Intelligent judging method for perfecting defects of digitalization of electric power instrument

Abstract

The application discloses an intelligent defect judgment method for digital improvement of electric power instruments, and relates to the technical field of data defect detection; the method comprises the following steps: sending a data collection task to each electric power instrument, generating a plurality of segment detection data according to the data collection task by each electric power instrument, and sending the segment detection data to the i-state grid through a data transmission channel; the i-state grid presets a plurality of data detection report templates according to the equipment types of each electric power equipment; whenever the i-state grid receives segment detection data, the segment detection data is input into the corresponding data detection report template after data processing, and a data detection report is generated; a defect judgment rule is set, and the data detection report is subjected to defect detection through the defect judgment rule; according to the defect detection result, the defect data is subjected to traceability query, and the data detection report is corrected according to the traceability query result.

Description

Technical Field

[0001] This invention relates to the field of data defect detection technology, specifically an intelligent method for determining defects in the digital improvement of power instruments. Background Technology

[0002] Digitalization of power instruments refers to the combination of traditional power instruments with digital technology to achieve digital detection and measurement of power systems. By digitizing power instruments, it is possible to collect voltage, current and power change data generated during the operation of the power system, convert the above data into data signals, and then send them to the cloud platform for analysis and prediction.

[0003] However, with the continuous expansion of the power system, the types of data generated by the power system are numerous and difficult to manage, and data loss or redundancy is likely to occur. Traditional digital improvement systems for power instruments generally have cumbersome procedures for data acquisition, data interaction, data analysis, and data storage, which leads to time delays and prevents timely acquisition and interactive analysis of detection data, thus delaying the analysis and judgment of detection data.

[0004] Therefore, how to reduce the number of processing steps for detection data while improving the processing efficiency of each step is a challenge of existing technologies. To address this, we provide an intelligent method for digitally improving defects in power instruments. Summary of the Invention

[0005] To address the aforementioned technical problems, the present invention aims to provide an intelligent method for determining defects in the digital perfection of power instruments.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for intelligently determining defects in the digitalization of power instruments, comprising the following steps:

[0008] Step 1: Pre-set several data transmission channels and send data acquisition tasks to each power instrument through the data transmission channels. Then, each power instrument generates several segments of detection data according to the data acquisition tasks and sends the segment detection data to iGuowang through the data transmission channels.

[0009] Step 2: iGuoGang presets several data detection report templates according to the equipment type of each power equipment. Whenever iGuoGang receives segment detection data, it processes the segment detection data and inputs it into the corresponding data detection report template to generate a data detection report.

[0010] Step 3: Set up defect judgment rules, and use these rules to perform defect detection on the data inspection report. Based on the defect detection results, trace the source of redundant or missing data, and then correct the data inspection report based on the traceability results.

[0011] Furthermore, the process of generating the fragment detection data includes:

[0012] A PMS cloud platform is set up, which is connected to the State Grid and several power instruments, and each power instrument is assigned a number.

[0013] The PSM cloud platform is configured with K data transmission channels to connect with power instruments and iGuo.com, where K is a natural number greater than 0.

[0014] The PMS cloud platform simultaneously sends data acquisition tasks to various power instruments through the data transmission channel. The data acquisition tasks include task execution time and sensor start and stop instructions.

[0015] Before the data acquisition task begins, each power instrument divides the task execution time into NUM task execution sub-times based on the total number and types of its own sensors, NUM, where NUM is a natural number greater than 0.

[0016] Then, the power instrument activates all sensors according to the sensor start command in the data acquisition task, generates segment detection data at the end of each task execution sub-time, and marks it with the number of the power instrument.

[0017] Furthermore, the data detection report template is provided with several data filling areas and one information filling area, and each data filling area is provided with data traction points and corresponding data index pointers, wherein the data index pointers are provided with data format feature points and data content feature points.

[0018] Furthermore, the feature is that the i-State Grid pre-sets an instrument information set for each power instrument, wherein the instrument information set includes the power instrument's number, equipment type, and the number and type of sensors it holds;

[0019] At the same time, iGuowang establishes several data processing nodes and a data index pool, with the data processing nodes directly connected to the data index pool;

[0020] Whenever iGuoWang receives a fragment detection data, it checks whether the data processing node already has the fragment detection data. If it does, the fragment detection data is transferred to the data waiting area. If it does not exist, the fragment detection data is directly transferred to the data processing node.

[0021] Furthermore, the process of processing the fragment detection data according to the data processing node and the data index pool includes:

[0022] When fragment detection data enters the data processing node, the data processing node extracts content features from the fragment detection data, then matches the data processing method according to the extraction results and generates fragment data. At the same time, it annotates the content features within the fragment data and sends the fragment data to the data index pool.

[0023] Before the data processing node converts the fragment detection data into fragment data, iGuoWang copies and stores the fragment detection data. After the data collection task is completed, the fragment detection data is spliced ​​together sequentially according to the number and collection time of each fragment detection data to obtain the collected data.

[0024] Set a threshold for the number of fragment data. Whenever the data index pool receives a fragment data, it checks whether the current number of fragment data is equal to the threshold. If it is less than the threshold, no operation is performed. If it is equal to the threshold, iGuo.net retrieves all data index pointers and sequentially performs index matching on all fragment data in the data index pool.

[0025] The data index pointer performs index matching on each data segment based on data format feature points and data content feature points. If the index matching fails, the index matches the next data segment.

[0026] If the index matches successfully, the data index pointer copies the corresponding fragment data and indexes the next fragment data to match;

[0027] After all data index pointers have been matched with each fragment of data in turn, the data index pool deletes all fragments of data.

[0028] Operation records are generated based on the process of converting fragment detection data into fragment data, mapping and matching fragment data with data index pointers, and generating data detection reports. At the same time as the data detection report is generated, iGuoWang integrates all corresponding operation records to generate a data detection report generation record.

[0029] Furthermore, the process of generating a detection data report based on the index matching result of the data index pointer includes:

[0030] iGuoWang retrieves the data inspection report template corresponding to the data index pointer containing fragmented data and generates a blank data inspection report;

[0031] Then, the data index pointer is matched with the data traction point on the blank data detection report. When the data index pointer and the data traction point are successfully matched, the data traction pointer transfers all the fragment data it carries to the corresponding data filling area.

[0032] When each power instrument determines the end of the task based on the task execution time, it sends a data collection end prompt to iGuowang. Then, iGuowang uses the number of the data segment in each data filling area in the blank data detection report to splice the data segment in sequence to obtain the corresponding detection data. At the same time, it fills the information filling area with the data from the instrument and equipment information to obtain the data detection report.

[0033] Once all blank data inspection reports have been converted into data inspection reports, the generation time of each data inspection report is marked, and then all data inspection reports are sent to the PMS cloud platform.

[0034] Furthermore, the process of generating the defect determination rules includes:

[0035] The PMS cloud platform has several historical data detection reports pre-stored, with three preset judgment keywords: device type, detection data type, and detection data volume.

[0036] Based on the primary classification results and the keywords for determining the data type, the data type of each data filling area in the historical data detection report is labeled with the data type.

[0037] Then, based on the amount of detected data, the keyword is determined to obtain the amount of detected data in each data filling area. The amount of detected data in the data filling areas with the same category and data type is overlapped to obtain the normal range of the amount of detected data in the corresponding data filling area.

[0038] By integrating the normal range of detection data from various historical detection data reports, several defect judgment rules for the corresponding equipment type are obtained.

[0039] Furthermore, the process of determining defects in the aforementioned detection data report includes:

[0040] When the PMS cloud platform receives a data detection report from iGuowang, it matches the corresponding defect judgment rules based on all the defect judgment rules for the corresponding equipment type in the information filling area of ​​the data detection report, and then matches the corresponding defect judgment rules based on the type and number of sensors in the information filling area.

[0041] Compare the amount of detected data in the data inspection report with the normal range of the corresponding amount of detected data in the defect judgment rules. If all the amount of detected data is within the normal range of the corresponding amount of detected data, then the corresponding data inspection report is judged to be normal.

[0042] If the amount of detected data is outside the normal range, the corresponding data detection report will be marked as an abnormal data detection report.

[0043] Furthermore, the process of tracing and correcting abnormal data detection reports includes:

[0044] If the amount of detected data exceeds the normal range, the generation process of the detection in the corresponding data filling area is found in the data detection report generation record, and then the redundant detection data fragments in the data filling area are deleted.

[0045] If the amount of detected data is less than the normal range, the generation process of the detection in the corresponding data filling area is found in the data detection report generation record. The corresponding collected data is then found according to the generation process, and the collected data is reprocessed. The missing detection data fragments in the data filling area are supplemented according to the data processing results.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] 1. This invention uses several preset data transmission channels to simultaneously send data acquisition tasks to various power instruments. Each power instrument then generates several segments of detection data based on the data acquisition tasks and sends these segments to iGuo.net via the data transmission channels. iGuo.net pre-sets several data detection report templates based on the equipment type of each power device. Whenever iGuo.net receives a segment of detection data, it processes the data and inputs it into the corresponding data detection report template to generate a data detection report. By uploading the detection data in fragmented form, distributed parallel processing of the detection data is achieved, effectively improving the processing efficiency of the detection data and reducing the processing steps.

[0048] 2. This invention sets up defect judgment rules and uses these rules to detect defects in data inspection reports. Based on the defect detection results, it traces and queries the source of redundant or missing data, and then corrects the data inspection report based on the traceability results. This achieves intelligent detection of missing or redundant data in data inspection reports, ensuring the accuracy of the data inspection reports. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0050] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0052] like Figure 1 As shown, an intelligent method for determining defects in the digitalization of power instruments includes the following steps:

[0053] Step 1: Pre-set several data transmission channels and send data acquisition tasks to each power instrument through the data transmission channels. Then, each power instrument generates several segments of detection data according to the data acquisition tasks and sends the segment detection data to iGuowang through the data transmission channels.

[0054] Specifically, a PMS cloud platform is set up, which is connected to the State Grid and several power instruments. Each power instrument is assigned a number, such as S1, S2, ..., S... n , where n is a natural number greater than 0 and n represents the total number of electrical instruments. It should be noted that the types and numbers of sensors on each electrical instrument are different.

[0055] The PSM cloud platform is configured with K data transmission channels to connect with power instruments and iGuo.com, where K is a natural number greater than 0 and less than or equal to n;

[0056] Then, the PMS cloud platform sends data acquisition tasks to each power instrument simultaneously through the data transmission channel. The data acquisition tasks include task execution time and sensor start and stop instructions.

[0057] When the PMS cloud platform determines that all power instruments have received the data acquisition task, each power instrument executes the data acquisition task synchronously.

[0058] Before the data acquisition task begins, each power instrument divides the task execution time into NUM task execution sub-times based on the sum of its own sensor quantity Num1 and sensor type quantity Num2.

[0059] Then, the power instrument activates all sensors according to the sensor start command in the data acquisition task, generates segment detection data at the end of each task execution sub-time, and marks the power instrument with its number, and then uploads the segment detection data to iGuo.com.

[0060] The i-State Grid pre-sets an instrument information set for each power instrument, which includes the instrument's number, equipment type, and the number and type of sensors it holds.

[0061] Step 2: iGuoGang presets several data detection report templates according to the equipment type of each power equipment. Whenever iGuoGang receives segment detection data, it processes the segment detection data and inputs it into the corresponding data detection report template to generate a data detection report.

[0062] Specifically, iGuoGang pre-sets corresponding data detection report templates based on the instrument information set of power instruments;

[0063] The data detection report template has several data filling areas and one information filling area. Each data filling area has a data traction point and a corresponding data index pointer. The data index pointer contains data format feature points and data content feature points.

[0064] Furthermore, iGuo.net establishes several data processing nodes, a data index pool, and a data waiting area, with the data processing nodes directly connected to the data index pool;

[0065] Whenever iGuoWang receives a fragment detection data, it checks whether the data processing node already has the fragment detection data. If it does, the fragment detection data is transferred to the data waiting area. If it does not exist, the fragment detection data is directly transferred to the data processing node.

[0066] It should be noted that after the fragment detection data within the data processing node has been processed, a fragment detection data is extracted from the data waiting area first.

[0067] The data processing node is configured with several data processing methods based on the data format feature points and data content feature points in the data index pointer;

[0068] When the fragment detection data enters the data processing node, the data processing node extracts content features from the fragment detection data and then matches the data processing method according to the extraction results.

[0069] The fragment detection data is processed according to the matching data processing method to generate fragment data. At the same time, the content features within the fragment data are labeled, and then the fragment data is sent to the data index pool.

[0070] It should be noted that before the data processing node converts the fragment detection data into fragment data, iGuoWang copies and stores the fragment detection data. After the data collection task is completed, the fragment detection data is spliced ​​together sequentially according to the number and collection time of each fragment detection data to obtain the collected data.

[0071] Set a threshold for the number of fragment data. Whenever the data index pool receives a fragment data, it checks whether the current number of fragment data is equal to the threshold. If it is less than the threshold, no operation is performed. If it is equal to the threshold, iGuo.net retrieves all data index pointers and sequentially performs index matching on all fragment data in the data index pool.

[0072] The data index pointer performs index matching on each data segment based on data format feature points and data content feature points. If the index matching fails, the index matches the next data segment.

[0073] If the index matches successfully, the data index pointer copies the corresponding fragment data and indexes the next fragment data to match;

[0074] After all data index pointers have been matched with each fragment of data in turn, the data index pool deletes all fragments of data.

[0075] iGuoWang retrieves the data inspection report template corresponding to the data index pointer containing fragmented data and generates a blank data inspection report;

[0076] Then, the data index pointer is matched with the data traction point on the blank data detection report. When the data index pointer and the data traction point are successfully matched, the data traction pointer transfers all the fragment data it carries to the corresponding data filling area.

[0077] When each power instrument determines the end of the task based on the task execution time, it sends a data collection end prompt to iGuowang. Then, iGuowang uses the number of the data segment in each data filling area in the blank data detection report to splice the data segment in sequence to obtain the corresponding detection data. At the same time, it fills the data filling information area in the instrument and equipment information to obtain the data detection report.

[0078] Once all blank data detection reports have been converted into data detection reports, the generation time of each data detection report is marked, and then all data detection reports are sent to the PMS cloud platform.

[0079] It should be noted that operation records are generated based on the process of converting fragment detection data into fragment data, mapping and matching fragment data with data index pointers, and generating data detection reports. At the same time as the data detection report is generated, iGuoWang integrates all corresponding operation records to generate a data detection report generation record, which is then sent to the PMS shared cloud platform together with the data detection report.

[0080] Step 3: Set up defect judgment rules, and use the defect judgment rules to perform defect detection on the data detection report. Based on the defect detection results, trace the source of redundant or missing data, and then correct the data detection report based on the source traceability results.

[0081] Specifically, the PMS cloud platform has several historical data detection reports pre-stored, and then sets defect judgment rules based on all data detection reports;

[0082] The process of setting defect judgment rules includes:

[0083] Three types of judgment keywords are preset: equipment type, detection data type, and detection data volume. The equipment type judgment keyword corresponds to the equipment type in the information filling area of ​​each historical data detection report. Then, the historical data detection reports are classified according to the equipment type judgment keyword.

[0084] The keywords for determining the data type and the keywords for determining the data volume correspond to the sensor type and the corresponding number of sensors in the information filling area of ​​each historical data detection report, respectively.

[0085] Based on the primary classification results and the keywords for determining the data type, the data type of each data filling area in the historical data detection report is labeled with the data type.

[0086] Then, based on the amount of detected data, the keyword is determined to obtain the amount of detected data in each data filling area. The amount of detected data in the data filling areas with the same category and data type is overlapped to obtain the normal range of the amount of detected data in the corresponding data filling area.

[0087] By integrating the normal range of the detection data volume in each historical detection data report, several defect judgment rules for the corresponding equipment type are obtained.

[0088] Furthermore, when the PMS cloud platform receives a data detection report from iGuowang, it matches the corresponding defect judgment rules based on all the defect judgment rules for the corresponding equipment type in the information filling area of ​​the data detection report, and then matches the corresponding defect judgment rules based on the type and number of sensors in the information filling area.

[0089] Compare the amount of detected data in the data detection report with the normal range of the corresponding amount of detected data in the defect judgment rules;

[0090] If all the test data is within the normal range for the corresponding test data, then the corresponding data test report is considered normal.

[0091] If the amount of detection data is outside the normal range, the corresponding data detection report is considered abnormal.

[0092] Furthermore, if the amount of detected data exceeds the normal range, the generation process of the detection in the corresponding data filling area is found in the data detection report generation record, and then the redundant detection data fragments in the data filling area are deleted.

[0093] If the amount of detected data is less than the normal range, then find the generation process of the corresponding data filling area in the data detection report generation record, find the corresponding collected data according to the generation process, and then reprocess the collected data. Based on the data processing results, supplement the missing detection data fragments in the data filling area.

[0094] When the PMS cloud platform deletes or supplements the detection data in the data filling area of ​​the abnormal data detection report, it re-detects the abnormal data detection report according to the defect judgment rules. If all the detection data is within the normal range of the corresponding detection data, the corresponding data detection report is judged to be normal.

[0095] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An intelligent method for determining defects in the digitalization of power instruments, characterized in that, Includes the following steps: Step 1: Pre-set several data transmission channels and send data acquisition tasks to each power instrument through the data transmission channels. Then, each power instrument generates several segments of detection data according to the data acquisition tasks and sends the segment detection data to iGuowang through the data transmission channels. Step 2: iGuoGang presets several data detection report templates according to the equipment type of each power equipment. Whenever iGuoGang receives segment detection data, it processes the segment detection data and inputs it into the corresponding data detection report template to generate a data detection report. Step 3: Set up defect judgment rules, and use the defect judgment rules to perform defect detection on the data detection report. Based on the defect detection results, trace the source of redundant or missing data, and then correct the data detection report based on the source traceability results. iGuo.net establishes several data processing nodes and a data index pool, with the data processing nodes directly connected to the data index pool; Whenever iGuoWang receives a fragment detection data, it checks whether the data processing node already has the fragment detection data. If it does, the fragment detection data is transferred to the data waiting area. If it does not exist, the fragment detection data is directly transferred to the data processing node. The process of processing the fragment detection data according to the data processing node and the data index pool includes: The data processing node extracts content features from the fragment detection data to generate fragment data, while labeling the content features within the fragment data and sending the fragment data to the data index pool. Before the data processing node converts the fragment detection data into fragment data, iGuoWang copies and stores the fragment detection data. After the data collection task is completed, the fragment detection data is spliced ​​together sequentially according to the number and collection time of each fragment detection data to obtain the collected data. Set a threshold for the number of fragment data. Whenever the data index pool receives a fragment data, it checks whether the current number of fragment data is equal to the threshold. If it is less than the threshold, no operation is performed. If it is equal to the threshold, iGuo.net retrieves all data index pointers and sequentially performs index matching on all fragment data in the data index pool. The data index pointer performs index matching on each data segment based on data format feature points and data content feature points. If the index matching fails, the index matches the next data segment. If the index matches successfully, the data index pointer copies the corresponding fragment data and indexes the next fragment data to match; After all data index pointers have been matched with each fragment of data in turn, the data index pool deletes all fragments of data. Operation records are generated based on the process of converting fragment detection data into fragment data, mapping and matching fragment data with data index pointers, and generating data detection reports. At the same time as the data detection report is generated, iGuoWang integrates all corresponding operation records to generate a data detection report generation record.

2. The intelligent judgment method for digital perfection defects of power instruments according to claim 1, characterized in that, The process of generating the fragment detection data includes: A PMS cloud platform is set up, which is connected to the State Grid and several power instruments, and each power instrument is assigned a number. The PSM cloud platform is configured with K data transmission channels to connect with power instruments and iGuo.com, where K is a natural number greater than 0. The PMS cloud platform simultaneously sends data acquisition tasks to various power instruments through the data transmission channel. The data acquisition tasks include task execution time and sensor start and stop instructions. Before the data acquisition task begins, each power instrument divides the task execution time into NUM task execution sub-times based on the total number and types of its own sensors, NUM, where NUM is a natural number greater than 0. Then, the power instrument activates all sensors according to the sensor start command in the data acquisition task, generates segment detection data at the end of each task execution sub-time, and marks it with the number of the power instrument.

3. The intelligent judgment method for digital improvement defects of power instruments according to claim 2, characterized in that, The data detection report template has several data filling areas and one information filling area. Each data filling area has a data traction point and a corresponding data index pointer. The data index pointer contains data format feature points and data content feature points.

4. The intelligent judgment method for digital perfection defects of power instruments according to claim 3, characterized in that, The i-State Grid pre-sets an instrument information set for each power instrument, which includes the instrument's number, equipment type, and the number and type of sensors it holds.

5. The intelligent judgment method for digital improvement defects of power instruments according to claim 4, characterized in that, The process of generating a detection data report based on the index matching result of the data index pointer includes: iGuoWang retrieves the data inspection report template corresponding to the data index pointer containing fragmented data and generates a blank data inspection report; Then, the data index pointer is matched with the data traction point on the blank data detection report. When the data index pointer and the data traction point are successfully matched, the data traction pointer transfers all the fragment data it carries to the corresponding data filling area. After each power instrument completes its task based on the execution time, the data segments are sequentially spliced ​​together to obtain the corresponding test data. At the same time, the data from the instrument information are filled into the information filling area to obtain the data test report.

6. The intelligent judgment method for digital perfection defects of power instruments according to claim 5, characterized in that, The process of generating the defect determination rules includes: The PMS cloud platform has several historical data detection reports pre-stored, with three preset judgment keywords: device type, detection data type, and detection data volume. Based on the primary classification results and the keywords for determining the data type, the data type of each data filling area in the historical data detection report is labeled with the data type. Then, based on the amount of detected data, the keyword is determined to obtain the amount of detected data in each data filling area. The amount of detected data in the data filling areas with the same category and data type is overlapped to obtain the normal range of the amount of detected data in the corresponding data filling area. By integrating the normal range of detection data from various historical detection data reports, several defect judgment rules for the corresponding equipment type are obtained.

7. The intelligent judgment method for digital improvement defects of power instruments according to claim 6, characterized in that, The process of determining defects in the aforementioned test data report includes: When the PMS cloud platform receives a data detection report from iGuowang, it matches the corresponding defect judgment rules based on all the defect judgment rules for the corresponding equipment type in the information filling area of ​​the data detection report, and then matches the corresponding defect judgment rules based on the type and number of sensors in the information filling area. Compare the amount of detected data in the data inspection report with the normal range of the corresponding amount of detected data in the defect judgment rules. If all the amount of detected data is within the normal range of the corresponding amount of detected data, then the corresponding data inspection report is judged to be normal. If the amount of detected data is outside the normal range, the corresponding data detection report will be marked as an abnormal data detection report.

8. The intelligent judgment method for digital perfection defects of power instruments according to claim 7, characterized in that, The process of tracing and correcting abnormal data detection reports includes: If the amount of detected data exceeds the two endpoints of the normal range, the generation process of the detection in the corresponding data filling area is found based on the data detection report, and then the redundant detection data fragments in the data filling area are deleted. If the amount of detected data is less than the two endpoints of the normal range, the generation process of the corresponding data filling area is found in the data detection report generation record. The corresponding collected data is then found according to the generation process, and the collected data is reprocessed. Based on the data processing results, the missing detection data fragments in the data filling area are supplemented.

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