Data management method for electrical equipment

Abstract

The invention relates to the technical field of equipment data management, in particular to a data management method for electrical equipment. The method comprises the following steps: determining an environment parameter of an abnormal data occurrence time point or marking the abnormal data based on the occurrence duration of the abnormal data in a single preset period and the repeated occurrence frequency of the marked abnormal data in a plurality of preset periods; determining to send the abnormal data to an environmental parameter abnormal database or analyze abnormal data change characteristics based on whether the abnormal data has environmental parameter fluctuation at the occurrence time point of the abnormal data and whether the abnormal data with the environmental parameter fluctuation at the occurrence time point is consistent with the periodic inspection data; determining to send the abnormal data to a corresponding abnormal database based on the abnormal data change characteristics; according to the abnormal data processing method and device, the abnormal data is sent to different abnormal databases by improving the analysis accuracy of the occurrence reasons of the abnormal data, so that the processing accuracy and efficiency of the abnormal data are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of device data management, and particularly to a data management method for electrical equipment. Background Art

[0002] Electrical equipment often operates in complex and diverse environments, including harsh conditions such as high temperature, high humidity, and strong electromagnetic interference. These environmental factors can have a significant impact on the performance of the equipment. Traditional data management of electrical equipment mostly relies on manual inspections and simple recording methods. This method has many drawbacks. On the one hand, there is a time interval for manual inspections, making it difficult to monitor the operating status of the equipment in real time and easily missing the critical time points of equipment abnormalities. On the other hand, the accuracy and integrity of the data recorded manually are difficult to guarantee, and the efficiency is low when dealing with a large amount of data, making it impossible to conduct in-depth analysis and mining of the data. With the increase in the number of equipment and the explosion of data volume, the traditional method can no longer meet the demand for efficient management of electrical equipment.

[0003] For example, Chinese Patent Application Publication No.: A Health Management System and Method for Electrical Equipment discloses a health management system and method for electrical equipment. By means of data fitting, a mapping relationship is established. For the input operating parameters, according to the mapping relationship, the final result is analyzed, and the data fitting method is continuously corrected according to the final result, and the data fitting method is continuously updated. The final result of the analysis is the secondary evaluation and prediction result of health assessment and fault prediction; data is collected by installing various sensors in the electrical equipment and through communication to obtain internal data, and by means of real-time monitoring, regular inspections, etc.; the invention not only establishes a data model and a neural network, but also conducts autonomous learning through data fitting, and through the mapping relationship therein, the health status of the electrical equipment is evaluated and the fault is predicted. The present invention includes three evaluation and prediction methods, and the health status of the electrical equipment is evaluated and the fault is predicted by combining the three evaluation and prediction methods, reducing the possibility of misjudgment.

[0004] However, the existing technology has the problems that the analysis of the causes of abnormal data is not accurate enough, resulting in the inability to send abnormal data to the corresponding abnormal database in time, resulting in low processing accuracy and low processing efficiency of abnormal data. Summary of the Invention

[0005] Therefore, the present invention provides a data management method for electrical equipment to overcome the problems in the prior art that the analysis of the causes of abnormal data is not accurate enough, resulting in the inability to send abnormal data to the corresponding abnormal database in time, resulting in low processing accuracy and low processing efficiency of abnormal data.

[0006] To achieve the above object, the present invention provides a data management method for electrical equipment, including:

[0007] Obtain the real-time monitoring data and periodic inspection data of the electrical equipment within a preset period, and preprocess the real-time monitoring data and the periodic inspection data to identify abnormal data;

[0008] Based on the occurrence duration of abnormal data within a single preset period and the recurrence frequency of the marked abnormal data within several preset periods, determine the environmental parameters at the time point when the abnormal data occurs or mark the abnormal data;

[0009] Based on whether there is environmental parameter fluctuation at the time point when the abnormal data occurs and whether the abnormal data with environmental parameter fluctuation at the time point of occurrence is consistent with the periodic inspection data, determine whether to send the abnormal data to the environmental parameter abnormal database or analyze the change characteristics of the abnormal data;

[0010] Match the change characteristics of the abnormal data with the change characteristics of historical abnormal data, and based on the comparison result of the similarity between the change characteristics of the abnormal data and the historical abnormal data and the preset similarity, determine whether to send the abnormal data to the historical abnormal database or send the abnormal data to the in-depth analysis abnormal database;

[0011] Obtain the growth amount and type of abnormal data in the abnormal database within a preset duration, and based on the growth amount and growth characteristics of the abnormal data, determine whether to adjust the preset occurrence duration and preset period, where the abnormal database includes an environmental parameter abnormal database, a historical abnormal database, and an in-depth analysis abnormal database.

[0012] Further, determining the environmental parameters at the time point when the abnormal data occurs or marking the abnormal data includes:

[0013] If the occurrence duration of abnormal data within a single preset period is less than the preset occurrence duration and the recurrence frequency of the marked abnormal data within several preset periods is less than the preset recurrence frequency, determine to mark the abnormal data;

[0014] If the occurrence duration of abnormal data within a single preset period is greater than or equal to the preset occurrence duration or the recurrence frequency of the marked abnormal data within several preset periods is greater than or equal to the preset recurrence frequency, determine to obtain the environmental parameters at the time point when the abnormal data occurs.

[0015] Further, the preset occurrence duration is determined according to the average value of the occurrence durations of abnormal data within several preset periods.

[0016] Further, determining whether to send the abnormal data to the environmental parameter abnormal database or analyze the change characteristics of the abnormal data includes:

[0017] If there are fluctuations in environmental parameters at the occurrence time point of the abnormal data and the abnormal data with environmental parameter fluctuations is consistent with the periodic inspection data, determine to send the abnormal data to the environmental parameter abnormal database;

[0018] If there are no fluctuations in environmental parameters at the occurrence time point of the abnormal data or the abnormal data with environmental parameter fluctuations is inconsistent with the periodic inspection data, determine to analyze the change characteristics of the abnormal data.

[0019] Further, determining whether there are fluctuations in environmental parameters at the occurrence time point of the abnormal data includes:

[0020] After identifying the abnormal data of the electrical equipment, obtain the occurrence time point of the abnormal data and the environmental parameter data within 5 minutes before and after it;

[0021] Compare the extracted environmental parameter data with the set fluctuation judgment threshold;

[0022] If it exceeds the fluctuation judgment threshold, it is determined that there are fluctuations in environmental parameters at the occurrence time point of the abnormal data.

[0023] Further, determining that the abnormal data with environmental parameter fluctuations is consistent with the periodic inspection data includes that there are fluctuations in environmental parameters at the occurrence time point of the abnormal data in the periodic inspection data.

[0024] Further, determining to send the abnormal data to the historical abnormal database or to send the abnormal data to the in-depth analysis abnormal database includes:

[0025] If the similarity between the change characteristics of the abnormal data and the change characteristics of the historical abnormal data is less than the preset similarity, determine to send the abnormal data to the historical abnormal database;

[0026] If the similarity between the change characteristics of the abnormal data and the change characteristics of the historical abnormal data is greater than or equal to the preset similarity, determine to send the abnormal data to the in-depth analysis abnormal database.

[0027] Further, determining whether to adjust the preset occurrence duration and the preset period includes:

[0028] If the growth amount of abnormal data in the abnormal database within the preset duration is greater than the preset data growth amount and the growth characteristic of the abnormal data is that the average duration is extended, determine to adjust the preset occurrence duration;

[0029] If the growth amount of abnormal data in the abnormal database within the preset duration is greater than the preset data growth amount and the growth characteristic of the abnormal data is that the recurrence frequency increases, determine to adjust the preset period.

[0030] Furthermore, the adjustment amount of the preset occurrence duration is negatively correlated with the growth amount of the abnormal data, and the adjustment amount of the preset period is negatively correlated with the growth amount of the abnormal data.

[0031] Furthermore, calculating the similarity between the abnormal data change feature and the historical abnormal data change feature includes:

[0032] Extract key change features from abnormal data;

[0033] Retrieving historical abnormal data related to the abnormal data from a historical abnormality database, and extracting key change features of the same type from the historical data;

[0034] Calculate the Euclidean distance between the current abnormal data change characteristics and the historical abnormal data change characteristics.

[0035] Compared with the prior art, the beneficial effect of the present invention is that the present invention determines whether to mark abnormal data or obtain environmental parameters according to the preset appearance duration and the preset repetition frequency, so that the judgment of abnormal situations is more accurate. For example, if the appearance duration of abnormal data in a single preset period is short and the repetition frequency is low, it means that the abnormal situation may be relatively mild, and only a simple mark is needed for subsequent observation. When the appearance duration is long or the repetition frequency is high, it indicates that the abnormal situation may be more serious. Obtaining environmental parameters helps to deeply analyze the cause of the abnormality, so as to accurately grasp the operating status of the equipment. This judgment method avoids obtaining environmental parameters for all abnormal data, saving resources and time costs. During the operation of the equipment, there are a large number of abnormal data. If environmental parameters are obtained for each abnormal data for analysis, a large amount of computing resources and time will be consumed. By setting a reasonable threshold, environmental parameters are only obtained when the abnormal situation is more serious, which improves the efficiency of data processing and concentrates resources on the analysis of key issues.

[0036] Furthermore, the present invention can more accurately determine the cause of abnormal data by judging the environmental parameter fluctuations at the time when the abnormal data occurs, and comparing the fluctuation data with the periodic inspection data. For example, in the case of No. 3 high-voltage switchgear, the correlation between temperature parameter fluctuations and abnormal current increases is clarified, which helps operation and maintenance personnel to quickly find the root cause of the problem, avoid blind investigation, and improve fault diagnosis efficiency. The decision to send abnormal data to the environmental parameter abnormality database or analyze the abnormal data change characteristics is based on the environmental parameter fluctuations and the consistency of the inspection data, which realizes the reasonable classification of abnormal data. The data sent to the environmental parameter abnormality database can provide a basis for studying the impact of environmental factors on equipment; and analyzing the abnormal data change characteristics is helpful to explore the potential problems of the equipment itself, and provide targeted directions for subsequent equipment maintenance and optimization. The above method improves the accuracy of the analysis of the cause of abnormal data, thereby improving the processing accuracy and efficiency of abnormal data.

[0037] Furthermore, by extracting the key change features of abnormal data and calculating the similarity with the data in the historical abnormal database, the present invention can quickly find historical cases similar to the current abnormality. In the example of abnormal increase in equipment temperature, Euclidean distance calculation is performed based on key features such as the temperature change rate, the highest temperature value, and the abnormal duration. If the similarity is high, it can quickly locate historical abnormalities that adopt the same repair method, provide reference for solving the current problem, save the time for fault troubleshooting and solution, and send abnormal data with similarity less than the preset similarity to the historical abnormal database, which can continuously enrich the historical abnormal data resources. These data contain detailed information on previous equipment abnormalities and corresponding repair methods. When encountering similar abnormalities in the future, historical experience can be directly borrowed, avoiding repeated labor, improving the repair efficiency, and reducing the repair cost. When the similarity between the change features of abnormal data and the change features of historical abnormal data is greater than or equal to the preset similarity, it is sent to the in-depth analysis abnormal database, which helps to focus on abnormalities that are different from historical situations and may have new problems or complex factors. Through the above method, the accuracy of analyzing the cause of abnormal data is improved, and thus the processing accuracy and processing efficiency of abnormal data are improved.

[0038] Furthermore, by analyzing the growth characteristics of abnormal data (such as the average duration prolonging or the recurrence frequency increasing), the present invention can dynamically adjust the monitoring duration and period. For example, when it is found that the average duration of abnormal data prolongs, the preset occurrence duration is appropriately shortened, which can capture abnormal situations more timely; when it is found that the recurrence frequency of abnormal data increases, the preset period is shortened, and monitoring and analysis can be performed more frequently, thereby improving the sensitivity to abnormal situations. Through the above method, the accuracy of analyzing the cause of abnormal data is improved, and thus the processing accuracy and processing efficiency of abnormal data are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is the flowchart of the data management method for electrical equipment in the embodiment of the present invention;

[0040] Figure 2 is the flowchart of determining whether there is environmental parameter fluctuation at the occurrence time point of abnormal data in the data management method for electrical equipment in the embodiment of the present invention;

[0041] Figure 3 is the flowchart of calculating the similarity between the change features of abnormal data and the change features of historical abnormal data in the data management method for electrical equipment in the embodiment of the present invention;

[0042] Figure 4This is the flowchart for determining whether to send the abnormal data to the environmental parameter abnormal database or analyze the change characteristics of the abnormal data in the data management method for electrical equipment according to the embodiments of the present invention. Detailed implementation manners

[0043] In order to make the objectives and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0044] The preferred implementation manners of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these implementation manners are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0045] Please refer to Figures 1 - 4 as shown in Figure 1 This is the flowchart for the data management method for electrical equipment according to the embodiments of the present invention; Figure 2 This is the flowchart for determining whether there is environmental parameter fluctuation at the occurrence time point of abnormal data in the data management method for electrical equipment according to the embodiments of the present invention; Figure 3 This is the flowchart for calculating the similarity between the change characteristics of abnormal data and the change characteristics of historical abnormal data in the data management method for electrical equipment according to the embodiments of the present invention; Figure 4 This is the flowchart for determining whether to send the abnormal data to the environmental parameter abnormal database or analyze the change characteristics of the abnormal data in the data management method for electrical equipment according to the embodiments of the present invention.

[0046] The data management method for electrical equipment according to the embodiments of the present invention includes:

[0047] Step S1, obtaining the real-time monitoring data and periodic inspection data of the electrical equipment within a preset period, and preprocessing the real-time monitoring data and the periodic inspection data to identify abnormal data;

[0048] Step S2, determining the environmental parameters at the occurrence time point of the abnormal data or marking the abnormal data based on the occurrence duration of the abnormal data within a single preset period and the repeated occurrence frequency of the marked abnormal data within several preset periods;

[0049] Step S3, determining whether to send the abnormal data to the environmental parameter abnormal database or analyze the change characteristics of the abnormal data based on whether there is environmental parameter fluctuation at the occurrence time point of the abnormal data and whether the abnormal data with environmental parameter fluctuation at the occurrence time point is consistent with the periodic inspection data;

[0050] Step S4: Match the abnormal data change characteristics with the historical abnormal data change characteristics, and determine whether to send the abnormal data to the historical abnormal database or the in-depth analysis abnormal database based on the comparison result between the similarity of the abnormal data change characteristics and the historical abnormal data change characteristics and the preset similarity;

[0051] Step S5: Obtain the abnormal data growth amount and abnormal data type in the abnormal database within a preset time period, and determine whether to adjust the preset occurrence duration and preset period based on the abnormal data growth amount and the growth characteristics of the abnormal data, where the abnormal database includes the environmental parameter abnormal database, the historical abnormal database, and the in-depth analysis abnormal database.

[0052] In the embodiment of the present invention, the real-time monitoring data includes but is not limited to "electrical equipment working current data, electrical equipment working voltage data, electrical equipment working power data, and electrical equipment working environment data", and the periodic inspection data includes but is not limited to "electrical equipment working status data, electrical equipment working parameter data, and electrical equipment working environment data". The preset period is set to 10 days, and the inspection period of the periodic inspection data is set to 2 days. The preprocessing of the real-time monitoring data and the periodic inspection data to identify abnormal data includes that noise, duplicate data, or error data may be mixed in the real-time monitoring data and the periodic inspection data during the acquisition and transmission process, and cleaning is required. For the electrical equipment working current data, check whether there are spike abnormal values caused by sensor failures, and if so, remove or correct them; check for duplicate records to ensure data accuracy. For the electrical equipment working status data in the periodic inspection data, if there are fuzzy or contradictory records, verification and correction are required. For example, if the equipment working status is recorded as "running" and "stopped" at the same time, it should be corrected according to the actual situation. Identifying abnormal data includes identifying abnormal data in the working parameter data of the electrical equipment that exceeds the normal working parameter range. The working parameter data of the electrical equipment includes but is not limited to "electrical equipment working current data, electrical equipment working voltage data, and electrical equipment working power data".

[0053] Specifically, in step S2, when determining the environmental parameters at the time point when abnormal data occurs or marking the abnormal data, determine the environmental parameters at the time point when abnormal data occurs or mark the abnormal data according to the occurrence duration of the abnormal data within a single preset period and the repeated occurrence frequency of the marked abnormal data within several preset periods;

[0054] When the occurrence duration of the abnormal data within a single preset period is less than the preset occurrence duration and the repeated occurrence frequency of the marked abnormal data within several preset periods is less than the preset repeated occurrence frequency, determine to mark the abnormal data;

[0055] When the occurrence duration of abnormal data within a single preset period is greater than or equal to the preset occurrence duration, or the recurrence frequency of the marked abnormal data within several preset periods is greater than or equal to the preset recurrence frequency, determine the environmental parameters at the time point when the abnormal data occurs.

[0056] In the embodiments of the present invention, the preset occurrence duration is one-third of the average value of the occurrence durations of abnormal data within several preset periods, and the preset recurrence frequency is the historical average value of the recurrence frequencies of the marked abnormal data. However, the above values are not limited to this, and those skilled in the art can also adjust the values according to actual needs.

[0057] The present invention determines whether to mark abnormal data or obtain environmental parameters based on the preset occurrence duration and the preset recurrence frequency, making the judgment of abnormal situations more accurate. For example, if the occurrence duration of abnormal data within a single preset period is short and the recurrence frequency is low, it indicates that the abnormal situation may be relatively mild, and only simple marking is required for subsequent observation. When the occurrence duration is long or the recurrence frequency is high, it indicates that the abnormal situation may be relatively serious, and obtaining environmental parameters helps to deeply analyze the cause of the abnormality, thereby accurately grasping the operating state of the device. This judgment method avoids obtaining environmental parameters for all abnormal data, saving resource and time costs. During the operation of the device, the number of abnormal data is numerous. If environmental parameters are obtained and analyzed for each abnormal data, a large amount of computing resources and time will be consumed. By setting reasonable thresholds, environmental parameters are only obtained when the abnormal situation is relatively serious, improving the efficiency of data processing and concentrating resources on the analysis of key issues.

[0058] Specifically, in step S3, when it is determined to send the abnormal data to the environmental parameter abnormal database or analyze the change characteristics of the abnormal data, determine whether to send the abnormal data to the environmental parameter abnormal database or analyze the change characteristics of the abnormal data according to whether there is environmental parameter fluctuation at the time point when the abnormal data occurs and whether the abnormal data with environmental parameter fluctuation at the time point is consistent with the periodic inspection data;

[0059] When there is environmental parameter fluctuation at the time point when the abnormal data occurs and the abnormal data with environmental parameter fluctuation is consistent with the periodic inspection data, determine to send the abnormal data to the environmental parameter abnormal database;

[0060] When there is no environmental parameter fluctuation at the time point when the abnormal data occurs or the abnormal data with environmental parameter fluctuation is inconsistent with the periodic inspection data, determine to analyze the change characteristics of the abnormal data.

[0061] Specifically, in step S3, the step of determining whether there is environmental parameter fluctuation at the time point when the abnormal data occurs:

[0062] Step S3301, after identifying the abnormal data of the electrical equipment, obtain the occurrence time point of the abnormal data and the environmental parameter data within 5 minutes before and after it;

[0063] Step S3302, compare the extracted environmental parameter data with the set fluctuation judgment threshold;

[0064] Step S3303, if it exceeds the fluctuation judgment threshold, it is determined that there is environmental parameter fluctuation at the occurrence time point of the abnormal data.

[0065] In the embodiment of the present invention, the environmental parameter data includes but is not limited to "temperature parameter data, humidity parameter data, and vibration parameter data". For example, in the real-time monitoring on a certain day, the system finds that the current data of the No. 3 high-voltage switch cabinet is abnormal. Under normal circumstances, the operating current of this switch cabinet is stable at about 800A, but at 9:30 in the morning, the current suddenly rises to 1200A, which significantly exceeds the normal range. This current data is identified as abnormal data. After the system identifies the abnormal data, it immediately obtains the environmental parameter data within 9:30 and 5 minutes before and after it (i.e., 9:25 - 9:35), compares the extracted environmental parameter data with the set fluctuation judgment threshold. If the environmental parameter data is greater than the fluctuation judgment threshold, it is determined that there is environmental parameter fluctuation at the occurrence time point of the abnormal data. The fluctuation judgment threshold is based on the historical average value of the environmental parameter change when there is no abnormal data for this electrical equipment. However, the above values are not limited to this, and those skilled in the art can also adjust this value according to actual needs.

[0066] In the embodiment of the present invention, determining that the abnormal data with environmental parameter fluctuations is consistent with the periodic inspection data includes that there are environmental parameter fluctuations at the occurrence time point of the abnormal data in the periodic inspection data. For example, at 9:30 am on a certain day, the current data of the No. 3 high-voltage switchgear shows an abnormality. The normal operating current is stable at about 800 A, and suddenly rises to 1200 A at this time. The system obtains the environmental parameter data (including temperature parameter data, humidity parameter data, and vibration parameter data) within 9:25 - 9:35, and compares it with the fluctuation judgment threshold set according to the historical average value of the environmental parameter changes when there is no abnormal data for this electrical equipment. It is found that there are environmental parameter fluctuations. Assuming that the inspection personnel conduct a periodic inspection of the No. 3 high-voltage switchgear every two days, and just completed an inspection at 9:00 am on the same day and recorded the relevant data. The inspection record shows that at the time point of 9:30 am (although the inspection was carried out at 9:00, the situation within a certain time range will be estimated): Normally, the historical average value of the ambient temperature where this switchgear is located is 25 °C, and the fluctuation judgment threshold is set at ±2 °C, that is, the normal fluctuation range is 23 °C - 27 °C. The inspection record estimates that the temperature at 9:30 is within the normal range, but the environmental parameter data of 9:30 obtained by the system shows that the temperature is 28 °C, exceeding the fluctuation judgment threshold, indicating that there are temperature parameter fluctuations. Since although the inspection record estimates that the environmental parameters are normal at 9:30, the data obtained by the actual system shows that at the occurrence time point (9:30) of this abnormal data (current increase), the temperature environmental parameter exceeds the fluctuation judgment threshold, that is, there are environmental parameter fluctuations at the occurrence time point of this abnormal data in the periodic inspection data, so it can be determined that the abnormal data with environmental parameter fluctuations is consistent with the periodic inspection data.

[0067] By determining the environmental parameter fluctuations at the occurrence time point of the abnormal data and comparing whether the fluctuation data is consistent with the periodic inspection data, the present invention can more accurately determine the cause of the abnormal data. For example, in the case of the No. 3 high-voltage switchgear, it is clear that the temperature parameter fluctuation is related to the abnormal increase in current, which helps the operation and maintenance personnel quickly find the root cause of the problem, avoid blind troubleshooting, and improve the fault diagnosis efficiency. Deciding to send the abnormal data to the environmental parameter abnormal database or analyzing the change characteristics of the abnormal data based on the environmental parameter fluctuations and the consistency of the inspection data realizes the reasonable classification of the abnormal data. The data sent to the environmental parameter abnormal database can provide a basis for studying the influence of environmental factors on the equipment; while analyzing the change characteristics of the abnormal data helps to dig out potential problems of the equipment itself and provide a targeted direction for subsequent equipment maintenance and optimization. Through the above method, the accuracy of analyzing the cause of the abnormal data is improved, and thus the processing accuracy and processing efficiency of the abnormal data are improved.

[0068] Specifically, in step S4, when it is determined to send the abnormal data to the historical abnormal database or to send the abnormal data to the in-depth analysis abnormal database, it is determined to send the abnormal data to the historical abnormal database or to send the abnormal data to the in-depth analysis abnormal database according to the comparison result between the similarity of the abnormal data change characteristics and the historical abnormal data change characteristics and the preset similarity;

[0069] When the similarity of the abnormal data change characteristics and the historical abnormal data change characteristics is less than the preset similarity, it is determined to send the abnormal data to the historical abnormal database;

[0070] When the similarity of the abnormal data change characteristics and the historical abnormal data change characteristics is greater than or equal to the preset similarity, it is determined to send the abnormal data to the in-depth analysis abnormal database.

[0071] In the embodiment of the present invention, the value of the preset similarity is the maximum similarity of the abnormal data change characteristics under several same maintenance methods, but the above value is not limited to this, and those skilled in the art can also adjust the value according to actual needs.

[0072] Specifically, in step S4, the steps of calculating the similarity between the abnormal data change characteristics and the historical abnormal data change characteristics include:

[0073] Step S4401, extract key change characteristics from the abnormal data;

[0074] Step S4402, retrieve historical abnormal data related to the abnormal data from the historical abnormal database, and extract the same type of key change characteristics from the historical data;

[0075] Step S4403, calculate the Euclidean distance between the current abnormal data change characteristics and the historical abnormal data change characteristics.

[0076] In an embodiment of the present invention, it is assumed that the temperature of the device is currently monitored to rise abnormally. The key change features extracted include calculating the difference between the current temperature and the temperature at the previous moment. For example, if the current temperature is 80 °C and the previous moment is 70 °C, the change rate is 10 °C per unit time. Record the highest temperature value during the abnormal period, record the duration of the abnormal temperature rise, retrieve records related to the current abnormal data from the historical abnormal database. For example, retrieve the historical records of the abnormal temperature rise of the device, extract the key change features of the same type as the current abnormal data from the retrieved historical abnormal data, calculate the change rate of the temperature in the historical abnormal data, record the highest temperature value in the historical abnormal data, record the duration of the abnormal temperature rise in the historical abnormal data, calculate the Euclidean distance between the current abnormal data and the historical abnormal data in terms of key features. The smaller the distance, the higher the similarity. For example, for the three features of temperature change rate, temperature peak value, and abnormal duration, calculate the differences between them respectively, and then find the square root of the sum of squares.

[0077] The present invention can quickly find historical cases similar to the current abnormality by extracting the key change features of the abnormal data and calculating the similarity with the data in the historical abnormal database. In the example of the abnormal temperature rise of the device, the Euclidean distance is calculated based on key features such as temperature change rate, highest temperature value, and abnormal duration. If the similarity is high, it can quickly locate the historical abnormality that uses the same repair method, providing a reference for solving the current problem, saving the time for troubleshooting and solving the problem. Send the abnormal data with a similarity less than the preset similarity to the historical abnormal database, which can continuously enrich the historical abnormal data resources. These data contain the detailed information of the previous device abnormalities and the corresponding repair methods. When encountering similar abnormalities in the future, it can directly draw on historical experience, avoid repeated labor, improve the repair efficiency, and reduce the repair cost. When the similarity between the change features of the abnormal data and the change features of the historical abnormal data is greater than or equal to the preset similarity, send it to the in-depth analysis abnormal database, which helps to focus on the abnormalities that are different from the historical situation and may have new problems or complex factors. By the above method, the accuracy of analyzing the cause of the abnormal data is improved, and thus the processing accuracy and processing efficiency of the abnormal data are improved.

[0078] Specifically, in step S5, when determining whether to adjust the preset occurrence duration and the preset period, according to the growth amount and type of abnormal data in the abnormal database within the preset duration, and based on the growth amount of the abnormal data and the growth characteristics of the abnormal data, determine whether to adjust the preset occurrence duration and the preset period;

[0079] When the growth amount of the abnormal data in the abnormal database within the preset duration is greater than the preset growth amount of the data and the growth characteristic of the abnormal data is that the average duration is extended, it is determined to adjust the preset occurrence duration;

[0080] When the growth amount of abnormal data in the abnormal database within the preset duration is greater than the preset data growth amount and the growth characteristic of the abnormal data is that the recurrence frequency increases, it is determined to adjust the preset cycle.

[0081] In the embodiment of the present invention, the preset duration is set to 30 days, the preset data growth amount is the average value of the growth amounts of abnormal data in the abnormal database within several preset durations, and the growth characteristic of the abnormal data that the recurrence frequency increases includes that the proportion of the abnormal data with repeated occurrences in the growing abnormal data is greater than the preset proportion. The value of the preset proportion is the proportion of the abnormal data with repeated occurrences in the growing abnormal data in the previous preset duration, but the above value is not limited to this, and those skilled in the art can also adjust this value according to actual needs.

[0082] In the embodiment of the present invention, the start and end times of all abnormal data are extracted from the abnormal database. For example, one over-temperature abnormal record of the UPS-3 device starts at 14:00 on January 15th and ends at 15:30 on January 15th, with a duration of 90 minutes. The total duration of all 150 abnormal data is counted as 12000 minutes, and the average duration is 12000÷150 = 80 minutes. Compared with the previous quarter, the average duration of abnormal data in the previous quarter was 60 minutes. Therefore, it is determined that there is a growth characteristic of an extended average duration.

[0083] In an embodiment of the present invention, it is assumed that a certain factory has 50 motor devices, which are used to drive various mechanical devices on the production line. To ensure the stable operation of the motor devices, an electrical equipment data management system is adopted to monitor the motor devices in real time and conduct periodic inspections. The preset duration in the system is set to 30 days, and the preset data growth amount is the average of the abnormal data growth amounts in the abnormal databases in several past 30-day periods. It is assumed that the abnormal data growth amounts in the abnormal databases in the past 5 30-day periods are 20, 22, 18, 25, and 23 respectively. Then the preset data growth amount is 21.6, and after rounding, the preset data growth amount is 22. At the same time, when the proportion of repeatedly occurring abnormal data in the increased abnormal data is greater than the preset proportion of 0.4, it is determined that the growth characteristic of the abnormal data is an increase in the repeated occurrence frequency. During the current 30-day monitoring period, the system collects and processes the real-time monitoring data and periodic inspection data of the motor devices, identifies the abnormal data and stores it in the abnormal database. After statistics, 30 new abnormal data are added to the abnormal database within the current 30 days, which is greater than the preset data growth amount of 22. By analyzing these 30 newly added abnormal data in detail, it is found that 13 of them are repeatedly occurring abnormal data. The repeatedly occurring abnormal data refers to the same type of abnormal situation occurring in the same motor device. For example, the motor No. 10 repeatedly shows the abnormal situation of excessive current at different time points, and the motor No. 25 repeatedly shows the abnormal situation of excessive temperature, etc. Calculate the proportion of the repeatedly occurring abnormal data in the newly added abnormal data as 0.43, which is greater than the preset proportion of 0.4. Therefore, it can be determined that the growth characteristic of the abnormal data is an increase in the repeated occurrence frequency.

[0084] Specifically, in step S5, when it is determined to adjust the preset occurrence duration, it is determined to adjust the preset occurrence duration with a first adjustment coefficient. When it is determined to adjust the preset period, it is determined to adjust the preset period with a second adjustment coefficient.

[0085] In the embodiment of the present invention, the value range of the first adjustment coefficient is set to 0.81 - 0.96, the preferred value of the first adjustment coefficient is 0.88, the value range of the second adjustment coefficient is set to 0.83 - 0.97, the preferred value of the second adjustment coefficient is 0.91. The adjustment amount of the preset occurrence duration is negatively correlated with the abnormal data growth amount, and the adjustment amount of the preset period is negatively correlated with the abnormal data growth amount. However, the above values are not limited to this, and those skilled in the art can also adjust the values according to actual needs.

[0086] By analyzing the growth characteristics of abnormal data (such as an extended average duration or an increased recurrence frequency), the present invention can dynamically adjust the monitoring duration and period. For example, when it is found that the average duration of abnormal data is extended, the preset occurrence duration can be appropriately shortened to capture abnormal situations more promptly; when it is found that the recurrence frequency of abnormal data increases, the preset period can be shortened to perform monitoring and analysis more frequently, thereby improving the sensitivity to abnormal situations. Through the above methods, the accuracy of analyzing the causes of abnormal data occurrences is improved, and thus the processing accuracy and efficiency of abnormal data are enhanced.

[0087] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easily understood by those skilled in the art that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

Claims

1. A data management method for electrical equipment, characterized in that: include: Acquire real-time monitoring data and periodic inspection data of electrical equipment within a preset period, and pre-process the real-time monitoring data and the periodic inspection data to identify abnormal data; Determine the environmental parameters for obtaining the time point at which the abnormal data occurs or mark the abnormal data based on the duration of the abnormal data occurring in a single preset period and the frequency of repeated occurrence of the abnormal data marked in several preset periods; Based on whether there is environmental parameter fluctuation at the time of occurrence of abnormal data and whether the abnormal data with environmental parameter fluctuation at the time of occurrence is consistent with the periodic inspection data, determine to send the abnormal data to the environmental parameter abnormality database or analyze the abnormal data change characteristics; Matching the abnormal data change characteristics with the historical abnormal data change characteristics, and determining whether to send the abnormal data to a historical abnormal data database or to send the abnormal data to a deep analysis abnormal data database based on a comparison result of the similarity between the abnormal data change characteristics and the historical abnormal data change characteristics and a preset similarity; Obtain the abnormal data growth amount and abnormal data type of the abnormal database within a preset time period, and determine whether to adjust the preset occurrence time and the preset period based on the abnormal data growth amount and the growth characteristics of the abnormal data, wherein the abnormal database includes an environmental parameter abnormality database, a historical abnormality database, and a deep analysis abnormality database.

2. The data management method for electrical equipment according to claim 1, characterized in that: Determining the environmental parameters of the time point at which the abnormal data occurs or marking the abnormal data includes: If the duration of occurrence of abnormal data in a single preset period is less than the preset duration and the repetition frequency of abnormal data marked in several preset periods is less than the preset repetition frequency, determine to mark the abnormal data; If the appearance duration of abnormal data in a single preset period is greater than or equal to the preset appearance duration or the repetition frequency of abnormal data marked in several preset periods is greater than or equal to the preset repetition frequency, determine the environmental parameters at the time point when the abnormal data occurs.

3. The data management method for electrical equipment according to claim 2, characterized in that: The preset occurrence duration is determined according to an average value of the occurrence durations of abnormal data within a plurality of preset periods.

4. The data management method for electrical equipment according to claim 2, characterized in that: Determining to send the abnormal data to the environmental parameter abnormality database or analyzing the abnormal data change characteristics includes: If there is environmental parameter fluctuation at the time point of occurrence of the abnormal data and the abnormal data with environmental parameter fluctuation is consistent with the periodic inspection data, determine to send the abnormal data to the environmental parameter abnormality database; If there is no environmental parameter fluctuation at the time when the abnormal data occurs, or the abnormal data with environmental parameter fluctuation is inconsistent with the periodic inspection data, determine and analyze the change characteristics of the abnormal data.

5. The data management method for electrical equipment according to claim 4, characterized in that: Determining whether there is environmental parameter fluctuation at the time when abnormal data occurs includes: After identifying the abnormal data of the electrical equipment, obtain the time point of the abnormal data and the environmental parameter data within 5 minutes before and after; Compare the extracted environmental parameter data with the set fluctuation judgment threshold; If the fluctuation judgment threshold is exceeded, it is determined that there is an environmental parameter fluctuation at the time when the abnormal data occurs.

6. The data management method for electrical equipment according to claim 3, characterized in that: It is determined that the abnormal data with environmental parameter fluctuation is consistent with the periodic inspection data includes that the environmental parameter fluctuation exists at the occurrence time point of the abnormal data in the periodic inspection data.

7. The data management method for electrical equipment according to claim 6, characterized in that: Determining to send the abnormal data to a historical abnormality database or to send the abnormal data to a deep analysis abnormality database includes: If the similarity between the abnormal data change characteristics and the historical abnormal data change characteristics is less than a preset similarity, determining to send the abnormal data to the historical abnormal data database; If the similarity between the abnormal data change characteristics and the historical abnormal data change characteristics is greater than or equal to a preset similarity, it is determined to send the abnormal data to the deep analysis abnormality database.

8. The data management method for electrical equipment according to claim 7, characterized in that: Determining whether to adjust the preset duration and the preset period includes: If the growth amount of abnormal data in the abnormal database within the preset time period is greater than the preset data growth amount and the growth characteristic of the abnormal data is that the average duration is prolonged, it is determined to adjust the preset occurrence duration; If the growth amount of abnormal data in the abnormal database within the preset time period is greater than the preset data growth amount and the growth characteristic of the abnormal data is an increase in the frequency of repeated occurrence, it is determined to adjust the preset period.

9. The data management method for electrical equipment according to claim 8, characterized in that: The adjustment amount of the preset occurrence duration is negatively correlated with the growth amount of the abnormal data, and the adjustment amount of the preset period is negatively correlated with the growth amount of the abnormal data.

10. The data management method for electrical equipment according to claim 9, characterized in that: Calculating the similarity between abnormal data change characteristics and historical abnormal data change characteristics includes: Extract key change features from abnormal data; Retrieving historical abnormal data related to the abnormal data from a historical abnormality database, and extracting key change features of the same type from the historical data; Calculate the Euclidean distance between the current abnormal data change characteristics and the historical abnormal data change characteristics.

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