Inspection processing method and system based on multi-source fusion terminal

Abstract

The invention provides an inspection processing method and system based on a multi-source fusion terminal, and belongs to the technical field of inspection management, and the method specifically comprises the steps: carrying out the determination of an infrared temperature measurement strategy when the partial discharge of a GIS part is abnormal according to the discrete distribution condition of the operation time period of the GIS in different operation load intervals, and carrying out the detection of the partial discharge of the GIS part based on the infrared temperature measurement strategy, and performing GIS inspection processing to obtain matching conditions of partial discharge detection results and infrared temperature measurement results in different operation load intervals, determining a target load interval based on the matching conditions, when partial discharge abnormal conditions of different parts of the GIS meet requirements, obtaining data of the target load interval, and storing the data of the target load interval in the GIS. And in combination with the deviation condition of the identification matching data in the target load interval and other target load intervals, the operation load interval of the active inspection processing of the GIS is determined, so that the reliability degree of partial discharge detection processing is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of inspection management, and particularly relates to an inspection processing method and system based on a multi-source fusion terminal. BACKGROUND

[0002] In order to realize GIS partial discharge detection processing, in the invention patent application CN202010117123.9 "System and method for detecting running GIS fault", the vibration signal, ultrasonic partial discharge signal and ultrahigh frequency partial discharge signal of GIS are collected, and the vibration signal, ultrasonic partial discharge signal and ultrahigh frequency partial discharge signal are transmitted to an upper computer, so that the detection efficiency of running GIS is effectively improved, but the above technical solution has the following technical problems: In some running load intervals, the reliability of the partial discharge detection result may not be high due to the influence of external environment, such as vibration, external noise and other factors, so that how to determine the active inspection processing strategy of GIS according to the matching degree of the infrared temperature measurement result under different partial discharge abnormal types, so as to ensure the timeliness and accuracy of the partial discharge monitoring processing becomes a technical problem to be solved.

[0003] To solve the above technical problems, the application provides an inspection processing method and system based on a multi-source fusion terminal. SUMMARY

[0004] To achieve the purpose of the application, the application adopts the following technical solutions: Specifically, the application provides an inspection processing method based on a multi-source fusion terminal, which specifically comprises: S1, based on the running data of GIS, determining the omission of the partial discharge measurement result of GIS in the running process, and based on the omission, determining that the infrared temperature measurement signal of GIS needs to be considered, and according to the distribution dispersion of the running period of GIS in different running load intervals, determining the infrared temperature measurement strategy when the components of GIS have partial discharge abnormalities; S2, based on the infrared temperature measurement strategy, performing the inspection processing of the GIS to obtain the matching condition of the partial discharge detection result and the infrared temperature measurement result in different running load intervals, and based on the matching condition, determining a target load interval, and when the partial discharge abnormality of different components of the GIS meets the requirements, entering the next step; S3, acquiring the target load interval data, combining the deviation condition of the identification matching data in the target load interval and other target load intervals, and determining the running load interval of the active inspection processing of the GIS.

[0005] The application has the following advantages: According to the distribution dispersion of the GIS in different operation load intervals, the infrared temperature measurement strategy of the GIS component in the partial discharge abnormality is determined, so that the distribution dispersion of the GIS in different operation load intervals is determined, and the operation load interval needing infrared temperature measurement is determined, so that the over-temperature condition of the GIS component in different partial discharge abnormality types is further determined, and the reliability of the partial discharge detection processing is improved based on the over-temperature condition.

[0006] Based on the target load interval, the deviation of the identification matching data in the target load interval and other target load intervals, the operation load interval of the GIS active inspection processing is determined, so that when the GIS has a certain degree of abnormality, the active inspection processing can be carried out in the load interval with obvious temperature rise characteristics when the partial discharge risk occurs, the difficulty of the inspection processing is reduced, and the reliability of the active inspection processing of the temperature rise abnormality in multiple load intervals is ensured. The accuracy of the partial discharge detection result is improved by further combining the over-temperature data.

[0007] Further, the operation data of the GIS includes operation time periods and partial discharge measurement characteristics in different operation time periods.

[0008] Further, the omission condition is the number of times of missing partial discharge abnormality in a certain operation load interval, that is, the number of times of missing, which specifically includes the number of times of missing in different operation load intervals.

[0009] Further, the determination of the infrared temperature measurement signal of the GIS specifically includes: Based on the omission condition, the number of times of missing in the partial discharge abnormality state in different operation load intervals is determined. Based on the number of times of missing, it is determined whether the infrared temperature measurement signal of the GIS needs to be considered.

[0010] Further, the method for determining the operation load interval of the GIS active inspection processing is: Based on the target load interval data, the number of target load intervals is determined. According to the identification matching data of the target load interval, the matching factor of the infrared temperature measurement result of the target load interval in different partial discharge abnormality types is determined. Based on the matching factor of the infrared temperature measurement result of the target load interval in different partial discharge abnormality types, the number of target load intervals and the matching factor of the infrared temperature measurement result of other target load intervals in the partial discharge abnormality type, the operation load interval of the GIS active inspection processing is determined.

[0011] In a second aspect, the present application provides a computer system, comprising a memory and a processor connected in communication, and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to implement the above-mentioned inspection processing method based on a multi-source fusion terminal.

[0012] Other features and advantages will be set forth in the descriptions that follow, and in part will be apparent from the description, or can be learned by practice of the application. The purposes and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

[0013] To make the above objectives, features and advantages of the present application more apparent, the following will specifically describe a preferred embodiment, and the accompanying drawings are referred to for a detailed description. BRIEF DESCRIPTION OF DRAWINGS

[0014] The above and other features and advantages of the present application will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0015] Figure 1 is a flowchart of an inspection processing method based on a multi-source fusion terminal; Figure 2 is a flowchart of determining an infrared temperature measurement signal that needs to consider GIS; Figure 3 is a flowchart of a method for determining an infrared temperature measurement strategy when a GIS component has a partial discharge anomaly. DETAILED DESCRIPTION

[0016] Example embodiments now will be described more fully hereinafter with reference to the accompanying drawings. Example embodiments, however, can be implemented in many different forms and should not be construed as limited to the implementations set forth herein; rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of example embodiments to those skilled in the art. Like reference numerals refer to like elements throughout the figures, and thus description of the same will be simplified or omitted.

[0017] The terms "one", "a", "an", "the", and "said" are used to indicate the existence of one or more than one element / portion / etc.; the terms "including" and "having" are used to indicate an open-ended inclusion of one or more elements / portion / etc. in the description of a process, method, or composition of matter.

[0018] Example 1 To solve the above problems, according to one aspect of the present application, as shown in Figure 1 An inspection processing method based on a multi-source fusion terminal is provided, and specifically includes: S1 determines the omission of the partial discharge measurement result of the GIS in the operation process based on the operation data of the GIS, and determines that the infrared temperature measurement signal of the GIS needs to be considered based on the omission, and determines the infrared temperature measurement strategy when the component of the GIS has a partial discharge anomaly according to the distribution dispersion of the operation period of the GIS in different operation load intervals. Further, the operation data of the GIS includes the operation period of the GIS and the partial discharge measurement characteristics in different operation periods.

[0019] Further, the omission is the number of times of undetected partial discharge anomalies in a certain operation load interval, i.e., the number of times of omission, which specifically includes the number of times of omission in different operation load intervals.

[0020] Specifically, the operation load interval is obtained by dividing the rated operation load interval of the GIS in an equal interval form.

[0021] The gas insulated metal enclosed switchgear (GIS) may cause partial discharge (partial discharge) due to insulation defects during operation. The partial discharge signal is usually monitored by a ultra-high frequency (UHF) sensor to diagnose abnormalities. However, in some operating conditions, the partial discharge signal may be masked or difficult to effectively capture, resulting in missed detection. The infrared temperature measurement technology can detect abnormalities caused by local overheating, which can be used as a supplement to partial discharge detection. The present embodiment aims to analyze the omission of partial discharge detection and intelligently determine whether the infrared temperature measurement signal of the GIS needs to be introduced.

[0022] Specifically, as shown in Figure 2 The determination that the infrared temperature measurement signal of the GIS needs to be considered specifically includes: S11 determines the number of times of omission in the partial discharge anomaly state in different operation load intervals based on the omission. Operation data of the GIS: including the operation period of the device, and the partial discharge measurement characteristics (such as discharge amount, discharge frequency, phase distribution, etc.) monitored in different operation periods.

[0023] Omission: refers to the situation that the partial discharge monitoring system does not detect the anomaly when the GIS actually has a partial discharge anomaly. The quantification is the number of times of omission, i.e., the number of times of undetected partial discharge anomaly in a certain operation load interval.

[0024] Operation load interval division: according to the rated operation load range of the GIS, an equal interval division method is used to generate multiple continuous operation load intervals. For example, if the rated load is 0-100%, it can be divided into 5 intervals: 0-20%, 20-40%, 40-60%, 60-80%, and 80-100%.

[0025] Based on the missing situation, the number of missing times in different load intervals is counted. According to historical data, the number of times of partial discharge anomalies that occur but are missed by the monitoring system in each operating load interval is counted.

[0026] Table 1: Partial discharge detection and missing statistics in different operating load intervals

[0027] S12 determines whether the infrared temperature measurement signal of the GIS needs to be considered based on the number of missing times.

[0028] It can be understood that determining whether the infrared temperature measurement signal of the GIS needs to be considered based on the number of missing times specifically includes: Taking the ratio of the number of missing times in the operating load interval to the number of partial discharge detection times in the operating load interval under the partial discharge anomaly state as the missing time ratio; According to the missing time ratio in the operating load interval, it is determined whether the infrared temperature measurement signal of the GIS needs to be considered.

[0029] Calculate the missing time ratio to determine whether the infrared temperature measurement signal needs to be introduced; Calculate the missing time ratio: formula: missing time ratio = (number of missing times in the operating load interval) / (total number of actual partial discharge anomalies in the operating load interval) According to Table 1, the 0-20% interval: 0 / 5 = 0, the 20-40% interval: 1 / 8 = 0.125, the 40-60% interval: 1 / 15 ≈ 0.067, the 60-80% interval: 4 / 22 ≈ 0.182, and the 80-100% interval: 1 / 12 ≈ 0.083.

[0030] Suppose the system preset missing ratio threshold is 0.15.

[0031] Decision logic: when the missing time ratio of any operating load interval is greater than the threshold, it is determined that a single partial discharge signal feature cannot effectively capture the abnormal state under the load, and the infrared temperature measurement signal of the GIS needs to be considered for fusion diagnosis.

[0032] Decision analysis: compare the ratio of each interval to the threshold (0.15): The ratio of the 60-80% interval (0.182) is greater than 0.15, and the conclusion is that since there is a significant missed detection rate (more than 18%) in the 60-80% high load interval, the system determines that the infrared temperature measurement signal of the GIS needs to be considered.

[0033] Specifically, when there is a running load interval with an omission frequency ratio greater than a preset ratio threshold, it is difficult to effectively obtain the partial discharge abnormal state by relying solely on the partial discharge signal characteristics, and therefore it is determined that the infrared temperature measurement signal of the GIS needs to be considered.

[0034] Specifically, as shown in Figure 3 The method for determining the infrared temperature measurement strategy when the GIS component has a partial discharge abnormality is as follows: After it is determined that the infrared temperature measurement signal needs to be introduced as a supplement to the partial discharge detection (as described in Embodiment 1), the next step is to develop an economic and efficient infrared temperature measurement execution strategy. This embodiment aims to intelligently determine which load intervals to focus on for infrared temperature measurement when a partial discharge abnormality occurs based on the distribution characteristics of the historical operating load of the GIS equipment, thereby achieving precise investment and efficient monitoring.

[0035] S21 determines the operating time period of the GIS in different operating load intervals based on the distribution dispersion of the operating time period of the GIS in the different operating load intervals; Step S1: Analyze the operating load interval distribution, collect the operating data of the GIS equipment for the past year, divide the operating load intervals at equal intervals according to the rated load, and count the cumulative operating time in each interval.

[0036] S22 determines the time length ratio of the operating load interval based on the operating time period of the GIS in the operating load interval; Table 2: Annual cumulative operating time distribution of GIS equipment in each operating load interval

[0037] S23 determines the infrared temperature measurement strategy when the GIS component has a partial discharge abnormality based on the time length ratio in different operating load intervals.

[0038] It can be understood that determining the infrared temperature measurement strategy when the GIS component has a partial discharge abnormality based on the time length ratio in different operating load intervals specifically includes: Case 1: If there is an operating load interval with a time length ratio greater than a preset time length ratio threshold, then only the infrared temperature measurement processing when there is a partial discharge abnormality needs to be performed in the operating load interval with a time length ratio greater than the preset time length ratio threshold.

[0039] Based on the time length ratio to develop the infrared temperature measurement strategy, the system preset time length ratio threshold is 25%. According to the data in Table 1, the following decision logic is applied: Case 1 judgment: Check if there is a load interval with a time length ratio > 70%.

[0040] Data shows: the time length of 20-40% interval accounts for 30% (all not more than 70%), the interval of 40-60% accounts for 25% (all not more than 70%), conclusion: there is no interval (20-40% load interval) that meets the conditions. Therefore, enter case 2.

[0041] Case 1 strategy execution: according to the rule, "only need to run in the running load interval with time length ratio greater than the preset time length ratio threshold value, and carry out infrared temperature measurement processing when there is partial discharge anomaly." Final strategy: only when the GIS device runs in the load interval with long running time, if partial discharge anomaly is detected at the same time, infrared temperature measurement is automatically triggered or strengthened. In other load intervals, even if there is partial discharge anomaly, infrared temperature measurement is not started (or only basic monitoring is maintained).

[0042] Strategy explanation: because the device runs in this load interval for the longest time (70% of the total running time), if temperature rise detection when partial discharge anomaly occurs can be achieved in the load interval, the temperature rise condition under partial discharge anomaly can be basically determined. Concentrating resources to enhance monitoring in this interval is the most cost-effective.

[0043] Case 2: if there is no running load interval with time length ratio greater than the preset time length ratio threshold value, judge whether the time length ratio of different running load intervals is less than the preset time length ratio threshold value, if yes, determine the infrared temperature measurement strategy of the components of the GIS when there is partial discharge anomaly, and carry out infrared temperature measurement processing in all running load intervals, if no, enter the next step; Judge whether all interval ratios are less than the preset threshold value (11%), no, there is an interval with an interval ratio not less than 11%, at this time, it is determined that the distribution of different running load intervals is relatively not very discrete, and therefore the next step is entered.

[0044] The running load interval with time length ratio not less than the preset time length ratio threshold value is regarded as the available load interval, and it is judged whether the sum of the time length ratios of the available load intervals is greater than the preset value, if yes, it is determined that the infrared temperature measurement strategy of the components of the GIS when there is partial discharge anomaly is carried out in all available load intervals, if no, the next step is entered; At this time, except for the 80-100% running load interval, all other running load intervals are not less than 11%, all intervals are listed as available load intervals, the sum of the time length ratios of the available load intervals is calculated, the sum of the ratios is 90%, it is determined that the infrared temperature measurement strategy of the components of the GIS when there is partial discharge anomaly is carried out in all available load intervals, that is, infrared temperature measurement is carried out in all available load intervals when partial discharge anomaly occurs.

[0045] determining whether the number of available load intervals is greater than a preset available load interval number threshold, if yes, determining the infrared temperature measurement strategy when the GIS component has a partial discharge abnormality, and performing infrared temperature measurement processing in all available load intervals, if not, determining the infrared temperature measurement strategy when the GIS component has a partial discharge abnormality according to the preset available load interval number threshold.

[0046] Specifically, the infrared temperature measurement strategy when the GIS component has a partial discharge abnormality is determined according to the preset available load interval number threshold, and specifically includes: Based on the preset available load interval number threshold, the determination of the operating load interval for infrared temperature measurement processing is performed according to the time length ratio from high to low.

[0047] In one specific embodiment, the rule is: based on the preset threshold (3), select the corresponding number of load intervals for temperature measurement according to the time length ratio from high to low.

[0048] Implementation: Select the top 3 intervals with the highest proportion from high to low, which are: 80-100% (25.8%), 60-80% (20.5%), and 40-60% (17.1%).

[0049] S2, based on the infrared temperature measurement strategy, performs the inspection processing of the GIS to obtain the matching condition of the partial discharge detection result and the infrared temperature measurement result in different operating load intervals, determines the target load interval based on the matching condition, and when the GIS has a partial discharge abnormality in different components, proceeds to the next step. Specifically, the method for determining the target load interval is: In the previous embodiment, we determined that infrared temperature measurement needs to be introduced in a specific load interval, and formulated the corresponding triggering strategy. This embodiment aims to further evaluate and verify the effectiveness of this multi-sensor fusion strategy, and identify the load interval with high correlation between partial discharge and infrared temperature measurement results, i.e. the "target load interval". In these intervals, infrared temperature measurement can effectively supplement partial discharge detection, verifying the value of fusion diagnosis, and can be used as a future focus monitoring area.

[0050] Determine the infrared temperature measurement result of the component with an abnormal partial discharge detection result in the operating load interval based on the matching condition of the partial discharge detection result and the infrared temperature measurement result in the operating load interval. Matching condition analysis object: In a certain operating load interval, all components determined by the partial discharge detection system to have an abnormality.

[0051] Infrared temperature measurement result anomaly definition: the infrared temperature measurement result of the part is abnormal, and the specific standard is that the temperature rise of the part and the adjacent similar part is significantly greater than the historical average temperature rise observed under the condition that the partial discharge detection result is normal. That is: ΔT (abnormal part) > ΔT (normal historical reference) + threshold.

[0052] Partial discharge anomaly type: different defect types classified according to the pattern characteristics (such as PRPD pattern) of the partial discharge signal, discharge level, phase distribution, etc. For example: Type_A: suspended potential discharge, Type_B: internal air gap discharge of insulation, Type_C: metal tip discharge, etc.

[0053] According to the infrared temperature measurement result, the proportion of the number of parts with abnormal infrared temperature measurement result in the parts with abnormal partial discharge detection result under different partial discharge anomaly types is determined. Select 60-80% operating load interval as evaluation object, there are 50 parts detected by partial discharge system in this interval. We synchronize infrared temperature measurement analysis on these parts, and count according to partial discharge anomaly type.

[0054] Table 3: Partial discharge-infrared matching statistics of each partial discharge anomaly type

[0055] Based on the proportion of the number of parts with abnormal infrared temperature measurement result in the parts with abnormal partial discharge detection result under different partial discharge anomaly types in the operating load interval, it is determined whether the operating load interval is the target load interval.

[0056] It should be noted that the part with abnormal infrared temperature measurement result is the part with temperature rise greater than that when the partial discharge detection result is not abnormal.

[0057] It can be understood that based on the proportion of the number of parts with abnormal infrared temperature measurement result in the parts with abnormal partial discharge detection result under different partial discharge anomaly types in the operating load interval, it is determined whether the operating load interval is the target load interval, which specifically includes: Based on the proportion of the number of parts with abnormal infrared temperature measurement result in the parts with abnormal partial discharge detection result under different partial discharge anomaly types in the operating load interval, it is determined whether the operating load interval is the target load interval, which specifically includes: According to the matching factor of the infrared temperature measurement result under different partial discharge anomaly types, it is determined whether the operating load interval is the target load interval.

[0058] It can be understood that when there is a partial discharge anomaly type with a matching factor of the infrared temperature measurement result greater than a preset matching factor threshold, it is determined that the operating load interval belongs to the target load interval.

[0059] Based on the matching factor to determine the target load interval set the matching factor threshold of 0.75, the first judgment: check if there is a matching factor > 0.75 partial discharge anomaly type, the data shows that the matching factor of Type_A is 0.90, and the matching factor of Type_C is 0.80, both of which are greater than 0.75. Conclusion: there is a type with a matching factor greater than the preset threshold. According to the rules, it is directly determined that the operating load interval (60-80%) belongs to the target load interval.

[0060] Decision explanation: In this load interval, for Type_A and Type_C, two common and dangerous partial discharge defects, infrared temperature measurement shows very high cooperative detection capability (matching factor reaches 80-90%). This strongly proves the effectiveness and high value of the "partial discharge triggers infrared" fusion strategy in this interval.

[0061] In addition, it should be noted that when there is no partial discharge anomaly type with a matching factor greater than the preset matching factor threshold, it is determined whether the matching factor of the infrared temperature measurement result under different partial discharge anomaly types is less than the preset factor threshold. If so, it is determined that the operating load interval does not belong to the target load interval, and if not, it proceeds to the next step; Suppose in another load interval (such as 20-40%), the first judgment is to check if there is a type with a matching factor > 0.70. The matching factors of all types (0.40, 0.50, 0.60, 0.60) are less than 0.70. Conclusion: enter the branch of "no" high matching type.

[0062] Secondary judgment: check if all types of matching factors are less than the threshold (0.70), yes, all matching factors are less than 0.70, conclusion: according to the rules, "determine that the operating load interval does not belong to the target load interval", decision explanation: in this load interval, the response of infrared temperature measurement to various partial discharge defects is not significant (the highest matching factor is only 60%), indicating that in this working condition, the effectiveness of infrared temperature measurement as a partial discharge supplement is limited, and it may not be worth as a key fusion monitoring interval.

[0063] The partial discharge anomaly type with a matching factor not less than the preset factor threshold is used as the available monitoring type, and according to the number of available monitoring types, it is determined whether the operating load interval belongs to the target load interval.

[0064] Further deduction (assuming there is a "not less than" threshold): Suppose the matching factor of Type_C is adjusted to 0.80, then Type_C becomes an available monitoring type, and the number of available monitoring types is 1. It is determined whether the number of available monitoring types (1) is greater than the preset monitoring type number threshold (2): 1<2, which does not meet the condition.

[0065] Final decision: since the number of available types does not reach the scale requirement, the system can still determine that the interval does not belong to the target load interval, or enter a more complex weighted decision.

[0066] It can be understood that when the number of available monitoring types is greater than the preset monitoring type number threshold, it is determined that the operating load interval belongs to the target load interval.

[0067] The embodiment provides a method for quantitatively evaluating the effectiveness of sensor fusion: Verify the effectiveness of the strategy: quantitatively evaluate the response degree of infrared temperature measurement to partial discharge anomalies in a specific load interval through the "matching factor", so as to verify whether the "introduction of infrared" strategy formulated in the early stage is really effective.

[0068] Identify high-value monitoring intervals: the identification of "target load intervals" provides data support for the priority allocation of operation and maintenance resources, and lays a foundation for further determining the load interval that needs to be actively inspected and processed.

[0069] Further, it is judged whether the GIS meets the requirements in terms of partial discharge anomalies of different components, specifically including: When the number of components with partial discharge anomalies in the GIS is within the preset range, it is determined that the GIS meets the requirements in terms of partial discharge anomalies of different components.

[0070] It can be understood that when the GIS does not meet the requirements in terms of partial discharge anomalies of different components, it directly proceeds to the shutdown maintenance processing.

[0071] Overall anomaly condition evaluation: count the total number of components with partial discharge anomalies in all components of the GIS at present (or in the last evaluation period), and make a satisfaction judgment: judge whether the total number of abnormal components is within the preset allowable range, for example, less than 3, then it is determined that the GIS meets the requirements in terms of partial discharge anomalies of different components.

[0072] S3 obtains the target load interval data, and combines the deviation of the identified matching data in the target load interval and other target load intervals to determine the operating load interval of the active inspection processing of the GIS.

[0073] Specifically, the method for determining the operating load interval of the active inspection processing of the GIS is: After identifying multiple "target load intervals" (i.e., intervals with strong correlation between partial discharge and infrared temperature measurement), in order to further optimize operation and maintenance resources, an "active inspection" strategy needs to be developed. Active inspection refers to scheduled on-site or remote focused inspection when the equipment is operating at a specific load. Due to limited resources, it is not possible to conduct high-intensity inspections in all target intervals, so it is necessary to prioritize and filter the target load intervals to determine the load intervals that most need active inspection.

[0074] Input: Target load interval set: Assume that through preliminary evaluation, several target load intervals are identified, such as interval A, interval B, interval C, and interval D.

[0075] Target load interval feature data: Matching data: Infrared temperature matching factor of each target load interval under different partial discharge anomaly types, which indicates the probability of synchronous discovery of this type of partial discharge anomaly by infrared temperature measurement.

[0076] Duration proportion: The proportion of this target load interval in the total historical running time.

[0077] Based on the target load interval data, determine the number of target load intervals; Step 1: Determine the number of target load intervals and basic conditions. Assume that through preliminary identification, four target load intervals are obtained, with the following basic data: Interval one: covers a load range of 20% to 40%, with a historical running time of 25% of the total time. Its main feature is that the infrared matching factor for this type of anomaly is as high as 95%.

[0078] Interval two: covers a load range of 40% to 60%, with a historical running time of 15% of the total time. Its main feature is that the infrared matching factor for this type of anomaly is 88%.

[0079] Interval three: covers a load range of 60% to 80%, with a historical running time of 35% of the total time. It has high matching factors for both suspended discharge and particle discharge anomalies, which are 90% and 85%, respectively.

[0080] Interval four: covers a load range of 80% to 100%, with a historical running time of 8% of the total time. Its main feature is that the infrared matching factor for this type of anomaly is 82%.

[0081] According to the identification matching data of the target load interval, determine the matching factor of the infrared temperature measurement result of the target load interval under different partial discharge anomaly types; The matching factor of the infrared temperature measurement result of the target load interval under different partial discharge anomaly types, the number of target load intervals, and the matching factor of the infrared temperature measurement result of other target load intervals under the partial discharge anomaly type are used to determine the operation load interval of the active inspection processing of the GIS.

[0082] Further, if the number of target load intervals is less than a preset target load interval number threshold, active inspection processing is required in different target load intervals.

[0083] First-level judgment: Based on the number of target intervals, the system preset target interval number threshold is 3. The number of target intervals currently identified is 4, which is not less than the threshold 3. Conclusion: Enter the next level of complex decision.

[0084] In addition, it can be understood that if the number of target load intervals is not less than the preset target load interval number threshold, the time length proportion of the target load interval is determined. If the time length proportion of the target load interval is not less than the preset time length proportion threshold, it is determined that active inspection processing is required in the target load interval.

[0085] Second-level judgment: Based on the time length proportion, the system preset time length proportion threshold is 20%.

[0086] Determine whether the time length proportion of each target interval reaches or exceeds this threshold: The time length proportion of interval one is 25%, which is greater than or equal to the threshold. Decision: Active inspection is required.

[0087] The time length proportion of interval two is 15%, which is less than the threshold.

[0088] The time length proportion of interval three is 35%, which is greater than or equal to the threshold. Decision: Active inspection is required.

[0089] The time length proportion of interval four is 8%, which is less than the threshold.

[0090] Further, if the time length proportion of the target load interval is less than the preset time length proportion threshold, if the target load interval has a matching factor, and the maximum partial discharge anomaly type in all target load intervals, it is determined that the target load interval is the operation load interval of the active inspection processing of the GIS. Otherwise, active inspection processing is not required in the target load interval.

[0091] This step targets those intervals (i.e. Interval Two and Interval Four) that do not meet the threshold in terms of time length proportion, but may have unique monitoring value. The judgment principle is: if the interval is highest in the infrared matching factor of a certain type of partial discharge abnormality, it needs to be actively inspected for its "unique advantage" in monitoring this type of defect, even if it does not run frequently.

[0092] Analyze which target interval has the highest matching factor under each main abnormality type: For the "suspended potential discharge" type, the interval with the highest matching factor is Interval One (95%).

[0093] For the "insulation internal air gap discharge" type, the interval with the highest matching factor is Interval Four (82%).

[0094] For the "free metal particle discharge" type, the interval with the highest matching factor is Interval Two (88%).

[0095] Application rules: Check Interval Two: It is the highest in the matching factor of the "free metal particle discharge" type. Therefore, Interval Two is determined to be actively inspected.

[0096] Check Interval Four: It is the highest in the matching factor of the "insulation internal air gap discharge" type. Therefore, Interval Four is determined to be actively inspected.

[0097] Four, final decision and strategy, comprehensive above judgment, finally determine the need to actively inspect the load interval: Interval One, Interval Two, Interval Three, Interval Four. All identified target intervals need to be inspected.

[0098] Inspection strategy refinement suggestions: For high time length proportion intervals (Interval One, Interval Three): High frequency regular inspection should be arranged, because these intervals have long equipment running time and sufficient risk exposure.

[0099] For feature advantage intervals (Interval Two, Interval Four): Inspection should be more targeted. For example, when the load is about to enter Interval Two, the parts prone to particle discharge should be checked; when entering Interval Four, the parts that may have insulation air gaps should be checked.

[0100] Five, special case deduction, scenario: target interval number is small Suppose only two target load intervals are identified, and the system preset threshold number is three.

[0101] Judgment: The number of target intervals (two) is less than the threshold (three).

[0102] Decision: According to the rules, active inspection is required in both target load intervals. This is a comprehensive coverage strategy when resources are relatively abundant.

[0103] This method realizes the accurate formulation of inspection strategy through hierarchical decision-making: Balancing frequency and characteristics: Both the load interval where the equipment is frequently operated and the interval where the monitoring effect on specific defects is best are considered, achieving comprehensive coverage of risk points.

[0104] Optimize resource allocation: The inspection priority and focus of different intervals are clear, making the most effective use of human and time resources.

[0105] Clear decision-making process: Based on clear thresholds and rules for automated judgment, subjective speculation is reduced, and the scientificity and consistency of decision-making are improved.

[0106] Flexible and adjustable strategy: By adjusting the preset number threshold and time proportion threshold, it can easily adapt to the operation and maintenance requirements of GIS equipment of different sizes and importance.

[0107] Embodiment 2 In a second aspect, the present application provides a computer system, comprising a memory and a processor connected in communication, and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to perform the above-mentioned inspection processing method based on a multi-source fusion terminal.

[0108] Each embodiment in the specification is described in a progressive manner, and the same and similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0109] The above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different than the order in the embodiments and still achieve the desired result. In addition, the processes depicted in the figures do not necessarily require the particular order shown or sequential order to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous or possible.

[0110] The above merely provides one or more embodiments of the present specification and is not intended to limit the present specification. One of ordinary skill in the art can make various modifications and changes to one or more embodiments of the present specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of the present specification should be included in the scope of claims of the present specification.

Claims

1. A method for inspection processing based on a multi-source fusion terminal, characterized in that, Specifically, it includes: Based on the GIS operation data, the omission of partial discharge measurement results during GIS operation is determined. Based on the omission, when it is determined that the infrared temperature measurement signal of GIS needs to be considered, the infrared temperature measurement strategy is determined when the components of the GIS have partial discharge anomalies, according to the distribution and dispersion of the GIS operation time in different operating load ranges. Based on the infrared temperature measurement strategy, the GIS is inspected and processed to obtain the matching results of partial discharge detection and infrared temperature measurement in different operating load ranges. The target load range is determined based on the matching results. When the partial discharge anomaly of different components of the GIS meets the requirements, the next step is performed. The target load range data is acquired, and the deviation between the identification and matching data within the target load range and other target load ranges is used to determine the operating load range for the GIS's proactive inspection processing.

2. The inspection processing method based on a multi-source fusion terminal as described in claim 1, characterized in that, The operational data of the GIS includes the GIS's runtime phases and the partial discharge measurement characteristics in different runtime phases.

3. The inspection processing method based on a multi-source fusion terminal as described in claim 1, characterized in that, The omission refers to the number of times a partial discharge anomaly exists but is not detected within a certain operating load range.

4. The inspection processing method based on a multi-source fusion terminal as described in claim 1, characterized in that, The operating load range is obtained by dividing the rated operating load range of the GIS into equal intervals.

5. The inspection processing method based on a multi-source fusion terminal as described in claim 1, characterized in that, The infrared thermography signals from GIS need to be considered, specifically including: Based on the omissions, the number of omissions under partial discharge anomaly conditions is determined within different operating load ranges; Based on the number of omissions, it is determined whether the infrared temperature measurement signal of the GIS needs to be considered.

6. The inspection processing method based on a multi-source fusion terminal as described in claim 5, characterized in that, Based on the number of omissions, determine whether the infrared temperature measurement signal of the GIS needs to be considered, specifically including: The ratio of the number of missed occurrences within the operating load range to the number of partial discharge detections under partial discharge abnormal conditions within the operating load range is taken as the missed occurrence ratio. Based on the ratio of missed occurrences within the operating load range, determine whether the infrared temperature measurement signal of the GIS needs to be considered.

7. The inspection processing method based on a multi-source fusion terminal as described in claim 6, characterized in that, When there is an operating load range where the ratio of missed occurrences is greater than a preset ratio threshold, it is difficult to effectively obtain the abnormal state of partial discharge by relying solely on the characteristics of partial discharge signals. Therefore, it is determined that the infrared temperature measurement signal of GIS needs to be considered.

8. The inspection processing method based on a multi-source fusion terminal as described in claim 1, characterized in that, The method for determining the operating load range of the GIS active inspection processing is as follows: Based on the target load range data, determine the number of target load ranges; Based on the identification and matching data of the target load range, determine the matching factor of the infrared thermometry results of the target load range under different partial discharge anomaly types; The operating load range for the active inspection processing of the GIS is determined by using the matching factor of the infrared thermometry results of the target load range under different partial discharge anomaly types, the number of target load ranges, and the matching factor of the infrared thermometry results of other target load ranges under the same partial discharge anomaly type.

9. The inspection processing method based on a multi-source fusion terminal as described in claim 8, characterized in that, If the number of target load intervals is less than the preset target load interval number threshold, then inspection processing is required in each target load interval.

10. A computer system, comprising: A memory and processor connected by communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes an inspection processing method based on a multi-source fusion terminal as described in any one of claims 1-9.

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