A method for quickly identifying unplanned power outage of voltage frequency mode

Abstract

The present application relates to the technical field of power system outage monitoring and identification, in particular to a voltage frequency mode unscheduled power outage fast identification method, voltage and frequency signals are acquired and synchronously collected to form a sampling relationship, the change direction of adjacent sampling points is extracted to establish a corresponding relationship, sections are divided and boundary positions are identified according to consistency, stable sections and fluctuation sections are distinguished, continuous segments and interval segments are formed according to section types, the breaking position is extracted and combined with the section types before and after to divide outage intervals and operation intervals, and the unscheduled power outage identification result is obtained. The present application forms a discrete sequence by the change direction of adjacent sampling points, establishes a corresponding relationship between voltage and frequency trends, divides sections according to consistency and indicates boundaries by change turning points, distinguishes stable and fluctuation sections within the sections, depicts section connection through continuous segments and interval relationship, and completes the division of outage intervals and operation intervals by combining the section types before and after.

Description

Technical Field

[0001] This invention relates to the field of power system outage monitoring and identification technology, and in particular to a method for rapid identification of unplanned power outages in voltage and frequency patterns. Background Technology

[0002] The field of power system outage monitoring and identification technology encompasses power grid operation status perception, voltage and frequency operation characteristic analysis, data acquisition in distribution and transmission links, and outage event discrimination mechanisms. Its core lies in constructing identification criteria for outage-related characteristics through continuous recording and comparative analysis of voltage amplitude change curves, frequency fluctuation trajectories, and sampling time-series data during power grid operation. Furthermore, by combining the temporal synchronization and spatial distribution relationships between different sampling points, the scope, duration, and type of outages are categorized. The overall technical system covers the setting of sampling rules for field measurement devices, the time-series processing of voltage and frequency data, continuous segment division methods, and outage discrimination logic based on segment characteristics, forming a complete monitoring chain from data acquisition to result determination.

[0003] The rapid identification method for unplanned power outages based on voltage and frequency patterns refers to a method that identifies unplanned power outage events by segmenting and analyzing the temporal variation patterns of voltage and frequency sampling data in the power grid and comparing their features. The technical aspects it addresses include the periodic acquisition of voltage and frequency sampling data, the division of continuous segments in the sampling sequence, the determination of consistency and differences between different segments, and the classification of power outage types based on segment attributes. Specifically, it synchronously records voltage and frequency data by setting a sampling time window, divides the continuous variation trend in the sampling sequence into intervals, marks intervals with consistent variation trends as matching segments, and marks intervals with inconsistent variation trends as non-matching segments, and identifies power outages based on the distribution of segments on the time axis, thereby completing the identification process for unplanned power outage events.

[0004] Existing methods focus on comparing overall trends, and segment division relies on continuous fluctuation patterns, lacking support from point-by-point changes. This makes it difficult to distinguish between local anomalies and actual power outages. Boundary positions in the sampling sequence are often inferred from the overall trend, which can easily lead to segment ambiguity when encountering slight frequency fluctuations or voltage fluctuations. Segment attribute division lacks internal structure characterization, making it difficult to distinguish between stable and fluctuating states. In actual operation, load switching can cause temporary mismatches, which are easily classified as abnormal segments, expanding the scope of power outage impact. There is a lack of analysis on the connection relationships between segments, and the determination of power outage intervals often relies on single segment attributes. During continuous operation, scattered misjudged intervals are prone to occur, affecting the overall consistency of judgment. Summary of the Invention

[0005] To address the technical problems existing in the prior art, embodiments of the present invention provide a method for rapid identification of unplanned power outages based on voltage frequency patterns. The technical solution is as follows: A method for rapid identification of unplanned power outages based on voltage frequency patterns includes the following steps: S1: Obtain the voltage amplitude signal output by the voltage transformer and the frequency change signal output by the frequency measuring device. Establish a sampling relationship based on a unified time. Extract the voltage change direction and frequency change direction of adjacent sampling points and match the time position to form a corresponding relationship, thereby obtaining the voltage-frequency change correspondence. S2: Based on the voltage-frequency change correspondence, extract the pairing relationship between voltage direction identifiers and frequency direction identifiers at adjacent change positions, form a consistent state according to the consistent relationship between voltage direction identifiers and frequency direction identifiers, and form interval segments corresponding to continuous time positions. Based on the pairing relationship, form interval boundary positions according to the change positions, and obtain the continuous change segment structure. S3: Based on the continuously changing segment structure, extract the pairing relationship between voltage direction identifier and frequency direction identifier within the segment, form a pairing distribution state according to the consistent relationship between voltage direction identifier and frequency direction identifier, divide the continuously consistent segment into stable segment, divide the alternating segment into fluctuating segment, and form the segment type distribution result; S4: Based on the segment type distribution results, extract the correspondence between segment type and time position, and correspond segments with the same type and continuous time to continuous segments, and segments with different types and discontinuous time to interval segments. Based on the positional relationship between continuous segments and interval segments, form a stable segment distribution structure to obtain a stable segment distribution structure. S5: Based on the stable segment distribution structure, extract the time position correspondence between continuous segments and interval segments, divide the power outage interval and the operation interval according to the interruption interval corresponding to the break position of the continuous segment and the segment type before and after the interruption interval, and obtain the unplanned power outage identification result.

[0006] As a further aspect of the present invention, the voltage-frequency change correspondence includes a voltage change trend sequence, a frequency change trend sequence, and a time-series matching relationship; the continuous change segment structure specifically includes a set of segment start and end positions, a segment length distribution, and segment boundary features; the segment type distribution results include stable segment identifiers, fluctuating segment identifiers, and segment type sequences; the stable segment distribution structure specifically includes continuous segment ranges, interval segment ranges, and segment combination forms; and the unplanned power outage identification results include power outage interval ranges, operating interval ranges, and interval status identifiers.

[0007] As a further aspect of the present invention, the step of obtaining the voltage frequency change correspondence is as follows: S101: Acquire the voltage amplitude signal output by the voltage transformer and the frequency change signal output by the frequency measuring device. Synchronously collect voltage sampling points and frequency sampling points according to a unified time. Detect the corresponding status of each sampling point's time position. Assign the voltage amplitude item and frequency change item corresponding to the time position to the same sampling unit. Mark the uncorresponding sampling items as empty sampling items to obtain the time registration sampling table. S102: Based on the time registration sampling table, extract the voltage amplitude trend and frequency change trend in adjacent sampling units, identify the voltage amplitude trend as rising, falling or flat, identify the frequency change trend as rising, falling or flat, and write the identified content into the voltage direction field and frequency direction field respectively to obtain the change direction identification field set. S103: Call the change direction identifier field set, match the voltage direction field and the frequency direction field according to the time position, write the voltage direction field and the frequency direction field at the same time position into the same pairing unit, detect the field integrity status of the pairing unit, arrange the field integrity pairing units in the order of time position, and obtain the voltage frequency change correspondence.

[0008] As a further aspect of the present invention, the step of obtaining the continuously changing segment structure is as follows: S201: Based on the voltage-frequency change correspondence, extract the voltage direction identifier and frequency direction identifier in adjacent change positions after arranging them according to time position, classify the two types of direction identifiers in the same change position into position pairing nodes, check the combination continuation status of the direction identifiers in adjacent position pairing nodes, mark the combination continuation item and the combination change item respectively, and obtain the set of adjacent change pairing nodes. S202: Call the adjacent change pairing node set, and based on the consistency relationship between voltage direction identifier and frequency direction identifier, check the consistency of the two types of direction identifiers in the position pairing node, mark the nodes with the same direction identifier as consistent, mark the nodes with different direction identifiers as inconsistent, and associate the combined change item with the corresponding time position to obtain the consistent state boundary identifier; S203: Based on the consistent state boundary identifier, extract the consecutive occurrence positions of the consistent state along the time axis, merge the consecutive occurrence positions into interval segments, extract the time position associated with the combined change item, mark the time position as the interval boundary position, and divide the interval segment around the interval boundary position to obtain the continuously changing segment structure.

[0009] As a further aspect of the present invention, the step of obtaining the segment type distribution result is as follows: S301: For the continuously changing segment structure, extract the pairing relationship between voltage direction identifier and frequency direction identifier within the segment, arrange the position pairing nodes in the order from the start position to the end position of the segment, compare the continuity state of the two types of direction identifier pairing relationship in adjacent position pairing nodes, write the continuous consistent state and the alternating change state into the node attributes respectively, and write the corresponding time position into the node attributes to obtain the segment state node trajectory. S302: Call the segment state node trajectory, perform continuous identification based on the consistent relationship between voltage direction identifier and frequency direction identifier, check the node attribute arrangement order within the same segment, connect the continuous consistent state into a consistent distribution range, connect the alternating change state into an alternating distribution range, write the start position and end position of the range into the segment attribute, and obtain the paired range attribute set; S303: Based on the pairing range attribute set, identify the segments corresponding to the consistent distribution range and divide them into stable segments, identify the segments corresponding to the alternating distribution range and divide them into fluctuating segments, and combine the segment types corresponding to the start and end positions of the segments to arrange the stable segments and fluctuating segments along the time position to obtain the segment type distribution result.

[0010] As a further aspect of the present invention, the step of obtaining the stable segment distribution structure is as follows: S401: Based on the distribution results of the segment types, extract the correspondence between segment types and time positions, arrange the segment type fields in the order of time positions, compare the consistency of segment type fields in adjacent time positions, write the consistent type items into the continuous candidate attribute, write the type change items into the interval candidate attribute, and write the corresponding start position and end position into the same attribute record to obtain the type time mapping chain. S402: Invoke the type time mapping chain, and based on the relationship between consistent segment types and continuous time positions, check the connection status of time positions in continuous candidate attributes, merge items with consistent types and continuous time into continuous segments, and based on the relationship between segment types changing and discontinuous time positions, merge items with changing types and discontinuous time into interval segments to obtain segment position boundary identifiers. S403: Based on the segment position boundary identifier, filter based on the positional relationship between continuous segments and interval segments, compare the boundary connection status of adjacent interval segments before and after continuous segments in stable segments, retain the continuous segments corresponding to stable segments, and remove the interval segments that do not form a boundary connection with the continuous segments in stable segments to obtain the distribution structure of stable segments.

[0011] As a further aspect of the present invention, the step of obtaining the unplanned power outage identification result is as follows: S501: Based on the stable segment distribution structure, extract the time position correspondence between continuous segments and interval segments, extract the segment type of continuous segments and the segment type of interval segments, arrange the time field of continuous segments and the time field of interval segments in the order of time position, compare the connection status of adjacent segments, and write the break position of continuous segments and the segment type before and after the break into the same attribute record to obtain the segment break attribute record. S502: Call the fragment breakage attribute record, form an interruption interval based on the breakage position of the continuous fragment, check the breakage start position and breakage end position in the interruption attribute, write the segment type in the interruption interval into the interruption interval field, write the segment types before and after the interruption interval into the corresponding relationship field, and obtain the corresponding field of the interruption interval. S503: Based on the corresponding fields of the interruption interval, and in conjunction with the segment types before and after the interruption interval, the corresponding relationship is compared, the fluctuating segment is identified as a continuous occurrence interval in the interruption interval and divided into a power outage interval, the stable segment is identified as a continuous occurrence interval in time position and divided into an operating interval, and the power outage interval and the operating interval are written into the interval status field to obtain the unplanned power outage identification result.

[0012] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: In this invention, a discrete sequence is formed by the changing direction of adjacent sampling points, establishing a point-to-point correspondence between voltage and frequency trends. Segment division is based on trend consistency, and boundary positions are directly indicated by changes and inflections, reducing amplitude fluctuation interference. Paired distribution states are introduced within segments, allowing stable segments and fluctuating segments to be distinguished based on overall continuity, avoiding segment misclassification caused by single-point anomalies. The segment connection structure is characterized by the continuity and interval relationship of segments, giving the interruption position an indicative feature. By combining the types of preceding and following segments, the determination of power outage intervals depends on contextual relationships rather than isolated segments, making the results continuously distributed on the time axis and enhancing stability under short-term disturbance scenarios. Attached Figure Description

[0013] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart illustrating the process of obtaining the voltage-frequency change correspondence in this invention. Figure 3 This is a flowchart illustrating the process of obtaining the continuously changing segment structure of the present invention. Figure 4 This is a flowchart illustrating the process of obtaining the segment type distribution results of the present invention. Figure 5 This is a flowchart illustrating the process of obtaining the stable segment distribution structure of the present invention. Figure 6 This is a flowchart illustrating the process of obtaining the unplanned power outage identification results according to the present invention. Detailed Implementation

[0014] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0015] refer to Figures 1 to 6 A method for rapid identification of unplanned power outages based on voltage frequency patterns, comprising the following steps: S1: Acquire the voltage amplitude signal output by the voltage transformer and the frequency change signal output by the frequency measuring device. Complete synchronous acquisition based on the same time to form a sampling relationship. Extract the voltage change direction of adjacent sampling points to form a voltage direction identifier. Extract the frequency change direction of adjacent sampling points to form a frequency direction identifier. Match the voltage direction identifier and the frequency direction identifier according to the time and position to form a one-to-one correspondence, and obtain the voltage frequency change correspondence. S2: Based on the correspondence between voltage and frequency changes, extract the pairing relationship between voltage direction identifiers and frequency direction identifiers at adjacent change positions. Compare the consistent relationship between voltage direction identifiers and frequency direction identifiers to form a consistent state. Combine the positions where the consistent state appears continuously on the time axis to form interval segments. Identify the interval boundary positions corresponding to the positions where the pairing relationship changes. Complete the segmentation around the interval boundary positions to obtain the continuous change segment structure. S3: For continuously changing segment structures, extract the pairing relationship between voltage direction identifiers and frequency direction identifiers within the segment. Based on the consistent relationship between voltage direction identifiers and frequency direction identifiers, continuously identify and form a pairing distribution state. Segments with consistently consistent pairing relationships are classified as stable segments, and segments with alternating pairing relationships are classified as fluctuating segments. Combine the segment type corresponding to the start and end positions of the segments to obtain the segment type distribution results. S4: Based on the distribution results of segment types, extract the correspondence between segment types and time positions. Form continuous segments based on consistent segment types and continuous time positions, and form interval segments based on changes in segment types and discontinuous time positions. Filter based on the positional relationship between continuous segments and interval segments to obtain a stable segment distribution structure. S5: Based on the stable segment distribution structure, extract the time position correspondence between continuous segments and interval segments, form interruption intervals according to the break position of continuous segments, compare the correspondences with the segment types before and after the interruption interval, identify the intervals in which fluctuating segments appear continuously in the interruption interval and classify them as power outage intervals, identify the intervals in which stable segments appear continuously in the time position and classify them as operating intervals, and obtain the unplanned power outage identification results.

[0016] The voltage and frequency change correspondence includes voltage change trend sequence, frequency change trend sequence, and time series matching relationship. The continuous change segment structure specifically includes the segment start and end position set, segment length distribution, and segment boundary characteristics. The segment type distribution results include stable segment identifier, fluctuating segment identifier, and segment type sequence. The stable segment distribution structure specifically includes continuous segment range, interval segment range, and segment combination form. The unplanned power outage identification results include power outage interval range, operating interval range, and interval status identifier.

[0017] Please see Figure 2 The steps to obtain the corresponding relationship between voltage and frequency changes are as follows: S101: Acquire the voltage amplitude signal output by the voltage transformer and the frequency change signal output by the frequency measuring device. Synchronously collect voltage sampling points and frequency sampling points according to a unified time. Detect the corresponding status of each sampling point's time position. Assign the voltage amplitude item and frequency change item corresponding to the time position to the same sampling unit. Mark the uncorresponding sampling items as empty sampling items to obtain the time registration sampling table. The system's underlying interface is invoked to extract the voltage amplitude signal sequence data item from the voltage transformer output terminal of the substation terminal acquisition equipment's bus monitoring node, and simultaneously extract the frequency change signal sequence data item from the frequency measurement device's output terminal. The time base parameter value set within the synchronous acquisition system, 2026-06-30 10:00:00.000, is extracted. The system's steady-state characteristic sequence output frequency parameter, 50Hz, is extracted, and the sampling time interval parameter value is obtained from the microprocessor's arithmetic unit, 20ms. Based on the time base parameter value and the sampling time interval parameter value, the sequence generation module is invoked to generate a set of theoretical sampling point timestamp sequences, extracting the first six theoretical timestamp node sequences as 0ms, 20ms, 40ms, 60ms, 80ms, and 100ms. For the extracted voltage amplitude signal sequence, the first to fourth groups of items with built-in time positions of 0ms, 20ms, 40ms, and 60ms are read from the register. The corresponding voltage amplitude parameters are 10.05kV, 10.02kV, 10.00kV, and 9.98kV, respectively. A null value check function is called to check the fifth group of items, returning a null pointer. The sixth group, with a built-in time position of 100ms, corresponds to a voltage amplitude parameter of 9.95kV. For the extracted frequency change signal sequence, the first to fifth groups of items with built-in time positions of 0ms, 20ms, 40ms, 60ms, and 80ms are read. The corresponding frequency change parameters are 50.01Hz, 50.00Hz, 49.99Hz, 49.98Hz, and 49.98Hz, respectively. The sixth group of items is checked and returns a null pointer. The logical comparison and judgment mechanism performs node state matching and judgment on the timestamp sequences of the first to fourth theoretical sampling points based on the time offset difference judgment condition. The voltage amplitude parameter and frequency change parameter at the corresponding time position are merged and written into the independent sampling unit sets corresponding to 0ms, 20ms, 40ms, and 60ms in memory and recorded as matching states. For the 80ms node of the fifth theoretical sampling point timestamp sequence, the traversal function is called to perform time position matching and judgment. Since the voltage data matching is empty, the address space corresponding to the voltage sampling parameter item of the 80ms node is directly assigned a value of negative 1. The frequency change parameter 49.98Hz is extracted, written into the unit, and a "no corresponding state record" label is attached. For the 100ms node of the sixth theoretical timestamp sequence, the time position matching and judgment is performed. Since the frequency data matching is empty, the address space of the corresponding frequency sampling parameter item is assigned a value of negative 1. The voltage amplitude parameter 9.95kV is extracted, written into the unit, and a "no corresponding state record" label is attached. All sampling unit sets with time indexes are summarized, arranged, and stored in the local database to obtain the time registration sampling table.

[0018] S102: Based on the time registration sampling table, extract the voltage amplitude trend and frequency change trend within adjacent sampling units, identify the voltage amplitude trend as rising, falling or flat, and identify the frequency change trend as rising, falling or flat. Write the identified content into the voltage direction field and frequency direction field respectively to obtain the change direction identifier field set. The system retrieves the time registration sampling table data stored in the local database. It initiates a loop read command on the data rows in the time registration sampling table, sorted in ascending order by time position parameters, to extract sampling interval units composed of adjacent time position parameter node sequences. It then sequentially obtains the voltage amplitude parameters and frequency change parameters at the start and end nodes of each interval unit. For the first sampling interval unit spanning from time node 0ms to 20ms, it extracts the starting voltage amplitude parameter of 10.05kV and the ending voltage amplitude parameter of 10.02kV, as well as the starting frequency change parameter of 50.01Hz and the ending frequency change parameter of 50.00Hz. The system then calls the calculation unit to obtain the voltage amplitude difference parameter within this interval unit as -0.03kV and the frequency change difference parameter as -0.01Hz. The parameter calibration module extracts 1000 consecutive sampling step-size data items from the historical steady-state operation database without switching operation cycles. The standard deviation mathematical operator is used to perform calculations on the extracted sequences, and a Gaussian distribution confidence principle constant amplification coefficient parameter is introduced. This yields a voltage stabilization boundary value of 0.009kV and a frequency stabilization boundary value of 0.003Hz. Therefore, the voltage stabilization reference range boundary within the system is set to -0.009kV to 0.009kV, and the frequency stabilization reference range boundary is set to -0.003Hz to 0.003Hz. For the first sampling interval unit, a numerical comparator is called to determine if the voltage amplitude difference parameter of -0.03kV and the frequency change difference parameter of -0.01Hz both meet the relationship of being less than the negative boundary. The corresponding voltage amplitude trend type attribute field and frequency change trend type attribute field for this interval are both written with the string data "decreasing". For the second sampling interval unit from 20ms to 40ms and the third sampling interval unit from 40ms to 60ms, after data extraction and comparator boundary condition judgment, the two corresponding direction type attribute fields are overwritten as the string data "decreasing". For the fourth sampling interval unit from 60ms to 80ms, the voltage amplitude parameter value record corresponding to the endpoint node 80ms is identified as negative 1, triggering the idle anomaly condition judgment mechanism. The voltage direction field attribute and frequency direction field attribute of this interval are synchronously assigned to the string data "idle type". The generated direction attribute field records are summarized and aggregated into the buffer according to the time node position to obtain a set of change direction identification fields with time mapping nodes.

[0019] S103: Call the change direction identifier field set, match the voltage direction field and frequency direction field according to the time position, write the voltage direction field and frequency direction field at the same time position into the same pairing unit, check the field integrity status of the pairing unit, arrange the field integrity pairing units in the order of time position, and obtain the voltage frequency change correspondence.

[0020] The system retrieves a set of fields with change direction identifiers for time-mapped nodes from the cache. It then calls a string processing function to extract and concatenate data for different time interval index positions within the field set. Specifically, it extracts the voltage direction field label "decreasing" and the frequency direction field label "decreasing" for the first sampling interval unit (0ms to 20ms), concatenates them to generate the first set of paired unit record objects, and allocates them to independent memory. The content record is a combination of "decreasing" and "decreasing". Simultaneously, it associates the corresponding time-mapped position label with an external pointer. The system then sequentially initiates a loop to extract bidirectional field labels for the second sampling interval unit (20ms to 40ms) and the third sampling interval unit (40ms to 60ms), concatenating them to generate the second and third sets of paired unit record objects, both containing the same combination of "decreasing" and "decreasing". Finally, it extracts the bidirectional field labels for the fourth sampling interval unit (60ms to 80ms), concatenates them to generate the fourth set of paired unit record objects, containing the combination of "empty type" and "empty type". Within the control unit, a constant variable for the judgment comparison string is defined as "empty type". Elements within the first, second, and third paired unit record objects are extracted. A homogeneity comparison module is called to perform a status comparison with the control variable. After confirming that none are equal to "empty type", the attribute writing tool is called to write the time position associated objects of these three paired units to the tag attribute value "complete label". Elements within the fourth paired unit record object are extracted, and the homogeneity comparison module is called to perform a comparison. After confirming that the condition of equal to "empty type" is met, the corresponding position is written to the tag attribute value "incomplete label". A filtering function is called to filter the paired unit sequence set based on the tag attribute results written to the records. A memory release instruction is called to directly remove data object records matching "incomplete label", retaining the set of data objects matching "complete label". The start time parameter values ​​(0ms, 20ms, 40ms) of the corresponding time position tags of the data objects in the retained set are extracted. A sorting algorithm is called to reallocate array index subscripts based on the size relationship of the time parameter values ​​to establish the storage order rules and tightly concatenate the outputs to obtain the voltage frequency change correspondence.

[0021] Please see Figure 3 The steps for obtaining the continuously changing segment structure are as follows: S201: Based on the correspondence between voltage and frequency changes, extract the voltage direction identifier and frequency direction identifier in adjacent change positions after arranging them according to time position, classify the two types of direction identifiers in the same change position into position pairing nodes, check the combination continuation status of the direction identifiers in adjacent position pairing nodes, mark the combination continuation item and the combination change item respectively, and obtain the set of adjacent change pairing nodes. The memory interface is called to extract the voltage frequency change correspondence of paired node data within consecutive time position intervals in chronological order. The memory buffer is then called to extract five consecutive, complete time position interval paired node sequences arranged according to a time base pattern. The results show that the sixth interval (100ms-120ms) shows "decreasing" and "unchanged" paired node records; the seventh interval (120ms-140ms) shows "increasing" and "unchanged" paired node records; the eighth interval (140ms-160ms) shows "increasing" and "unchanged" paired node records; the ninth interval (160ms-180ms) shows "decreasing" and "increasing" paired node records; and the tenth interval (180ms-200ms) shows "decreasing" and "increasing" paired node records. The array insertion command is invoked to merge the records in the first interval (0ms to 20ms), the second interval (20ms to 40ms), and the third interval (40ms to 60ms) of the original matrix set with the newly extracted records, arranging them sequentially according to the numerical value of the starting point of the time parameter span into the updated position-paired node sequence set. The loop comparator is invoked to perform a comparison and verification operation on the direction combination content continuation state within the paired node set corresponding to adjacent time nodes. The position-corresponding string judgment logic is invoked to determine that the corresponding elements of the first interval node and the second interval node are completely equal. The assignment command is invoked to assign the integer value code 1 to the corresponding state of the adjacent node position and write the label record item "Combination Continuation Item" into the structure. The judgment logic is invoked to confirm that the corresponding elements of the second interval node and the third interval node are completely equal, assigning the value code 1 and writing "Combination Continuation Item" into the structure. The judgment logic is invoked to confirm that the corresponding elements of the sixth interval node and the seventh interval node are completely equal, assigning the value code 0 to the corresponding state of the intersection position and writing the label record item "Combination Change Item". The decision logic is invoked to confirm that the contents of the seventh and eighth interval nodes are completely equal, assigning a numerical code of 1 and writing it to the "Combined Continuation Item". The decision logic is invoked to confirm that the corresponding elements of the eighth and ninth interval nodes are not completely equal, assigning a numerical code of 0 and writing it to the "Combined Change Item". The decision logic is invoked to confirm that the contents of the ninth and tenth intervals are completely equal, assigning a numerical code of 1 and writing it to the "Combined Continuation Item". All label sequence items with numerical codes 0 and 1 are attached to the corresponding interval connection gap time node positions and stored in the structured database, resulting in a set of adjacent change paired nodes.

[0022] S202: Call the adjacent change pairing node set, and based on the consistency relationship between voltage direction identifier and frequency direction identifier, check the consistency of the two types of direction identifiers in the position pairing node, mark the nodes with the same direction identifier as consistent, mark the nodes with different direction identifiers as inconsistent, and associate the combined change item with the corresponding time position to obtain the consistent state boundary identifier; The system retrieves a set of adjacent change pairing nodes stored in a structured database, containing numerical code labels. It then uses a string comparator to perform a consistency logic comparison between the voltage direction identifier parameter value and the frequency direction identifier parameter value recorded within the paired nodes at corresponding positions in each independent interval of the sequence set. For the first interval (0ms to 20ms), if both the first and second element parameter values ​​within the identified node are "decreasing," the system calls a logical discrimination function to determine if the string equality condition is met. It then calls a variable modifier to assign the corresponding internal state variable of the paired node at that position to the state code parameter value 2 and appends the string name "Consistent State." The system performs internal element comparisons for the second interval (20ms to 40ms) and the third interval (40ms to 60ms), confirming that the parameter values ​​are consistently "decreasing." The system then calls a variable modifier to assign the corresponding state variables to the state code parameter value 2 and append the name "Consistent State." Extract data records from the 100ms to 120ms node in the sixth interval. Identify that the first element parameter value is "decreasing" and the second element parameter value is "unchanged," determining that the two are inconsistent. Assign the internal state variable to the state code parameter value 3 and attach the name "Inconsistent State." Perform a comparison operation on the internal elements of the seventh, eighth, ninth, and tenth intervals to confirm that the extracted elements in each interval are inconsistent. Call the variable modifier to assign the value code 3 and attach the name "Inconsistent State" to each interval. After completing the identification and marking of internal elements in a single interval, call the query command to extract the specific boundary time position node parameters corresponding to all records with a value code equal to 0 and an attached "Combined Change Item" label. Obtain the first logically consistent time position parameter node, which is the boundary between the sixth and seventh intervals, as 120ms. Obtain the second logically consistent time position parameter node, which is the boundary between the eighth and ninth intervals, as 160ms. The extracted specific associated boundary point time and location parameter node records are written as boundary constants into the dedicated location boundary pointer record table structure as isolation boundary identifiers. The data structure packaging instruction is called to package and export the status code value and associated string name to obtain a consistent status boundary identifier.

[0023] S203: Based on the consistent state boundary identifier, extract the locations where the consistent state occurs continuously along the time axis, merge the locations where the continuous occurrence occurs into interval segments, extract the time positions associated with the combined change items, mark the time positions as the interval boundary positions, and divide the interval segments around the interval boundary positions to obtain the continuously changing segment structure.

[0024] Extract consistent state boundary identifiers. The classification and filtering algorithm module is invoked to perform a location operation mechanism based on the continuity of the state code parameter values ​​within the corresponding intervals. An independent traversal pointer is established, and a loop reading action is performed at the beginning of the node sequence set to extract the internal state code parameter values ​​corresponding to each interval in turn. When the logical comparator detects that the state code values ​​of the first, second, and third intervals are all 2, the time span recorder is triggered to verify that they form a seamless structure covering 0ms to 60ms in terms of time parameters. The start time parameter value of 0ms and the end time parameter value of 60ms are extracted, integrated, and written into the first independent memory area, and the storage name is defined as "Interval Segment One". Advancing the traversal pointer to read subsequent areas reveals that the state code values ​​recorded within the sixth to tenth intervals, covering a span of 100ms to 200ms, are all 3. A logical judgment instruction is invoked to identify that this does not meet the prerequisite of consecutive state values ​​of 2, and the direct extraction and merging of the first and last time spans of this continuous range is skipped. The system retrieves the position boundary pointer record table structure, extracts the first boundary marker time parameter node constant of 120ms and the second boundary marker time parameter node constant of 160ms, and, for the span sequence domain of 100ms to 200ms with state changes, calls the slicing operator to perform a cutting operation at this time position using the extracted time boundary parameter values. This extracts three independent sub-intervals: a first segment spanning 100ms to 120ms, a second segment spanning 120ms to 160ms, and a third segment spanning 160ms to 200ms. The array recombination instruction then stores the merged first segment (0ms to 60ms) and the list of combinations of spans of the various independent regions extracted by the cutting operation in ascending order of the time starting parameter into the segmentation table, resulting in a continuously changing segment structure.

[0025] Please see Figure 4 The steps to obtain the segment type distribution results are as follows: S301: For continuously changing segment structures, extract the pairing relationship between voltage direction identifiers and frequency direction identifiers within the segment, arrange the position pairing nodes in the order from the start position to the end position of the segment, compare the continuity status of the pairing relationship between the two types of direction identifiers in adjacent position pairing nodes, write the continuous consistent state and the alternating change state into the node attributes respectively, and write the corresponding time position into the node attributes to obtain the segment state node trajectory. Extract the structure of continuously changing segments. For "Interval Segment 1," which begins with a start time node of 0ms and ends with a time node of 60ms, the extraction operator is invoked to extract all positional pairing node parameters and start / end time parameters for the first, second, and third intervals within the sequence. A comparison operation is performed on the string indicating the continuation of pairing relationships between adjacent sequences. If the internal pairing relationship parameter records of the first and second interval nodes are completely equal, a character-type feature code A and the external description string "continuously consistent state" are written to the corresponding time range position and stored in the attribute record covering the span from 0ms to 40ms. If the parameter contents of the second and third interval nodes are completely equal, feature code A and "continuously consistent state" are written again, extending and updating the covered time position parameters to 60ms. For the three sub-intervals falling within 100ms to 200ms after boundary segmentation, the internal pairing relationship parameter records corresponding to the sixth to tenth intervals are extracted. It is determined that the pairing relationship parameter strings of connected intervals are not completely equal or that the parameters at both ends of a single interval are unequal. Within each slice node covering the 100ms to 200ms period, a character-type feature code B and an external description string "alternating state" are sequentially generated and written. The start and end parameters of the time segments 100ms to 120ms, 120ms to 160ms, and 160ms to 200ms that have undergone the comparison intervals are entered into the accompanying node attributes. The structural information containing specific feature code parameters and specific record strings is merged and sorted according to the timeline progression to obtain the segment state node trajectory.

[0026] S302: Call the segment status node trajectory, perform continuous identification based on the consistent relationship between voltage direction identifier and frequency direction identifier, check the node attribute arrangement order within the same segment, connect the continuous consistent states into a consistent distribution range, connect the alternating states into an alternating distribution range, write the start and end positions of the range into the segment attributes, and obtain the paired range attribute set; The system retrieves the segment status node trajectory, which contains the mapping relationship between feature code parameters and specific record string time series. It performs code consistency and continuity identification on the segment status node trajectory, extracting feature code A attached to the first item of the sequence, and temporarily storing the start time parameter value (0ms) and end time parameter value (20ms) in memory as a baseline. It then extracts feature code A attached to the next adjacent sequence position and the start time parameter value (20ms), verifies the seamless time connection, updates the end parameter value to 40ms, and continues to extract feature code A for subsequent records and verify the connection, updating the end parameter value to 60ms. When a subsequent span feature code change causes a matching interruption, it extracts the span limit parameters from 0ms to 60ms within the collector, merges them into a separate data interval parameter domain package, and names it "Consistent Distribution Range". Using the same mechanism, it extracts the subsequent region feature code B, performs time continuity node connection verification and termination parameter point expansion update operations, obtains the start time parameter value (100ms) and the last node's end time parameter value (200ms), encapsulates them into an independent data interval parameter domain package, and names it "Alternating Distribution Range". After loading the supplementary data sequence and reading the record area with start and end parameter values ​​from 240ms to 300ms, and verifying that the feature code parameter A is correctly connected, it is merged into a parameter domain package with a time span of 240ms to 300ms and named "Consistent Distribution Range". All independent span domain packages covering the start and end time node limit parameter values ​​and their respective name attributes are summarized to obtain the paired range attribute set.

[0027] S303: Based on the paired range attribute set, identify the segments corresponding to the consistent distribution range and divide them into stable segments, identify the segments corresponding to the alternating distribution range and divide them into fluctuating segments, and combine the segment type corresponding to the start and end positions of the segments to arrange the stable segments and fluctuating segments along the time position to obtain the segment type distribution result.

[0028] Extract the paired range attribute set. Iterate through the independent span data domain encapsulation component information within the set, extracting the name string parameter value "consistent distribution range" from the first data domain encapsulation, and obtaining the associated absolute time start span parameter value of 0ms and end span parameter value of 60ms. Under the condition that the name string parameter value is equal to the baseline string constant, trigger the system state translation action, translating the segment type attribute data item corresponding to the position within the range of start parameter value 0ms to end parameter value 60ms into the segment type string label "stable segment". Extract the name string parameter value "alternating distribution range" from the next data domain encapsulation, obtaining the corresponding start span time parameter value of 100ms and end span time parameter value of 200ms, and translating the segment type attribute data item falling within the time range of 100ms to 200ms into the string label "fluctuating segment". Read the supplementary segment sequence record position name string parameter value "consistent distribution range", obtain the start and end span parameters 240ms and 300ms, and translate the position interval type into the label "stable segment". Extract all newly assigned representative type label parameters and their corresponding span start and end time position parameters, and store them sequentially into the corresponding block record rows and columns according to the increasing dimension from left to right in the global time coordinate system to obtain the segment type distribution result.

[0029] Please see Figure 5 The steps for obtaining the stable segment distribution structure are as follows: S401: Based on the distribution results of segment types, extract the correspondence between segment types and time positions, arrange the segment type fields in the order of time positions, compare the consistency of segment type fields in adjacent time positions, write the consistent type items into the continuous candidate attribute, write the type change items into the interval candidate attribute, and write the corresponding start position and end position into the same attribute record to obtain the type time mapping chain. The distribution results of the segment types are retrieved. Based on the ascending order of the absolute time start parameter position, the first record object item and its time span start position parameter 0ms, end position parameter 60ms, and segment type field "stable segment" are extracted. The second record object item and its time parameters 100ms and 200ms are extracted, along with the segment type field "fluctuating segment". The third record object item and its time parameters 240ms and 300ms are extracted, along with the segment type field "stable segment". A consistent state logic comparison is performed on the segment type fields of adjacent sorted record objects. If the content of the first object item "stable segment" is inconsistent with the content of the second object item "fluctuating segment", the association sequence containing this change state feature is assigned the candidate classification attribute identifier character label "interval candidate attribute" and the code constant D. The end position parameter 60ms of the first object item and the start position parameter 100ms of the second object item are written into the dedicated attribute data record row for storage. If the content of the second object item is inconsistent with that of the third object item, the result is recorded in the classification character label "Interval Candidate Attribute," and the intersection point parameters 200ms and 240ms are entered into the classification record with code D. Load the fourth object item with start and end parameters from 300ms to 360ms and content designated as "Stable Segment." If the content of the third object item is consistent with that of the fourth object item, the records involved in the comparison are stored in the specific classification character label "Continuous Candidate Attribute," i.e., a structure with the code constant C, and the 300ms time point parameter of the joining item is extracted and written into the attribute table record. Merge all independent record information containing code labels to obtain the type-time mapping chain.

[0030] S402: Call the type-time mapping chain, check the connection status of time position in continuous candidate attributes based on the relationship of consistent segment type and continuous time position, merge the consistent type and continuous time items into continuous segments, and merge the type changed and time broken items into interval segments based on the relationship of segment type change and discontinuous time position, and obtain the segment position boundary identifier. Invoke type time mapping. Extract the record data item group with mounting code C, obtain the end time parameter value of the third object (300ms) and the start time parameter value of the fourth object (300ms) within this group, determine that the two time parameter value nodes are seamlessly connected, perform span superposition and merging on the object segment regions participating in the cross-boundary comparison, generate an independent data block with an absolute span of 240ms to 360ms, and write it into the collection array storage space named "Continuous Segment". Extract the record information of the data group with mounting code D, obtain the first boundary end time point parameter of 60ms associated with the first group of records and the start time point parameter of the adjacent subsequent interval (100ms), determine that the two nodes cannot be seamlessly connected, extract the free interval of the segment spanning the 60ms to 100ms gap as a separate block, name it, and write it into the collection array named "Interval Segment". Extract the end boundary time parameter of 200ms and the start boundary time parameter of 240ms from the next set of records, code D. Determine that the two nodes cannot achieve a seamless connection, and strip the 200ms to 240ms region covered by their connection and write it into another independent set of "interval segments". Summarize the extreme value data of specific locations classified as "continuous segments" and "interval segments" to obtain the segment location boundary identifiers.

[0031] S403: Based on the segment position boundary identifier, filter based on the positional relationship between continuous segments and interval segments, compare the boundary connection status of adjacent interval segments before and after continuous segments in stable segments, retain the continuous segments corresponding to stable segments, and remove the interval segments that do not form a boundary connection with the continuous segments in stable segments to obtain the distribution structure of stable segments.

[0032] Extract the segment location boundary markers. Extract the start boundary time pole parameter 240ms and the end boundary time pole parameter 360ms corresponding to the data blocks classified as "continuous segments". Traverse and search all isolated "interval segment" blocks in the array library to determine the time span position data. Extract the start boundary time parameter point 60ms and the end boundary time parameter point 100ms of the first "interval segment" record block. Identify that the start and end poles of the first "interval segment" do not have a time edge contact with the pole parameters of the continuous stable segments. Remove and discard the block entries covering the span of 60ms to 100ms from the array memory. Extract the end boundary time parameter point 240ms of the second "interval segment" data block. Identify that it seamlessly matches the start parameter of the continuous stable segment, forming a boundary contact. The main body of the successfully matched continuous stable segment spanning 240ms to 360ms and its associated immediate preceding interval segment are retained in the core storage array for continued holding. After the complete collision comparison traversal is fully executed, the data set of the coherent combination module blocks of the locked effective stable segments is encapsulated to obtain the stable segment distribution structure.

[0033] Please see Figure 6The steps for obtaining the results of unplanned power outage identification are as follows: S501: Based on the stable segment distribution structure, extract the time position correspondence between continuous segments and interval segments, extract the segment type of continuous segments and the segment type of interval segments, arrange the time fields of continuous segments and interval segments in the order of time position, compare the connection status of adjacent segments, and write the break position of continuous segments and the segment type before and after the break into the same attribute record to obtain the segment break attribute record. The stable segment distribution structure is invoked. The start position parameter (240ms) and end position parameter (360ms) of the time span of the retained continuous segments are extracted. Simultaneously, the start position parameter (100ms) and end position parameter (240ms) of the associated preceding spans are extracted. The specific segment type string labels attached to each span segment are extracted. A rearranged sequence array is built based on the absolute time coordinates. Records containing the stable block span record and its attached string content "stable segment," as well as the corresponding position parameter records and their extracted string content for the fluctuation exposure segment span range, are combined and cross-aligned in the system list sequence cell. The 100ms time parameter value of the starting interface extreme point representing the interruption and fluctuation of the stable continuous running state in the preceding position is extracted. The 240ms time parameter value of the starting extreme point in the subsequent region of the queue that has resumed stable running state is extracted. It is confirmed that an absolute distance exists between the two extreme points, causing the continuous continuation condition to be invalid and confirming the break in the system's continuous running state. The joint confirmation of the leading edge disintegration point parameter 100ms and the trailing edge recovery point parameter 240ms as boundary items, extract the type label "stable segment" in front of the boundary and the type label "stable segment" after the recovery position, combine and encapsulate the extreme point parameters 100ms and 240ms and the corresponding boundary environment label to obtain the fragment breakage attribute record.

[0034] S502: Call the fragment breakage attribute record, form an interruption interval based on the breakage position of the continuous fragment, check the breakage start position and breakage end position in the interruption attribute, write the segment type in the interruption interval into the interruption interval field, write the segment types before and after the interruption interval into the corresponding relationship field, and obtain the corresponding field of the interruption interval. The fragment break attribute records are retrieved. Based on the extracted time pole coordinates, a corresponding evaluation framework to be labeled is established, covering the entire actual span and assigning the span range the identifier "Interruption Interval". The data pole at 100ms, representing the starting point of the break, and the data starting point at 240ms, representing the recovery to a stable state, are verified. The blank time distance between these two points is identified as the boundary coverage of the interruption interval. The segment type string field label information originally assigned to the records falling within the 100ms to 240ms range is extracted, and the feature of this region is "fluctuation segment". This string feature data "fluctuation segment" is inserted as a filler value and stored in a dedicated memory table space named "Interruption Interval Field" in the data structure. Read the segment type string "stable segment" attached to the preceding segment before the collapse position in front of the cross section and the segment type string "stable segment" attached to the following segment after the recovery point. Map the homogeneous background feature strings covering both sides of the interface into a specific record memory area named "correspondence field". Merge the embedded abnormal attributes and external background attributes to obtain the comprehensive interrupt interval correspondence field.

[0035] S503: Based on the corresponding fields of the interruption interval, and in conjunction with the segment types before and after the interruption interval, the corresponding relationship is compared. The fluctuating segment is identified as a continuous occurrence interval in the interruption interval and divided into a power outage interval. The stable segment is identified as a continuous occurrence interval in time position and divided into an operating interval. The power outage interval and the operating interval are written into the interval status field to obtain the unplanned power outage identification result.

[0036] Extract the corresponding fields for the comprehensive interruption interval. Extract the segment background property type string of the outer cross-sections before and after the interval stored in the "correspondence field" location, confirm that the type of both cross-sections is "stable segment", and read the "interruption interval field" to identify the interruption interval coverage label as "fluctuation segment". Obtain the lower limit of the fluctuation time limit benchmark parameter of 50ms, retrieve the experience time span parameter of 140ms covering the start and end points of specific events from 100ms to 240ms, and verify that the span parameter of 140ms is greater than the fluctuation span threshold parameter of 50ms. Based on this logical truth value verification condition, rename and overwrite the interruption interval record framework with a span from 100ms to 240ms in the result output structure to divide it into the formal status label "power outage interval". For the remaining time range segments in the list, reverse identification and judgment are performed. Addressing reveals that the segments spanning 0ms to 60ms and 240ms to 360ms are both full-time, uninterrupted "stable segments" (labeled as such). The continuity condition is satisfied, and these continuous, unisolated absolute timeline segments are uniformly classified as formal status labels "operating intervals." The generated "outage interval" label data records and "operating interval" label data records are arranged according to time parameters and written into the interval status field master record matrix sequence library file to obtain the unplanned power outage identification results.

[0037] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for rapid identification of unplanned power outages in voltage frequency patterns, characterized in that, Includes the following steps: S1: Obtain the voltage amplitude signal output by the voltage transformer and the frequency change signal output by the frequency measuring device. Establish a sampling relationship based on a unified time. Extract the voltage change direction and frequency change direction of adjacent sampling points and match the time position to form a corresponding relationship, thereby obtaining the voltage-frequency change correspondence. S2: Based on the voltage-frequency change correspondence, extract the pairing relationship between voltage direction identifiers and frequency direction identifiers at adjacent change positions, form a consistent state according to the consistent relationship between voltage direction identifiers and frequency direction identifiers, and form interval segments corresponding to continuous time positions. Based on the pairing relationship, form interval boundary positions according to the change positions, and obtain the continuous change segment structure. S3: Based on the continuously changing segment structure, extract the pairing relationship between voltage direction identifier and frequency direction identifier within the segment, form a pairing distribution state according to the consistent relationship between voltage direction identifier and frequency direction identifier, divide the continuously consistent segment into stable segment, divide the alternating segment into fluctuating segment, and form the segment type distribution result; S4: Based on the segment type distribution results, extract the correspondence between segment type and time position, correspond segments with the same type and continuous time to continuous segments, correspond segments with different types and discontinuous time to interval segments, and form a stable segment distribution structure according to the positional relationship between continuous segments and interval segments to obtain a stable segment distribution structure.

2. The method for rapid identification of unplanned power outages based on voltage frequency patterns according to claim 1 is characterized in that: The voltage-frequency change correspondence includes voltage change trend sequence, frequency change trend sequence, and time-series matching relationship. The continuous change segment structure specifically includes the segment start and end position set, segment length distribution, and segment boundary characteristics. The segment type distribution result includes stable segment identifier, fluctuating segment identifier, and segment type sequence. The stable segment distribution structure specifically includes continuous segment range, interval segment range, and segment combination form.

3. The method for rapid identification of unplanned power outages based on voltage frequency patterns according to claim 1 is characterized in that: The steps for obtaining the corresponding relationship of voltage frequency changes are as follows: S101: Acquire the voltage amplitude signal output by the voltage transformer and the frequency change signal output by the frequency measuring device. Synchronously collect voltage sampling points and frequency sampling points according to a unified time. Detect the corresponding status of each sampling point's time position. Assign the voltage amplitude item and frequency change item corresponding to the time position to the same sampling unit. Mark the uncorresponding sampling items as empty sampling items to obtain the time registration sampling table. S102: Based on the time registration sampling table, extract the voltage amplitude trend and frequency change trend in adjacent sampling units, identify the voltage amplitude trend as rising, falling or flat, identify the frequency change trend as rising, falling or flat, and write the identified content into the voltage direction field and frequency direction field respectively to obtain the change direction identification field set. S103: Call the change direction identifier field set, match the voltage direction field and the frequency direction field according to the time position, write the voltage direction field and the frequency direction field at the same time position into the same pairing unit, detect the field integrity status of the pairing unit, arrange the field integrity pairing units in the order of time position, and obtain the voltage frequency change correspondence.

4. The method for rapid identification of unplanned power outages based on voltage frequency patterns according to claim 1 is characterized in that: The steps for obtaining the continuously changing segment structure are as follows: S201: Based on the voltage-frequency change correspondence, extract the voltage direction identifier and frequency direction identifier in adjacent change positions after arranging them according to time position, classify the two types of direction identifiers in the same change position into position pairing nodes, check the combination continuation status of the direction identifiers in adjacent position pairing nodes, mark the combination continuation item and the combination change item respectively, and obtain the set of adjacent change pairing nodes. S202: Call the adjacent change pairing node set, and based on the consistency relationship between voltage direction identifier and frequency direction identifier, check the consistency of the two types of direction identifiers in the position pairing node, mark the nodes with the same direction identifier as consistent, mark the nodes with different direction identifiers as inconsistent, and associate the combined change item with the corresponding time position to obtain the consistent state boundary identifier; S203: Based on the consistent state boundary identifier, extract the consecutive occurrence positions of the consistent state along the time axis, merge the consecutive occurrence positions into interval segments, extract the time position associated with the combined change item, mark the time position as the interval boundary position, and divide the interval segment around the interval boundary position to obtain the continuously changing segment structure.

5. The method for rapid identification of unplanned power outages based on voltage frequency patterns according to claim 1 is characterized in that: The steps for obtaining the segment type distribution results are as follows: S301: For the continuously changing segment structure, extract the pairing relationship between voltage direction identifier and frequency direction identifier within the segment, arrange the position pairing nodes in the order from the start position to the end position of the segment, compare the continuity state of the two types of direction identifier pairing relationship in adjacent position pairing nodes, write the continuous consistent state and the alternating change state into the node attributes respectively, and write the corresponding time position into the node attributes to obtain the segment state node trajectory. S302: Call the segment state node trajectory, perform continuous identification based on the consistent relationship between voltage direction identifier and frequency direction identifier, check the node attribute arrangement order within the same segment, connect the continuous consistent state into a consistent distribution range, connect the alternating change state into an alternating distribution range, write the start position and end position of the range into the segment attribute, and obtain the paired range attribute set; S303: Based on the pairing range attribute set, identify the segments corresponding to the consistent distribution range and divide them into stable segments, identify the segments corresponding to the alternating distribution range and divide them into fluctuating segments, and combine the segment types corresponding to the start and end positions of the segments to arrange the stable segments and fluctuating segments along the time position to obtain the segment type distribution result.

6. The method for rapid identification of unplanned power outages based on voltage frequency patterns according to claim 1 is characterized in that: The steps for obtaining the stable segment distribution structure are as follows: S401: Based on the distribution results of the segment types, extract the correspondence between segment types and time positions, arrange the segment type fields in the order of time positions, compare the consistency of segment type fields in adjacent time positions, write the consistent type items into the continuous candidate attribute, write the type change items into the interval candidate attribute, and write the corresponding start position and end position into the same attribute record to obtain the type time mapping chain. S402: Invoke the type time mapping chain, and based on the relationship between consistent segment types and continuous time positions, check the connection status of time positions in continuous candidate attributes, merge items with consistent types and continuous time into continuous segments, and based on the relationship between segment types changing and discontinuous time positions, merge items with changing types and discontinuous time into interval segments to obtain segment position boundary identifiers. S403: Based on the segment position boundary identifier, filter based on the positional relationship between continuous segments and interval segments, compare the boundary connection status of adjacent interval segments before and after continuous segments in stable segments, retain the continuous segments corresponding to stable segments, and remove the interval segments that do not form a boundary connection with the continuous segments in stable segments to obtain the distribution structure of stable segments.

7. The method for rapid identification of unplanned power outages based on voltage frequency patterns according to claim 1 is characterized in that, The method further includes step S5: S5: Based on the stable segment distribution structure, extract the time position correspondence between continuous segments and interval segments, divide the power outage interval and the operation interval according to the interruption interval corresponding to the break position of the continuous segment, and combine the segment types before and after the interruption interval to obtain the unplanned power outage identification result. The unplanned power outage identification results include the power outage range, the operating range, and the range status identifier.

8. The method for rapid identification of unplanned power outages based on voltage frequency patterns according to claim 7 is characterized in that: The steps for obtaining the unplanned power outage identification result are as follows: S501: Based on the stable segment distribution structure, extract the time position correspondence between continuous segments and interval segments, extract the segment type of continuous segments and the segment type of interval segments, arrange the time field of continuous segments and the time field of interval segments in the order of time position, compare the connection status of adjacent segments, and write the break position of continuous segments and the segment type before and after the break into the same attribute record to obtain the segment break attribute record. S502: Call the fragment breakage attribute record, form an interruption interval based on the breakage position of the continuous fragment, check the breakage start position and breakage end position in the interruption attribute, write the segment type in the interruption interval into the interruption interval field, write the segment types before and after the interruption interval into the corresponding relationship field, and obtain the corresponding field of the interruption interval. S503: Based on the corresponding fields of the interruption interval, and in conjunction with the segment types before and after the interruption interval, the corresponding relationship is compared, the fluctuating segment is identified as a continuous occurrence interval in the interruption interval and divided into a power outage interval, the stable segment is identified as a continuous occurrence interval in time position and divided into an operating interval, and the power outage interval and the operating interval are written into the interval status field to obtain the unplanned power outage identification result.

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