; S13, construct cross-channel response time difference data based on the response start time point of each channel in the channel response segment, and input the response time difference data into the delay correction model to generate delay alignment labels; S14, filter channel response segments that meet the response synchronization by delay alignment labels, and calculate the matching value based on the control command data and the channel response segments to generate causal verification labels; S15, construct an operation response mapping chain based on causal verification labels arranged in chronological order, and generate state evolution label data based on continuous non-closed-loop events in the mapping chain; The steps to obtain multi-channel status monitoring data are as follows: S111: Current channel data is acquired by the current sensor on the main circuit of the circuit breaker according to a preset sampling time interval; S112, voltage channel data is acquired by voltage sensors on the incoming and outgoing sides of the circuit breaker according to a preset sampling time interval; S113, vibration channel data is acquired by a triaxial vibration acceleration sensor installed on the circuit breaker housing at a preset sampling time interval; S114, Perform time series integration processing based on the sampling timestamps of current channel data, voltage channel data and vibration channel data to generate multi-channel status monitoring data; The steps for integrating current channel data, voltage channel data, and vibration channel data are as follows: S114.1 Extract the sampling timestamp fields of current channel data, voltage channel data and vibration channel data; S114.2, Construct a time index list based on all sampled timestamp fields; S114.3, Perform synchronous resampling of current channel data, voltage channel data and vibration channel data based on time index list; S114.4 outputs the synchronous resampling results as multi-channel status monitoring data; The steps in S12 are as follows: S121, Calculate the adjacent timestamp interval based on the timestamp field in the control command data; S122, Generate sliding alignment window boundaries based on adjacent timestamp intervals and set sliding step size; S123, multi-channel status monitoring data is captured by sliding the alignment window boundaries to obtain channel response segments; The steps for generating a channel response fragment are as follows: S123.1, Extract the start and end times of each sliding alignment window; S123.2, extract multi-channel status monitoring data within the window range based on the start time and end time; S123.3, sort the captured multi-channel status monitoring data in ascending order by timestamp; S123.4 outputs the multi-channel status monitoring data arranged in ascending order of timestamps as channel response segments; The steps to construct cross-channel response time difference data are as follows: S131, extract the response start time points of current channel data, voltage channel data and vibration channel data in the channel response segment; S132, calculate the response start time difference between each pair of channels; S133, combine the response start time differences between all channel pairs into cross-channel response time difference data; S134, Input the cross-channel response time difference data into the delay correction model to generate delay alignment labels; The steps to generate cross-channel response time difference data are as follows: S133.1, Perform difference calculation on the response start time points of the current channel data and the voltage channel data to obtain the current and voltage start time difference; S133.2, Perform difference calculation on the response start time points of the current channel data and the vibration channel data to obtain the current vibration start time difference value; S133.3, calculate the difference between the response start time points of the voltage channel data and the vibration channel data to obtain the voltage vibration start time difference value; S133.4, summarize the current and voltage start time difference, current vibration start time difference, and voltage vibration start time difference into cross-channel response time difference data.

2. The method for full life-cycle monitoring of circuit breaker operating status based on remote monitoring according to claim 1, characterized in that, The steps to generate causal verification labels are as follows: S141, based on the delay alignment label, filter out channel response segments whose response start time difference is lower than the set synchronization threshold; S142, Extract the control command data corresponding to the synchronous channel response segment; S143, perform matching value calculation based on the time interval and amplitude change between control command data and channel response segments; S144, mark channel response fragments with matching values ​​exceeding a set threshold as causal verification labels.

3. The method for full life-cycle monitoring of circuit breaker operating status based on remote monitoring according to claim 2, characterized in that, The steps to calculate the matching value are as follows: S143.1 Extract the action type field and timestamp field from the control instruction data; S143.2, Extract the response amplitude and response start time point from the channel response segment; S143.3, perform difference calculation between the timestamp field of the control command data and the response start time of the channel response segment to obtain the time interval value; S143.4 uses the time interval value and the response amplitude as joint inputs to calculate the matching value.

4. The method for full life-cycle monitoring of circuit breaker operating status based on remote monitoring according to claim 3, characterized in that, The steps for generating state evolution label data based on consecutive non-closed-loop events in this mapping chain are as follows: S151, pair and connect adjacent causal verification tags arranged in chronological order to generate mapping chain units; S152, perform time series splicing processing based on all mapping chain units to construct the operation response mapping chain; S153, Detect whether there is a mapping chain unit in the operation response mapping chain with an unclosed end node; S154, mark the continuous non-closed-loop mapping chain units as state evolution label data; The logic for marking state evolution and marking data is as follows: S154.1 Extract the start and end nodes of all mapping chain units in the operation response mapping chain; S154.2, filter out mapping chain units whose end node does not appear in any starting node; S154.3, perform aggregated index processing on consecutive unclosed loop mapping chain units in adjacent time intervals; S154.4 outputs the unclosed-loop mapping chain units after aggregated index processing as state evolution marker data.

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