Quality control method, device and equipment applied to lightning pulse data and medium

Abstract

The present disclosure relates to the technical field of meteorological warning, in particular to a quality control method and device applied to lightning pulse data, equipment and medium. The quality control method comprises: receiving lightning pulse data collected by a plurality of devices constituting a lightning monitoring network, the lightning monitoring network being composed of a local device and adjacent site devices; the lightning pulse data comprising data volume, peak electric field data and peak magnetic field data; respectively controlling the data volume, peak electric field data and peak magnetic field data, and attaching a quality control code to the lightning pulse data after quality control; and storing the lightning pulse data after quality control. The technical solution above controls the lightning pulse data obtained by the local station through multiple judgment methods, calculates the lightning occurrence position and intensity using the lightning pulse data after quality control, improves the lightning monitoring positioning accuracy, and makes it better serve lightning warning, lightning protection, lightning research, lightning monitoring, etc.

Description

Technical Field

[0001] This disclosure relates to the field of meteorological early warning technology, specifically to a quality control method, device, equipment, and medium for lightning pulse data. Background Technology

[0002] The data detected by lightning locators is called lightning pulse data. The National Lightning Data Processing Center processes lightning pulse data sent from various meteorological monitoring stations to calculate the location and intensity of lightning strikes. Therefore, lightning pulse data is the foundation of lightning monitoring and location, and its quality directly affects the accuracy of lightning monitoring and location.

[0003] In existing technologies, meteorological monitoring stations use lightning pulse data detected and collected for lightning monitoring and location. However, since effective methods are not used to control the quality of the collected lightning pulse data, its direct use will have an adverse impact on lightning warning, lightning protection, lightning research, and lightning monitoring in the region. Summary of the Invention

[0004] To address the problems in the related technologies, this disclosure provides a method, apparatus, device, and medium for quality control of lightning pulse data.

[0005] In a first aspect, this disclosure provides a quality control method for lightning pulse data.

[0006] Specifically, the quality control method applied to lightning pulse data includes:

[0007] The system receives lightning pulse data collected by several devices forming a lightning monitoring network, which consists of local equipment and equipment from adjacent sites. The lightning pulse data includes data volume, peak electric field data, and peak magnetic field data.

[0008] The data volume, peak electric field data, and peak magnetic field data are respectively subjected to quality control, and the quality control code after quality control is attached to the lightning pulse data.

[0009] The lightning pulse data is stored after quality control.

[0010] Optionally, the step of performing quality control on the data volume, peak electric field data, and peak magnetic field data respectively, and appending the quality control code after quality control to the lightning pulse data, includes:

[0011] If the amount of data within a unit of time meets a preset condition, the skewness coefficient of the data amount of the local station equipment and the adjacent station equipment is calculated. If the data amount meets a threshold range, the quartile method is used to determine whether the data amount of the local station equipment is abnormal.

[0012] Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

[0013] Optionally, the step of performing quality control on the data volume, peak electric field data, and peak magnetic field data respectively, and appending the quality control code after quality control to the lightning pulse data, further includes:

[0014] Peak electric field data is selected from historical lightning pulse data collected by the equipment at this station. The mean and standard deviation are calculated by taking the natural logarithm of the absolute value of the peak electric field data. The historical lightning pulse data refers to data that has participated in the location calculation of multiple stations and whose location results are located within the lightning monitoring network.

[0015] Obtain real-time lightning pulse data collected by the equipment at this station, and take the natural logarithm of the absolute value of the real-time peak electric field data;

[0016] Anomalies are identified by using the mean and standard deviation to determine the natural logarithm of the absolute value of the real-time peak electric field data.

[0017] Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

[0018] Optionally, the step of performing quality control on the data volume, peak electric field data, and peak magnetic field data respectively, and appending the quality control code after quality control to the lightning pulse data, further includes:

[0019] Select data from historical lightning pulse data collected by the equipment at this station that participate in the location calculation of multiple stations and whose location results are located within the lightning monitoring network;

[0020] A prediction model for the peak magnetic field data is obtained based on the peak electric field data and peak magnetic field data in the lightning pulse data.

[0021] The system acquires real-time lightning pulse data collected by the equipment at this station, extracts real-time peak electric field data and real-time peak magnetic field data, inputs the real-time peak electric field data into the prediction model to obtain predicted peak magnetic field data, and obtains residuals based on the real-time peak magnetic field data and the predicted peak magnetic field data.

[0022] The standardized residual is calculated based on the residual and the standard deviation of the residual;

[0023] Anomaly detection is performed using the values ​​of the standardized residuals.

[0024] Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

[0025] Optionally, the lightning pulse data may also include the following characteristic parameters: the steepest point magnetic field data, the waveform peak time, and the waveform zero-crossing time.

[0026] Anomaly detection is performed on the values ​​of several characteristic parameters continuously received by the local equipment.

[0027] Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

[0028] Optional, also includes:

[0029] The count number of the lightning pulse data collected by the equipment at this station within a preset time period is recorded.

[0030] Use the quartile method to determine whether the count number is abnormal;

[0031] Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

[0032] Optional, also includes:

[0033] The network utilization rate of each site device in the lightning monitoring network within the preset time period is calculated.

[0034] Based on whether the network utilization rate meets the preset threshold range, it is determined whether the lightning pulse data is abnormal;

[0035] Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

[0036] Secondly, this disclosure provides a quality control device for lightning pulse data.

[0037] Specifically, the quality control device applied to lightning pulse data includes:

[0038] The receiving module is configured to receive lightning pulse data collected by several devices forming a lightning monitoring network, which consists of local equipment and equipment at adjacent sites; the lightning pulse data includes data volume, peak electric field data, and peak magnetic field data.

[0039] The quality control module is configured to perform quality control on the data volume, peak electric field data, and peak magnetic field data respectively, and to attach the quality control code after quality control to the lightning pulse data.

[0040] The storage module is configured to store the lightning pulse data after quality control.

[0041] Optionally, the quality control module includes:

[0042] The data processing unit is configured to count the amount of data per unit time. If a preset condition is met, it calculates the skewness coefficient of the data amount of the local device and the adjacent site devices. If the threshold range is met, it uses the quartile method to determine whether the data amount of the local device is abnormal.

[0043] The first output unit is configured to output a corresponding quality control code based on the judgment result and attach the quality control code to the lightning pulse data.

[0044] Optionally, the quality control module further includes:

[0045] The first acquisition unit is configured to select peak electric field data from historical lightning pulse data collected by the equipment at this station, and calculate the average value and standard deviation by taking the natural logarithm of the absolute value of the peak electric field data; wherein, the historical lightning pulse data is data that participates in the location calculation of multiple stations and whose location results are located within the lightning monitoring network;

[0046] The acquisition unit is configured to acquire real-time lightning pulse data collected by the equipment at this station, and take the natural logarithm of the absolute value of the real-time peak electric field data therein;

[0047] The first judgment unit is configured to use the average value and standard deviation to make anomaly judgments on the natural logarithm of the absolute value of the real-time peak electric field data.

[0048] The second output unit is configured to output a corresponding quality control code based on the judgment result and attach the quality control code to the lightning pulse data.

[0049] Optionally, the quality control module further includes:

[0050] The second acquisition unit is configured to select data from historical lightning pulse data acquired by the local station equipment that participates in the location calculation of multiple stations and whose location result is located within the lightning monitoring network.

[0051] The prediction unit is configured to obtain a prediction model for the peak magnetic field data based on the peak electric field data and peak magnetic field data in the lightning pulse data;

[0052] The residual calculation unit is configured to acquire real-time lightning pulse data collected by the equipment at this station, extract real-time peak electric field data and real-time peak magnetic field data, input the real-time peak electric field data into the prediction model to obtain predicted peak magnetic field data, and obtain residuals based on the real-time peak magnetic field data and the predicted peak magnetic field data.

[0053] The standardized residual calculation unit is configured to calculate the standardized residual based on the residual and the standard deviation of the residual;

[0054] The second judgment unit is configured to use the value of the standardized residual to make anomaly judgments;

[0055] The third output unit is configured to output a corresponding quality control code based on the judgment result and attach the quality control code to the lightning pulse data.

[0056] Optionally, the lightning pulse data may also include the following characteristic parameters: the steepest point magnetic field data, the waveform peak time, and the waveform zero-crossing time.

[0057] The quality control device applied to lightning pulse data further includes a feature parameter judgment module, configured as follows:

[0058] Anomaly detection is performed on the values ​​of several characteristic parameters continuously received by the local equipment.

[0059] Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

[0060] Optionally, the quality control device applied to lightning pulse data further includes a counting and numbering judgment module, configured as follows:

[0061] The count number of the lightning pulse data collected by the equipment at this station within a preset time period is recorded.

[0062] Use the quartile method to determine whether the count number is abnormal;

[0063] Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

[0064] Optionally, the quality control device applied to lightning pulse data further includes a network utilization rate judgment module, configured as follows:

[0065] The network utilization rate of each site device in the lightning monitoring network within the preset time period is calculated.

[0066] Based on whether the network utilization rate meets the preset threshold range, it is determined whether the lightning pulse data is abnormal;

[0067] Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

[0068] Thirdly, embodiments of this disclosure provide an electronic device including a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method as described in any of the first aspects.

[0069] Fourthly, this disclosure provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the method as described in any of the first aspects.

[0070] The quality control method for lightning pulse data provided in this disclosure includes: receiving lightning pulse data collected by several devices forming a lightning monitoring network, wherein the lightning monitoring network consists of local station devices and adjacent station devices; the lightning pulse data includes data volume, peak electric field data, and peak magnetic field data; performing quality control on the data volume, peak electric field data, and peak magnetic field data respectively, and attaching the quality control code to the lightning pulse data; and storing the quality-controlled lightning pulse data. The above technical solution uses multiple judgment methods to perform quality control on the lightning pulse data acquired by the local station, and uses the quality-controlled lightning pulse data to calculate the lightning occurrence location, intensity, etc., thereby improving the accuracy of lightning monitoring and positioning, and enabling it to better serve lightning early warning, lightning protection, lightning research, and lightning monitoring.

[0071] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0072] Other features, objects, and advantages of this disclosure will become more apparent from the following detailed description of non-limiting embodiments, taken in conjunction with the accompanying drawings. In the drawings:

[0073] Figure 1 A flowchart illustrating a quality control method for lightning pulse data according to an embodiment of the present disclosure is shown.

[0074] Figure 2 A structural block diagram of a quality control device for lightning pulse data according to an embodiment of the present disclosure is shown.

[0075] Figure 3 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown.

[0076] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the method according to embodiments of the present disclosure is shown. Detailed Implementation

[0077] In the following, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings to enable those skilled in the art to readily implement them. Furthermore, for clarity, portions unrelated to the description of exemplary embodiments have been omitted from the drawings.

[0078] In this disclosure, it should be understood that terms such as “comprising” or “having” are intended to indicate the presence of features, figures, steps, behaviors, components, parts or combinations thereof disclosed in this specification, and are not intended to exclude the possibility of the presence or addition of one or more other features, figures, steps, behaviors, components, parts or combinations thereof.

[0079] It should also be noted that, unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other. This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0080] In this disclosure, any operation involving the acquisition of user information or user data, or the display of user information or user data to others, is an operation authorized or confirmed by the user, or actively selected by the user.

[0081] The data detected by lightning locators is called lightning pulse data. The National Lightning Data Processing Center processes lightning pulse data sent from various meteorological monitoring stations to calculate the location and intensity of lightning strikes. Therefore, lightning pulse data is the foundation of lightning monitoring and location, and its quality directly affects the accuracy of lightning monitoring and location.

[0082] In existing technologies, meteorological monitoring stations use lightning pulse data detected and collected for lightning monitoring and location. However, since effective methods are not used to control the quality of the collected lightning pulse data, its direct use will have an adverse impact on lightning warning, lightning protection, lightning research, and lightning monitoring in the region.

[0083] Figure 1 A flowchart illustrating a quality control method for lightning pulse data according to an embodiment of the present disclosure is shown.

[0084] like Figure 1 As shown, the quality control method applied to lightning pulse data includes the following steps S101-S103:

[0085] Step S101: Receive lightning pulse data collected by several devices that make up the lightning monitoring network, which consists of local devices and devices at adjacent sites; the lightning pulse data includes data volume, peak electric field data, and peak magnetic field data.

[0086] Step S102: Perform quality control on the data volume, peak electric field data, and peak magnetic field data respectively, and attach the quality control code after quality control to the lightning pulse data;

[0087] Step S103: Store the lightning pulse data after quality control.

[0088] The quality control method for lightning pulse data provided in this disclosure uses multiple judgment methods to perform quality control on the lightning pulse data acquired by the station. The quality-controlled lightning pulse data is used to calculate the location and intensity of lightning occurrence, which improves the accuracy of lightning monitoring and positioning, enabling it to better serve lightning early warning, lightning protection, lightning research, and lightning monitoring.

[0089] According to embodiments of this disclosure, step S101 involves receiving lightning pulse data collected by several devices forming a lightning monitoring network, which consists of local equipment and equipment from adjacent sites. The lightning pulse data includes data volume, peak electric field data, and peak magnetic field data. In this step, the lightning monitoring network is composed of lightning locators (e.g., DDW1 type lightning locators) from at least four adjacent sites, used to monitor lightning activity 24 hours a day, 365 days a year. The lightning pulse data refers to the data detected by the lightning locators at each site. The National Lightning Data Processing Center calculates the location and intensity of lightning strikes by processing the lightning pulse data sent by each site. Therefore, lightning pulse data is the foundation of lightning monitoring and location, and its quality directly affects the accuracy of lightning monitoring and location. The lightning pulse data also includes the following characteristic parameters: steepest point magnetic field data, waveform peak time, and waveform zero-crossing time.

[0090] According to embodiments of this disclosure, step S102 involves performing quality control on the data volume, peak electric field data, and peak magnetic field data, respectively, and appending the quality control code to the lightning pulse data. The quality control code can be "1" for "suspicious," "0" for "correct," "9" for "no quality control," etc. Specifically, if the lightning pulse data is incorrect, quality control code 2 is output; if the lightning pulse data is suspicious, quality control code 1 is output; if the lightning pulse data is correct, quality control code 0 is output. Furthermore, the quality control code can also be any other form, and this disclosure does not limit this. For example, the quality control code includes at least one of the following forms: numbers, letters, or symbols.

[0091] According to an embodiment of this disclosure, the step of performing quality control on the data volume, peak electric field data, and peak magnetic field data respectively, and attaching the quality control code after quality control to the lightning pulse data, includes: statistically analyzing the data volume per unit time; if a preset condition is met, calculating the skewness coefficient of the data volume of the local station equipment and the adjacent station equipment; if a threshold range is met, using the quartile method to determine whether the data volume of the local station equipment is abnormal; outputting the corresponding quality control code according to the determination result, and attaching the quality control code to the lightning pulse data.

[0092] The skewness coefficient disclosed herein measures the degree of data skewness as the ratio of the difference between the mean and the median to the standard deviation. A positive skewness coefficient indicates that the data is positively or right-skewed, meaning that the data sample contains many small data points, with most below the mean. A negative skewness coefficient indicates that the data is negatively or left-skewed, meaning that the data sample contains many large data points, with most above the mean. The smaller the absolute value of the skewness coefficient, the less biased the data; the larger the absolute value of the skewness coefficient, the greater the bias.

[0093] According to embodiments of this disclosure, the quartiles refer to the values ​​at three dividing points in statistics, where all values ​​are arranged from smallest to largest and divided into four parts. Each part contains 25% of the data, and the middle quartile is the median. Therefore, the commonly referred to quartiles are the values ​​at the 25th percentile (called the lower quartile) and the values ​​at the 75th percentile (called the upper quartile). This disclosure uses the quartile method to compare the overall situation of lightning pulse data from various sites to determine if there are any outliers.

[0094] According to embodiments of this disclosure, in the step of calculating the data volume of lightning pulse data within a unit time period, if a preset condition is met, then the skewness coefficient of the data volume of the local equipment and adjacent equipment is calculated; if a threshold range is met, then the quartile method is used to determine whether the data volume of the local equipment is abnormal. The data volume of the lightning pulse data refers to the data volume statistically analyzed within a preset time period, and the preset condition is the threshold range that the statistically analyzed data volume within that preset time period must meet. For example, it could be that the statistically analyzed data volume is greater than 100 records within one hour. If the statistically analyzed data volume of lightning pulse data within a unit time period does not meet the preset condition, then a quality control code indicating "no quality control" is added to the data.

[0095] Furthermore, after the amount of lightning pulse data within the statistical unit time meets the preset condition, the skewness coefficient of the data amount of the local equipment and the adjacent equipment is calculated. The threshold range that the calculated skewness coefficient must meet includes: the skewness coefficient of the data amount of the local equipment and the adjacent equipment is greater than 1; or the skewness coefficient of the data amount of the local equipment and the adjacent equipment is less than -1.

[0096] Furthermore, after the calculated skewness coefficient meets the threshold range, the quartile method is used to determine whether the data volume of the local station device is abnormal. Specifically, this includes calculating the maximum and minimum estimates of the quartiles based on the data volume of the local station device and adjacent station devices; if the data volume of the local station device is greater than the maximum estimate, the data volume of the local station device is confirmed to be abnormal; or if the data volume of the local station device is less than the minimum estimate, the data volume of the local station device is confirmed to be abnormal; or if the data volume of the local station device is less than the maximum estimate but greater than the minimum estimate, the data volume of the local station device is confirmed to be normal.

[0097] The formulas for calculating the maximum and minimum estimates of the quartiles are as follows:

[0098] Maximum estimated value = Q3 + k(Q3 - Q1) (1)

[0099] Minimum estimate = Q1 - k(Q3 - Q1) (2)

[0100] In the formula, Q1 is the lower quartile;

[0101] Q2 is the median;

[0102] Q3 is the upper quartile;

[0103] k = 1.5 (moderately abnormal) or k = 3 (extremely abnormal).

[0104] Specifically, the maximum and minimum estimated values ​​calculated when k = 1.5 can be used to assess the data as moderately abnormal; the maximum and minimum estimated values ​​calculated when k = 3 can be used to assess the data as extremely abnormal. For example, by substituting the upper quartile, lower quartile, and median of the data volume of the station's equipment into formulas (1) and (2), and taking k = 1.5, the maximum and minimum estimated values ​​are obtained. Then, when the data volume of the station's equipment is greater than the maximum estimated value, it is confirmed that the data volume of the station's equipment is moderately abnormal; or when the data volume of the station's equipment is less than the minimum estimated value, it is confirmed that the data volume of the station's equipment is moderately abnormal; or when the data volume of the station's equipment is less than the maximum estimated value but greater than the minimum estimated value, it is confirmed that the data volume of the station's equipment is normal.

[0105] For example, specific steps in embodiments of this disclosure include:

[0106] The first step is to receive lightning pulse data collected by the lightning monitoring network consisting of the equipment at this station and the equipment at four adjacent stations, and to count the amount of lightning pulse data in each hour. If the count of the data at this station is less than 100, a quality control code "9" is added to the lightning pulse data of this station's DDW1 to indicate "no quality control". If the count of the data at this station is greater than or equal to 100, then proceed to the second step.

[0107] The second step is to calculate the skewness coefficient of the lightning pulse data volume of this station and the four adjacent stations. When the skewness coefficient is less than 1 or greater than -1, the quality control code "0" is added to the DDW1 lightning pulse data of this station to indicate "correct". When the skewness coefficient is greater than 1 or less than -1, the third step is continued.

[0108] The third step is to calculate the maximum and minimum estimated values ​​among the quartiles of the data volume at this station. If the data volume at this station is greater than the maximum estimated value or less than the minimum estimated value among the quartiles, then add a quality control code "1" to the DDW1 lightning pulse data at this station to indicate "suspicious"; otherwise, add a quality control code "0" to indicate "correct".

[0109] The fourth step is to store the lightning pulse data after quality control.

[0110] Furthermore, according to embodiments of this disclosure, the step of performing quality control on the data volume, peak electric field data, and peak magnetic field data respectively, and attaching the quality control code after quality control to the lightning pulse data, further includes: selecting peak electric field data from historical lightning pulse data collected by the local station equipment, and calculating the average value and standard deviation by taking the natural logarithm of the absolute value of the peak electric field data; wherein, the historical lightning pulse data is data that has participated in the positioning calculation of multiple stations and whose positioning results are located within the lightning monitoring network; acquiring real-time lightning pulse data collected by the local station equipment, and taking the natural logarithm of the absolute value of the real-time peak electric field data therein; using the average value and standard deviation to perform anomaly judgment on the natural logarithm of the absolute value of the real-time peak electric field data; outputting the corresponding quality control code according to the judgment result, and attaching the quality control code to the lightning pulse data.

[0111] According to embodiments of this disclosure, in the step of selecting peak electric field data from historical lightning pulse data collected by the station's equipment, and calculating the average and standard deviation by taking the natural logarithm of the absolute value of the peak electric field data; wherein, the historical lightning pulse data is data that has participated in the location calculation of multiple stations and whose location results are located within the lightning monitoring network, the equipment for collecting lightning pulse data can be a DDW1 type lightning locator, and the other stations are the adjacent stations of this station, with a number of at least 4, that is, selecting data from the historical lightning pulse data collected by this station that has also been collected by at least 4 other adjacent stations, and selecting the historical lightning pulse data from the first year since the station's equipment started operating, this can ensure the accuracy of the peak electric field data in the lightning pulse data, and the average and standard deviation calculated by taking the natural logarithm of it are used as a benchmark to measure the quality of the real-time lightning pulse data of the station's equipment.

[0112] According to embodiments of this disclosure, in the step of acquiring real-time lightning pulse data collected by the local station equipment and taking the natural logarithm of the absolute value of the real-time peak electric field data, the real-time lightning pulse data collected by the local station equipment does not require that it be simultaneously collected by other stations. For quality control of the lightning pulse data from the local station equipment, a natural logarithm can be taken synchronously for each lightning pulse data point acquired in real time, performing a quality control step.

[0113] According to embodiments of this disclosure, the step of using the average value and standard deviation to determine anomalies in the natural logarithm of the absolute value of the real-time peak electric field data includes the following steps: if the absolute value of the difference between the natural logarithm of the absolute value of the real-time peak electric field data and the average value is greater than three times the standard deviation, then the real-time lightning pulse data is determined to be erroneous; if the absolute value of the difference between the natural logarithm of the absolute value of the real-time peak electric field data and the average value is less than or equal to three times the standard deviation, but greater than two times the standard deviation, then the real-time lightning pulse data is determined to be suspicious; if the absolute value of the difference between the natural logarithm of the absolute value of the real-time peak electric field data and the average value is less than or equal to two times the standard deviation, then the real-time lightning pulse data is determined to be correct.

[0114] According to embodiments of this disclosure, in the step of outputting a corresponding quality control code based on the judgment result and attaching the quality control code to the lightning pulse data, the quality control code includes at least one of the following forms: numbers, letters, and symbols. For example, if the real-time lightning pulse data is incorrect, quality control code 2 is output; if the real-time lightning pulse data is questionable, quality control code 1 is output; if the real-time lightning pulse data is correct, quality control code 0 is output. Furthermore, if the equipment at this station has not operated for the predetermined time, quality control code 9 is directly output, indicating that no quality control has been performed. For example, if the equipment at this station has been operating for less than one year, quality control code 9 is directly output, indicating that the data has not undergone quality control.

[0115] The specific steps of the embodiments of this disclosure include: using lightning pulse data collected by the local equipment within the first year of operation and participating in the positioning of four or more stations in the network, recording the peak electric field in the lightning pulse data as E, and calculating the average value A and standard deviation S of ln(|E|). Quality control is performed on the lightning pulse data observed in real time by the equipment at this station. When the absolute value of the difference between ln(|E|) of the real-time lightning pulse data and A obtained from the above statistics is greater than 2 times S and less than or equal to 3 times S, a quality control code "1" is added to the lightning pulse data, indicating that the data is suspicious; when the absolute value of the difference between ln(|E|) of the real-time lightning pulse data and A is greater than 3 times S, a quality control code "2" is added to the lightning pulse data, indicating that the data is incorrect; when the absolute value of the difference between ln(|E|) of the real-time lightning pulse data and A is less than or equal to 2 times S, a quality control code "0" is added to the lightning pulse data, indicating that the data is correct; if the current equipment has been running for less than 1 year, a quality control code "9" is added to the lightning pulse data, indicating that the data has not been quality controlled.

[0116] Furthermore, the step of performing quality control on the data volume, peak electric field data, and peak magnetic field data respectively, and attaching the quality control code to the lightning pulse data, further includes: selecting data from historical lightning pulse data collected by the station's equipment that participated in the multi-site positioning calculation and whose positioning results are located within the lightning monitoring network; obtaining a prediction model for the peak magnetic field data based on the peak electric field data and peak magnetic field data in the lightning pulse data; acquiring real-time lightning pulse data collected by the station's equipment, extracting real-time peak electric field data and real-time peak magnetic field data, inputting the real-time peak electric field data into the prediction model to obtain predicted peak magnetic field data, obtaining residuals based on the real-time peak magnetic field data and the predicted peak magnetic field data; calculating standardized residuals based on the residuals and the standard deviation of the residuals; using the value of the standardized residuals for anomaly judgment; outputting the corresponding quality control code according to the judgment result, and attaching the quality control code to the lightning pulse data.

[0117] According to embodiments of this disclosure, in the step of selecting data from historical lightning pulse data collected by the local station equipment that participates in the location calculation of multiple stations and whose location results are located within the lightning monitoring network, the historical lightning pulse data selected is data from the first year since the local station equipment began operation. That is, data collected by at least four other adjacent stations is selected from the historical lightning pulse data collected by this station, and the selection of historical lightning pulse data from the first year since the local station equipment began operation ensures the accuracy of the lightning pulse data, making the prediction model trained using it more effective.

[0118] According to the embodiments of this disclosure, in the step of obtaining the prediction model of the peak magnetic field data based on the peak electric field data and peak magnetic field data in the lightning pulse data, the lightning pulse data includes peak electric field data, north-south peak magnetic field data, east-west peak magnetic field data, etc. The peak electric field data in this disclosure is positive peak electric field data or negative peak electric field data; the peak magnetic field data in this disclosure is calculated from the north-south peak magnetic field data and east-west peak magnetic field data collected by the station equipment, as shown in formula (3):

[0119]

[0120] Among them, y i This represents the peak magnetic field data at time i;

[0121] B ns This represents the north-south peak magnetic field data at time i;

[0122] B ew This represents the peak magnetic field data at time i.

[0123] Furthermore, the prediction model for the peak magnetic field data can be a univariate linear regression equation, as shown in formula (4):

[0124]

[0125] in, This represents the predicted peak magnetic field data at time i;

[0126] x i This represents the peak electric field data at time i;

[0127] a and b are constants.

[0128] Specifically, when training the model, the peak magnetic field data of the north and south and the peak magnetic field data of the east and west collected at time i in the first year of operation of the station equipment are first substituted into formula (3) to calculate the peak magnetic field data at time i; then the peak magnetic field data at time i is used as the predicted peak magnetic field data at time i, and together with the peak electric field data at time i collected by the station equipment, it is substituted into formula (4) to train and obtain the constants a and b of the univariate linear regression equation, and finally the prediction model is determined.

[0129] Wherein, the peak electric field data can be positive peak electric field data or negative peak electric field data. Therefore, the prediction model for obtaining the peak magnetic field data based on the peak electric field data and peak magnetic field data in the lightning pulse data includes: a first prediction model for obtaining the peak magnetic field data based on the positive peak electric field data and peak magnetic field data; and a second prediction model for obtaining the peak magnetic field data based on the negative peak electric field data and peak magnetic field data. Further, the step of inputting the real-time peak electric field data into the prediction model to obtain predicted peak magnetic field data includes: if the real-time peak electric field data is the positive peak electric field data, then inputting it into the first prediction model to obtain predicted peak magnetic field data; if the real-time peak electric field data is the negative peak electric field data, then inputting it into the second prediction model to obtain predicted peak magnetic field data.

[0130] According to the embodiments of this disclosure, in the steps of acquiring real-time lightning pulse data collected by the station equipment, extracting real-time peak electric field data and real-time peak magnetic field data, inputting the real-time peak electric field data into the prediction model to obtain predicted peak magnetic field data, and obtaining the residual based on the real-time peak magnetic field data and the predicted peak magnetic field data, the method for calculating the residual is as shown in formula (5):

[0131]

[0132] Where ei represents the residual;

[0133] y i This represents the real-time peak magnetic field data at time i;

[0134] This represents the predicted peak magnetic field data at time i.

[0135] Specifically, the residual calculation process is as follows: First, substitute the north-south peak magnetic field data and east-west peak magnetic field data collected at time i in the first year of operation of the equipment at this station into formula (3) to calculate the peak magnetic field data y at time i. i ; Take the peak electric field data at time i x i Substituting the data into the prediction model trained by formula (4), the predicted peak magnetic field data at time i is calculated. Substituting both into formula (5) yields the residual ei.

[0136] According to an embodiment of this disclosure, in the step of calculating the standardized residual based on the residual and the standard deviation of the residual, the method for calculating the standardized residual is shown in formula (6):

[0137] Zei=ei / Se (6)

[0138] Where Zei represents the standardized residual;

[0139] ei represents the residual;

[0140] Se represents the standard deviation of the residuals.

[0141] The standard deviation Se of the residuals can be calculated using the standard deviation statistical formula commonly used in existing technologies, which will not be elaborated here.

[0142] According to an embodiment of this disclosure, in the step of using the value of the standardized residual to determine anomalies, the determination method is as follows: when the absolute value of the standardized residual is less than or equal to 2, the real-time lightning pulse data collected by the station equipment is confirmed to be normal; when the absolute value of the standardized residual is greater than 2 and less than or equal to 3, the real-time lightning pulse data collected by the station equipment is confirmed to be suspicious; when the absolute value of the standardized residual is greater than 3, the real-time lightning pulse data collected by the station equipment is confirmed to be erroneous.

[0143] For example, checking the consistency of the electric and magnetic fields in lightning pulse data collected by the DDW1 lightning locator can involve using lightning pulse data from four or more stations within a network over a year, calculating the univariate linear regression equations for the positive and negative peak electric field data (x) and peak magnetic field data (y), and the residuals of the peak magnetic field data in the lightning pulse data. Where y i These are measured peak magnetic field data. The predicted value is obtained based on the estimated regression equation. Then, the standardized residual Zei = ei / Se of the peak magnetic field data in the lightning pulse data is calculated, where Se is the standard deviation of the statistically obtained residual. When the absolute value of the standardized residual Zei is less than or equal to 2, a quality control code "0" is added to the lightning pulse data to indicate that it is correct; when the absolute value of the standardized residual Zei is greater than 2 and less than or equal to 3, a quality control code "1" is added to the lightning pulse data to indicate that it is questionable; when the absolute value of the standardized residual Zei is greater than 3, a quality control code "2" is added to the lightning pulse data to indicate that it is incorrect; if the current equipment has been in operation for less than 1 year, a quality control code "9" is added to the lightning pulse data to indicate that no quality control has been performed.

[0144] Furthermore, the quality control method applied to lightning pulse data further includes the following characteristic parameters: north-south peak magnetic field, east-west peak magnetic field, peak electric field, steepest point magnetic field data, waveform peak time, and waveform zero-crossing time. The quality control method also includes: performing anomaly judgment on the values ​​of several characteristic parameters continuously received by the local station equipment; outputting a corresponding quality control code based on the judgment result, and attaching the quality control code to the lightning pulse data. For example, if two or more of the seven fields—north-south peak magnetic field, east-west peak magnetic field, peak electric field, steepest point magnetic field, waveform peak time, waveform zero-crossing time, and count number—remain unchanged for N consecutive values, then it is confirmed as "suspicious"; otherwise, it is confirmed as "correct"; where N is greater than or equal to 10. Preferably, N is 10.

[0145] Furthermore, the quality control method applied to lightning pulse data also includes: statistically analyzing the count numbers of the lightning pulse data collected by the local equipment within a preset time period; determining whether the count numbers are abnormal using the quartile method; outputting a corresponding quality control code based on the determination result, and attaching the quality control code to the lightning pulse data. For example, the maximum and minimum estimated values ​​of the quartiles of the counter numbers within a second predetermined time range are statistically analyzed. Data whose counter numbers are greater than the maximum estimated value or less than the minimum estimated value are identified as "suspicious," otherwise they are identified as "correct."

[0146] Furthermore, the quality control method applied to lightning pulse data also includes: statistically analyzing the network utilization rate of each station device in the lightning monitoring network within a preset time period; determining whether the lightning pulse data is abnormal based on whether the network utilization rate meets a preset threshold range; outputting a corresponding quality control code based on the determination result, and attaching the quality control code to the lightning pulse data. For example, the network utilization rate can be quality controlled as follows: selecting lightning locators whose network utilization rate is less than a predetermined percentage within a first predetermined time range, and confirming the collected lightning pulse data that did not participate in network positioning as "suspicious," otherwise confirming it as "correct." The preset threshold could be, for example, 10%.

[0147] According to embodiments of this disclosure, step S103, namely the step of storing the quality-controlled lightning pulse data, includes storing the quality-controlled lightning pulse data with an additional quality control code in a preset format. The preset format may also include various fields, such as a frame start field, frame end field, reserved bit field, device number field, lightning pulse data return stroke type field, year, month, day, hour, minute, second, 0.1 microsecond field of the lightning time, lightning longitude field, latitude field, intensity field, altitude field, and station field involved in the calculation, etc., which are not limited in this disclosure.

[0148] Figure 2 A structural block diagram of a quality control device for lightning pulse data according to an embodiment of the present disclosure is shown. This device can be implemented as part or all of an electronic device through software, hardware, or a combination of both.

[0149] like Figure 2 As shown, the quality control device 200 applied to lightning pulse data includes:

[0150] The receiving module 210 is configured to receive lightning pulse data collected by several devices constituting a lightning monitoring network, wherein the lightning monitoring network consists of local devices and devices at adjacent sites; the lightning pulse data includes data volume, peak electric field data, and peak magnetic field data.

[0151] The quality control module 220 is configured to perform quality control on the data volume, peak electric field data, and peak magnetic field data respectively, and to attach the quality control code after quality control to the lightning pulse data.

[0152] Storage module 230 is configured to store the lightning pulse data after quality control.

[0153] The quality control device for lightning pulse data provided in this embodiment of the present disclosure performs quality control on the lightning pulse data acquired by the station through various judgment methods. The quality-controlled lightning pulse data is used to calculate the location and intensity of lightning occurrence, thereby improving the accuracy of lightning monitoring and positioning, and enabling it to better serve lightning early warning, lightning protection, lightning research, lightning monitoring, etc.

[0154] According to an embodiment of this disclosure, the quality control module 220 includes:

[0155] The data processing unit is configured to count the amount of data per unit time. If a preset condition is met, it calculates the skewness coefficient of the data amount of the local device and the adjacent site devices. If the threshold range is met, it uses the quartile method to determine whether the data amount of the local device is abnormal.

[0156] The first output unit is configured to output a corresponding quality control code based on the judgment result and attach the quality control code to the lightning pulse data.

[0157] According to embodiments of this disclosure, the quality control module 220 further includes:

[0158] The first acquisition unit is configured to select peak electric field data from historical lightning pulse data collected by the equipment at this station, and calculate the average value and standard deviation by taking the natural logarithm of the absolute value of the peak electric field data; wherein, the historical lightning pulse data is data that participates in the location calculation of multiple stations and whose location results are located within the lightning monitoring network;

[0159] The acquisition unit is configured to acquire real-time lightning pulse data collected by the equipment at this station, and take the natural logarithm of the absolute value of the real-time peak electric field data therein;

[0160] The first judgment unit is configured to use the average value and standard deviation to make anomaly judgments on the natural logarithm of the absolute value of the real-time peak electric field data.

[0161] The second output unit is configured to output a corresponding quality control code based on the judgment result and attach the quality control code to the lightning pulse data.

[0162] According to embodiments of this disclosure, the quality control module 220 further includes:

[0163] The second acquisition unit is configured to select data from historical lightning pulse data acquired by the local station equipment that participates in the location calculation of multiple stations and whose location result is located within the lightning monitoring network.

[0164] The prediction unit is configured to obtain a prediction model for the peak magnetic field data based on the peak electric field data and peak magnetic field data in the lightning pulse data;

[0165] The residual calculation unit is configured to acquire real-time lightning pulse data collected by the equipment at this station, extract real-time peak electric field data and real-time peak magnetic field data, input the real-time peak electric field data into the prediction model to obtain predicted peak magnetic field data, and obtain residuals based on the real-time peak magnetic field data and the predicted peak magnetic field data.

[0166] The standardized residual calculation unit is configured to calculate the standardized residual based on the residual and the standard deviation of the residual;

[0167] The second judgment unit is configured to use the value of the standardized residual to make anomaly judgments;

[0168] The third output unit is configured to output a corresponding quality control code based on the judgment result and attach the quality control code to the lightning pulse data.

[0169] According to embodiments of this disclosure, the lightning pulse data further includes the following characteristic parameters: the steepest point magnetic field data, the waveform peak time, and the waveform zero-crossing time.

[0170] The quality control device 200 applied to lightning pulse data further includes a feature parameter judgment module, configured as follows:

[0171] Anomaly detection is performed on the values ​​of several characteristic parameters continuously received by the local equipment.

[0172] Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

[0173] According to embodiments of this disclosure, the quality control device 200 applied to lightning pulse data further includes a counting and numbering judgment module, configured as follows:

[0174] The count number of the lightning pulse data collected by the equipment at this station within a preset time period is recorded.

[0175] Use the quartile method to determine whether the count number is abnormal;

[0176] Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

[0177] According to embodiments of this disclosure, the quality control device 200 applied to lightning pulse data further includes a network utilization rate judgment module, configured as follows:

[0178] The network utilization rate of each site device in the lightning monitoring network within the preset time period is calculated.

[0179] Based on whether the network utilization rate meets the preset threshold range, it is determined whether the lightning pulse data is abnormal;

[0180] Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

[0181] This disclosure also discloses an electronic device, Figure 3 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown.

[0182] like Figure 3 As shown, the electronic device includes a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the following method steps:

[0183] The system receives lightning pulse data collected by several devices forming a lightning monitoring network, which consists of local equipment and equipment from adjacent sites. The lightning pulse data includes data volume, peak electric field data, and peak magnetic field data.

[0184] The data volume, peak electric field data, and peak magnetic field data are respectively subjected to quality control, and the quality control code after quality control is attached to the lightning pulse data.

[0185] The lightning pulse data is stored after quality control.

[0186] The technical solution provided in this disclosure uses multiple judgment methods to perform quality control on the lightning pulse data acquired by this station, and uses the quality-controlled lightning pulse data to calculate the location and intensity of lightning occurrence, thereby improving the accuracy of lightning monitoring and positioning, and enabling it to better serve lightning early warning, lightning protection, lightning research, lightning monitoring, etc.

[0187] According to embodiments of this disclosure, the step of performing quality control on the data volume, peak electric field data, and peak magnetic field data respectively, and appending the quality control code after quality control to the lightning pulse data, includes:

[0188] If the amount of data within a unit of time meets a preset condition, the skewness coefficient of the data amount of the local station equipment and the adjacent station equipment is calculated. If the data amount meets a threshold range, the quartile method is used to determine whether the data amount of the local station equipment is abnormal.

[0189] Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

[0190] According to embodiments of this disclosure, the step of performing quality control on the data volume, peak electric field data, and peak magnetic field data respectively, and appending the quality control code after quality control to the lightning pulse data, further includes:

[0191] Peak electric field data is selected from historical lightning pulse data collected by the equipment at this station. The mean and standard deviation are calculated by taking the natural logarithm of the absolute value of the peak electric field data. The historical lightning pulse data refers to data that has participated in the location calculation of multiple stations and whose location results are located within the lightning monitoring network.

[0192] Obtain real-time lightning pulse data collected by the equipment at this station, and take the natural logarithm of the absolute value of the real-time peak electric field data;

[0193] Anomalies are identified by using the mean and standard deviation to determine the natural logarithm of the absolute value of the real-time peak electric field data.

[0194] Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

[0195] According to embodiments of this disclosure, the step of performing quality control on the data volume, peak electric field data, and peak magnetic field data respectively, and appending the quality control code after quality control to the lightning pulse data, further includes:

[0196] Select data from historical lightning pulse data collected by the equipment at this station that participate in the location calculation of multiple stations and whose location results are located within the lightning monitoring network;

[0197] A prediction model for the peak magnetic field data is obtained based on the peak electric field data and peak magnetic field data in the lightning pulse data.

[0198] The system acquires real-time lightning pulse data collected by the equipment at this station, extracts real-time peak electric field data and real-time peak magnetic field data, inputs the real-time peak electric field data into the prediction model to obtain predicted peak magnetic field data, and obtains residuals based on the real-time peak magnetic field data and the predicted peak magnetic field data.

[0199] The standardized residual is calculated based on the residual and the standard deviation of the residual;

[0200] Anomaly detection is performed using the values ​​of the standardized residuals.

[0201] Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

[0202] According to embodiments of this disclosure, the lightning pulse data further includes the following characteristic parameters: the steepest point magnetic field data, the waveform peak time, and the waveform zero-crossing time.

[0203] Anomaly detection is performed on the values ​​of several characteristic parameters continuously received by the local equipment.

[0204] Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

[0205] According to embodiments of this disclosure, it further includes:

[0206] The count number of the lightning pulse data collected by the equipment at this station within a preset time period is recorded.

[0207] Use the quartile method to determine whether the count number is abnormal;

[0208] Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

[0209] According to embodiments of this disclosure, it further includes:

[0210] The network utilization rate of each site device in the lightning monitoring network within the preset time period is calculated.

[0211] Based on whether the network utilization rate meets the preset threshold range, it is determined whether the lightning pulse data is abnormal;

[0212] Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

[0213] Figure 4A schematic diagram of the structure of a computer system suitable for implementing the method according to embodiments of the present disclosure is shown.

[0214] like Figure 4 As shown, the computer system includes a processing unit that can execute various methods described above based on a program stored in a read-only memory (ROM) or a program loaded from a storage portion into a random access memory (RAM). The RAM also stores various programs and data required for the operation of the computer system. The processing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0215] The following components are connected to the I / O interface: input sections including keyboards, mice, etc.; output sections including cathode ray tubes (CRTs), liquid crystal displays (LCDs), and speakers; storage sections including hard disks, etc.; and communication sections including network interface cards such as LAN cards and modems. The communication section performs communication processes via a network such as the Internet. Drives are also connected to the I / O interface as needed. Removable media, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on the drive as needed so that computer programs read from them can be installed into the storage section as needed. The processing unit can be implemented as a CPU, GPU, TPU, FPGA, NPU, etc.

[0216] In particular, according to embodiments of this disclosure, the methods described above can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program containing program code for performing the methods described above. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium.

[0217] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0218] The units or modules described in the embodiments of this disclosure can be implemented in software or programmable hardware. The described units or modules can also be located in a processor, and the names of these units or modules do not necessarily constitute a limitation on the unit or module itself.

[0219] In another aspect, this disclosure also provides a computer-readable storage medium, which may be a computer-readable storage medium included in the electronic device or computer system described above; or it may be a standalone computer-readable storage medium not assembled into a device. The computer-readable storage medium stores one or more programs, which are used by one or more processors to perform the methods described in this disclosure.

[0220] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

Claims

1. A quality control method for lightning pulse data, characterized in that, include: Receives lightning pulse data collected by several devices that make up a lightning monitoring network, which consists of local equipment and equipment at adjacent sites; The lightning pulse data includes data volume, peak electric field data, and peak magnetic field data; The data volume, peak electric field data, and peak magnetic field data are respectively subjected to quality control, and the quality control code after quality control is attached to the lightning pulse data. If the amount of data within a unit of time meets a preset condition, the skewness coefficient of the data amount of the local station equipment and the adjacent station equipment is calculated. If the data amount meets a threshold range, the quartile method is used to determine whether the data amount of the local station equipment is abnormal. Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data; Peak electric field data is selected from historical lightning pulse data collected by the equipment at this station. The mean and standard deviation are calculated by taking the natural logarithm of the absolute value of the peak electric field data. The historical lightning pulse data refers to data that has participated in the location calculation of multiple stations and whose location results are located within the lightning monitoring network. Obtain real-time lightning pulse data collected by the equipment at this station, and take the natural logarithm of the absolute value of the real-time peak electric field data; The natural logarithm of the absolute value of the real-time peak electric field data is used to determine anomalies based on the mean and standard deviation; the corresponding quality control code is output according to the determination result and the quality control code is attached to the lightning pulse data; Select data from historical lightning pulse data collected by the equipment at this station that participate in the location calculation of multiple stations and whose location results are located within the lightning monitoring network; A prediction model for the peak magnetic field data is obtained based on the peak electric field data and peak magnetic field data in the lightning pulse data. The system acquires real-time lightning pulse data collected by the equipment at this station, extracts real-time peak electric field data and real-time peak magnetic field data, inputs the real-time peak electric field data into the prediction model to obtain predicted peak magnetic field data, and obtains residuals based on the real-time peak magnetic field data and the predicted peak magnetic field data. The standardized residual is calculated based on the residual and the standard deviation of the residual; Anomaly detection is performed using the values ​​of the standardized residuals. Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data; The lightning pulse data is stored after quality control.

2. The quality control method according to claim 1, characterized in that, The lightning pulse data also includes the following characteristic parameters: the steepest point magnetic field data, the waveform peak time, and the waveform zero-crossing time. Anomaly detection is performed on the values ​​of several characteristic parameters continuously received by the local equipment. Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

3. The quality control method according to claim 1, characterized in that, Also includes: The count number of the lightning pulse data collected by the equipment at this station within a preset time period is recorded. Use the quartile method to determine whether the count number is abnormal; Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

4. The quality control method according to claim 1, characterized in that, Also includes: The network utilization rate of each site device in the lightning monitoring network within the preset time period is calculated. Based on whether the network utilization rate meets the preset threshold range, it is determined whether the lightning pulse data is abnormal; Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data.

5. A quality control device for lightning pulse data, characterized in that, include: The receiving module is configured to receive lightning pulse data collected by several devices forming a lightning monitoring network, which consists of local equipment and equipment at adjacent sites; the lightning pulse data includes data volume, peak electric field data, and peak magnetic field data. The quality control module is configured to perform quality control on the data volume, peak electric field data, and peak magnetic field data respectively, and to attach the quality control code after quality control to the lightning pulse data. If the amount of data within a unit of time meets a preset condition, the skewness coefficient of the data amount of the local station equipment and the adjacent station equipment is calculated. If the data amount meets a threshold range, the quartile method is used to determine whether the data amount of the local station equipment is abnormal. Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data; Peak electric field data is selected from historical lightning pulse data collected by the equipment at this station. The mean and standard deviation are calculated by taking the natural logarithm of the absolute value of the peak electric field data. The historical lightning pulse data refers to data that has participated in the location calculation of multiple stations and whose location results are located within the lightning monitoring network. Obtain real-time lightning pulse data collected by the equipment at this station, and take the natural logarithm of the absolute value of the real-time peak electric field data; The natural logarithm of the absolute value of the real-time peak electric field data is used to determine anomalies based on the mean and standard deviation; the corresponding quality control code is output according to the determination result and the quality control code is attached to the lightning pulse data; Select data from historical lightning pulse data collected by the equipment at this station that participate in the location calculation of multiple stations and whose location results are located within the lightning monitoring network; A prediction model for the peak magnetic field data is obtained based on the peak electric field data and peak magnetic field data in the lightning pulse data. The system acquires real-time lightning pulse data collected by the equipment at this station, extracts real-time peak electric field data and real-time peak magnetic field data, inputs the real-time peak electric field data into the prediction model to obtain predicted peak magnetic field data, and obtains residuals based on the real-time peak magnetic field data and the predicted peak magnetic field data. The standardized residual is calculated based on the residual and the standard deviation of the residual; Anomaly detection is performed using the values ​​of the standardized residuals. Based on the judgment result, the corresponding quality control code is output and the quality control code is attached to the lightning pulse data; The storage module is configured to store the lightning pulse data after quality control.

6. An electronic device, characterized in that, The method includes a memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the steps of the method according to any one of claims 1-4.

7. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by a processor, the computer instructions implement the steps of the method described in any one of claims 1-4.

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