Lightning pulse data electromagnetic field consistency quality control method, device, equipment and medium
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CMA METEOROLOGICAL OBSERVATION CENT
- Filing Date
- 2022-12-19
- Publication Date
- 2026-06-19
Smart Images
Figure CN116008697B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of meteorological early warning technology, specifically to a method, device, equipment, and medium for quality control of electromagnetic field consistency of 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 related technologies, this disclosure provides a method, apparatus, device, and medium for quality control of electromagnetic field consistency in lightning pulse data.
[0005] In a first aspect, this disclosure provides a method for quality control of electromagnetic field consistency in lightning pulse data.
[0006] Specifically, the method for quality control of electromagnetic field consistency in lightning pulse data includes:
[0007] Select data from historical lightning pulse data collected by the equipment at this station that participates in the location calculation of multiple stations and whose location results are located within the lightning monitoring network, wherein the lightning monitoring network consists of this station and multiple other stations;
[0008] 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.
[0009] 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.
[0010] The standardized residual is calculated based on the residual and the standard deviation of the residual;
[0011] Anomaly detection is performed using the values of the standardized residuals.
[0012] Output the corresponding quality control label based on the judgment result.
[0013] Optionally, the peak electric field data can be positive peak electric field data or negative peak electric field data;
[0014] The 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 includes:
[0015] Based on the positive peak electric field data and the peak magnetic field data, a first prediction model for the peak magnetic field data is obtained;
[0016] A second prediction model for the peak magnetic field data is obtained based on the negative peak electric field data and the peak magnetic field data.
[0017] Optionally, inputting the real-time peak electric field data into the prediction model to obtain the predicted peak magnetic field data includes:
[0018] If the real-time peak electric field data is the positive peak electric field data, then input the first prediction model to obtain the predicted peak magnetic field data;
[0019] If the real-time peak electric field data is the negative peak electric field data, then input the second prediction model to obtain the predicted peak magnetic field data.
[0020] Optionally, the anomaly detection using the standardized residual values includes:
[0021] When the absolute value of the standardized residual is less than or equal to 2, the real-time lightning pulse data collected by the equipment at this station is confirmed to be normal.
[0022] 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 equipment at this station is confirmed to be suspicious.
[0023] When the absolute value of the standardized residual is greater than 3, the real-time lightning pulse data collected by the equipment at this station is confirmed to be incorrect.
[0024] Optionally, the step of outputting a corresponding quality control identifier based on the judgment result includes:
[0025] If the real-time lightning pulse data is correct, output quality control flag 0;
[0026] If the real-time lightning pulse data is questionable, output quality control flag 1;
[0027] If the real-time lightning pulse data is incorrect, output quality control flag 2.
[0028] Optionally, the historical lightning pulse data is selected from the data of the first year since the equipment started operating at this station.
[0029] Optional, also includes:
[0030] If the equipment at this station does not operate within the predetermined time, quality control flag 9 will be output directly, indicating that no quality control has been performed.
[0031] Secondly, this disclosure provides a lightning pulse data electromagnetic field consistency quality control device.
[0032] Specifically, the lightning pulse data electromagnetic field consistency quality control device includes:
[0033] The acquisition module is configured to select data from historical lightning pulse data acquired by the equipment at this station that participates in the location calculation of multiple stations and whose location results are located within the lightning monitoring network, wherein the lightning monitoring network consists of this station and multiple other stations;
[0034] The prediction module 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;
[0035] The residual calculation module 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.
[0036] The standardized residual calculation module is configured to calculate the standardized residual based on the residual and the standard deviation of the residual;
[0037] The judgment module is configured to use the value of the standardized residual to make anomaly judgments;
[0038] The output module is configured to output the corresponding quality control identifier based on the judgment result.
[0039] Optionally, the peak electric field data can be positive peak electric field data or negative peak electric field data;
[0040] The prediction module includes:
[0041] Based on the positive peak electric field data and the peak magnetic field data, a first prediction model for the peak magnetic field data is obtained;
[0042] A second prediction model for the peak magnetic field data is obtained based on the negative peak electric field data and the peak magnetic field data.
[0043] Optionally, the residual calculation module includes:
[0044] If the real-time peak electric field data is the positive peak electric field data, then input the first prediction model to obtain the predicted peak magnetic field data;
[0045] If the real-time peak electric field data is the negative peak electric field data, then input the second prediction model to obtain the predicted peak magnetic field data.
[0046] Optionally, the determination module includes:
[0047] When the absolute value of the standardized residual is less than or equal to 2, the real-time lightning pulse data collected by the equipment at this station is confirmed to be normal.
[0048] 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 equipment at this station is confirmed to be suspicious.
[0049] When the absolute value of the standardized residual is greater than 3, the real-time lightning pulse data collected by the equipment at this station is confirmed to be incorrect.
[0050] Optionally, the output module includes:
[0051] If the real-time lightning pulse data is correct, output quality control flag 0;
[0052] If the real-time lightning pulse data is questionable, output quality control flag 1;
[0053] If the real-time lightning pulse data is incorrect, output quality control flag 2.
[0054] Optionally, the historical lightning pulse data is selected from the data of the first year since the equipment started operating at this station.
[0055] Optional, also includes:
[0056] If the equipment at this station does not operate within the predetermined time, quality control flag 9 will be output directly, indicating that no quality control has been performed.
[0057] 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.
[0058] 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.
[0059] The method for quality control of electromagnetic field consistency of lightning pulse data according to embodiments of this disclosure includes: 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 a lightning monitoring network, wherein the lightning monitoring network consists of the local station and multiple other stations; 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 local 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, 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 to perform anomaly judgment; and outputting corresponding quality control indicators according to the judgment results. The above technical solution uses an electromagnetic field consistency judgment method to perform quality control on the lightning pulse data acquired by this 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.
[0060] 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
[0061] 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:
[0062] Figure 1 A flowchart illustrating a method for electromagnetic field consistency quality control of lightning pulse data according to an embodiment of the present disclosure is shown.
[0063] Figure 2 A structural block diagram of a lightning pulse data electromagnetic field consistency quality control device according to an embodiment of the present disclosure is shown.
[0064] Figure 3 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown.
[0065] 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
[0066] 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.
[0067] 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.
[0068] 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.
[0069] 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.
[0070] 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.
[0071] 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.
[0072] Figure 1 A flowchart illustrating a method for electromagnetic field consistency quality control of lightning pulse data according to an embodiment of the present disclosure is shown.
[0073] like Figure 1 As shown, the lightning pulse data electromagnetic field consistency quality control method includes the following steps S101-S106:
[0074] Step S101: Select data from the historical lightning pulse data collected by the equipment at this station that participates in the location calculation of multiple stations and whose location results are located within the lightning monitoring network, wherein the lightning monitoring network consists of this station and multiple other stations;
[0075] Step S102: 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;
[0076] Step S103: Obtain 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 the residual based on the real-time peak magnetic field data and the predicted peak magnetic field data;
[0077] Step S104: Calculate the standardized residual based on the residual and the standard deviation of the residual;
[0078] Step S105: Use the value of the standardized residual to determine anomalies;
[0079] Step S106: Output the corresponding quality control label based on the judgment result.
[0080] The electromagnetic field consistency quality control method 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 an electromagnetic field consistency judgment method. 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, and lightning monitoring.
[0081] According to embodiments of this disclosure, the lightning monitoring network of this disclosure is a lightning monitoring network composed of multiple lightning locators (e.g., DDW1 type lightning locators) for monitoring lightning activity 24 hours a day, 365 days a year.
[0082] According to embodiments of this disclosure, the lightning pulse data refers to data detected by lightning locators at various stations. The National Lightning Data Processing Center calculates the location and intensity of lightning strikes by processing the lightning pulse data sent by each station. Therefore, lightning pulse data is the foundation of lightning monitoring and location, and its quality directly affects the accuracy of lightning monitoring and location.
[0083] According to embodiments of this disclosure, step S101 involves selecting data from historical lightning pulse data collected by the local station's equipment that participates in the location calculation of multiple stations and whose location results are located within the lightning monitoring network. In this step, where the lightning monitoring network consists of the local station and multiple other stations, the historical lightning pulse data selected is data from the first year since the local station's equipment began operation. Specifically, data collected by the local station from historical lightning pulse data that was also collected by at least four other adjacent stations is selected, and the historical lightning pulse data selected is data from the first year since the local station's equipment began operation. This ensures the accuracy of the lightning pulse data, making the prediction model trained using it more effective.
[0084] According to the embodiments of this disclosure, in step S102, which is the step of 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, 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 equipment at this station, as shown in formula (1):
[0085]
[0086] Among them, y i This represents the peak magnetic field data at time i;
[0087] B ns This represents the north-south peak magnetic field data at time i;
[0088] B ew This represents the peak magnetic field data at time i.
[0089] Furthermore, the prediction model for the peak magnetic field data can be a univariate linear regression equation, as shown in formula (2):
[0090]
[0091] in, This represents the predicted peak magnetic field data at time i;
[0092] x i This represents the peak electric field data at time i;
[0093] a and b are constants.
[0094] 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 (1) 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 (2) to train and obtain the constants a and b of the univariate linear regression equation, and finally the prediction model is determined.
[0095] 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 the 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 the 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 the predicted peak magnetic field data.
[0096] According to the embodiments of this disclosure, in step S103, which involves 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 (3):
[0097]
[0098] Where ei represents the residual;
[0099] y i This represents the real-time peak magnetic field data at time i;
[0100] This represents the predicted peak magnetic field data at time i.
[0101] 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 (1) 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 (2), the predicted peak magnetic field data at time i is calculated. Substituting both into formula (3) yields the residual ei.
[0102] According to an embodiment of this disclosure, in step S104, which is 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 (4):
[0103] Zei=ei / Se(4)
[0104] Where Zei represents the standardized residual;
[0105] ei represents the residual;
[0106] Se represents the standard deviation of the residuals.
[0107] 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.
[0108] According to an embodiment of this disclosure, in step S105, which is the step of using the value of the standardized residual to make an anomaly judgment, the judgment method is specifically as follows: when the absolute value of the standardized residual is less than or equal to 2, it is confirmed that the real-time lightning pulse data collected by the station equipment is normal; when the absolute value of the standardized residual is greater than 2 and less than or equal to 3, it is confirmed that the real-time lightning pulse data collected by the station equipment is suspicious; when the absolute value of the standardized residual is greater than 3, it is confirmed that the real-time lightning pulse data collected by the station equipment is incorrect.
[0109] According to embodiments of this disclosure, step S106, which is the step of outputting a corresponding quality control identifier based on the judgment result, specifically includes: if the real-time lightning pulse data is correct, output quality control identifier 0; if the real-time lightning pulse data is questionable, output quality control identifier 1; if the real-time lightning pulse data is incorrect, output quality control identifier 2. Further, if the equipment at this station does not operate for the predetermined time, quality control identifier 9 is directly output, indicating that no quality control has been performed. The quality control identifier includes at least one of the following forms: numbers, letters, or symbols.
[0110] 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, the output flag "0" indicates correctness; when the absolute value of the standardized residual Zei is greater than 2 and less than or equal to 3, the output flag "1" indicates doubt; when the absolute value of the standardized residual Zei is greater than 3, the output flag "2" indicates error; if the current equipment has been in operation for less than 1 year, the output flag "9" indicates that no quality control has been performed.
[0111] Figure 2A structural block diagram of a lightning pulse data electromagnetic field consistency quality control device according to an embodiment of the present disclosure is shown.
[0112] This device can be implemented as part or all of an electronic device through software, hardware, or a combination of both.
[0113] like Figure 2 As shown, the lightning pulse data electromagnetic field consistency quality control device 200 includes:
[0114] The acquisition module 210 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 results are located within the lightning monitoring network, wherein the lightning monitoring network consists of the local station and multiple other stations;
[0115] Prediction module 220 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;
[0116] The residual calculation module 230 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.
[0117] The standardized residual calculation module 240 is configured to calculate the standardized residual based on the residual and the standard deviation of the residual;
[0118] The judgment module 250 is configured to use the value of the standardized residual to make anomaly judgments;
[0119] Output module 260 is configured to output the corresponding quality control identifier based on the judgment result.
[0120] The lightning pulse data electromagnetic field consistency quality control device provided in this embodiment of the present disclosure performs quality control on the lightning pulse data acquired by the station through an electromagnetic field consistency judgment method. 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.
[0121] According to embodiments of this disclosure, the peak electric field data is either positive peak electric field data or negative peak electric field data;
[0122] The prediction module 220 includes:
[0123] Based on the positive peak electric field data and the peak magnetic field data, a first prediction model for the peak magnetic field data is obtained;
[0124] A second prediction model for the peak magnetic field data is obtained based on the negative peak electric field data and the peak magnetic field data.
[0125] According to an embodiment of this disclosure, the residual calculation module 230 includes:
[0126] If the real-time peak electric field data is the positive peak electric field data, then input the first prediction model to obtain the predicted peak magnetic field data;
[0127] If the real-time peak electric field data is the negative peak electric field data, then input the second prediction model to obtain the predicted peak magnetic field data.
[0128] According to an embodiment of this disclosure, the determination module 250 includes:
[0129] When the absolute value of the standardized residual is less than or equal to 2, the real-time lightning pulse data collected by the equipment at this station is confirmed to be normal.
[0130] 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 equipment at this station is confirmed to be suspicious.
[0131] When the absolute value of the standardized residual is greater than 3, the real-time lightning pulse data collected by the equipment at this station is confirmed to be incorrect.
[0132] According to an embodiment of this disclosure, the output module 260 includes:
[0133] If the real-time lightning pulse data is correct, output quality control flag 0;
[0134] If the real-time lightning pulse data is questionable, output quality control flag 1;
[0135] If the real-time lightning pulse data is incorrect, output quality control flag 2.
[0136] According to an embodiment of this disclosure, the historical lightning pulse data is selected from the data of the first year since the equipment started operating at this station.
[0137] According to embodiments of this disclosure, the lightning pulse data electromagnetic field consistency quality control device 200 further includes:
[0138] If the equipment at this station does not operate within the predetermined time, quality control flag 9 will be output directly, indicating that no quality control has been performed.
[0139] 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.
[0140] like Figure 3As 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:
[0141] Select data from historical lightning pulse data collected by the equipment at this station that participates in the location calculation of multiple stations and whose location results are located within the lightning monitoring network, wherein the lightning monitoring network consists of this station and multiple other stations;
[0142] 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.
[0143] 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.
[0144] The standardized residual is calculated based on the residual and the standard deviation of the residual;
[0145] Anomaly detection is performed using the values of the standardized residuals.
[0146] Output the corresponding quality control label based on the judgment result.
[0147] The technical solution provided in this disclosure uses an electromagnetic field consistency judgment method to perform quality control on the lightning pulse data acquired by the 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.
[0148] According to embodiments of this disclosure, the peak electric field data is either positive peak electric field data or negative peak electric field data;
[0149] The 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 includes:
[0150] Based on the positive peak electric field data and the peak magnetic field data, a first prediction model for the peak magnetic field data is obtained;
[0151] A second prediction model for the peak magnetic field data is obtained based on the negative peak electric field data and the peak magnetic field data.
[0152] According to embodiments of this disclosure, the step of inputting the real-time peak electric field data into the prediction model to obtain predicted peak magnetic field data includes:
[0153] If the real-time peak electric field data is the positive peak electric field data, then input the first prediction model to obtain the predicted peak magnetic field data;
[0154] If the real-time peak electric field data is the negative peak electric field data, then input the second prediction model to obtain the predicted peak magnetic field data.
[0155] According to embodiments of this disclosure, the anomaly detection using the standardized residual values includes:
[0156] When the absolute value of the standardized residual is less than or equal to 2, the real-time lightning pulse data collected by the equipment at this station is confirmed to be normal.
[0157] 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 equipment at this station is confirmed to be suspicious.
[0158] When the absolute value of the standardized residual is greater than 3, the real-time lightning pulse data collected by the equipment at this station is confirmed to be incorrect.
[0159] According to embodiments of this disclosure, the step of outputting a corresponding quality control identifier based on the judgment result includes:
[0160] If the real-time lightning pulse data is correct, output quality control flag 0;
[0161] If the real-time lightning pulse data is questionable, output quality control flag 1;
[0162] If the real-time lightning pulse data is incorrect, output quality control flag 2.
[0163] According to an embodiment of this disclosure, the historical lightning pulse data is selected from the data of the first year since the equipment started operating at this station.
[0164] According to embodiments of this disclosure, it further includes:
[0165] If the equipment at this station does not operate within the predetermined time, quality control flag 9 will be output directly, indicating that no quality control has been performed.
[0166] 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.
[0167] 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.
[0168] 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.
[0169] 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.
[0170] 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.
[0171] 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.
[0172] 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.
[0173] 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 method for quality control of electromagnetic field consistency in lightning pulse data, characterized in that, include: Select data from historical lightning pulse data collected by the equipment at this station that participates in the location calculation of multiple stations and whose location results are located within the lightning monitoring network, wherein the lightning monitoring network consists of this station and multiple other stations; 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. Output the corresponding quality control label based on the judgment result.
2. The quality control method according to claim 1, characterized in that, The peak electric field data can be positive peak electric field data or negative peak electric field data; The 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 includes: Based on the positive peak electric field data and the peak magnetic field data, a first prediction model for the peak magnetic field data is obtained; A second prediction model for the peak magnetic field data is obtained based on the negative peak electric field data and the peak magnetic field data.
3. The quality control method of claim 2, wherein, The step of inputting the real-time peak electric field data into the prediction model to obtain the predicted peak magnetic field data includes: If the real-time peak electric field data is the positive peak electric field data, then input the first prediction model to obtain the predicted peak magnetic field data; If the real-time peak electric field data is the negative peak electric field data, then input the second prediction model to obtain the predicted peak magnetic field data.
4. The quality control method of claim 1, wherein, The anomaly detection using the standardized residual values includes: When the absolute value of the standardized residual is less than or equal to 2, the real-time lightning pulse data collected by the equipment at this station 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 equipment at this station 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 equipment at this station is confirmed to be incorrect.
5. The quality control method of claim 4, wherein, The step of outputting corresponding quality control indicators based on the judgment result includes: If the real-time lightning pulse data is correct, output quality control flag 0; If the real-time lightning pulse data is questionable, output quality control flag 1; If the real-time lightning pulse data is incorrect, output quality control flag 2.
6. The quality control method according to claim 1, characterized in that, The historical lightning pulse data is selected from the data of the first year since the equipment started operating at this station.
7. The quality control method of claim 6, wherein, Also includes: If the equipment at this station does not operate within the predetermined time, quality control flag 9 will be output directly, indicating that no quality control has been performed.
8. A lightning pulse data electromagnetic field uniformity control device, characterized in that, include: The acquisition module is configured to select data from historical lightning pulse data acquired by the equipment at this station that participates in the location calculation of multiple stations and whose location results are located within the lightning monitoring network, wherein the lightning monitoring network consists of this station and multiple other stations; The prediction module 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; The residual calculation module 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. The standardized residual calculation module is configured to calculate the standardized residual based on the residual and the standard deviation of the residual; The judgment module is configured to use the value of the standardized residual to make anomaly judgments; The output module is configured to output the corresponding quality control identifier based on the judgment result.
9. An electronic device, comprising: 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 1-7.
10. A computer readable storage medium having stored thereon computer instructions, wherein, When executed by a processor, the computer instructions implement the steps of the method described in any one of claims 1-7.