CN116008697A - Lightning pulse data electromagnetic field consistency property control method, device, equipment and medium - Google Patents
Lightning pulse data electromagnetic field consistency property control method, device, equipment and medium Download PDFInfo
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Abstract
The disclosure relates to the technical field of meteorological early warning, in particular to a lightning pulse data electromagnetic field consistency property control method, device, equipment and medium. According to the lightning pulse data electromagnetic field consistency property control method, the quality control is carried out on the lightning pulse data acquired by the station, and the lightning occurrence position, the intensity and the like are calculated by using the quality-controlled lightning pulse data, so that the lightning monitoring and positioning accuracy is improved, and the lightning early warning, the lightning stroke protection, the lightning research, the lightning monitoring and the like can be better served.
Description
Technical Field
The disclosure relates to the technical field of meteorological early warning, in particular to a lightning pulse data electromagnetic field consistency property control method, device, equipment and medium.
Background
The data detected by the lightning positioning instrument are called lightning pulse data, and the national lightning data processing center calculates the lightning occurrence position, strength and the like by processing the lightning pulse data sent by each meteorological monitoring station. Therefore, the lightning pulse data is the basis of lightning monitoring and positioning, and the quality of the lightning pulse data directly influences the accuracy of the lightning monitoring and positioning.
In the prior art, each weather monitoring station uses detected and collected lightning pulse data to carry out lightning monitoring and positioning, and as the quality control of the collected lightning pulse data is not carried out by using an effective method, the direct use can have adverse effects on lightning early warning, lightning stroke protection, lightning research and lightning monitoring in the area.
Disclosure of Invention
In order to solve the problems in the related art, the embodiments of the present disclosure provide a lightning pulse data electromagnetic field consistency property control method, device, equipment and medium.
In a first aspect, an embodiment of the disclosure provides a lightning pulse data electromagnetic field consistency property control method.
Specifically, the lightning pulse data electromagnetic field consistency property control method comprises the following steps:
selecting data which participates in positioning calculation of a plurality of stations and the positioning result of which is positioned in a lightning monitoring network from historical lightning pulse data acquired by local station equipment, wherein the lightning monitoring network consists of the local station and a plurality of other stations;
obtaining a prediction model of the peak magnetic field data based on the peak electric field data and the peak magnetic field data in the lightning pulse data;
acquiring real-time lightning pulse data acquired by 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, and obtaining residual errors based on the real-time peak magnetic field data and the predicted peak magnetic field data;
calculating a standardized residual error based on the residual error and a standard deviation of the residual error;
performing abnormality judgment by using the numerical value of the standardized residual error;
and outputting a corresponding quality control identifier according to the judging result.
Optionally, the peak electric field data is positive peak electric field data or negative peak electric field data;
the predicting model for obtaining the peak magnetic field data based on the peak electric field data and the peak magnetic field data in the lightning pulse data comprises the following steps:
obtaining a first prediction model of the peak magnetic field data based on the positive peak electric field data and the peak magnetic field data;
and obtaining a second prediction model of the peak magnetic field data based on the negative peak electric field data and the peak magnetic field data.
Optionally, the 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, inputting the first prediction model to obtain predicted peak magnetic field data;
and if the real-time peak electric field data is the negative peak electric field data, inputting the second prediction model to obtain the predicted peak magnetic field data.
Optionally, the performing anomaly determination by using the value of the normalized residual error includes:
when the absolute value of the numerical value of the standardized residual error is less than or equal to 2, confirming that the real-time lightning pulse data acquired by the equipment at the station are normal;
when the absolute value of the numerical value of the standardized residual error is more than 2 and less than or equal to 3, confirming that the real-time lightning pulse data collected by the equipment at the station is suspicious;
and when the absolute value of the numerical value of the standardized residual error is larger than 3, confirming that the real-time lightning pulse data acquired by the local station equipment is wrong.
Optionally, outputting the corresponding quality control identifier according to the judgment result includes:
outputting a quality control mark 0 if the real-time lightning pulse data is correct;
if the real-time lightning pulse data are suspicious, outputting a quality control identifier 1;
and if the real-time lightning pulse data is wrong, outputting a quality control mark 2.
Optionally, the historical lightning pulse data selects data of 1 st year when the self-operation of the self-station equipment is performed.
Optionally, the method further comprises:
if the operation of the equipment of the station does not meet the preset time, the quality control mark 9 is directly output, and the quality control is not performed.
In a second aspect, in an embodiment of the disclosure, a lightning pulse data electromagnetic field consistency property control device is provided.
Specifically, the lightning pulse data electromagnetic field consistency property control device comprises:
the system comprises an acquisition module, a monitoring module and a control module, wherein the acquisition module is configured to select data which participates in positioning calculation of a plurality of stations and the positioning result is positioned in a lightning monitoring network from historical lightning pulse data acquired by local station equipment, and the lightning monitoring network consists of the local station and a plurality of other stations;
a prediction module configured to obtain a prediction model of the peak magnetic field data based on the peak electric field data and the peak magnetic field data in the lightning pulse data;
the residual calculation module is configured to acquire real-time lightning pulse data acquired by the local station equipment, 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 residual errors based on the real-time peak magnetic field data and the predicted peak magnetic field data;
a normalized residual calculation module configured to calculate a normalized residual based on the residual and a standard deviation of the residual;
a judging module configured to perform abnormality judgment using the numerical value of the normalized residual error;
and the output module is configured to output a corresponding quality control identifier according to the judging result.
Optionally, the peak electric field data is positive peak electric field data or negative peak electric field data;
the prediction module comprises:
obtaining a first prediction model of the peak magnetic field data based on the positive peak electric field data and the peak magnetic field data;
and obtaining a second prediction model of the peak magnetic field data based on the negative peak electric field data and the peak magnetic field data.
Optionally, the residual calculation module includes:
if the real-time peak electric field data is the positive peak electric field data, inputting the first prediction model to obtain predicted peak magnetic field data;
and if the real-time peak electric field data is the negative peak electric field data, inputting the second prediction model to obtain the predicted peak magnetic field data.
Optionally, the judging module includes:
when the absolute value of the numerical value of the standardized residual error is less than or equal to 2, confirming that the real-time lightning pulse data acquired by the equipment at the station are normal;
when the absolute value of the numerical value of the standardized residual error is more than 2 and less than or equal to 3, confirming that the real-time lightning pulse data collected by the equipment at the station is suspicious;
and when the absolute value of the numerical value of the standardized residual error is larger than 3, confirming that the real-time lightning pulse data acquired by the local station equipment is wrong.
Optionally, the output module includes:
outputting a quality control mark 0 if the real-time lightning pulse data is correct;
if the real-time lightning pulse data are suspicious, outputting a quality control identifier 1;
and if the real-time lightning pulse data is wrong, outputting a quality control mark 2.
Optionally, the historical lightning pulse data selects data of 1 st year when the self-operation of the self-station equipment is performed.
Optionally, the method further comprises:
if the operation of the equipment of the station does not meet the preset time, the quality control mark 9 is directly output, and the quality control is not performed.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including a memory and a processor, wherein the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method of any one of the first aspects.
In a fourth aspect, in an embodiment of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer instructions which, when executed by a processor, implement a method according to any of the first aspects.
The lightning pulse data electromagnetic field consistency property control method provided by the embodiment of the disclosure comprises the following steps: selecting data which participates in positioning calculation of a plurality of stations and the positioning result of which is positioned in a lightning monitoring network from historical lightning pulse data acquired by local station equipment, wherein the lightning monitoring network consists of the local station and a plurality of other stations; obtaining a prediction model of the peak magnetic field data based on the peak electric field data and the peak magnetic field data in the lightning pulse data; acquiring real-time lightning pulse data acquired by 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, and obtaining residual errors based on the real-time peak magnetic field data and the predicted peak magnetic field data; calculating a standardized residual error based on the residual error and a standard deviation of the residual error; performing abnormality judgment by using the numerical value of the standardized residual error; and outputting a corresponding quality control identifier according to the judging result. According to the technical scheme, the quality control is carried out on the lightning pulse data acquired by the station through the electromagnetic field consistency judging method, the lightning generation position, the strength and the like are calculated by using the quality-controlled lightning pulse data, the lightning monitoring positioning accuracy is improved, and the lightning early warning, lightning stroke protection, lightning research, lightning monitoring and the like can be better served.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
Other features, objects and advantages of the present disclosure will become more apparent from the following detailed description of non-limiting embodiments, taken in conjunction with the accompanying drawings. In the drawings:
FIG. 1 illustrates a flow chart of a lightning pulse data electromagnetic field consistency property control method according to an embodiment of the disclosure.
FIG. 2 illustrates a block diagram of a lightning pulse data electromagnetic field consistency property control device in accordance with an embodiment of the present disclosure.
Fig. 3 shows a block diagram of an electronic device according to an embodiment of the disclosure.
Fig. 4 shows a schematic diagram of a computer system suitable for use in implementing a method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. In addition, for the sake of clarity, portions irrelevant to description of the exemplary embodiments are omitted in the drawings.
In this disclosure, it should be understood that terms such as "comprises" or "comprising," etc., are intended to indicate the presence of features, numbers, steps, acts, components, portions, or combinations thereof disclosed in this specification, and are not intended to exclude the possibility that one or more other features, numbers, steps, acts, components, portions, or combinations thereof are present or added.
In addition, it should be noted that, without conflict, the embodiments of the present disclosure and features of the embodiments may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
In the present disclosure, if an operation of acquiring user information or user data or an operation of presenting user information or user data to another person is referred to, the operations are all operations authorized, confirmed, or actively selected by the user.
The data detected by the lightning positioning instrument are called lightning pulse data, and the national lightning data processing center calculates the lightning occurrence position, strength and the like by processing the lightning pulse data sent by each meteorological monitoring station. Therefore, the lightning pulse data is the basis of lightning monitoring and positioning, and the quality of the lightning pulse data directly influences the accuracy of the lightning monitoring and positioning.
In the prior art, each weather monitoring station uses detected and collected lightning pulse data to carry out lightning monitoring and positioning, and as the quality control of the collected lightning pulse data is not carried out by using an effective method, the direct use can have adverse effects on lightning early warning, lightning stroke protection, lightning research and lightning monitoring in the area.
FIG. 1 illustrates a flow chart of a lightning pulse data electromagnetic field consistency property control method according to an embodiment of the disclosure.
As shown in FIG. 1, the lightning pulse data electromagnetic field consistency property control method comprises the following steps S101-S106:
step S101: selecting data which participates in positioning calculation of a plurality of stations and the positioning result of which is positioned in a lightning monitoring network from historical lightning pulse data acquired by local station equipment, wherein the lightning monitoring network consists of the local station and a plurality of other stations;
step S102: obtaining a prediction model of the peak magnetic field data based on the peak electric field data and the peak magnetic field data in the lightning pulse data;
step S103: acquiring real-time lightning pulse data acquired by 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, and obtaining residual errors based on the real-time peak magnetic field data and the predicted peak magnetic field data;
step S104: calculating a standardized residual error based on the residual error and a standard deviation of the residual error;
step S105: performing abnormality judgment by using the numerical value of the standardized residual error;
step S106: and outputting a corresponding quality control identifier according to the judging result.
According to the lightning pulse data electromagnetic field consistency property control method provided by the embodiment of the disclosure, the quality control is performed on the lightning pulse data acquired by the station through the electromagnetic field consistency judgment method, and the lightning generation position, the intensity and the like are calculated by using the quality-controlled lightning pulse data, so that the lightning monitoring and positioning accuracy is improved, and the lightning pulse data electromagnetic field consistency property control method can be better used for lightning early warning, lightning stroke protection, lightning research, lightning monitoring and the like.
According to an embodiment of the present disclosure, the lightning monitoring network of the present disclosure is a lightning monitoring network consisting of a multi-station lightning locator (which may be a DDW1 type lightning locator, for example) for monitoring lightning activity 24 hours a day at 365 days a year.
According to an embodiment of the present disclosure, lightning pulse data of the present disclosure refers to data detected by lightning locators of each station. The national lightning data processing center calculates the lightning occurrence position, strength and the like by processing the lightning pulse data sent by each station. Therefore, the lightning pulse data is the basis of lightning monitoring and positioning, and the quality of the lightning pulse data directly influences the accuracy of the lightning monitoring and positioning.
According to the embodiment of the disclosure, step S101 of the disclosure is to select data which participates in positioning calculation of a plurality of sites and has positioning results in a lightning monitoring network from historical lightning pulse data collected by the local site equipment, wherein in the step of forming the lightning monitoring network by the local site and a plurality of other sites, the historical lightning pulse data selects data of 1 st year when the local site equipment runs by itself. The historical lightning pulse data collected by the station is selected from the data collected by other adjacent at least 4 stations, and the historical lightning pulse data is selected from the data of the 1 st year when the station equipment runs by itself, so that the accuracy of the lightning pulse data can be ensured, and the prediction model trained by the device is more effective.
According to an embodiment of the present disclosure, step S102 of the present disclosure is a step of obtaining a prediction model of the peak magnetic field data based on peak electric field data and peak magnetic field data in the lightning pulse data, where the lightning pulse data includes peak electric field data, north-south peak magnetic field data, east-west peak magnetic field data, and the like. The peak electric field data of the present disclosure is positive peak electric field data or negative peak electric field data; the peak magnetic field data are obtained by calculating north-south peak magnetic field data and east-west peak magnetic field data acquired by local station equipment, and the specific formula is shown in the formula (1):
wherein y is i Peak magnetic field data at time i;
B ns the south-north peak magnetic field data at the moment i is represented;
B ew and represents the east-west peak magnetic field data at time i.
Further, the predictive model of the peak magnetic field data may be a unitary linear regression equation, such as equation (2):
wherein, the liquid crystal display device comprises a liquid crystal display device,
the predicted peak magnetic field data at the moment i is represented;
x i the peak electric field data at the moment i is represented;
a and b are constants.
Specifically, when a model is trained, firstly substituting north-south peak magnetic field data and east-west peak magnetic field data of i moment acquired by the self-operation of the station equipment in the 1 st year into a formula (1), and calculating to obtain peak magnetic field data of i moment; and taking the peak magnetic field data at the moment i as predicted peak magnetic field data at the moment i, substituting the predicted peak magnetic field data at the moment i and the peak electric field data at the moment i acquired by the local station equipment into a formula (2), training to obtain constants a and b of a unitary linear regression equation, and finally determining a prediction model.
Wherein, the peak electric field data may be positive peak electric field data or negative peak electric field data, and the predicting model based on the peak electric field data and the peak magnetic field data in the lightning pulse data includes: obtaining a first prediction model of the peak magnetic field data based on the positive peak electric field data and the peak magnetic field data; and obtaining a second prediction model of the peak magnetic field data based on the negative peak electric field data and the peak magnetic field data. Further, the 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, inputting the first prediction model to obtain predicted peak magnetic field data; and if the real-time peak electric field data is the negative peak electric field data, inputting the second prediction model to obtain the predicted peak magnetic field data.
According to an embodiment of the present disclosure, step S103 of the present disclosure is a step of acquiring real-time lightning pulse data acquired by a local station device, 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 a residual error based on the real-time peak magnetic field data and the predicted peak magnetic field data, where a residual error calculating method is shown in formula (3):
wherein ei represents the residual error;
y i real-time peak magnetic field data representing instant i;
Specifically, the residual calculation process is as follows: firstly substituting south-north peak magnetic field data and east-west peak magnetic field data of i moment acquired by the self-running station equipment in the 1 st year into a formula (1), and calculating to obtain peak magnetic field data y of the i moment i The method comprises the steps of carrying out a first treatment on the surface of the Peak electric field data x at moment i i Substituting the predicted peak value magnetic field data into a predicted model trained by the formula (2) to calculate and obtain predicted peak value magnetic field data at the moment i
And substituting the two into the formula (3) to obtain a residual error ei.
According to an embodiment of the present disclosure, in step S104 of the present disclosure, that is, the step of calculating the normalized residual based on the residual and the standard deviation of the residual, the calculation method of the normalized residual is shown in formula (4):
Zei＝ei/Se(4)
wherein Zei represents a normalized residual;
ei represents the residual;
se represents the standard deviation of the residual error.
The calculation method of the standard deviation Se of the residual error may adopt a standard deviation statistical formula commonly used in the prior art, and will not be described herein.
According to an embodiment of the present disclosure, in step S105 of the present disclosure, that is, the step of performing abnormality determination by using the value of the normalized residual error, the determination method specifically includes: when the absolute value of the numerical value of the standardized residual error is less than or equal to 2, confirming that the real-time lightning pulse data acquired by the equipment at the station are normal; when the absolute value of the numerical value of the standardized residual error is more than 2 and less than or equal to 3, confirming that the real-time lightning pulse data collected by the equipment at the station is suspicious; and when the absolute value of the numerical value of the standardized residual error is larger than 3, confirming that the real-time lightning pulse data acquired by the local station equipment is wrong.
According to an embodiment of the present disclosure, step S106 of the present disclosure, namely, a step of outputting a corresponding quality control identifier according to a determination result, specifically includes: outputting a quality control mark 0 if the real-time lightning pulse data is correct; if the real-time lightning pulse data are suspicious, outputting a quality control identifier 1; and if the real-time lightning pulse data is wrong, outputting a quality control mark 2. Further, if the operation of the local station equipment does not meet the preset time, the quality control mark 9 is directly output, and the quality control is not performed. Wherein, the quality control mark at least comprises any one form of the following: numbers, letters, symbols.
For example, the process of checking whether the electric field and magnetic field consistency of the lightning pulse data collected by the DDW1 lightning locator are normal may be to use the current device for 1 year to participate in the lightning pulse data located in four stations and above, respectively calculate the unitary linear regression equation of the positive and negative peak electric field data (x) and the peak magnetic field data (y), and the residual error of the peak magnetic field data in the lightning pulse data
Wherein y is i Is the measured peak magnetic field data,/>
Based on estimated returnsThe predicted value obtained by the equation is then calculated, and a normalized 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 derived residual. When the absolute value of the normalized residual Zei is less than or equal to 2, the output flag "0" indicates correct; when the absolute value of the normalized residual Zei is more than 2 and less than or equal to 3, then the output identifier "1" indicates suspicious; when the absolute value of the normalized residual Zei is greater than 3, then the output flag "2" represents an error; if the current equipment running time is less than 1 year, the output identifier of '9' indicates that the quality control is not performed.
FIG. 2 illustrates a block diagram of a lightning pulse data electromagnetic field consistency property control device in accordance with an embodiment of the present disclosure.
The apparatus may be implemented as part or all of an electronic device by software, hardware, or a combination of both.
As shown in fig. 2, the lightning pulse data electromagnetic field coincidence quality control device 200 includes:
the acquisition module 210 is configured to select data which participates in positioning calculation of a plurality of sites and the positioning result is positioned in a lightning monitoring network from historical lightning pulse data acquired by the local station equipment, wherein the lightning monitoring network consists of the local station and a plurality of other sites;
a prediction module 220 configured to obtain a prediction model of the peak magnetic field data based on the peak electric field data and the peak magnetic field data in the lightning pulse data;
the residual calculation module 230 is configured to obtain real-time lightning pulse data collected by the local station equipment, 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 residual errors based on the real-time peak magnetic field data and the predicted peak magnetic field data;
a normalized residual calculation module 240 configured to calculate a normalized residual based on the residual and a standard deviation of the residual;
a judging module 250 configured to perform abnormality judgment using the value of the normalized residual;
the output module 260 is configured to output a corresponding quality control identifier according to the determination result.
According to the lightning pulse data electromagnetic field consistency property control device provided by the embodiment of the disclosure, the quality control is carried out on the lightning pulse data acquired by the station through the electromagnetic field consistency judging method, and the lightning occurrence position, the strength and the like are calculated by using the quality-controlled lightning pulse data, so that the lightning monitoring and positioning accuracy is improved, and the lightning monitoring and positioning device can be better used for lightning early warning, lightning stroke protection, lightning research, lightning monitoring and the like.
According to an embodiment of the present disclosure, the peak electric field data is positive peak electric field data or negative peak electric field data;
the prediction module 220 includes:
obtaining a first prediction model of the peak magnetic field data based on the positive peak electric field data and the peak magnetic field data;
and obtaining a second prediction model of the peak magnetic field data based on the negative peak electric field data and the peak magnetic field data.
According to an embodiment of the present disclosure, the residual calculation module 230 includes:
if the real-time peak electric field data is the positive peak electric field data, inputting the first prediction model to obtain predicted peak magnetic field data;
and if the real-time peak electric field data is the negative peak electric field data, inputting the second prediction model to obtain the predicted peak magnetic field data.
According to an embodiment of the disclosure, the determining module 250 includes:
when the absolute value of the numerical value of the standardized residual error is less than or equal to 2, confirming that the real-time lightning pulse data acquired by the equipment at the station are normal;
when the absolute value of the numerical value of the standardized residual error is more than 2 and less than or equal to 3, confirming that the real-time lightning pulse data collected by the equipment at the station is suspicious;
and when the absolute value of the numerical value of the standardized residual error is larger than 3, confirming that the real-time lightning pulse data acquired by the local station equipment is wrong.
According to an embodiment of the present disclosure, the output module 260 includes:
outputting a quality control mark 0 if the real-time lightning pulse data is correct;
if the real-time lightning pulse data are suspicious, outputting a quality control identifier 1;
and if the real-time lightning pulse data is wrong, outputting a quality control mark 2.
According to an embodiment of the disclosure, the historical lightning pulse data selects data of 1 st year of self-operation of the local station equipment.
According to an embodiment of the present disclosure, the lightning pulse data electromagnetic field consistency property control device 200 further includes:
if the operation of the equipment of the station does not meet the preset time, the quality control mark 9 is directly output, and the quality control is not performed.
The present disclosure also discloses an electronic device, and fig. 3 shows a block diagram of the electronic device according to an embodiment of the present disclosure.
As shown in fig. 3, the electronic device comprises a memory and a processor, wherein the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to perform the method steps of:
selecting data which participates in positioning calculation of a plurality of stations and the positioning result of which is positioned in a lightning monitoring network from historical lightning pulse data acquired by local station equipment, wherein the lightning monitoring network consists of the local station and a plurality of other stations;
obtaining a prediction model of the peak magnetic field data based on the peak electric field data and the peak magnetic field data in the lightning pulse data;
acquiring real-time lightning pulse data acquired by 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, and obtaining residual errors based on the real-time peak magnetic field data and the predicted peak magnetic field data;
calculating a standardized residual error based on the residual error and a standard deviation of the residual error;
performing abnormality judgment by using the numerical value of the standardized residual error;
and outputting a corresponding quality control identifier according to the judging result.
According to the technical scheme provided by the embodiment of the disclosure, the quality control is carried out on the lightning pulse data acquired by the station through the electromagnetic field consistency judging method, and the lightning generation position, the strength and the like are calculated by using the quality-controlled lightning pulse data, so that the lightning monitoring and positioning accuracy is improved, and the lightning early warning, the lightning stroke protection, the lightning research, the lightning monitoring and the like can be better served.
According to an embodiment of the present disclosure, the peak electric field data is positive peak electric field data or negative peak electric field data;
the predicting model for obtaining the peak magnetic field data based on the peak electric field data and the peak magnetic field data in the lightning pulse data comprises the following steps:
obtaining a first prediction model of the peak magnetic field data based on the positive peak electric field data and the peak magnetic field data;
and obtaining a second prediction model of the peak magnetic field data based on the negative peak electric field data and the peak magnetic field data.
According to an embodiment of the disclosure, the 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, inputting the first prediction model to obtain predicted peak magnetic field data;
and if the real-time peak electric field data is the negative peak electric field data, inputting the second prediction model to obtain the predicted peak magnetic field data.
According to an embodiment of the disclosure, the performing anomaly determination by using the value of the normalized residual error includes:
when the absolute value of the numerical value of the standardized residual error is less than or equal to 2, confirming that the real-time lightning pulse data acquired by the equipment at the station are normal;
when the absolute value of the numerical value of the standardized residual error is more than 2 and less than or equal to 3, confirming that the real-time lightning pulse data collected by the equipment at the station is suspicious;
and when the absolute value of the numerical value of the standardized residual error is larger than 3, confirming that the real-time lightning pulse data acquired by the local station equipment is wrong.
According to an embodiment of the disclosure, outputting the corresponding quality control identifier according to the determination result includes:
outputting a quality control mark 0 if the real-time lightning pulse data is correct;
if the real-time lightning pulse data are suspicious, outputting a quality control identifier 1;
and if the real-time lightning pulse data is wrong, outputting a quality control mark 2.
According to an embodiment of the disclosure, the historical lightning pulse data selects data of 1 st year of self-operation of the local station equipment.
According to an embodiment of the present disclosure, further comprising:
if the operation of the equipment of the station does not meet the preset time, the quality control mark 9 is directly output, and the quality control is not performed.
Fig. 4 shows a schematic diagram of a computer system suitable for use in implementing a method according to an embodiment of the present disclosure.
As shown in fig. 4, the computer system includes a processing unit that can execute the various methods in the above embodiments according to a program stored in a Read Only Memory (ROM) or a program loaded from a storage section into a Random Access Memory (RAM). In the RAM, various programs and data required for the operation of the computer system are also stored. The processing unit, ROM and RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.
The following components are connected to the I/O interface: an input section including a keyboard, a mouse, etc.; an output section including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc., and a speaker, etc.; a storage section including a hard disk or the like; and a communication section including a network interface card such as a LAN card, a modem, and the like. The communication section performs a communication process via a network such as the internet. The drives are also connected to the I/O interfaces as needed. Removable media such as magnetic disks, optical disks, magneto-optical disks, semiconductor memories, and the like are mounted on the drive as needed so that a computer program read therefrom is mounted into the storage section as needed. The processing unit may be implemented as a processing unit such as CPU, GPU, TPU, FPGA, NPU.
In particular, according to embodiments of the present disclosure, the methods described above may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program comprising program code for performing the method described above. In such embodiments, the computer program may be downloaded and installed from a network via a communication portion, and/or installed from a removable medium.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules referred to in the embodiments of the present disclosure may be implemented in software or in programmable hardware. The units or modules described may also be provided in a processor, the names of which in some cases do not constitute a limitation of the unit or module itself.
As another aspect, the present disclosure also provides a computer-readable storage medium, which may be a computer-readable storage medium included in the electronic device or the computer system in the above-described embodiments; or may be a computer-readable storage medium, alone, that is not assembled into a device. The computer-readable storage medium stores one or more programs for use by one or more processors in performing the methods described in the present disclosure.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention referred to in this disclosure is not limited to the specific combination of features described above, but encompasses other embodiments in which any combination of features described above or their equivalents is contemplated without departing from the inventive concepts described. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).
Claims (10)
1. A lightning pulse data electromagnetic field consistency property control method, comprising the steps of:
selecting data which participates in positioning calculation of a plurality of stations and the positioning result of which is positioned in a lightning monitoring network from historical lightning pulse data acquired by local station equipment, wherein the lightning monitoring network consists of the local station and a plurality of other stations;
obtaining a prediction model of the peak magnetic field data based on the peak electric field data and the peak magnetic field data in the lightning pulse data;
acquiring real-time lightning pulse data acquired by 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, and obtaining residual errors based on the real-time peak magnetic field data and the predicted peak magnetic field data;
calculating a standardized residual error based on the residual error and a standard deviation of the residual error;
performing abnormality judgment by using the numerical value of the standardized residual error;
and outputting a corresponding quality control identifier according to the judging result.
2. The quality control method according to claim 1, wherein the peak electric field data is positive peak electric field data or negative peak electric field data;
the predicting model for obtaining the peak magnetic field data based on the peak electric field data and the peak magnetic field data in the lightning pulse data comprises the following steps:
obtaining a first prediction model of the peak magnetic field data based on the positive peak electric field data and the peak magnetic field data;
and obtaining a second prediction model of the peak magnetic field data based on the negative peak electric field data and the peak magnetic field data.
3. The quality control method according to claim 2, wherein the inputting the real-time peak electric field data into the predictive model to obtain predicted peak magnetic field data includes:
if the real-time peak electric field data is the positive peak electric field data, inputting the first prediction model to obtain predicted peak magnetic field data;
and if the real-time peak electric field data is the negative peak electric field data, inputting the second prediction model to obtain the predicted peak magnetic field data.
4. The quality control method according to claim 1, wherein the performing anomaly determination using the value of the normalized residual error includes:
when the absolute value of the numerical value of the standardized residual error is less than or equal to 2, confirming that the real-time lightning pulse data acquired by the equipment at the station are normal;
when the absolute value of the numerical value of the standardized residual error is more than 2 and less than or equal to 3, confirming that the real-time lightning pulse data collected by the equipment at the station is suspicious;
and when the absolute value of the numerical value of the standardized residual error is larger than 3, confirming that the real-time lightning pulse data acquired by the local station equipment is wrong.
5. The quality control method according to claim 4, wherein outputting the corresponding quality control identifier according to the determination result includes:
outputting a quality control mark 0 if the real-time lightning pulse data is correct;
if the real-time lightning pulse data are suspicious, outputting a quality control identifier 1;
and if the real-time lightning pulse data is wrong, outputting a quality control mark 2.
6. The method according to claim 1, wherein,
and the historical lightning pulse data selects data of the 1 st year when the self-operation of the self-station equipment is performed.
7. The quality control method according to claim 6, further comprising:
if the operation of the equipment of the station does not meet the preset time, the quality control mark 9 is directly output, and the quality control is not performed.
8. A lightning pulse data electromagnetic field coincidence quality control device, comprising:
the system comprises an acquisition module, a monitoring module and a control module, wherein the acquisition module is configured to select data which participates in positioning calculation of a plurality of stations and the positioning result is positioned in a lightning monitoring network from historical lightning pulse data acquired by local station equipment, and the lightning monitoring network consists of the local station and a plurality of other stations;
a prediction module configured to obtain a prediction model of the peak magnetic field data based on the peak electric field data and the peak magnetic field data in the lightning pulse data;
the residual calculation module is configured to acquire real-time lightning pulse data acquired by the local station equipment, 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 residual errors based on the real-time peak magnetic field data and the predicted peak magnetic field data;
a normalized residual calculation module configured to calculate a normalized residual based on the residual and a standard deviation of the residual;
a judging module configured to perform abnormality judgment using the numerical value of the normalized residual error;
and the output module is configured to output a corresponding quality control identifier according to the judging result.
9. An electronic device comprising a memory and a processor; wherein the memory is for storing one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method steps of any of claims 1-7.
10. A computer readable storage medium having stored thereon computer instructions, which when executed by a processor, implement the method steps of any of claims 1-7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117748437A (en) * | 2024-02-20 | 2024-03-22 | 中国人民解放军空军预警学院 | Strong electromagnetic pulse protection method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117748437A (en) * | 2024-02-20 | 2024-03-22 | 中国人民解放军空军预警学院 | Strong electromagnetic pulse protection method and system |
| CN117748437B (en) * | 2024-02-20 | 2024-05-28 | 中国人民解放军空军预警学院 | Strong electromagnetic pulse protection method and system |
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