CN113327022B - Lightning protection safety risk management system and method - Google Patents

Lightning protection safety risk management system and method Download PDF

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CN113327022B
CN113327022B CN202110539370.2A CN202110539370A CN113327022B CN 113327022 B CN113327022 B CN 113327022B CN 202110539370 A CN202110539370 A CN 202110539370A CN 113327022 B CN113327022 B CN 113327022B
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lightning
data
thunderstorm
risk
module
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CN113327022A (en
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曾宇
曾武
余蜀豫
高荣生
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Chongqing Laiting Lightning Protection Technology Co ltd
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Chongqing Laiting Lightning Protection Technology Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
    • G01R19/165—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values
    • G01R19/16566—Circuits and arrangements for comparing voltage or current with one or several thresholds and for indicating the result not covered by subgroups G01R19/16504, G01R19/16528, G01R19/16533
    • G01R19/16571—Circuits and arrangements for comparing voltage or current with one or several thresholds and for indicating the result not covered by subgroups G01R19/16504, G01R19/16528, G01R19/16533 comparing AC or DC current with one threshold, e.g. load current, over-current, surge current or fault current
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
    • G01R19/165—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values
    • G01R19/17—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values giving an indication of the number of times this occurs, i.e. multi-channel analysers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R27/00—Arrangements for measuring resistance, reactance, impedance, or electric characteristics derived therefrom
    • G01R27/02—Measuring real or complex resistance, reactance, impedance, or other two-pole characteristics derived therefrom, e.g. time constant
    • G01R27/20—Measuring earth resistance; Measuring contact resistance, e.g. of earth connections, e.g. plates
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R29/00—Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
    • G01R29/08—Measuring electromagnetic field characteristics
    • G01R29/0807—Measuring electromagnetic field characteristics characterised by the application
    • G01R29/0814—Field measurements related to measuring influence on or from apparatus, components or humans, e.g. in ESD, EMI, EMC, EMP testing, measuring radiation leakage; detecting presence of micro- or radiowave emitters; dosimetry; testing shielding; measurements related to lightning
    • G01R29/0842—Measurements related to lightning, e.g. measuring electric disturbances, warning systems
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    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/95—Radar or analogous systems specially adapted for specific applications for meteorological use
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
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    • G—PHYSICS
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
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    • G06N3/048—Activation functions
    • G—PHYSICS
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    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention discloses a lightning protection safety risk management system, which comprises: the data acquisition module is used for remotely acquiring lightning protection safety performance data of each lightning protection component of the project to be evaluated; the lightning fixed-point monitoring module is used for monitoring the lightning activity of the lightning monitoring sites in a set area in real time; the lightning dynamic risk assessment module is used for dynamically analyzing and assessing lightning activity background rules, lightning activity paths and lightning disaster risks of the project to be assessed according to the data monitored by the lightning fixed-point monitoring module in real time, and giving lightning protection suggestions; the early warning module is used for sending out early warning information according to the early warning range of the position of the item to be evaluated of the thunder and lightning. The system monitors the lightning activities by remotely acquiring the safety performance of each lightning protection component and combining meteorological real-time detection data, dynamically evaluates lightning risks, can give lightning protection suggestions and send out early warning information in real time, and better plays a role in lightning disaster evaluation.

Description

Lightning protection safety risk management system and method
Technical Field
The invention relates to the technical field of lightning protection monitoring, in particular to a lightning protection safety risk management system and a lightning protection safety risk management method.
Background
Lightning is common disastrous weather, and can cause damage to buildings, power supply and distribution systems and low-voltage electrical systems due to huge current, hot high temperature and strong electromagnetic radiation to generate huge damage in a moment, so that great economic loss and adverse social effects are caused. For a long time, the thunder and lightning disasters bring serious casualties and economic losses to China, thousands of people are killed by lightning each year, and the economic losses reach billions of yuan.
The lightning protection measure of the system is a key means of lightning protection, the lightning protection measure is influenced by factors such as the use environment, the use duration and the like, the safety performance of the lightning protection measure is required to be regularly detected to meet the lightning protection requirement, generally, the safety performance is monitored more than once every year, but in actual operation, the lightning protection measure has the following problems: 1. the quantity of the detected substances is too large, and the coverage of annual human detection is very low; 2. part of the detection cost is too high, and the detected human cost and traffic cost exceed the bearable range for projects such as gas pipelines, high-speed railway lines, remote schools, forest lightning towers and the like; 3. the safety performance of the existing lightning protection detection is comprehensively judged by fully considering factors such as environment, soil, season, test means and the like, but the current detection uses single detection equipment to carry out one-time test on a test object, and the result cannot well reflect the real state of the safety performance; 4. at present, manual detection has poor timeliness, detection is performed only once in one year under common conditions, and detection cannot be obtained for a long time under many conditions, and the condition that the running state information of the lightning protection device is not acceptable to an internet of things system for a long time; 5. the current manual detection results are a large number of specific numerical values, and non-professional users are difficult to read.
In recent years, 5G networks, Internet of things and big data construction are vigorously developed, lightning protection safety performance detection is required to be regularly carried out on related infrastructure such as 5G communication base stations and Internet of things sensors, manpower detection is far beyond the coverage of intelligent networks, meanwhile, part of infrastructure is built in remote areas, forest lightning stroke fire monitoring facilities or forest lightning protection towers are usually built in deep mountains without channels, time and manpower consumed by one-time detection are huge, meanwhile, the infrastructure is very easy to suffer from lightning strokes, and the safety performance of the infrastructure needs to be concerned all the time.
The existing lightning disaster risk assessment system uses relative values when lightning disaster loss and lightning disaster risks are processed, most parameters give certain typical values in forms such as tables, and the value relation is not tight and high precision is not easy to achieve. Each standard requires precision to obtain the lightning stroke effective area of an evaluation object (a building or a service facility), the lightning stroke effective area is multiplied by the local lightning stroke density to obtain the number of possible lightning strokes, then the lightning disaster risk value is obtained according to different requirements, and finally the calculated value is compared with the allowable lightning disaster risk level (the lightning disaster risk can be borne). Their limitations and disadvantages are also significant: the selection of parameters for determining damage is mostly based on experience; parameters such as lightning stroke frequency, lightning probability, lightning endurance and the like need quantitative research with experimental basis, and key parameters for restricting evaluation level need to be determined; the understanding of the risk by the standard is not comprehensive enough, the description of the evaluation principle and the evaluation flow is not clear, the evaluation system is complex, and the result is likely to vary from person to person.
The lightning disaster risk assessment method can be collectively called as static risk assessment, historical accumulated data and on-site investigation data are used for assessment, a mean value judgment is given to the current risk state of an assessment object, certain guide value is provided for the traditional new construction or new planning project, the traditional new construction or new planning project is not suitable for the current social development, no relevant research exists at home and abroad at present, no concept similar to dynamic risk assessment exists, and more research is focused on predicting the future probability and risk through analysis of a large amount of historical data.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a lightning protection safety risk management system and a lightning protection safety risk management method, which can be used for monitoring lightning activities by remotely acquiring the safety performance of each lightning protection component and combining with meteorological real-time detection data, dynamically evaluating lightning risks, giving lightning protection suggestions and sending early warning information in real time, and better playing the role of lightning disaster evaluation.
In a first aspect, the lightning protection security risk management system provided by the present invention includes: a data acquisition module, a thunder fixed-point monitoring module, a thunder risk dynamic evaluation module and an early warning module,
the data acquisition module is used for remotely acquiring lightning protection safety performance data of each lightning protection component of the project to be evaluated;
the lightning fixed-point monitoring module is used for monitoring the lightning activity of the lightning monitoring sites in a set area in real time;
the lightning dynamic risk assessment module is used for dynamically analyzing and assessing lightning activity background rules, lightning activity paths and lightning disaster risks of the project to be assessed according to data monitored by the lightning fixed-point monitoring module in real time, and giving lightning protection suggestions;
the early warning module is used for sending out early warning information according to the early warning range of the position of the item to be evaluated of the thunder and lightning.
In a second aspect, the lightning protection security risk management method provided by the present invention is applicable to the system described in the above embodiment, and the method includes the following steps:
the data acquisition module acquires lightning protection safety performance data of a project to be evaluated;
the lightning fixed-point monitoring module monitors the lightning activities of lightning monitoring sites in a set area in real time;
the lightning risk dynamic evaluation module carries out dynamic analysis and evaluation on the lightning activity background rule, the lightning activity path and the lightning disaster risk of the project to be evaluated according to the data monitored by the lightning fixed-point monitoring module in real time, and gives a lightning protection suggestion;
the early warning module sends out early warning information in an early warning range of the position of the project to be evaluated according to thunder and lightning.
The invention has the beneficial effects that:
according to the lightning protection safety risk management system and method provided by the embodiment of the invention, the safety performance of each lightning protection component is remotely acquired, the lightning activity is monitored by combining with meteorological real-time detection data, the lightning risk is dynamically evaluated, a lightning protection suggestion can be given, early warning information can be sent out in real time, and the lightning disaster evaluation effect can be better played.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
Fig. 1 shows a block diagram of a lightning protection security risk management system according to a first embodiment of the present invention;
FIG. 2 shows an annual distribution map of a project site in a first embodiment of the invention;
FIG. 3 is a distribution diagram of the earth-moon distribution of a project in the first embodiment of the present invention;
FIG. 4 is a diagram showing a daily distribution of a location of a project in a first embodiment of the present invention;
FIG. 5 is a density distribution diagram of a location of a project according to the first embodiment of the present invention;
FIG. 6 is a diagram showing an intensity distribution of a location of an item in the first embodiment of the present invention;
FIG. 7 is a diagram showing a distribution of probability of lightning intensity accumulation at a location of an item in the first embodiment of the present invention;
FIG. 8 is a lightning azimuth distribution diagram of a location of an item according to a first embodiment of the invention;
FIG. 9 is a lightning intensity azimuth distribution diagram of a location of an item according to a first embodiment of the invention;
FIG. 10 is a view showing a dynamic lightning risk evaluation structure of a Chongqing area according to a first embodiment of the present invention;
fig. 11 shows a flowchart of a lightning protection security risk management method according to a second embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
As shown in fig. 1, a block diagram of a lightning protection security risk management system according to a first embodiment of the present invention is shown, including: the lightning protection safety performance monitoring system comprises a data acquisition module, a lightning fixed-point monitoring module, a lightning risk dynamic evaluation module and an early warning module, wherein the data acquisition module is used for remotely acquiring lightning protection safety performance data of each lightning protection component of a project to be evaluated; the lightning fixed-point monitoring module is used for monitoring the lightning activity of the lightning monitoring sites in a set area in real time; the lightning dynamic risk assessment module is used for dynamically analyzing and assessing lightning activity background rules, lightning activity paths and lightning disaster risks of the project to be assessed according to data monitored by the lightning fixed-point monitoring module in real time, and giving lightning protection suggestions; the early warning module is used for sending out early warning information according to the early warning range of the position of the item to be evaluated of the thunder and lightning.
Lightning protection safety performance data acquisition is divided into three types according to different acquisition methods, 1, lightning receiving current acquisition is performed, a Rogowski coil is additionally arranged on an external direct-strike prevention component to serve as a sensor, a lightning current detector is used for calculating lightning current polarity, lightning strike times, a lightning strike peak value, lightning strike occurrence time and the like in real time, and acquired data are transmitted to a lightning dynamic risk assessment module; 2. the method is characterized in that data acquisition of an internal weak current system is carried out, a multi-interface intelligent electronic module is adopted, photoelectric isolated data input and output interfaces are configured, three-phase voltage, degradation data, SPD running state, leakage current, overcurrent data and the like are acquired in real time and transmitted back to a lightning dynamic risk evaluation module; 3. the method is characterized in that grounding and equipotential data are acquired, a grounding resistance value and a soil resistivity value are acquired respectively through a tripolar method and a quadrapole method, an equipotential connection state is acquired in real time, and data are transmitted back to a lightning dynamic risk assessment module.
In this embodiment, the data acquisition module includes a lightning detector, a monitoring sensor and a ground resistance monitor, and the lightning detector is used for acquiring lightning strike data of the lightning rod; the data that is exposed to lightning strikes includes corresponding lightning strike amperage, time of occurrence, polarity, and waveform data. The monitoring sensor is used for collecting the running state, temperature, overcurrent intensity and waveform data of the project equipment (such as a distribution box) to be evaluated. The grounding resistance detector is used for collecting the numerical value of the grounding resistance on the grounding device, collecting the numerical value of the grounding resistance at regular intervals, if the numerical value exceeds a safety threshold, the early warning module can send out early warning information, and if the numerical value does not exceed the safety threshold, the display is normal. Whether the data collected by the data collection module meets the requirements of related technical standards or not and the error of the consistency of the data collected by the data collection module and the manual monitoring data cannot be too large. Through a large amount of field tests, debugging and simulation, the lightning protection safety performance is more perfect in online acquisition, the accuracy and the authenticity of online monitoring data results are evaluated, and the error of the data acquisition module and manual detection in the embodiment is not more than 10%.
The basic data for lightning monitoring is derived from ADTD and a self-established mixed baseline lightning detection network. The thunder and lightning monitoring network ADTD in Chongqing is built in 2007 and 4 months, consists of a lightning locator, a central data processing station, a user data service network and a graphic display terminal, comprises a main station (a sand terrace dam) and four sub stations (Yunyang, city gate, Yunyang and stone pillar), and is networked with thunder and lightning monitoring stations in adjacent areas of Sichuan, Shaanxi, Hubei, Guizhou province and the like. The lightning monitoring network realizes automatic monitoring of the time, the position (longitude and latitude), the peak value and the polarity of lightning current of the ground lightning, the clock frequency of the lightning monitoring network is 16MHz at most, and the processing time of each lightning strike back is about 1 ms.
The hybrid baseline lightning detection network can more quickly and accurately identify strong convection events such as lightning by means of the latest electronic technology and machine learning technology, the unique hybrid technology of 'long baseline + ultra-long baseline' really realizes real-time tracking of large-range strong convection, and through an efficient calculation method, the hybrid baseline lightning detection network can accurately capture strong convection kernels, provide technical support for high-quality early warning and prediction, and also provide effective technical support for disaster prevention and exploration. The detection range covers the whole Chongqing city and most southwest areas, and more refined services can be provided for various industries. The lightning activity can be monitored in real time based on the two detection data, and the accuracy is 500 meters; and predicting the moving path of the lightning in the future of 1 hour with the precision of 1000 meters.
The lightning dynamic risk assessment module comprises a lightning activity background law analysis unit, and the lightning activity background law analysis unit adopts a mixed baseline lightning detection system to detect a strong convection event; calculating the ground wave propagation time of the lightning event by adopting an ionosphere reflected wave identification method; and counting, calculating and analyzing the lightning activities within the position range of the location of the project and the time length to be analyzed to obtain the space-time distribution rule of the lightning activities. Selecting a certain determined range and a determined time period, acquiring specific lightning activity data of the certain determined range, calculating a lightning activity space-time distribution rule through an activity background rule algorithm, and drawing visual graphs such as a lightning frequency annual change graph, a lightning daily annual change graph, a lightning monthly change graph, a lightning frequency daily change graph, a lightning density distribution graph, a lightning average intensity distribution graph, a lightning intensity accumulation probability distribution graph, a lightning azimuth rose graph, a lightning average intensity rose graph and the like through a specific visual processing method to better show the lightning activity rule in the region. For example: the minimum unit of the statistical time is 5 natural years, and the statistics is more representative by adopting 10 natural years. According to the floor area and the shape of the project, the statistical range adopts four levels of radius 5 kilometers, 10 kilometers, 20 kilometers and 50 kilometers. And counting lightning data and drawing a distribution diagram from the space-time distribution, wherein the annual distribution diagram of the place of a certain project is shown in figure 2, counting the lightning frequency of ten years of the place of the project, and displaying the distribution of the histogram. The distribution diagram of the place and the month where a certain project is located is shown in fig. 3, the average lightning frequency of the place where the project is located in ten years and months is counted, and the high-occurrence month and the low-occurrence month of lightning activities can be clearly shown by using a bar chart. Such as: the daily distribution graph of the location of a certain project is shown in fig. 4, the average lightning frequency of the location of the project in each hour is counted and displayed by a bar chart, and the peak time and the valley time of lightning activities can be clearly displayed. The density distribution map of the location of a certain project is shown in fig. 5, the number of lightning occurrences per square kilometer of the location of the project is counted by utilizing a Kingson interpolation method, the lightning occurrences are shown on a GIS map, and the high-occurrence position of the lightning activity is visually shown by using rainbow seven colors as color codes. The intensity distribution map of the location of a certain project is shown in fig. 6, the intensity distribution value of lightning at the location of the project is counted by utilizing a Kingson interpolation method, the intensity distribution value is displayed on a GIS map, and the high-rise location of lightning activities is visually displayed by taking rainbow seven colors as color codes. The distribution diagram of the cumulative probability of the lightning intensity of the location of a certain project is shown in fig. 7, the lightning intensity of the location of the project in ten years is counted, the cumulative probability of the lightning intensity is represented by a curve, and the diagram can be used as an important technical basis for protecting the radius, which is a key parameter of lightning protection design. The lightning azimuth distribution map of the location of a certain project is shown in fig. 8, the distribution of the lightning occurrence times in 8 directions of the location of the project is counted, and the radar distribution map is used for drawing, so that the strength of lightning activities in all directions is visually shown. The lightning intensity azimuth distribution map of the location of a certain project is shown in fig. 9, the lightning current intensity distribution in 8 directions of the location of the project is counted, the radar distribution map is used for drawing, and the strength of lightning activities in all directions is visually shown. And drawing the lightning activity background law into an intuitive graph, visualizing statistical data and better showing the lightning activity law in the region.
The thunder and lightning activity background law analysis unit calculates the thunder and lightning activity law by using data detected by the mixed baseline thunder and lightning detection system and an ionosphere reflected wave identification method, can quickly and accurately analyze the thunder and lightning activity law, has more representative and guidance values in an analysis result, generates visual graphs of the thunder and lightning activity law, facilitates visual checking of personnel, and provides technical support for active lightning protection.
Thunder and lightning developments risk assessment module includes thunder and lightning activity path analysis unit, thunder and lightning activity path analysis unit is according to atmospheric electric field and lightning location data prediction thunderstorm cloud position, specifically includes:
and A, acquiring atmospheric electric field data of the project area to be evaluated, and calculating the accumulated difference value to judge whether a thunderstorm occurs in the project area to be evaluated.
And B: the lightning positioning data of the project area to be evaluated is obtained, and thunderstorm information in the project area to be evaluated is analyzed by adopting a cluster analysis method.
And C: and constructing and training a thunderstorm movement relative speed recognition model, inputting atmospheric electric field time sequence data of a project area to be evaluated into the trained thunderstorm movement relative speed recognition model to judge the thunderstorm relative movement information, and obtaining a judgment result.
Step D: and constructing and training a thunderstorm path prediction model, processing the lightning positioning data, inputting the lightning positioning data into the trained thunderstorm path prediction model to predict the thunderstorm movement track, and correcting the predicted thunderstorm movement track by using the judgment result.
The lightning activity path analysis unit solves the problem of refinement of lightning nowcasting, lightning early warning signals sent by meteorological departments are lightning early warnings on the surface, the coverage is large, the administrative area is usually used as the minimum unit, in other words, the lightning early warning signals are not refined enough, the lightning early warning signals cannot be refined to a point, and the guiding value of specific units is low. Meanwhile, the lightning early warning signal is a time scale from two hours to six hours, and the delay is too large for a specific unit. The lightning activity path analysis unit develops a spatial clustering lightning path prediction algorithm by utilizing an atmospheric electric field instrument and lightning positioning data, and predicts and early warns lightning to a project point accurately and time is as fine as 1 hour.
In the step a, the method specifically comprises: the electric field jumping phenomenon before the lightning is generated is captured by an accumulative difference method, and the calculation formula is as follows.
Figure BDA0003071064990000091
Wherein E is the atmospheric electric field value, E (t)0) Is t0Value of atmospheric electric field at time, E (t)1) Is t1Value of atmospheric electric field at time t0And t1Two adjacent moments are separated by the sampling time interval of the atmospheric electric field instrument t1-t 015 min. When the value E (t)' reaches a set threshold value, the thunderstorm can be considered to occur in the early warning area, and the selection of the threshold value needs to be determined according to local weather and geographic conditions.
Specifically, collecting atmospheric electric field data in the early warning area, calculating an atmospheric electric field accumulated difference value within 15min according to a formula (1), and considering that thunderstorm activity exists in the early warning area when an early warning threshold value is reached.
In the step B, the lightning location data is monitoring data of the lightning location system of the meteorological department, and includes information such as time, longitude and latitude, strength, and steepness, and the time and longitude and latitude are used in this embodiment. The method specifically comprises the steps of arranging the lightning location data into sequence data arranged at intervals of 10min, and converting the sequence data into a CSV comma separation file.
The Clustering analysis of the lightning positioning data in the step B is Based on DBSCAN (sensitivity-Based Spatial Clustering of Applications with Noise, Density-Based Clustering method), which is a Density-Based Spatial Clustering algorithm, has high Clustering speed, can effectively process Noise points and find Spatial clusters of any shape; the number of clusters to be divided is not required to be input; the method has the advantages of being capable of finding out noise points in data, insensitive to noise and the like, and is a method widely applied to the data mining technology. Some preliminary knowledge should be mastered using the DBSCAN clustering algorithm, assuming my sample set is D ═ (x)1,x2,...,xm) Then the following concepts and notations are introduced:
1) epsilon neighborhood: for xjE.g. D, whose e neighborhood contains the sum x in the sample set DjA set of subsamples having a distance of not more than epsilon, i.e.
Figure BDA0003071064990000092
The number of this subsample set is denoted as | N ∈ (x)i)|。
2) Core object: for any sample xj ∈ D, if the N ∈ (xj) corresponding to its ε neighborhood contains at least MinPts samples, i.e., if | N ∈ (xj) | ≧ MinPts, xj is the core object.
3) The density is up to: if xi is located in the epsilon neighborhood of xj, and xj is a core object, xi is said to be directed by the xj density. Note that the opposite does not necessarily hold, i.e., it cannot be said that xj is passed through by xi density at this time, unless xi is also the core object.
4) The density can reach: for xi and xj, xj is said to be reachable by xi density if there are sample sequences p1, p2, …, pT, satisfying p1 ═ xi, pT ═ xj, and pT +1 is reached by pT density. That is, the density can be achieved to satisfy transitivity. At this time, the transfer samples p1, p2, … and pT-1 in the sequence are all core objects, because only the core objects can make other sample densities reach through. Note that the density can be achieved without satisfying the symmetry, which can be derived from the asymmetry of the density through.
5) Density connection: for xi and xj, if there is a core object sample xk, making both xi and xj reachable by xk density, xi and xj are said to be connected in density. Note that the density connectivity is such that symmetry is satisfied.
The step B is realized by the following steps:
step B1: inputting a parameter field radius epsilon, a threshold MinPts and a lightning positioning data set D;
step B2: randomly selecting a point p in the data set D, and judging whether the point p is a core point;
step B3: if the point p is a core point, finding out all points with reachable direct density in the neighborhood;
step B4: whether the points in the data set D are judged completely or not is judged, and if not, the steps B.2-B.4 are repeated;
step B5: combining the points with the reachable density and expanding the clusters;
step B6: and outputting the target cluster set.
The step C specifically comprises the following steps:
step C1, combining the lightning location data, marking the data of the thunderstorm close to the early warning area and far from the early warning area manually, dividing the training set and the verification set according to the proportion of 7:3, and carrying out normalization processing on the data, wherein the normalization aims at preventing the neuron output saturation phenomenon caused by the overlarge net input absolute value, leading the neural network to be converged rapidly, and the calculation formula is shown as a formula (2),
Figure BDA0003071064990000111
wherein, x [ n ]]Is the nth value, x, of the data sequencemin、xmaxAre the minimum and maximum values of the data sequence,
Figure BDA0003071064990000112
is x [ n ]]The normalized value of (a).
Step C2, establishing a thunderstorm movement relative speed recognition model by using a one-dimensional convolution neural network, setting super parameters such as iteration rounds, learning rate and the like, inputting data into the neural network for training, and evaluating and fixing the model when the number of training rounds is reached;
step C3: and normalizing the atmospheric electric field time sequence data to be predicted, and inputting the normalized atmospheric electric field time sequence data into the recognition model to obtain the movement direction of the thunderstorm relative to the atmospheric electric field instrument.
The step D specifically comprises the following steps:
step D1: b, calculating the mass center of the lightning cluster by using the output result of the step B and a formula (3), and establishing a thunderstorm movement path data set;
Figure BDA0003071064990000113
wherein (x)i，yi) The coordinates of the ith lightning in the lightning cluster and (x, y) the coordinates of the centroid of the lightning cluster.
And removing the short-duration thunderstorm path data, and arranging the short-duration thunderstorm path data into data required by a thunderstorm path prediction model, wherein the continuous 6 thunderstorm position data is one sample. The data set is normalized and the maximum and minimum values are recorded. And dividing the normalized data into a training set and a verification set according to the proportion of 7: 3.
The thunderstorm path prediction model is based on a GRU (gated Recurrent Unit) gated cyclic unit structure network, is a variant of a traditional RNN (cyclic neural network), can effectively capture semantic association between Long sequences like an LSTM (Long Short-Term Memory), and relieves gradient disappearance or explosion phenomena, and meanwhile, the structure and the calculation of the model are simpler than those of the LSTM. The GRU unit has two gates, a reset gate that determines how to combine the new input with the previous memory and an update gate that determines how much of the previous memory is to be used, the calculation of GRU is as follows:
zt＝σ(Wz·[ht-1,xt]+bu) (4)
rt＝σ(Wr·[ht-1,xt]+br) (5)
h't＝tanh(W·[rt*ht-1,xt]) (6)
ht＝(1-zt)*ht-1+zt*h't (7)
wherein σ is sigmoid activation function, tanh is tanh activation function, Wz、WrW is the weight, bu、brTo be offset, ytAs output at the current time, yt-1The output of the last moment. Equation (4) is called the update gate and equation (5) is called the reset gate.
Step D2: and establishing and training a thunderstorm path prediction model based on the GRU recurrent neural network.
A three-layer GRU recurrent neural network is established by utilizing paddlepaddlele, the network is set to be a training mode, and hyper-parameters such as iteration turns and learning rate are set. Training and learning the thunderstorm movement path prediction model read in by the training set data processed in the step D1 to obtain a trained thunderstorm movement path prediction model, and predicting the last 3 data by using the first 3 data; and after the number of training rounds is reached, inputting the verification set data into the trained thunderstorm movement path prediction model for verification and evaluation, fixing the model, and finishing the training and learning of the thunderstorm movement path prediction model.
When the thunderstorm path prediction model of the GRU recurrent neural network is trained in the step D2, the optimizer is an Adam optimizer, the activation function is relu, dropout is adopted for improving the generalization of the network, and the discarding rate is 0.3.
The loss function adopted in step D2 is an MSE (mean square error) loss function, and the calculation formula is as follows:
Figure BDA0003071064990000121
wherein y _ is a predicted value of the neural network, i.e. y in formula (7)tAnd y is the tag value, i.e. the true location of the thunderstorm at that moment in the dataset.
And D3, processing the lightning positioning data, inputting the processed lightning positioning data into a thunderstorm path prediction model, and predicting the thunderstorm movement track.
Processing the lightning positioning data according to the methods in the steps B and D1 to obtain thunderstorm position data; normalizing the thunderstorm position data by using the maximum value and the minimum value recorded in the step D1; and setting the thunderstorm path prediction model as a prediction mode, and inputting the data after normalization processing to obtain a prediction result.
Step D4: and correcting the predicted thunderstorm movement track by using the calculation result of the step C3.
The software and hardware environment used in step C, D is: the operating system is windows 10; the CPU is AMD R7-2700; the display card is NVIDIA RTX-2060; the AI frame is hundred degrees paddlepaddle; the memory is 32G; the programming language is python.
The thunder and lightning activity path analysis unit predicts the thunderstorm movement track by utilizing atmospheric electric field data, lightning positioning data and a spatial clustering analysis method, realizes real-time thunder and lightning early warning monitoring and real-time hidden danger risk assessment, provides more refined thunder and lightning activity prediction abstinence, can accurately predict the thunderstorm movement track, and predicts and early warns thunder and lightning accurately to a project area to be assessed.
The lightning risk dynamic evaluation module comprises a lightning disaster risk analysis unit, wherein the lightning disaster risk analysis unit is used for constructing a factor set of a lightning disaster risk evaluation system, determining a decision set of the grade of an evaluation index, establishing the weight of each factor in the evaluation factor set, establishing a comprehensive evaluation matrix, performing comprehensive evaluation by applying a synthesis algorithm of fuzzy transformation, and determining the lightning disaster risk grade.
The risk of the lightning disaster is inaccurate and ambiguous, the cause of the lightning disaster is not completely known, the evaluation information is not complete, the inaccuracy of the evaluation result is reflected by a proper mode in the approach of solving the problem, and the current fuzzy comprehensive evaluation method can better solve the problem. The lightning disaster risk is a fuzzy event, generally speaking, the dynamic lightning disaster risk can be divided into four levels of low risk, medium risk, high risk and very high risk, and then a corresponding equation is established to calculate to obtain a final lightning risk level.
Generally, establishing a fuzzy evaluation model mainly involves the following three elements:
(1) a factor set U;
(2) a decision set V;
(3) and judging the relationship by using a single factor.
Lightning disaster risk assessment indexes are many and complex, so a three-level fuzzy comprehensive evaluation model is used. The method comprises the following steps:
(1) factor set U for constructing evaluation system
U＝{u1,u2,···,un} (8)
(2) Decision set V for determining index
V＝{v1,v2,···,vn} (9)
The decision set is the grade of the evaluation index, and is a reference standard for determining the membership degree of the index, and is generally a boundary value.
(3) Establishing a weight distribution vector A of m evaluation factors
Each factor in the evaluation factor set has a different status in the "evaluation target", that is, each evaluation factor occupies a different proportion in the comprehensive evaluation, which is called a weight value. At present, most weight value methods belong to subjective judgment, the specific disaster mechanism of lightning cannot be well explained at present, and the weight values of evaluation factors of all layers are divided equally, so that the final evaluation result is prevented from being influenced too much by one evaluation factor due to immature artificial concept.
(4) Establishing a comprehensive evaluation matrix R
Establishing a fuzzy mapping from U to V for the lowest index of the evaluation parameters, wherein the judgment membership degree vector of the ith index is Ri＝[ri1,ri2,···,rim]Then, the membership matrix with m evaluation indexes is:
Figure BDA0003071064990000141
(5) selecting a synthesis algorithm for comprehensive evaluation
The fuzzy comprehensive evaluation result is a fuzzy subset on the decision set V, and the synthetic operation formula applying the fuzzy transformation is as follows:
Figure BDA0003071064990000142
natural disasters are the result of a disaster-bearing body failing to adapt or adjust to environmental changes. The risk of lightning disaster is the same as that of other natural disasters, and is also a product of interaction of various factors, which is influenced by a regional natural system, a social system and a combination relationship thereof. The conclusion is obtained by carrying out deep research on the lightning disasters in Chongqing areas: the lightning disaster risk of a certain area is formed by the mutual interlinkage of 4 risk factors including lightning disaster risk (H), exposure (E), vulnerability (V) of a disaster bearing body and disaster prevention and reduction capability (C), and is expressed by a formula: r is H.E.V.C. The 4 risk factors are relatively general abstract concepts, specific parameters influencing the risk of the lightning disaster are selected, on the basis of fully investigating the topographic and topographic features, climate backgrounds and lightning disaster forming characteristics of the Chongqing area, a dynamic lightning risk assessment structure of the Chongqing area is provided as shown in a figure 10, the risk levels of assessment indexes are reasonably divided, classification standards are determined, appropriate membership functions are selected, then the membership degrees of the lightning disaster risks at all levels are sequentially solved, and finally the final lightning disaster risk level is calculated by adopting an appropriate weighting method.
The membership functions are struts of fuzzy sets, the fuzzy sets are completely described by the membership functions, parameters selected by dynamic lightning disaster evaluation can be found out after corresponding statistics according to the actual conditions of the project, most membership functions can be processed by adopting K-time parabolic distribution, and the K-time parabolic distribution of partial small, partial large and intermediate types respectively corresponds to four levels of dynamic lightning risks of the project:
the small-sized device is as follows:
Figure BDA0003071064990000151
large-scale:
Figure BDA0003071064990000152
intermediate type:
Figure BDA0003071064990000153
obtaining a comprehensive evaluation result, losing more useful information by using a common maximum membership principle, and finally obtaining the following formula by adopting proper weighted average processing:
F＝1×b1+3×b2+5×b3+7×b4 (15)。
the lightning disaster risk analysis unit shows main lightning risk sources, risk sources and risk distribution in detail, and provides good technical support for reducing the risk of lightning disaster accidents. And performing weighted analysis on the data acquired by the data acquisition module and the thunder background activity rule, systematically displaying the positions of the lightning protection potential safety hazards and the improvement suggestions, and transmitting the positions and the improvement suggestions to the client in real time in a visual and visual mode.
According to the lightning protection safety risk management system provided by the embodiment of the invention, the safety performance of each lightning protection component is remotely acquired, the lightning activity is monitored by combining with meteorological real-time detection data, the lightning risk is dynamically evaluated, a lightning protection suggestion can be given, early warning information can be sent out in real time, and the lightning disaster evaluation effect can be better played.
In the first embodiment, a lightning protection security risk management system is provided, and correspondingly, a lightning protection security risk management method is also provided in the present application. Please refer to fig. 11, which is a flowchart illustrating a lightning protection security risk management method according to a second embodiment of the present invention. Since the method embodiments are substantially similar to the apparatus embodiments, they are described in a relatively simple manner, and reference may be made to the apparatus embodiments for some of their relevant descriptions. The method embodiments described below are merely illustrative.
As shown in fig. 11, a flowchart of a lightning protection security risk management method provided by a second embodiment of the present invention is shown, and is applicable to the system described in the first embodiment, where the method includes:
the data acquisition module acquires lightning protection safety performance data of a project to be evaluated;
the lightning fixed-point monitoring module monitors the lightning activities of lightning monitoring sites in a set area in real time;
the lightning risk dynamic evaluation module carries out dynamic analysis and evaluation on the lightning activity background rule, the lightning activity path and the lightning disaster risk of the project to be evaluated according to the data monitored by the lightning fixed-point monitoring module in real time, and gives a lightning protection suggestion;
and the early warning module sends out early warning information to the abnormal data and the lightning in the early warning range of the position of the project to be evaluated.
Specifically, the thunder and lightning risk dynamic assessment module assesses the thunder and lightning activity background law of the location of the item to be assessed according to the data monitored in real time by the thunder and lightning fixed-point monitoring module, and specifically comprises the following steps:
detecting a strong convection event with a mixed baseline lightning detection system;
calculating the ground wave propagation time of the lightning event by adopting an ionosphere reflected wave identification method;
and counting, calculating and analyzing the lightning activities within the position range of the location of the project and the time length to be analyzed to obtain the space-time distribution rule of the lightning activities.
The thunder and lightning risk dynamic assessment module assesses the thunder and lightning activity path of the location of the item to be assessed according to the data monitored in real time by the thunder and lightning fixed-point monitoring module, and specifically comprises the following steps:
the method comprises the steps of obtaining atmospheric electric field data, calculating accumulated difference values to judge whether a thunderstorm occurs in a project area to be evaluated or not, obtaining lightning positioning data, clustering and analyzing thunderstorm information in the project area to be evaluated to obtain an analysis result, judging thunderstorm relative motion information by using the atmospheric electric field event sequence data, predicting a thunderstorm moving track by using the lightning positioning data, and correcting the predicted thunderstorm moving track by using the analysis result.
The thunder and lightning risk dynamic assessment module carries out dynamic analysis and assessment on the project self thunder and lightning disaster risk of the location of the project to be assessed according to the data monitored in real time by the thunder and lightning fixed-point monitoring module, and the dynamic assessment module specifically comprises the following steps:
the method comprises the steps of constructing a factor set of a lightning disaster risk assessment system, determining a decision set of assessment index levels, establishing the weight of each factor in the assessment factor set, establishing a comprehensive assessment matrix, performing comprehensive assessment by applying a fuzzy transformation synthesis algorithm, and determining the lightning disaster risk level.
The above is a description of an embodiment of a lightning protection security risk management method according to a second embodiment of the present invention.
The lightning protection safety risk management method provided by the invention and the lightning protection safety risk management system have the same inventive concept and the same beneficial effects, and are not repeated herein.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.

Claims (7)

1. A lightning protection security risk management system, comprising: a data acquisition module, a thunder fixed-point monitoring module, a thunder risk dynamic evaluation module and an early warning module,
the data acquisition module is used for remotely acquiring lightning protection safety performance data of each lightning protection component of the project to be evaluated;
the lightning fixed-point monitoring module is used for monitoring the lightning activity of the lightning monitoring sites in a set area in real time;
the lightning dynamic risk assessment module is used for dynamically analyzing and assessing lightning activity background rules, lightning activity paths and lightning disaster risks of the project to be assessed according to data monitored by the lightning fixed-point monitoring module in real time, and giving lightning protection suggestions;
the early warning module is used for sending early warning information to abnormal data and lightning in an early warning range of the position of the project to be evaluated;
thunder and lightning developments risk assessment module includes thunder and lightning activity path analysis unit, thunder and lightning activity path analysis unit is according to atmospheric electric field and lightning location data prediction thunderstorm cloud position, specifically includes: acquiring atmospheric electric field data, calculating accumulated difference values and judging whether a thunderstorm occurs in a project area to be evaluated; acquiring lightning positioning data, clustering and analyzing thunderstorm information in a project area to be evaluated to obtain an analysis result, judging thunderstorm relative motion information by using atmospheric electric field event sequence data, predicting a thunderstorm moving track by using the lightning positioning data, and correcting the predicted thunderstorm moving track by using the analysis result; the method specifically comprises the following steps:
calculating the mass center of the lightning cluster by utilizing thunderstorm information, and establishing a thunderstorm moving path data set;
establishing and training a thunderstorm path prediction model based on a GRU (generalized regression Unit) recurrent neural network;
processing the lightning positioning data, inputting the processed lightning positioning data into a thunderstorm path prediction model, and predicting a thunderstorm movement track;
and correcting the predicted thunderstorm movement track by using the obtained movement direction of the thunderstorm relative to the atmospheric electric field instrument.
2. The lightning protection security risk management system of claim 1 wherein the lightning dynamic risk assessment module comprises a lightning activity context law analysis unit that detects strong convection events with a mixed baseline lightning detection system; calculating the ground wave propagation time of the lightning event by adopting an ionosphere reflected wave identification method; and (4) counting, calculating and analyzing the lightning activities within the position range of the location of the project and the time length to be analyzed to obtain the spatial-temporal distribution rule of the lightning activities.
3. The lightning protection security risk management system according to claim 1, wherein the lightning risk dynamic evaluation module includes a lightning risk analysis unit, and the lightning risk analysis unit is configured to construct a factor set of a lightning risk evaluation system, determine a decision set of a grade of an evaluation index, establish a weight of each factor in the evaluation factor set, establish a comprehensive evaluation matrix, perform comprehensive evaluation by applying a synthesis algorithm of fuzzy transformation, and determine a lightning risk grade.
4. The lightning protection safety risk management system according to claim 1, wherein the data collection module comprises a lightning detector, a monitoring sensor and a ground resistance monitor,
the lightning detector is used for acquiring lightning stroke data of the lightning rod;
the monitoring sensor is used for acquiring the running state, temperature, overcurrent intensity and waveform data of the project equipment to be evaluated;
the grounding resistance detector is used for collecting the numerical value of the grounding resistance on the grounding device.
5. A lightning protection security risk management method, adapted to the lightning protection security risk management system of claim 1, the method comprising the steps of:
the data acquisition module acquires lightning protection safety performance data of a project to be evaluated;
the lightning fixed-point monitoring module monitors the lightning activities of lightning monitoring sites in a set area in real time;
the lightning risk dynamic evaluation module carries out dynamic analysis and evaluation on the lightning activity background rule, the lightning activity path and the lightning disaster risk of the project to be evaluated according to the data monitored by the lightning fixed-point monitoring module in real time, and gives a lightning protection suggestion;
the early warning module sends out early warning information to the abnormal data and the lightning in the early warning range of the position of the project to be evaluated;
the thunder and lightning risk dynamic assessment module assesses the thunder and lightning activity path of the location of the item to be assessed according to the data monitored in real time by the thunder and lightning fixed-point monitoring module, and specifically comprises the following steps:
acquiring atmospheric electric field data, calculating accumulated differential values to judge whether a thunderstorm occurs in a project area to be evaluated or not, acquiring lightning positioning data, clustering and analyzing thunderstorm information in the project area to be evaluated to obtain an analysis result, judging thunderstorm relative motion information by using the atmospheric electric field event sequence data, predicting a thunderstorm moving track by using the lightning positioning data, and correcting the predicted thunderstorm moving track by using the analysis result; the method specifically comprises the following steps:
calculating the mass center of the lightning cluster by utilizing thunderstorm information, and establishing a thunderstorm moving path data set;
establishing and training a thunderstorm path prediction model based on a GRU (generalized regression Unit) recurrent neural network;
processing the lightning positioning data, inputting the processed lightning positioning data into a thunderstorm path prediction model, and predicting a thunderstorm movement track;
and correcting the predicted thunderstorm movement track by using the obtained movement direction of the thunderstorm relative to the atmospheric electric field instrument.
6. The lightning protection safety risk management method according to claim 5, wherein the lightning risk dynamic assessment module assesses the background law of lightning activity at the location of the item to be assessed according to the data monitored by the lightning fixed-point monitoring module in real time, and specifically comprises:
detecting a strong convection event with a mixed baseline lightning detection system;
calculating the ground wave propagation time of the lightning event by adopting an ionosphere reflected wave identification method;
and counting, calculating and analyzing the lightning activities within the position range of the location of the project and the time length to be analyzed to obtain the space-time distribution rule of the lightning activities.
7. The lightning protection safety risk management method according to claim 6, wherein the lightning risk dynamic evaluation module performs dynamic analysis and evaluation on the lightning disaster risk of the project to be evaluated according to the data monitored by the lightning fixed-point monitoring module in real time, and specifically comprises:
the method comprises the steps of constructing a factor set of a lightning disaster risk assessment system, determining a decision set of assessment index levels, establishing the weight of each factor in the assessment factor set, establishing a comprehensive assessment matrix, performing comprehensive assessment by applying a fuzzy transformation synthesis algorithm, and determining the lightning disaster risk level.
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