A switchgear cabinet online monitoring analysis and fault prediction method and system

Abstract

The present application relates to the technical field of equipment failure prediction, in particular to a switch cabinet online monitoring analysis and fault prediction method and system, comprising the following steps: collecting voltage sequence, identifying main peak and recording, extracting main peak back section, analyzing amplitude change to judge discontinuous characteristics, constructing trajectory trend sequence to identify continuous deviation, cross comparing abnormal period to count deviation direction, determining abnormal section to output diagnosis result, in the present application, through extraction and sequential processing of voltage main peak characteristics during contact conduction, combined with amplitude comparison of main peak back sampling section, accurate identification of transient abnormality is realized, through inclination angle change analysis of main peak trajectory in continuous period, deviation behavior of operation trend is identified, through cross comparison of abnormal period and trajectory deviation, abnormal section with consistent and concentrated distribution is positioned, based on period number and direction characteristics, deviation trend is comprehensively judged, timeliness and accuracy of fault prediction are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of equipment failure prediction, in particular to a switchgear online monitoring analysis and failure prediction method and system. BACKGROUND

[0002] The technical field of equipment failure prediction involves the continuous perception of the running state of industrial equipment, the identification of failure signs, and the monitoring and prediction of trend development. Its core tasks include the collection of equipment running data, the categorical analysis of historical running states, the detection of abnormalities based on data variation rules, and the quantitative prediction and judgment of failure probability. The methodological characteristics of this technical field are to collect and analyze parameter information in real time during the running of equipment, combined with existing failure modes and evolution rules, to identify and predict potential equipment failures. The traditional switchgear online monitoring analysis and failure prediction method refers to the switchgear used for power distribution and protection in high-voltage power transmission and distribution systems. Its failure identification relies on the periodic collection of basic running parameters such as temperature, current, and voltage, and determines whether the equipment is abnormal by setting fixed thresholds for these parameters. The data collection method generally uses power sensors installed at key nodes to periodically upload data to a local monitoring terminal and compares them with pre-set rules to identify failure risks.

[0003] The existing technology relies on fixed-point collection and fixed threshold comparison of static parameters such as temperature, current, and voltage, which cannot accurately capture weak fluctuations and early abnormal signals during operation. The short-term voltage changes during contact switching are difficult to respond completely, making it difficult to detect early trend failures in a timely manner. Especially in scenarios with frequent load changes or strong environmental interference, misjudgment and missed judgment are likely to occur. Periodically uploaded data lack the ability to depict dynamic evolution processes, and it is difficult to identify the process of failure development by comparing single-point data with pre-set rules. In practical applications, data anomalies often occur but cannot be located to the fault section, reducing the reliability and decision effectiveness of the system. SUMMARY

[0004] To solve the technical problems existing in the prior art, the present application provides a switchgear online monitoring analysis and failure prediction method, comprising the following steps:

[0005] To achieve the above-mentioned purpose, the present application adopts the following technical solution: a switchgear online monitoring analysis and failure prediction method, comprising the following steps:

[0006] S1: Collect the voltage sampling sequence during the conduction of the switchgear contact, identify the main peak feature points in the cycle, and record the time and amplitude information. Construct the main peak feature sequence in cycle order;

[0007] S2: based on the main peak feature sequence, extracting the main peak sampling segment after the sampling segment, analyzing the amplitude change between the sequences, judging whether there is discontinuous feature, screening abnormal period number and offset direction, and generating transient abnormal period information;

[0008] S3: according to the main peak feature sequence and the transient abnormal period information, using the main peak amplitude and time information in the continuous period, constructing the trajectory change trend sequence, judging whether the trajectory exists continuous offset by comparing the change direction of the inclination angle of adjacent line segments, and generating the trajectory offset period list;

[0009] S4: cross comparison of the transient abnormal period information and the trajectory offset period list is carried out, the offset direction in the matching period is counted, the abnormal behavior concentrated interval is identified, and the period matching abnormal identification group is generated;

[0010] S5: according to the number of continuous periods, the direction consistency and the distribution in the period matching abnormal identification group, the abnormal period section is determined, the associated contact number, the offset feature and the trend label are output, and the fault prediction diagnosis result is generated.

[0011] As a further scheme of the application, the main peak feature sequence includes the main peak time point, the main peak amplitude, the main peak sequence sorting position, the transient abnormal period information includes the abnormal period number, the abnormal offset direction and the discontinuous feature amplitude, the trajectory offset period list includes the offset period number, the offset trend angle change and the trend continuity mark, the period matching abnormal identification group includes the matching period number, the directionality statistical result and the concentrated distribution interval range, and the fault prediction diagnosis result includes the contact number, the offset behavior feature and the trend label.

[0012] As a further scheme of the application, the specific steps of S1 are:

[0013] S101: obtaining the voltage sampling sequence during the conduction of the switch cabinet contact, arranging the data points in time sequence, constructing the voltage waveform curve, identifying the section with obvious fluctuation amplitude change, selecting the position with change rate exceeding the set threshold, and extracting the point with the highest voltage value in the section, and generating the voltage jump point sequence;

[0014] S102: calling the voltage value of the point in the voltage jump point sequence and the adjacent point data, screening the main peak point meeting the condition that the voltage value is higher than that of the adjacent point, recording the time and amplitude information of the main peak point, and arranging it into a structured data table in time sequence, and generating the main peak feature data set;

[0015] S103: according to the time field and period number information in the main peak feature data set, extracting the amplitude value and time value of each main peak point in period order, and combining them into a continuous data sequence, and obtaining the main peak feature sequence.

[0016] As a further scheme of the present application, the specific steps of S2 are:

[0017] S201: Based on the main peak feature sequence, a fixed length voltage sampling segment after the time corresponding to the main peak point in each period is extracted, the data sequence is arranged according to the period number, a sampling segment set indexed by the period number is constructed, the fluctuation range information of the voltage value in the time dimension is called, and the period sampling change amplitude value is established;

[0018] S202: According to the period sampling change amplitude value, the change amplitude difference value sequence between adjacent periods is called, whether each difference value exceeds the threshold range is judged according to the set discontinuous identification threshold, and the period number corresponding to the exceeder is extracted as an abnormal candidate period to generate a mutation period number list;

[0019] S203: The voltage sampling segment corresponding to each period number in the mutation period number list is called, the voltage value fluctuation trend direction change sign is compared according to the voltage value change direction of the starting segment and the ending segment, the voltage offset direction of the corresponding period is judged, the period number and the offset direction are combined as structured data, and the transient abnormal period information is obtained.

[0020] As a further scheme of the present application, the specific steps of S3 are:

[0021] S301: The main peak corresponding to the amplitude and time information in the continuous period is extracted by calling the transient abnormal period information and the main peak feature sequence, the main peak amplitude and time in the same period are combined after corresponding, the point set is constructed according to the period order, and the line segment is generated by connecting in time sequence to establish the main peak trajectory line segment group;

[0022] S302: According to the point information of adjacent line segments in the main peak trajectory line segment group, the corresponding inclination angle value is calculated, the angle change sequence is constructed, whether the line segment trend is consistent is judged according to the direction difference of adjacent angle change in the sequence, the line segment set with the direction continuous characteristic is identified, and the trajectory direction continuous section group is generated;

[0023] S303: According to the period index in the trajectory direction continuous section group, it is checked whether there is a continuous offset of the line segment direction in the corresponding time sequence, the direction section meeting the continuous time requirement is screened, the corresponding period range and direction are recorded, and the trajectory offset period list is established.

[0024] As a further scheme of the present application, the specific steps of S4 are:

[0025] S401: The period number in the transient abnormal period information and the trajectory offset period list is obtained, the matching is performed according to the number order, the data items with the same period number are selected as matching samples to establish a matching period number set;

[0026] S402: Call the matching period number set, and the offset characteristic directionality data corresponding to each period is divided into three categories of positive direction, reverse direction and no direction according to the offset direction category, the frequency of each category appearing in all matching periods is counted, and the offset direction distribution value is generated;

[0027] S403: According to the offset direction distribution value, the concentrated occurrence interval of single direction in continuous period is extracted, the position of direction corresponding period number is recognized, the corresponding period position number is marked, and the period matching abnormality identification group is generated.

[0028] As a further scheme of the application, the specific steps of S5 are:

[0029] S501: According to the period number marked in the period matching abnormality identification group, the sequential continuity between adjacent period numbers is judged, the continuous period number section is extracted, the number section meeting the continuity condition is screened, and the continuous period section number value is generated.

[0030] S502: Call the continuous period section number value, get the offset direction information in the number section, identify the distribution of direction type, screen the period section with consistent direction type, and generate the direction consistent number section value.

[0031] S503: According to the direction consistent number section value, the offset trend characteristic and contact number information of the corresponding number section are extracted, the concentration degree of number section distribution is judged, and the trend type is marked to generate the switchgear fault prediction diagnosis result.

[0032] As a further scheme of the application, the switch cabinet contact is a metal contact element used for conducting or disconnecting the main loop current in the switch cabinet, which belongs to the typical conductive interface structure in high-voltage electrical equipment;

[0033] The voltage sampling sequence is a voltage data set formed by sampling the voltage signal generated in the process of conducting the switch cabinet contact at regular intervals, which is collected by the sensor at a fixed sampling frequency;

[0034] The main peak feature point is the sampling point with the maximum amplitude in a period in the voltage sampling sequence;

[0035] The main peak feature sequence is a data sequence formed by arranging the main peak feature points identified in multiple periods in time sequence.

[0036] As a further scheme of the application, the sampling section is a section of voltage sampling data region extending backward based on the main peak feature point;

[0037] The discontinuous feature refers to the abnormal change mode of voltage waveform mutation, sharp turning or curve fault in the sampling section, which represents the non-stable disturbance phenomenon of the signal in a short time.

[0038] The offset direction refers to the change trend monitored when analyzing the voltage or peak data of adjacent periods, which is manifested as an increasing or decreasing path of the value in the time or amplitude dimension;

[0039] The trajectory change trend sequence is a line segment path composed of a plurality of main peak feature points, recording the change trend of the peak position in the time and amplitude dual axes;

[0040] The inclination angle change direction refers to the change trend of the angle formed between adjacent line segments in the trajectory change trend sequence, that is, the angle continuously increases or decreases;

[0041] The abnormal behavior concentrated distribution interval refers to an interval in which the data segments with abnormal fluctuations in multiple detection periods are continuously concentrated in a single time range;

[0042] The fault pre-diagnosis result is a state judgment result output based on the analysis of abnormal period data and trajectory change characteristics.

[0043] An online monitoring and analysis and fault prediction system for a switchgear cabinet of a switch, comprising:

[0044] The cycle feature extraction module acquires a voltage signal sequence during contact conduction in the switchgear cabinet of the switch, performs segmentation processing according to the conduction period, identifies the voltage maximum point in the period and records the corresponding time position and amplitude, constructs a sequence set including all cycle main peak features, and generates a main peak feature sequence;

[0045] The transient screening module extracts the voltage sampling segment after the main peak position based on the main peak feature sequence, judges the amplitude change trend of the sampling points in adjacent periods, identifies the cycle number with mutation characteristics, and classifies the cycle number according to the change direction to generate transient abnormal period information;

[0046] The offset identification module calls the main peak feature sequence and the transient abnormal period information, constructs a trajectory change sequence according to the time and amplitude of the main peak points of consecutive periods, analyzes the direction characteristics of the trajectory slope change between adjacent periods, and screens and discriminates the corresponding period according to the abnormal period number to generate a trajectory offset period list;

[0047] The abnormal integration module matches the records with the same cycle number according to the transient abnormal period information and the trajectory offset period list, counts the offset direction of the cycle and judges the continuity and concentration of the direction distribution, identifies the cycle range with consistent characteristics, and generates a cycle matching abnormal identification group;

[0048] The fault judgment module calls the period matching exception identification group, extracts the number distribution, direction feature and period quantity index in the period section, combines the period marking classification of the corresponding contact number in the switch cabinet of the switch, judges the period sequence with behavior characteristics, and generates a fault pre-judgment diagnosis result.

[0049] Compared with the prior art, the application has the advantages and positive effects that:

[0050] In the application, through extraction and serialization processing of the voltage main peak feature during the contact conduction period, combined with comparison of the change amplitude of the sampling section after the main peak, the transient abnormality is accurately identified, further through analysis of the inclination angle change of the main peak track in the continuous period, the deviation behavior of the operation trend is identified, through cross comparison of the abnormal period and the track deviation, the abnormal section with consistent and concentrated distribution is located, the deviation trend is judged based on the period quantity and the direction feature, and the timeliness and accuracy of fault prediction are effectively improved. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0052] Figure 1 It is a step flowchart of the application.

[0053] Figure 2 It is a S1 refinement diagram of the application.

[0054] Figure 3 It is a S2 refinement diagram of the application.

[0055] Figure 4 It is a S3 refinement diagram of the application.

[0056] Figure 5 It is a S4 refinement diagram of the application.

[0057] Figure 6 It is a S5 refinement diagram of the application.

[0058] Figure 7 It is a system module diagram of the application. DETAILED DESCRIPTION

[0059] The technical solutions in the application will be described below with reference to the drawings.

[0060] In the embodiments of the present application, the words such as "example", "for example" are used to represent an example, illustration, or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "example" is intended to present the concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0061] In the embodiments of the present application, "image" and "picture" can be used interchangeably at times, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized. "Of", "corresponding" and "corresponding" can be used interchangeably at times, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized.

[0062] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1, and the meanings expressed are consistent when the distinction is not emphasized.

[0063] In order to make the technical problems, technical schemes and advantages to be solved by the present application more clear, the following will be described in detail in conjunction with the drawings and specific embodiments.

[0064] Please refer to Figure 1 The embodiments of the present application provide a switchgear online monitoring analysis and fault prediction method, comprising the following steps:

[0065] S1: Obtain a voltage sampling sequence during contact conduction of the switchgear, identify main peak characteristic points in a period, and record corresponding time and amplitude information, and generate a main peak characteristic sequence in order of period;

[0066] The switchgear contact is a metal contact element used for conducting or disconnecting the main loop current in the switchgear, which belongs to a typical conductive interface structure in high-voltage electrical equipment and is a direct source point of voltage collection in online monitoring;

[0067] The voltage sampling sequence is a voltage data set formed by timing sampling of the voltage signal generated in the conduction process of the switchgear contact, which is collected by the sensor at a fixed sampling frequency and reflects the waveform trend of the voltage change over time;

[0068] The main peak characteristic point is a sampling point with the maximum amplitude in a period in the voltage sampling sequence, which is used to reflect the maximum fluctuation point of the voltage curve in the conduction state and is a key characteristic basis for judging the waveform trend and abnormality;

[0069] The main peak feature sequence is a data sequence formed by arranging the main peak feature points identified in multiple cycles in time sequence, used for analyzing the voltage trajectory trend and stability change between cycles;

[0070] S2: Based on the main peak feature sequence, the sampling segment after the main peak of each cycle is extracted for analysis, whether there is a discontinuous feature is judged by the change amplitude between sequences, the cycle number and the offset direction of the abnormal period are screened, and the transient abnormal period information is generated;

[0071] The sampling segment is a segment of voltage sampling data region extending backward from the main peak feature point, used to reflect the behavior pattern of voltage fluctuation in a short time after the main peak, especially for identifying the bounce type transient change feature;

[0072] The discontinuous feature refers to the abnormal change mode of voltage waveform mutation, sharp turning or curve fault in the sampling segment, which represents the non-stable disturbance phenomenon of the signal in a short time;

[0073] The offset direction refers to the change trend monitored when analyzing the voltage or wave peak data of adjacent cycles, which is represented as the increasing or decreasing path of the value in the time or amplitude dimension, used to identify the evolution direction of abnormal behavior;

[0074] S3: Call the transient abnormal period information and the main peak feature sequence, establish a trajectory change trend sequence according to the amplitude and time information of the main peak in consecutive cycles, judge whether the trajectory exists continuous offset by comparing the change direction of the inclination angle between adjacent line segments, and generate a trajectory offset period list;

[0075] The trajectory change trend sequence is a line segment path composed of consecutive main peak feature points, recording the change direction of the wave peak position in the time and amplitude dual axis, used to judge whether the waveform deviates from the original stable trajectory;

[0076] The inclination angle change direction refers to the change trend of the included angle formed between adjacent line segments in the trajectory change trend sequence, that is, the angle continuously increases or decreases, used to identify whether the trajectory is in continuous drift state;

[0077] S4: Cross compare the transient abnormal period information and the trajectory offset period list by cycle number, count the directionality of the offset feature in the matching period, identify the abnormal behavior concentrated distribution interval, and generate a cycle matching abnormal identification group;

[0078] The abnormal behavior concentrated distribution interval refers to the interval in which the data segments with abnormal fluctuations in multiple detection cycles are continuously gathered in a single time range, used to determine the position of the potential risk concentrated area;

[0079] S5: According to the number of consecutive periods marked in the period matching abnormality identification group, the consistency of the direction and the centralized distribution, the period section is judged, and the associated contact number, the offset behavior characteristics and the trend label are output, and the fault prediction diagnosis result is generated;

[0080] The trend label is a classification label based on the period offset directionality, continuity and amplitude characteristics, used for pattern recognition and output classification of period behavior, including "continuous drift" and "instantaneous jump";

[0081] The switchgear fault prediction diagnosis result is a state judgment result output based on abnormal period data and trajectory change characteristics, including contact number, fault type characteristics and trend label information, which is used as a decision basis for system operation and maintenance.

[0082] The main peak feature sequence includes the main peak time point, the main peak amplitude and the main peak sequence sorting position, the transient abnormal period information includes the abnormal period number, the abnormal offset direction and the discontinuous feature amplitude, the trajectory offset period list includes the offset period number, the offset trend angle change and the trend continuity label, the period matching abnormality identification group includes the matching period number, the directionality statistical result and the centralized distribution interval range, and the switchgear fault prediction diagnosis result includes the contact number, the offset behavior characteristics and the trend label.

[0083] Please refer to Figure 2 , the specific steps of S1 are as follows:

[0084] S101: Obtain the voltage sampling sequence during the conduction of the switch cabinet contact, arrange the data points in time sequence, construct the voltage waveform curve, identify the section with the fluctuation amplitude change exceeding the set threshold, select the position with the change rate exceeding the set threshold, and extract the point with the highest voltage value in the section, to generate the voltage jump point sequence;

[0085] During the contact conduction of the switch cabinet, the closing state is set as the sampling trigger signal by the high-speed data acquisition device, the sampling frequency is set to 100 kHz, which is equivalent to sampling once every 10 microseconds, and the sampling duration is set to 20 milliseconds. The voltage values collected during the period are arranged in a sequence in time order, which is composed of multiple data points, such as the first point voltage of 120, followed by 125, 130, 185, 160, etc. The voltage difference between each adjacent point is compared, and if the voltage of a certain point rises by more than 20 compared to the previous moment, it is considered to enter the fluctuation section, for example, from 130 to 185, the voltage difference is 55, which has exceeded the set threshold, and it is determined that this section is a voltage mutation section. On this basis, the voltage change rate is calculated, and if the voltage rises by 50 in 10 microseconds, the change rate is 5 per microsecond, which exceeds the set rate threshold of 2 per microsecond, confirming that this point is a rate change point. In the marked fluctuation section, the voltage values of each point are compared, and the maximum value is extracted, such as a voltage value of 145, 172, 165, and 178 in a certain section. Take 178 as the maximum voltage point in this section. Finally, multiple maximum voltage points that meet the conditions are sorted in time order to form a point sequence representing the characteristics of rapid voltage change. Each item in the sequence contains time and voltage value.

[0086] S102: Call the voltage value of the point in the voltage jump point sequence and the adjacent point data, filter the main peak point that meets the condition of voltage value higher than the adjacent point, record the time and amplitude information of the main peak point, and arrange it into a structured data table in time order to generate a main peak feature data set;

[0087] According to the voltage jump point sequence, the voltage value of each point is compared with the voltage values of the two adjacent points before and after it. If the voltage of the middle point is higher than that of the two points on both sides and the difference is greater than 10, it is marked as a main peak point. For example, a set of three-point voltage is 160, 190, and 175. The middle point is 30 higher than the left side and 15 higher than the right side, both of which exceed the set standard, confirming that it is a main peak point. The time of this point is recorded as a timestamp, such as 0.008 seconds, and the voltage is 190. At the same time, the cycle number information such as the 5th cycle is added. All main peak points are arranged in time order to form a structured data set, each row containing time, voltage value, and cycle number. During the collection process, if multiple main peak points appear continuously, they still need to be compared and judged for difference one by one to ensure that only the points with voltage significantly higher than the adjacent points are selected. The comparison process can be performed through logical judgment, such as continuous voltage values of 172, 198, and 185, where 198 is higher than the left and right, which is a main peak. If the values are 182, 183, and 180, the difference is too small, which does not meet the condition, and it is determined to be a non-main peak. All main peak data obtained through screening is arranged to form a data set for subsequent analysis.

[0088] S103: According to the time field and cycle number information in the main peak feature data set, the amplitude value and time value of each main peak point are extracted in cycle order, and combined into a continuous data sequence to obtain the main peak feature sequence;

[0089] Based on the main peak feature data set, the time and voltage amplitude of the main peak point in each cycle are extracted in cycle number order to form a main peak sequence that evolves with the cycle. If the cycle numbers are 1, 2, and 3, the main peak points appear at 0.005 seconds, 0.025 seconds, and 0.045 seconds, and the corresponding voltages are 192, 185, and 198, then these three groups of data constitute a continuous sequence. If there are multiple main peak points in a cycle, the one with the maximum voltage value is selected, for example, in cycle 2, there are three points with voltage values of 176, 185, and 180, then 185 is selected as the representative main peak. In the extraction process, it is determined whether each main peak belongs to the cycle by the cycle start time and cycle duration. For example, if the cycle time is 20 milliseconds, the first cycle is from 0 to 0.02 seconds, and the second cycle is from 0.02 to 0.04 seconds. If the main peak time is 0.021 seconds, it belongs to the second cycle. The sorted data sequence will be used to show the time position and amplitude change of the main peak in different cycles to form a complete main peak feature sequence.

[0090] Please refer to Figure 3 , the specific steps of S2 are:

[0091] S201: Based on the main peak feature sequence, extract the fixed length voltage sampling segment after the time corresponding to the main peak point in each cycle, arrange the data sequence according to the cycle number, and construct a sampling segment set indexed by cycle number. Call the fluctuation range information of the voltage value in the time dimension to establish the cycle sampling change amplitude value.

[0092] Based on the sequence of pre-peak characteristics, the voltage data in a fixed length time interval after the occurrence time of the main peak point in each cycle needs to be sampled. The sampling length can be set to 5 ms. Under the condition of a sampling frequency of 100 kHz, it is equivalent to extracting 500 consecutive voltage data points per cycle. Each data point contains the sampling time and the corresponding voltage value. During the extraction process, the main peak point time is taken as the starting point, and the time window is extended in the same length backward, and the corresponding paragraph voltage data is extracted from the original voltage sequence as the sampling segment of the cycle. For example, if the main peak point time is 0.015 s, the sampling segment range is 0.015 s to 0.020 s. The data points in this segment are, for example, 0.01501 s voltage is 180, 0.01502 s is 185, etc. Each sampling segment is indexed by the cycle number to which it belongs, and the sampling segment set is composed in order of the number. After all the sampling segments are extracted, the voltage data in each segment is analyzed, and the maximum and minimum values of the voltage in the segment are found respectively, and the difference between the two is calculated as the voltage fluctuation amplitude of the cycle. For example, the maximum value is 210, the minimum value is 175, and the fluctuation amplitude is 35. In this way, the same processing is performed on all cycles to obtain a complete set of voltage fluctuation amplitude values, each of which is associated with the corresponding cycle number to form a cycle sampling change amplitude value set, which serves as the basis for subsequent abnormal cycle judgment.

[0093] S202: According to the cycle sampling change amplitude value, the change amplitude difference value sequence between adjacent cycles is called, and whether each difference value exceeds the threshold range is judged according to the set discontinuous recognition threshold value, and the cycle number corresponding to the exceeding one is extracted as the abnormal candidate cycle to generate a mutation cycle number list;

[0094] According to the periodic sampling change amplitude value set, the fluctuation amplitude values between adjacent two periods are compared, the difference values are calculated, and a difference value sequence is formed, for example, the fluctuation of the 4th period is 38, and the fluctuation of the 3rd period is 26, so the difference value is 12. This process is performed on all periods to obtain a complete difference value sequence. When judging whether the difference value is abnormal, a threshold for identification needs to be set. The threshold can be set according to the historical data fluctuation range of the equipment operation, for example, set to 15, which means that when the fluctuation change between adjacent two periods exceeds 15, it can be determined that the period may have an abnormal situation. When the difference value between periods exceeds the set value, for example, the 5th period is 50, the 4th period is 30, the difference value is 20, which is greater than the threshold 15, and the 5th period is included in the abnormal candidate period. Here, the absolute value is used to judge the positive or negative difference value to avoid missing the mutation period in the downward trend. The whole judgment process is performed on each period, and the period numbers that exceed the set threshold are extracted and arranged into a list. The list only contains the period numbers with obvious fluctuation mutation, for example, the 3rd, 6th and 8th periods have difference values of 18, 22 and 19 respectively, and the final list is the set of numbers 3, 6 and 8, indicating that these periods have significant mutation behavior.

[0095] S203: Call the voltage sampling segment corresponding to each period number in the mutation period number list, compare the voltage value fluctuation trend direction change sign according to the voltage value change direction of the starting segment and the ending segment, judge the voltage offset direction of the corresponding period, combine the period number and the offset direction to form structured data, and obtain the transient abnormal period information;

[0096] For each period in the mutation period number list, the voltage sampling segment corresponding to the period is called, and the voltage values at the starting position and the ending position in the segment are compared to judge the voltage change direction in the period. The comparison method is to take the average voltage of the first few points in the sampling segment as the starting value and the average voltage of the last few points as the ending value to reduce the influence of single-point mutation interference, for example, the starting 3 points voltage is 172, 175 and 178, the average is 175, the ending 3 points is 193, 195 and 191, the average is 193, so the ending value is higher than the starting value, and it is judged that the voltage offset direction of the period is positive, if the starting value is 190 and the ending value is 175, it is negative. In the judgment process, if the starting and ending difference value is within 5, it is considered that the trend is not obvious, and the direction is not recorded. This process is performed on all mutation periods, and each period number and its offset direction form structured data, such as period number 5 offset direction positive, period number 6 offset direction negative, and period number 8 no obvious offset. After all the results are arranged, a complete transient abnormal period information set is formed, which is used for further fault identification or trend analysis.

[0097] Please refer to Figure 4 , the specific steps of S3 are:

[0098] S301: Call the transient anomaly period information and the main peak feature sequence, extract the amplitude and time information corresponding to the main peak in the continuous period, combine the main peak amplitude and time corresponding to the same period, construct the point set according to the period order, and connect the line segment according to the time sequence to establish the main peak trajectory line segment group;

[0099] Call the transient anomaly period information and the main peak feature sequence, extract the amplitude and time information corresponding to the main peak in the continuous period, combine the main peak amplitude and time corresponding to the same period, construct the point set according to the period order, and connect the line segment according to the time sequence to establish the main peak trajectory line segment group;

[0100] S302: According to the point information of the adjacent line segments in the main peak trajectory line segment group, calculate the corresponding inclination angle value, construct the angle change sequence, judge whether the line segment trend is consistent according to the direction difference of the adjacent angle change in the sequence, identify the line segment set with the direction continuous feature, and generate the trajectory direction continuous segment group;

[0101] Based on the established main peak trajectory line segment group, the numerical values of the two end points of any pair of adjacent line segments are extracted segment by segment, so as to obtain the voltage amplitude change and time span between adjacent line segments, and the change trend between the two is combined to estimate the line segment direction. The speed of voltage change with time is taken as the reference to set the line segment direction. The increase in voltage amplitude represents the upward direction, and the decrease represents the downward direction. For example, from 215 to 218, the direction is upward, and from 218 to 213, the direction is downward. The extraction and direction estimation operations are performed on the whole line segment in sequence to form a group of angle sequences representing the trajectory change trend. In order to avoid errors in direction change judgment, a judgment condition can be set between angles, such as the change direction of two consecutive line segments being consistent, which is considered as no change in trajectory direction. If there is a sudden change in direction, it is a direction switching point, such as from continuous upward to downward, which is marked as direction change. In addition, a direction change tolerance value can be set to evaluate the degree of continuity, such as 30, which represents that the trend change of two segments does not exceed the range, and the direction is consistent. When the voltage value change trend of adjacent line segments is consistent and the time difference does not exceed the set upper limit, it is considered as direction continuity, and the same segment is merged, and all line segments are repeatedly processed to finally form a trajectory direction continuous segment group with consistent direction, which is provided for further behavior judgment.

[0102] S303: According to the period index in the trajectory direction continuous segment group, it is checked whether the line segment direction in the corresponding time sequence exists continuous deviation, the direction segment satisfying the continuous time requirement is screened, the corresponding period range and direction are recorded, and the trajectory deviation period list is established;

[0103] According to the period number information contained in the trajectory direction continuous segment group, the time segments corresponding to each period are returned and extracted, and it is checked whether the trajectory line segment direction in each segment remains consistent. The direction judgment is based on the fact that the voltage change trend in continuous multiple periods does not reverse. If the trend remains rising or falling in the whole segment, it is judged as a direction stable segment. Then, combined with the time span condition, the continuous deviation time threshold is set as 30 ms. If the interval between the start and end time of a certain direction continuous segment reaches or exceeds the value, the segment is identified as a deviation segment satisfying the continuous condition. For example, the segment continues from 0.135 s to 0.175 s, with a total of 40 ms, which meets the time requirement. Then, the segment deviation direction and period number range are recorded. If the direction is positive, it is recorded as positive deviation, and if the direction is negative, it is recorded as negative deviation. Finally, all the segment numbers and directions satisfying the continuous direction condition are sorted to construct the trajectory deviation period list. The list content includes the period start and end numbers corresponding to each segment satisfying the condition and the corresponding deviation direction. For example, the period numbers 7 to 11 are positive, and the period numbers 12 to 15 are negative.

[0104] Please refer to Figure 5 , and the specific steps of S4 are as follows:

[0105] S401: Obtain the period number of the transient abnormal period information and the trajectory offset period list, match them in order, filter the data items with the same period number as the matching samples, and establish the matching period number set;

[0106] After obtaining the period number of the transient abnormal period information and the trajectory offset period list, extract and arrange them in ascending order, then compare them by sequential matching. First, extract the abnormal period set number, such as 7, 8, 9, 10, 12, 13. Then extract the number set in the trajectory offset period list, such as offset sections 7 to 9, 12 to 13, which expand to 7, 8, 9, 12, 13. Then check if each item in the abnormal period set is completely consistent with the offset period number, including value and arrangement order. If an item in the abnormal period set, such as 10, is not in the offset period list, exclude the entire group. Set the matching standard during the screening process, i.e. all numbers must be completely consistent and not missing. Use comparison-based lookup logic to verify if there are any missing or extra items. For period numbers that completely match, such as 7, 8, 9, 12, 13, add them to the matching sample set after confirming that they meet the conditions. The period numbers in this set form the basis data for subsequent analysis. Remove period number pairs that do not form a complete match, thus constructing a period set that meets the exact matching standard.

[0107] S402: Call the matching period number set, and for each period's offset characteristic directionality data, divide it into three categories: positive, negative, and no direction. Calculate the frequency of each category in all matching periods and generate the offset direction distribution value.

[0108] Based on the established matching period number set, extract the offset directionality features corresponding to each period one by one. The direction categories are divided into three types: positive, negative, and no direction, represented by symbols +, -, and 0 respectively. For example, the direction of number 7 is positive, the direction of number 8 is positive, the direction of number 9 is negative, the direction of number 12 is no direction, and the direction of number 13 is positive. The direction sequence is +, +, -, 0, +. Next, count the frequency of each direction category. Positive direction appears 3 times, negative direction appears 1 time, and no direction appears 1 time. The total number is 5, which translates to percentages of 60%, 20%, and 20% respectively. The entire process uses group counting method for classification and statistics, which can be achieved by manual recording or simple programming. Record the number of occurrences for each direction category and divide by the total number of matching samples to get the proportion. For example, the proportion of positive direction is 3 divided by 5, which equals 60%. The proportions of negative and no direction are both 1 divided by 5, which equals 20%. Finally, a complete offset direction distribution value set is formed, which will serve as an important basis for judging the concentration degree of direction trend.

[0109] S403: Based on the offset direction distribution value, extract the concentrated occurrence interval of a single direction in a continuous period, identify the position of the period number corresponding to the direction, mark the corresponding period position number, and generate a period matching anomaly identification group.

[0110] Based on the directions that appear most frequently in the offset direction distribution values, we retrieve whether there is a continuous concentrated interval for that direction in the directional sequence corresponding to the matching period number set. For example, if a positive direction appears twice consecutively in period numbers 7 and 8, and although there are reverse and undirected occurrences in between, it appears again in number 13. Since this occurrence does not form a continuous interval, only the positive continuous interval formed by numbers 7 and 8 is retained. After extracting these period numbers, their positions in the original matching set are marked. For example, number 7 is the first position in the set, and number 8 is the second position. These numbers are then assigned positions. The numbers are identified and organized into a periodic matching anomaly identification group. The data structure of this group includes the period number range and the corresponding direction. For example, the identification group content is "number 7 to 8, positive direction". When judging the concentration interval, a continuity judgment standard needs to be set. For example, at least two consecutive periods have the same direction. If it only appears once, it does not constitute continuity. The directional sequence is scanned from beginning to end using a position sliding method. Each time, the current position and several subsequent positions are checked to see if the direction is consistent. If they are, the number interval and its direction type are recorded. After completion, all period number ranges that meet the central tendency are output and summarized into anomaly identification groups.

[0111] Please see Figure 6 The specific steps of S5 are as follows:

[0112] S501: Based on the period number marked in the period matching anomaly identifier group, determine the sequential continuity between adjacent period numbers, extract continuous period number segments, filter number segments that meet the continuity condition, and generate continuous period segment number values.

[0113] Based on the cycle numbers marked in the cycle matching anomaly identifier group, all numbers are first collected and organized into a sequence, and then sorted in ascending order of numerical value. Next, the numerical differences between adjacent numbers are compared sequentially. If the difference between two adjacent numbers is 1, it is considered a continuous cycle; otherwise, it is considered a discontinuous node. Multiple number segments are constructed through this sequential comparison method. Each number segment consists of continuous cycles. For example, if the number sequence is 7, 8, 9, 11, 12, 14, then 7 to 9 are continuous, 11 and 12 are also continuous, while numbers 10 and 13 are absent, resulting in segmentation. The final number segments are 7 to 9 and 11 to 12, which are recorded as continuous cycle segment numbers. To enhance the accuracy of the judgment, filtering conditions need to be set, such as retaining only number segments with a length of at least 2. If a number segment contains only one number, such as 14, it is discarded because it does not meet the minimum segment length condition. The segment length threshold can be set to 2, meaning that each number segment should contain at least 2 consecutive numbers. All number segments that meet this condition are organized into the final result, forming the data foundation for subsequent directional analysis.

[0114] S502: Call the continuous periodic segment number value, obtain the offset direction information within the number segment, identify the distribution of direction types, filter periodic segments with the same direction type, and generate numbered segment values ​​with the same direction.

[0115] The system retrieves the continuous periodic segment number values, reads each period number contained within it segment by segment, and searches for the corresponding offset direction information. Direction values ​​are generally recorded in three categories: + for positive, - for negative, and 0 for no direction. By statistically analyzing all direction types contained in each number segment, it determines whether the segment has consistent directions. For example, number segments 7 to 9 contain +, +, and - directions, representing two types of directions, indicating inconsistent directions. Number segments 11 to 12, however, contain only - directions, making them consistent directions. The consistency of directions is determined by checking if all direction values ​​within a number segment are identical. If the number of direction types is only one, it is considered consistent. Direction information can be obtained by indexing each row in an established data table. A threshold condition can be set for consistency, such as requiring over 80% of the period directions in a number segment to be the same. When dealing with number segments like 7 to 9 containing multiple directions, those that do not meet the consistency condition are discarded. Finally, all number segment values ​​with consistent directions are collected and archived to form consistent direction number segment values, laying the foundation for subsequent offset trend and equipment correlation analysis.

[0116] S503: Based on the consistent numbering segment value, extract the offset trend characteristics and contact number information of the corresponding numbering segment, determine the degree of concentration of the numbering segment distribution, mark it in combination with the trend type, and generate fault prediction and diagnosis results.

[0117] Based on the consistent numbering segment values, the offset trend characteristics and contact number data corresponding to each numbering segment are extracted. The offset trend is usually recorded as three types: rising, falling, or stable. The contact number is used to identify the switchgear component to which each cycle belongs. By analyzing the trend data of each cycle in the numbering segment, it is determined whether the trend type is consistent. For example, if all trends in a certain numbering segment are rising, then the trend of that segment is identified as rising. If there is a mixed trend, that segment is not included in further judgment. At the same time, the contact number information of the corresponding cycle is extracted. For example, if the contact number of cycles 11 and 12 is CT01, it means that the numbering segment corresponds to a single contact, which is used for judgment. The degree of concentration can be determined by setting conditions. For example, if more than 80% of the contact numbers in a numbering segment are the same, the distribution can be considered concentrated. If there are 5 cycles in a numbering segment and 4 cycles have the same contact number, accounting for 80%, the concentration condition is met. Otherwise, the distribution is considered scattered. After completing the trend consistency and concentration judgment, the numbering segments that meet the conditions are marked. For example, if the trend of numbering segments 11 to 12 is upward and the contact numbers are the same, it is marked as an upward trend and concentrated contact. Finally, a set of switchgear fault prediction and diagnosis results is constructed. The content structure is usually a combination of three elements: numbering segment range, trend type, and contact concentration status.

[0118] Please see Figure 7 An online monitoring, analysis, and fault prediction system for switch cabinets includes:

[0119] The periodic feature extraction module acquires the voltage signal sequence during the contact conduction period in the switch cabinet, segments it according to the conduction period, identifies the voltage maximum point in the period and records the corresponding time position and amplitude, constructs a sequence set including the main peak features of all periods, and generates the main peak feature sequence.

[0120] The transient screening module extracts the voltage sampling segment after the main peak position based on the main peak feature sequence, judges the amplitude change trend of the sampling points in adjacent periods, identifies the period number with abrupt change characteristics, and classifies them in combination with the change direction to generate transient abnormal period information.

[0121] The offset recognition module calls the main peak feature sequence and transient abnormal cycle information, constructs the trajectory change sequence based on the time and amplitude of the main peak point in the continuous cycle, analyzes the directional characteristics of the trajectory slope change between adjacent cycles, and combines the abnormal cycle number to filter and identify the corresponding cycle, generating a list of trajectory offset cycles.

[0122] The anomaly integration module matches records with common period numbers based on transient anomaly period information and trajectory offset period list, counts the offset direction of the period and judges the continuity and concentration of the direction distribution, identifies period ranges with consistent characteristics, and generates period matching anomaly identifier groups.

[0123] The fault diagnosis module calls the period matching anomaly identifier group to extract the number distribution, direction features and period quantity indicators within the period segment. It then combines these with the corresponding contact numbers in the switch cabinet to classify the period markings, determine the period sequences with behavioral characteristics, and generate fault prediction and diagnosis results.

[0124] 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 online monitoring, analysis, and fault prediction of a switch cabinet, characterized in that, Includes the following steps: S1: Collect the voltage sampling sequence during the contact conduction period of the switch cabinet, identify the main peak feature points within the period, record the time and amplitude information, and construct the main peak feature sequence in the period order; S2: Based on the main peak feature sequence, extract the sampling segment after the main peak, analyze the amplitude changes between sequences, determine whether there are discontinuous features, filter the abnormal cycle number and offset direction, and generate transient abnormal cycle information. S3: Based on the main peak feature sequence and the transient abnormal period information, using the main peak amplitude and time information in the continuous period, construct the trajectory change trend sequence, and by comparing the direction of the change in the tilt angle of adjacent line segments, determine whether there is a continuous trajectory deviation and generate a trajectory deviation period list. S4: Perform cross-comparison between the transient abnormal period information and the trajectory offset period list, count the offset direction in the matching period, identify the concentrated distribution interval of abnormal behavior, and generate a period matching abnormal identifier group. S5: Based on the number of consecutive cycles, direction consistency and distribution in the cycle matching anomaly identifier group, determine the abnormal cycle segment, output the associated contact number, offset feature and trend label, and generate the fault prediction and diagnosis result.

2. The method for online monitoring, analysis, and fault prediction of switch cabinets according to claim 1, characterized in that, The main peak feature sequence includes the main peak time point, main peak amplitude, and main peak sequence sorting position. The transient abnormal cycle information includes the abnormal cycle number, abnormal offset direction, and discontinuous characteristic amplitude. The trajectory offset cycle list includes the offset cycle number, offset trend angle change, and trend continuity mark. The cycle matching abnormal identification group includes the matching cycle number, directional statistical results, and concentrated distribution interval range. The fault prediction and diagnosis results include the contact number, offset behavior characteristics, and trend label.

3. The method for online monitoring, analysis, and fault prediction of switch cabinets according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Obtain the voltage sampling sequence during the contact conduction period of the switch cabinet, organize the data points in chronological order, construct the voltage waveform curve, identify the sections with significant fluctuation amplitude changes, select the positions where the rate of change exceeds the set threshold, and extract the points with the highest voltage values ​​within the sections to generate a voltage jump point sequence. S102: Call the voltage values ​​of the points in the voltage jump point sequence and the data of adjacent points, filter the main peak points that meet the condition that the voltage value is higher than that of the adjacent points, record the time and amplitude information of the main peak points, and organize them into a structured data table in chronological order to generate the main peak feature dataset. S103: Based on the time field and period number information in the main peak feature dataset, extract the amplitude value and time value of each main peak point in periodic order, and combine them into a continuous data sequence to obtain the main peak feature sequence.

4. The online monitoring, analysis, and fault prediction method for switch cabinets according to claim 3, characterized in that, The specific steps of S2 are as follows: S201: Based on the main peak feature sequence, extract a fixed-length voltage sampling segment after the time corresponding to the main peak point in each period, organize the data sequence according to the period number, construct a sampling segment set indexed by the period number, call the fluctuation range information of the voltage value in the time dimension, and establish the period sampling change amplitude value. S202: Based on the periodic sampling change amplitude value, call the change amplitude difference sequence between adjacent periods, and determine whether each difference exceeds the threshold range according to the set discontinuity identification threshold. Extract the period number corresponding to the one that exceeds the threshold as an abnormal candidate period and generate a list of mutation period numbers. S203: Call the voltage sampling segment corresponding to each cycle number in the mutation cycle number list, compare the voltage value change direction of the start segment and end segment with the sign of the voltage value fluctuation trend direction change, determine the voltage offset direction of the corresponding cycle, combine the cycle number and offset direction into structured data, and obtain transient abnormal cycle information.

5. The method for online monitoring, analysis, and fault prediction of switch cabinets according to claim 4, characterized in that, The specific steps for S3 are as follows: S301: Call the transient abnormal periodic information and the main peak feature sequence, extract the amplitude and time information corresponding to the main peak in the continuous period, combine the corresponding amplitude and time of the main peak in the same period, construct a point set according to the period order, and connect them according to the time order to generate line segments and establish a main peak trajectory line segment group. S302: Based on the point information of adjacent line segments in the main peak trajectory line segment group, calculate the corresponding tilt angle value, construct the angle change sequence, and determine whether the trend of the line segments is consistent based on the difference in the direction of the adjacent angle changes in the sequence, identify the set of line segments with continuous direction characteristics, and generate a trajectory direction continuous segment group. S303: Based on the period index in the continuous segment group of the trajectory direction, check whether there is a continuous offset in the direction of the line segment in the corresponding time series, filter the direction segments that meet the duration requirements, record the corresponding period range and direction, and establish a trajectory offset period list.

6. The method for online monitoring, analysis, and fault prediction of switch cabinets according to claim 5, characterized in that, The specific steps of S4 are as follows: S401: Obtain the transient abnormal cycle information and the cycle number in the trajectory offset cycle list, match them in the order of the numbers, filter out data items with the same cycle number as matching samples, and establish a set of matching cycle numbers; S402: Call the set of matching cycle numbers, and for the offset feature directional data corresponding to each cycle, divide it into three categories according to the offset direction: forward, reverse and undirected. Count the frequency of each category in all matching cycles and generate the offset direction distribution value. S403: Based on the offset direction distribution value, extract the concentrated occurrence interval of a single direction in a continuous period, identify the position of the period number corresponding to the direction, mark the corresponding period position number, and generate a period matching anomaly identifier group.

7. The method for online monitoring, analysis, and fault prediction of switch cabinets according to claim 6, characterized in that, The specific steps of S5 are as follows: S501: Based on the period number marked in the period matching anomaly identifier group, determine the sequential continuity between adjacent period numbers, extract continuous period number segments, filter number segments that meet the continuity condition, and generate continuous period segment number values. S502: Call the continuous periodic segment number value, obtain the offset direction information within the number segment, identify the distribution of direction types, filter periodic segments with the same direction type, and generate a numbered segment value with the same direction. S503: Based on the consistent numbering segment value, extract the offset trend features and contact number information of the corresponding numbering segment, determine the degree of concentration of the numbering segment distribution, mark it in combination with the trend type, and generate fault prediction and diagnosis results.

8. The method for online monitoring, analysis, and fault prediction of switch cabinets according to claim 1, characterized in that, The switch cabinet contacts are metal contact elements in the switch cabinet used to conduct or disconnect the main circuit current, and belong to the typical conductive interface structure in high-voltage electrical equipment. The voltage sampling sequence is a set of voltage data formed by periodically sampling the voltage signal generated during the conduction process of the switch cabinet contacts, and is collected by the sensor at a fixed sampling frequency; The main peak feature point is the sampling point with the largest amplitude in one period of the voltage sampling sequence; The main peak feature sequence is a data sequence formed by arranging the main peak feature points identified in multiple periods in chronological order.

9. The method for online monitoring, analysis, and fault prediction of switch cabinets according to claim 1, characterized in that, The sampling segment is a voltage sampling data area that extends backward from the main peak feature point. The discontinuity feature refers to the voltage waveform abrupt changes, sharp turns, or abnormal curve discontinuities that occur within the sampling segment, characterizing the unstable disturbance phenomenon of the signal in a short period of time. The offset direction refers to the changing trend monitored when analyzing voltage or peak data of adjacent periods, which is manifested as an increasing or decreasing path of the value in the time or amplitude dimension. The trajectory change trend sequence is a line segment path composed of multiple consecutive main peak feature points, recording the change trend of the peak position on the dual axes of time and amplitude. The direction of the tilt angle change refers to the changing trend of the angle formed between adjacent line segments in the trajectory change trend sequence, that is, the angle continuously increases or decreases; The abnormal behavior concentration distribution interval refers to the interval in which data segments with abnormal fluctuations in multiple detection cycles continuously cluster within a single time range. The fault prediction and diagnosis results are state judgment results output after analyzing abnormal periodic data and trajectory change characteristics.

10. A system for online monitoring, analysis, and fault prediction of switch cabinets, characterized in that, The system is used to implement the online monitoring, analysis, and fault prediction method for a switch cabinet as described in any one of claims 1-9, and the system includes: The periodic feature extraction module acquires the voltage signal sequence during the contact conduction period in the switch cabinet, segments it according to the conduction period, identifies the voltage maximum point in the period and records the corresponding time position and amplitude, constructs a sequence set including the main peak features of all periods, and generates the main peak feature sequence. The transient screening module extracts the voltage sampling segment after the main peak position based on the main peak feature sequence, judges the amplitude change trend of the sampling points in adjacent periods, identifies the period number with abrupt change characteristics, and classifies them in combination with the change direction to generate transient abnormal period information. The offset recognition module calls the main peak feature sequence and the transient abnormal cycle information, constructs a trajectory change sequence based on the time and amplitude of the main peak point in the continuous cycle, analyzes the directional characteristics of the trajectory slope change between adjacent cycles, and combines the abnormal cycle number to filter and identify the corresponding cycle, generating a trajectory offset cycle list. The anomaly integration module matches records with common period numbers based on the transient anomaly period information and the trajectory offset period list, counts the offset direction of the period and judges the continuity and concentration of the direction distribution, identifies period ranges with consistent characteristics, and generates a period matching anomaly identifier group. The fault diagnosis module calls the period matching anomaly identifier group, extracts the number distribution, direction features and period quantity indicators within the period segment, and classifies the period markings in combination with the corresponding contact numbers in the switch cabinet to determine the period sequence with behavioral characteristics and generate fault prediction and diagnosis results.

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