A power equipment fault data processing method and system based on big data

Abstract

The application discloses a kind of electric power equipment fault data processing method and system based on big data, and the present application relates to electric power equipment technical field, it solves the problem that due to the data missing of some monitoring items, the data of other monitoring items will be abnormal, leading to the misjudgment of its electric power data fault, the present application is based on the abnormal signal identified, whether the monitoring item associated with the corresponding abnormal signal exists data missing condition, if there is data missing, then by confirming the missing time, the correlation adjustment of time characteristics is carried out, based on the data verification result after adjustment, whether there is data missing condition is assessed, if there is data missing condition, signal display is carried out in time, if there is no data missing condition, it is just that there is data anomaly, this kind of identification processing mode can effectively guarantee the comprehensiveness of its electric power equipment fault data confirmation and processing, and better data processing effect can be achieved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power equipment, in particular to a power equipment fault data processing method and system based on big data. BACKGROUND

[0002] The power equipment refers to various equipment used in power generation, power transmission, power transformation, power distribution and power utilization;

[0003] The application with the patent publication No. CN118035924A discloses a power equipment fault data processing method and system based on power big data, which comprises the following steps: collecting the fault data of an electric energy meter in real time, pre-processing the collected fault data, and performing qualified detection to provide comprehensive and accurate data support for fault judgment of the electric energy meter, calculating the fault characteristic coefficient of the electric energy meter, determining the abnormal degree of the electric energy meter fault, being beneficial to accurate diagnosis of the electric energy meter fault, calculating the fault characteristic difference value of the electric energy meter based on the fault data of the electric energy meter, calculating the influence value of the data deviation on the fault characteristic coefficient of the electric energy meter based on the obtained fault characteristic difference value of the electric energy meter and the t-statistics of two groups of data, analyzing the influence degree of the data deviation on the electric energy meter fault according to the influence value, being beneficial to reducing the misjudgment rate of the electric energy meter fault degree, and being also beneficial to corresponding optimization and adjustment of the electric energy meter, improving the metering accuracy and operation stability thereof.

[0004] In the fault analysis and processing process of the power equipment associated data, whether the corresponding data belongs to abnormal data is generally evaluated based on the belonging condition of the corresponding data and the corresponding preset interval, so as to perform fault determination, but this kind of mode is one-sided, because the corresponding power equipment data is easy to have data missing in the actual processing process, due to the data missing of certain monitoring items, the data of other monitoring items is abnormal, which leads to misjudgment of the power data fault, thereby causing great problems in the accuracy of the fault processing process of the power equipment. SUMMARY

[0005] In view of the defects of the prior art, the application provides a power equipment fault data processing method and system based on big data, which solves the problem that due to the data missing of certain monitoring items, the data of other monitoring items is abnormal, which leads to misjudgment of the power data fault.

[0006] To achieve the above purpose, the application is implemented by the following technical scheme: a power equipment fault data processing method based on big data, comprising the following steps:

[0007] Step 1: Analyze the periodic data generated by the specified power equipment in the past historical data. From the monitoring data associated with several different monitoring items, determine the data trends associated with different monitoring items. Then, lock the related monitoring items with correlation from the data trends confirmed in the same period and calibrate them. The specific sub-steps are as follows:

[0008] S11. For each group of power equipment, determine the data monitoring items associated with the power equipment, and then extract a set of periodic data from the historical completed data monitored by the power equipment. The periodic data has a period duration of T, where T is a preset value, and each moment in the periodic data includes monitoring data for all data monitoring items of the power equipment.

[0009] S12. Based on the periodic data confirmed by the corresponding power equipment, the monitoring data belonging to different data monitoring items in the periodic data are sorted based on their time sequence, and the data sequences belonging to the different data monitoring items of the power equipment are confirmed;

[0010] S13. Confirm the change data of each data sequence at adjacent moments. The change data is the monitoring data of the next moment minus the monitoring data of the previous moment. The change data between different adjacent moments of different data monitoring items are calibrated as BH. i-k , where i represents different data monitoring items and k represents different adjacent moments;

[0011] S14. Based on the confirmed data sequences belonging to different data monitoring items, randomly select a group of data sequences as the main sequence, and then randomly select a group of data sequences from the remaining data sequences as the secondary sequence. The combination of the main sequence and the secondary sequence is calibrated as a combined sequence. The change data of the main sequence corresponding to adjacent moments is used as the main change data, and the change data of the secondary sequence corresponding to adjacent moments is used as the secondary change data. The following is used: Q k-q =Main variable data ÷ Secondary variable data to confirm the comparison trend between the same period Q k-q , where k represents different adjacent moments, q represents different combination sequences, and within each different combination sequence, the secondary sequences are different, and the main sequences are the same;

[0012] Based on the different comparison trends Q confirmed at different adjacent moments in the corresponding combination sequence k-q , compare several groups of trends Q k-q Perform variance processing to confirm the associated approved value H q , select H q min is taken as the sequence to be determined, and H is identified. q The result of checking min with the preset value Y1: If H q min>Y1, then the data monitoring item associated with the main sequence in the pending sequence is marked as a single monitoring item.q min≤Y1, then the data monitoring items associated with the main sequence and the secondary sequence in the pending sequence are marked as associated monitoring items, where Y1 is a preset value;

[0013] S15. For other data sequences that have not been associated with monitoring items or single monitoring items, the same method as step S14 is used to sequentially process the remaining data sequences until several data sequences are marked as associated monitoring items or single monitoring items;

[0014] Step 2: Perform fault confirmation on the monitoring parameters associated with each group of monitoring items in the power equipment to identify whether the monitoring parameters associated with the corresponding monitoring items are abnormal. If there is an abnormality, calibrate the abnormal monitoring items. If there is no abnormality, no calibration is performed. The specific sub-steps are as follows:

[0015] The monitoring parameters of each group of monitoring items in the power equipment are calibrated as CS i , and the monitoring parameter CS associated with the corresponding monitoring item i Compare with the preset interval. The endpoint values ​​of the preset interval associated with each group of monitoring items are all preset values. If CS i ∈ preset interval, it represents the monitoring parameter CS of this monitoring item at the current moment i No abnormalities, if CS i ∉Preset interval represents the monitoring parameter CS associated with the corresponding monitoring item at the current moment i There is an abnormality, and the monitoring item corresponding to the current moment is marked as an abnormal monitoring item;

[0016] Step 3: Based on the abnormal monitoring items calibrated for the corresponding power equipment, prioritize identifying whether such abnormal monitoring items belong to single monitoring items or associated monitoring items. Based on the identification results, confirm the analysis signal or fault signal, and display the confirmed signal. The specific sub-steps are:

[0017] Based on the abnormal monitoring item calibrated at the current moment, identify whether this abnormal monitoring item belongs to the single monitoring item shown. If it does, directly generate a fault signal for direct display. If it does not, it means that this abnormal monitoring item is associated with other monitoring items, that is, this abnormal monitoring item belongs to the associated monitoring item, so generate an analysis signal, and then perform subsequent processing on the generated analysis signal;

[0018] Step 4: Based on the abnormal monitoring item calibrated by the corresponding power equipment and the associated analysis signal, initially lock the other monitoring items associated with the abnormal monitoring item from the associated monitoring items of the abnormal monitoring item, then lock a set of verification cycles, and perform verification analysis on the monitoring data associated with the abnormal monitoring item and other monitoring items within the verification cycle to assess whether the abnormal monitoring item is a data missing abnormality or a fault abnormality. The specific sub-steps are as follows:

[0019] S41, based on the abnormal monitoring item and the analysis signal, determining other monitoring items from the associated monitoring items to which the abnormal monitoring item belongs, and marking the other monitoring items determined by the abnormal monitoring item as associated sub-items;

[0020] S42: Define a set of verification periods, where the verification period is a preset period, and the abnormal time associated with the abnormal monitoring item is located in the middle of the verification period. Confirm the monitoring data generated by the abnormal monitoring item within the verification period in sequence, and sort the confirmed monitoring data according to the chronological order to confirm the abnormal monitoring item data sequence.

[0021] Then, the monitoring data associated with the corresponding associated sub-items within this verification period are sorted according to the time sequence to confirm the data sequence of the associated sub-items;

[0022] S43. Based on the abnormal moment identified in the abnormal monitoring item data sequence, mark the group of moments preceding the abnormal moment as previous moments. From the associated sub-item data sequence, identify the calibration moment associated with the previous moment. The calibration moment and the previous moment are contemporaneous. Following the calibration moment identified in the associated sub-item data sequence, identify a group of missing moments. Make the missing moments contemporaneous with the abnormal moment. Adjust the time features associated with subsequent different monitoring data in the associated sub-item data sequence.

[0023] S44. Verify the adjusted associated sub-item data sequence and the abnormal monitoring item data sequence: confirm the change data between adjacent moments in the two data sequences, mark the change data at adjacent moments in the associated sub-item data sequence as first data, and then synchronously lock the change data at adjacent moments in the abnormal monitoring item data sequence and mark it as second data. The change data is determined in the same manner as the corresponding change data in step S13. The change data of the two adjacent moments before and after the abnormal moment are not verified. Similarly, the change data of the two adjacent moments before and after the missing data are not verified synchronously.

[0024] S45. Identify whether the data sequence associated with the abnormal monitoring item in step S14 belongs to the primary sequence or the secondary sequence in the associated monitoring item:

[0025] If it belongs to the main sequence, the formula is: first data ÷ second data = data trend, to confirm the data trend of the associated sub-item data sequence and the abnormal monitoring item data sequence between adjacent moments;

[0026] If it belongs to a subsequence, use: second data ÷ first data = data trend to confirm the data trend of the associated sub-item data sequence and the abnormal monitoring item data sequence between adjacent moments;

[0027] S46, the confirmed several groups of data trends are subjected to variance processing, a calibration variance is locked, and a correlation monitoring item determined by the correlation monitoring item is confirmed from the processed historical data, if the calibration variance is less than or equal to the correlation monitoring item, a data missing signal is generated and displayed, representing that the correlation monitoring item of the abnormal monitoring item exists in a data missing condition, causing the abnormal monitoring item to appear in a data abnormal condition; if the calibration variance is greater than the correlation monitoring item, an abnormal monitoring fault signal is generated and displayed.

[0028] Preferably, a power equipment fault data processing system based on big data comprises:

[0029] The correlation monitoring item calibration end performs data analysis on the periodic data generated by the specified power equipment in the past historical data, determines the data trends associated with different monitoring items from the monitoring data associated with several different monitoring items, locks the related monitoring items existing in correlation from the simultaneously confirmed data trends and performs calibration, and locks the correlation monitoring item or single monitoring item based on the calibration result;

[0030] The abnormal monitoring item calibration end performs fault confirmation on the monitoring parameters associated with each group of monitoring items in the power equipment, identifies whether the monitoring parameters associated with the corresponding monitoring items are abnormal, if there is an abnormality, the abnormal monitoring item is calibrated, and if there is no abnormality, no calibration is performed;

[0031] The signal preliminary confirmation end, based on the abnormal monitoring items calibrated for the corresponding power equipment, preferentially identifies whether the abnormal monitoring items belong to single monitoring items or belong to correlation monitoring items, confirms the analysis signal or the fault signal based on the identification result, and displays the confirmed signal;

[0032] The abnormality judgment center, based on the abnormal monitoring items calibrated for the corresponding power equipment and the associated analysis signal, preliminarily locks other monitoring items associated with the abnormal monitoring item from the correlation monitoring items existing in the abnormal monitoring item, and locks a group of verification periods, and performs verification analysis on the monitoring data associated with the abnormal monitoring item and other monitoring items in the verification period, and evaluates whether the abnormal monitoring item belongs to a data missing abnormality or a fault abnormality.

[0033] The present application provides a power equipment fault data processing method and system based on big data. Compared with the prior art, the following beneficial effects are achieved:

[0034] The present application monitors the monitoring data associated with different monitoring items in each power equipment, and identifies the monitoring items existing in correlation based on the correlation characteristics between the data. By confirming the monitoring items existing in correlation and the monitoring items not existing in any correlation, whether the monitoring data of the monitoring items is abnormal can be effectively confirmed, so as to achieve better data precision confirmation effect and guarantee the specific accuracy of subsequent data confirmation.

[0035] In the subsequent data verification and evaluation process, based on the confirmed abnormal signal, it is identified whether there is any data missing in the monitoring item associated with the corresponding abnormal signal. If there is data missing, the time feature correlation adjustment is performed by confirming the missing moment. Based on the adjusted data verification result, it is evaluated whether there is data missing. If there is data missing, the signal is displayed in time. If there is no data missing, it means that there is data abnormality. This identification and processing method can effectively ensure the comprehensiveness of the confirmation and processing of the power equipment fault data, and can achieve better data processing effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Schematic diagram of the process of the present invention;

[0037] Figure 2 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] First embodiment: Please refer to Figure 1 , the present application provides a method for processing power equipment fault data based on big data, comprising the following steps:

[0040] Step 1: Perform data analysis on the periodic data generated by the specified power equipment in the past historical data, determine the data trends associated with different monitoring items from the monitoring data associated with several different monitoring items, and then lock the related monitoring items with correlation from the data trends confirmed in the same period and calibrate them. Specifically, in the corresponding power equipment, there are related monitoring items with strong correlation, such as voltage and current. The correlation is very strong. When the voltage changes, the related current will also change. Similarly, in the actual data fault verification and identification process, for the related monitoring items with correlation, among the two groups of related related monitoring items, one group has a fault and the other group does not have a fault. In this case, there may be a data missing problem in the actual processing process. Under normal circumstances, the values ​​of the two groups of equipment with strong enough correlation are both in a state of correlated change, so that the related signals are displayed;

[0041] The specific sub-steps for determining the presence of associated monitoring items within the power equipment are:

[0042] S11, for each group of power equipment, determine the data monitoring items associated with the power equipment (that is, the specific numerical items designated by the system for monitoring, the parameters irrelevant to the power equipment are not recorded in the data monitoring items), and extract a set of periodic data from the historical completed data monitored by the power equipment, the periodic data has a periodic length T, T is a preset value, the specific value is determined by the operator according to experience, and each time point in the periodic data includes the monitoring data of all data monitoring items of the power equipment (that is, each data monitoring item has corresponding monitoring data at the corresponding time point);

[0043] S12, based on the periodic data confirmed by the corresponding power equipment, and based on the time sequence relationship, the monitoring data in the periodic data belonging to different data monitoring items are sorted, and the data sequence belonging to different data monitoring items of the power equipment is confirmed;

[0044] S13, confirm the change data of each group of data sequence adjacent time, the change data is the monitoring data of the next time minus the monitoring data of the previous time, and the change data between different adjacent time belonging to different data monitoring items is marked as BH i-k , where i represents different data monitoring items, and k represents different adjacent time;

[0045] S14, based on the data sequence confirmed to belong to different data monitoring items, randomly select a group of data sequence as the main sequence, and randomly select a group of data sequence from the remaining data sequence as the secondary sequence, mark the combination of the main sequence and the secondary sequence as the combination sequence (the combination sequence includes two groups of data sequence), the change data of the main sequence corresponding to the adjacent time as the main variable data, and the change data of the secondary sequence corresponding to the adjacent time as the secondary variable data, and adopt: Q k-q = main variable data ÷ secondary variable data to confirm the comparison trend Q k-q , where k represents different adjacent time (that is, the same period in two data sequences), and q represents different combination sequences, and in each different combination sequence, the secondary sequence is different, and the main sequence is the same;

[0046] Based on the different comparison trends Q k-q confirmed by different adjacent time in the corresponding combination sequence, a plurality of comparison trends Q k-q are subjected to variance processing to confirm the associated calibration value H q , select the combination sequence associated with H q min as the to-be-determined sequence, identify the comparison result of H q min and the preset value Y1: if H q min > Y1, the data monitoring item associated with the main sequence in the to-be-determined sequence is marked as a single monitoring item, and if H qmin≤Y1, then the data monitoring items associated with the main sequence and the secondary sequence in the pending sequence are marked as associated monitoring items, where Y1 is a preset value, and its specific value is determined by the operator based on experience;

[0047] S15. For other data sequences that have not been associated with monitoring items or single monitoring items, the same method as step S14 is used to sequentially process the remaining data sequences until several data sequences are marked as associated monitoring items or single monitoring items;

[0048] Specifically, the so-called associated monitoring items are those where the two corresponding groups of data monitoring items are highly correlated. For example, data monitoring items such as voltage and current will definitely be classified into the corresponding associated monitoring items. The so-called single monitoring item is that the data associated with this item has no correlation with the data of any other items. In this case, such unrelated data monitoring items are called single monitoring items.

[0049] Step 2: Perform fault confirmation on the monitoring parameters associated with each group of monitoring items in the power equipment, and identify whether the monitoring parameters associated with the corresponding monitoring items are abnormal. If there is an abnormality, calibrate the abnormal monitoring items. If there is no abnormality, no calibration is performed. The specific sub-steps for identifying whether the corresponding monitoring items are abnormal are as follows:

[0050] S21, calibrate the real-time monitoring parameters of each group of monitoring items in the power equipment as CS i , and the monitoring parameter CS associated with the corresponding monitoring item i Compare with the preset interval. The endpoint values ​​of the preset interval associated with each group of monitoring items are preset values, which are prepared in advance by relevant operators based on experience. i ∈ preset interval, it represents the monitoring parameter CS of this monitoring item at the current moment i No abnormalities, if CS i ∉Preset interval represents the monitoring parameter CS associated with the corresponding monitoring item at the current moment i If an abnormality occurs, the corresponding monitoring item at the current moment will be marked as an abnormal monitoring item. The preset interval associated with each different monitoring item is different. The specific value range of the corresponding preset interval is determined by the relevant operator based on the standard operating conditions of the corresponding equipment to lock the standard interval associated with the corresponding monitoring item for comprehensive confirmation;

[0051] Step 3: Based on the abnormal monitoring items calibrated for the corresponding power equipment, prioritize identifying whether such abnormal monitoring items belong to single monitoring items or associated monitoring items. Based on the identification results, confirm the analysis signal or fault signal, and display the confirmed signal. The specific sub-steps for identification are:

[0052] S31. Based on the abnormal monitoring item calibrated at the current moment, identify whether the abnormal monitoring item belongs to the single monitoring item shown. If so, directly generate a fault signal for direct display. If not, it means that the abnormal monitoring item is associated with other monitoring items, that is, the abnormal monitoring item belongs to the associated monitoring items, so generate an analysis signal, and perform subsequent processing on the generated analysis signal.

[0053] Step 4: Based on the abnormal monitoring item calibrated for the corresponding power equipment and the associated analysis signal, initially lock other monitoring items associated with the abnormal monitoring item from the associated monitoring items of the abnormal monitoring item, then lock a set of verification cycles, and perform verification analysis on the monitoring data associated with the abnormal monitoring item and other monitoring items within the verification cycle to assess whether the abnormal monitoring item is a data missing anomaly or a fault anomaly. The specific sub-steps of the assessment are as follows:

[0054] S41, based on the abnormal monitoring item and the analysis signal, determining other monitoring items from the associated monitoring items to which the abnormal monitoring item belongs, and marking the other monitoring items determined by the abnormal monitoring item as associated sub-items;

[0055] S42. Define a set of verification periods, where the verification periods are preset periods, and the specific duration of the preset periods is determined by relevant operators based on experience. The abnormal time associated with the abnormal monitoring item (that is, the time corresponding to the monitoring item being determined as an abnormal monitoring item) is located in the middle of the verification period. The monitoring data generated by the abnormal monitoring item within the verification period are sequentially confirmed, and the confirmed monitoring data are sorted according to the chronological order to confirm the abnormal monitoring item data sequence.

[0056] Then, the monitoring data associated with the corresponding associated sub-items within this verification period are sorted according to the time sequence to confirm the data sequence of the associated sub-items;

[0057] S43. Based on the abnormal moment confirmed in the abnormal monitoring item data sequence, a group of moments preceding the abnormal moment are calibrated as previous moments. A calibration moment associated with the previous moment is confirmed from the associated sub-item data sequence, and the calibration moment and the previous moment are simultaneously located. A group of missing moments is confirmed subsequent to the calibration moment confirmed in the associated sub-item data sequence, so that the missing moment and the abnormal moment are simultaneously located. The time features associated with subsequent different monitoring data in the associated sub-item data sequence are adjusted (when a group of missing moments is inserted, all other subsequently associated moments need to be incremented by 1, so that all associated related moments are extended one moment later to form another group of moments).

[0058] S44. Verify the adjusted associated sub-item data sequence and the abnormal monitoring item data sequence: confirm the change data between adjacent moments in the two data sequences, mark the change data at adjacent moments in the associated sub-item data sequence as first data, and then synchronously lock the change data at adjacent moments in the abnormal monitoring item data sequence and mark it as second data. The change data is determined in the same manner as the corresponding change data in step S13. The change data of the two adjacent moments before and after the abnormal moment are not verified. Similarly, the change data of the two adjacent moments before and after the missing data are not verified synchronously.

[0059] S45. Identify whether the data sequence associated with the abnormal monitoring item in step S14 belongs to the primary sequence or the secondary sequence in the associated monitoring item:

[0060] If it belongs to the main sequence, the formula is: first data ÷ second data = data trend, to confirm the data trend of the associated sub-item data sequence and the abnormal monitoring item data sequence between adjacent moments;

[0061] If it belongs to a subsequence, use: second data ÷ first data = data trend to confirm the data trend of the associated sub-item data sequence and the abnormal monitoring item data sequence between adjacent moments;

[0062] S46. Perform variance processing on the confirmed data trends, lock the calibration variance, and then confirm the associated verified value determined for this associated monitoring item from the processed historical data (that is, the data in step S14). If the calibration variance is less than or equal to the associated verified value, a data missing signal is generated for display, indicating that there is data missing for the associated monitoring item of this abnormal monitoring item, resulting in data abnormality for this abnormal monitoring item. If the calibration variance is greater than the associated verified value, an abnormal monitoring fault signal is generated for display for external personnel to view, indicating that there is a monitoring fault in this processing process and relevant processing needs to be carried out in a timely manner.

[0063] Specifically, with respect to whether there are abnormalities in the corresponding data monitoring items, priority is given to whether there is data missing. If there is data missing, the correlation adjustment of the time characteristics is performed by confirming the missing time. Based on the adjusted data verification results, it is assessed whether there is data missing. If there is data missing, the signal is displayed in a timely manner. If there is no data missing, it means that there is a data abnormality, which leads to a signal abnormality in the corresponding monitoring item.

[0064] Second embodiment: Combination Figure 2 , a power equipment fault data processing system based on big data, comprising:

[0065] The associated monitoring item calibration end analyzes the periodic data generated by the specified power equipment in the past historical data, determines the data trends associated with different monitoring items from the monitoring data associated with several different monitoring items, and then locks the related monitoring items from the data trends confirmed in the same period and calibrates them. Based on the calibration results, the associated monitoring items or single monitoring items are locked;

[0066] The abnormal monitoring item calibration end performs fault confirmation on the monitoring parameters associated with each group of monitoring items in the power equipment, and identifies whether the monitoring parameters associated with the corresponding monitoring items are abnormal. If there is an abnormality, the abnormal monitoring item is calibrated; if there is no abnormality, no calibration is performed;

[0067] The initial signal confirmation terminal, based on the abnormal monitoring items calibrated by the corresponding power equipment, prioritizes identifying whether such abnormal monitoring items belong to single monitoring items or associated monitoring items. Based on the identification results, it confirms the analysis signal or fault signal and displays the confirmed signal;

[0068] The abnormality determination center, based on the abnormal monitoring items calibrated by the corresponding power equipment and the associated analysis signals, initially locks other monitoring items associated with the abnormal monitoring item from the associated monitoring items of the abnormal monitoring item, and then locks a group of verification cycles. It verifies and analyzes the monitoring data associated with the abnormal monitoring item and other monitoring items within the verification cycle, and assesses whether the abnormal monitoring item is a data missing abnormality or a fault abnormality.

[0069] The third embodiment: This embodiment includes the entire implementation process of the above two groups of embodiments during the specific implementation process.

[0070] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0071] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for processing power equipment fault data based on big data, characterized in that: The following steps are involved: Step 1: Analyze the periodic data generated by the specified power equipment in the past historical data. From the monitoring data associated with several different monitoring items, determine the data trends associated with different monitoring items. Then, lock the related monitoring items with correlation from the data trends confirmed in the same period and calibrate them. The specific sub-steps are as follows: S11. For each group of power equipment, determine the data monitoring items associated with the power equipment, and then extract a set of periodic data from the historical completed data monitored by the power equipment. The periodic data has a period duration of T, where T is a preset value, and each moment in the periodic data includes monitoring data for all data monitoring items of the power equipment. S12. Based on the periodic data confirmed by the corresponding power equipment, the monitoring data belonging to different data monitoring items in the periodic data are sorted based on their time sequence, and the data sequences belonging to the different data monitoring items of the power equipment are confirmed; S13. Confirm the change data of each data sequence at adjacent moments. The change data is the monitoring data of the next moment minus the monitoring data of the previous moment. The change data between different adjacent moments of different data monitoring items are calibrated as BH. i-k , where i represents different data monitoring items and k represents different adjacent moments; S14. Based on the confirmed data sequences belonging to different data monitoring items, randomly select a group of data sequences as the main sequence, and then randomly select a group of data sequences from the remaining data sequences as the secondary sequence. The combination of the main sequence and the secondary sequence is calibrated as a combined sequence. The change data of the main sequence corresponding to adjacent moments is used as the main change data, and the change data of the secondary sequence corresponding to adjacent moments is used as the secondary change data. The following is used: Q k-q =Main variable data ÷ Secondary variable data to confirm the comparison trend between the same period Q k-q , where k represents different adjacent moments, q represents different combination sequences, and within each different combination sequence, the secondary sequences are different, and the main sequences are the same; Based on the different comparison trends Q confirmed at different adjacent moments in the corresponding combination sequence k-q , compare several groups of trends Q k-q Perform variance processing to confirm the associated approved value H q , select H q min is taken as the sequence to be determined, and H is identified. q The result of checking min with the preset value Y1: If H q min>Y1, then the data monitoring item associated with the main sequence in the pending sequence is marked as a single monitoring item. q min≤Y1, then the data monitoring items associated with the main sequence and the secondary sequence in the pending sequence are marked as associated monitoring items, where Y1 is a preset value; S15. For other data sequences that have not been associated with monitoring items or single monitoring items, the same method as step S14 is used to sequentially process the remaining data sequences until several data sequences are marked as associated monitoring items or single monitoring items; Step 2: Perform fault confirmation on the monitoring parameters associated with each group of monitoring items in the power equipment to identify whether the monitoring parameters associated with the corresponding monitoring items are abnormal. If there is an abnormality, calibrate the abnormal monitoring items; if there is no abnormality, no calibration is performed; Step 3: Based on the abnormal monitoring items calibrated for the corresponding power equipment, prioritize identifying whether such abnormal monitoring items belong to single monitoring items or associated monitoring items. Based on the identification results, confirm the analysis signal or fault signal, and display the confirmed signal; Step 4. Based on the abnormal monitoring items calibrated by the corresponding power equipment and the associated analysis signals, initially lock the other monitoring items associated with this abnormal monitoring item from the associated monitoring items of the abnormal monitoring item, then lock a group of verification cycles, and perform verification and analysis on the monitoring data associated with this abnormal monitoring item and other monitoring items within the verification cycle to assess whether this abnormal monitoring item is a data missing abnormality or a fault abnormality.

2. The method for processing power equipment fault data based on big data according to claim 1, characterized in that: In step 2, the specific sub-steps for identifying whether the corresponding monitoring item is abnormal are: The monitoring parameters of each group of monitoring items in the power equipment are calibrated as CS i , and the monitoring parameter CS associated with the corresponding monitoring item i Compare with the preset interval. The endpoint values ​​of the preset interval associated with each group of monitoring items are all preset values. If CS i ∈ preset interval, it represents the monitoring parameter CS of this monitoring item at the current moment i No abnormalities, if CS i ∉Preset interval represents the monitoring parameter CS associated with the corresponding monitoring item at the current moment i There is an anomaly, and the monitoring item corresponding to the current moment is marked as an abnormal monitoring item.

3. The method for processing power equipment fault data based on big data according to claim 1, characterized in that: In step 3, the specific sub-steps for confirming the analysis signal or the fault signal are: Based on the abnormal monitoring item calibrated at the current moment, identify whether this abnormal monitoring item belongs to the single monitoring item represented. If it does, directly generate a fault signal for direct display. If it does not, it means that this abnormal monitoring item has other associated monitoring items, that is, this abnormal monitoring item belongs to the associated monitoring item, so generate an analysis signal, and then process the generated analysis signal subsequently.

4. The method for processing power equipment fault data based on big data according to claim 1, characterized in that: In step 4, the specific sub-steps for evaluating whether the abnormal monitoring item is a data missing abnormality or a fault abnormality are: S41, based on the abnormal monitoring item and the analysis signal, determining other monitoring items from the associated monitoring items to which the abnormal monitoring item belongs, and marking the other monitoring items determined by the abnormal monitoring item as associated sub-items; S42: Define a set of verification periods, where the verification period is a preset period, and the abnormal time associated with the abnormal monitoring item is located in the middle of the verification period. Confirm the monitoring data generated by the abnormal monitoring item within the verification period in sequence, and sort the confirmed monitoring data according to the chronological order to confirm the abnormal monitoring item data sequence. Then, the monitoring data associated with the corresponding associated sub-items within this verification period are sorted according to the time sequence to confirm the data sequence of the associated sub-items; S43. Based on the abnormal moment identified in the abnormal monitoring item data sequence, mark the group of moments preceding the abnormal moment as previous moments. From the associated sub-item data sequence, identify the calibration moment associated with the previous moment. The calibration moment and the previous moment are contemporaneous. Following the calibration moment identified in the associated sub-item data sequence, identify a group of missing moments. Make the missing moments contemporaneous with the abnormal moment. Adjust the time features associated with subsequent different monitoring data in the associated sub-item data sequence. S44. Verify the adjusted associated sub-item data sequence and the abnormal monitoring item data sequence: confirm the change data between adjacent moments in the two data sequences, mark the change data at adjacent moments in the associated sub-item data sequence as first data, and then synchronously lock the change data at adjacent moments in the abnormal monitoring item data sequence and mark it as second data. The change data is determined in the same manner as the corresponding change data in step S13. The change data of the two adjacent moments before and after the abnormal moment are not verified. Similarly, the change data of the two adjacent moments before and after the missing data are not verified synchronously. S45. Identify whether the data sequence associated with the abnormal monitoring item in step S14 belongs to the primary sequence or the secondary sequence in the associated monitoring item: If it belongs to the main sequence, the formula is: first data ÷ second data = data trend, to confirm the data trend of the associated sub-item data sequence and the abnormal monitoring item data sequence between adjacent moments; If it belongs to a subsequence, use: second data ÷ first data = data trend to confirm the data trend of the associated sub-item data sequence and the abnormal monitoring item data sequence between adjacent moments; S46. Perform variance processing on the confirmed data trends of several groups, lock the calibration variance, and then confirm the associated verification value determined by this associated monitoring item from the processed historical data. If the calibration variance ≤ the associated verification value, a data missing signal is generated for display, indicating that there is data missing in the associated monitoring item of this abnormal monitoring item, resulting in data anomaly in this abnormal monitoring item.

5. The method for processing power equipment fault data based on big data according to claim 4, characterized in that: In step S46, if the calibration variance is greater than the associated verified value, an abnormal monitoring fault signal is generated for display.

6. A power equipment fault data processing system based on big data, the processing system operates according to the power equipment fault data processing method based on big data according to claims 1-5, characterized in that: include: The associated monitoring item calibration end analyzes the periodic data generated by the specified power equipment in the past historical data, determines the data trends associated with different monitoring items from the monitoring data associated with several different monitoring items, and then locks the related monitoring items from the data trends confirmed in the same period and calibrates them. Based on the calibration results, the associated monitoring items or single monitoring items are locked; The abnormal monitoring item calibration end performs fault confirmation on the monitoring parameters associated with each group of monitoring items in the power equipment, and identifies whether the monitoring parameters associated with the corresponding monitoring items are abnormal. If there is an abnormality, the abnormal monitoring item is calibrated; if there is no abnormality, no calibration is performed; The initial signal confirmation terminal, based on the abnormal monitoring items calibrated by the corresponding power equipment, prioritizes identifying whether such abnormal monitoring items belong to single monitoring items or associated monitoring items. Based on the identification results, it confirms the analysis signal or fault signal and displays the confirmed signal; The abnormality determination center, based on the abnormal monitoring items calibrated by the corresponding power equipment and the associated analysis signals, initially locks other monitoring items associated with the abnormal monitoring item from the associated monitoring items of the abnormal monitoring item, and then locks a group of verification cycles. It verifies and analyzes the monitoring data associated with the abnormal monitoring item and other monitoring items within the verification cycle, and assesses whether the abnormal monitoring item is a data missing abnormality or a fault abnormality.

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