CN104200281A - Method and system for predicting thunder cloud moving path based on lightning location system - Google Patents
Method and system for predicting thunder cloud moving path based on lightning location system Download PDFInfo
- Publication number
- CN104200281A CN104200281A CN201410422077.8A CN201410422077A CN104200281A CN 104200281 A CN104200281 A CN 104200281A CN 201410422077 A CN201410422077 A CN 201410422077A CN 104200281 A CN104200281 A CN 104200281A
- Authority
- CN
- China
- Prior art keywords
- lightning
- thunder
- time period
- thunderstorm
- feature point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Abstract
The invention provides a method for predicting a thunder cloud moving path based on a lightning location system. The method comprises the steps of establishing a clustering analysis database, obtaining lightning data in the same time period, calculating the distance between each lightning point and a neighboring lightning point according to each lightning point and building a lightning data spatial position relation table according to the lightning points and the distances, calculating the density relation of any two lightning points, clustering the lightning data according to the density relation of any two lightning points to generate a thunder cloud and obtaining the position characteristic points of the thunder cloud, linearly fitting the position characteristic points of the thunder cloud according to the displacement change of position characteristic points of the thunder cloud as time goes by, and predicting the position of the thunder cloud in next time period. The method for predicting the thunder cloud moving path based on the lightning location system is capable of predicting the position of the thunder cloud more accurately. The invention also provides a system for predicting the thunder cloud moving path based on the lightning location system.
Description
Technical field
The present invention relates to thunder and lightning prediction and early warning technology field, particularly a kind of Forecasting Methodology and system of the thunder cloud mobile route based on lightning location system.
Background technology
At present, thundercloud path prediction has been carried out a large amount of deep research at meteorological field, has obtained great successes.The real time data that researchist mainly gathers according to Doppler radar, utilizes the methods such as extrapolation method, pattern-recongnition method, cross correlation algorithm to realize the identification of thunder cloud, tracking and prediction.But, concerning the special industries such as electric power enterprise, be more concerned about that ground thunderbolt is at the thunderbolt probability of specific region, and less with the achievement in research of ground thunderbolt probability under its area coverage about thundercloud position.Therefore,, although the technology that thundercloud position is followed the tracks of, predicted is relatively ripe, its achievement in research still cannot effectively be used in special industries such as electric power.In recent years, along with flourish in whole world every field of informationization, data mining technology also causes various countries scholar's very big concern.At present, there is a kind of method based on the motion of space density clustering prediction thunder and lightning, the method adopts computing machine to process thunder and lightning automatic monitoring data, set up cluster analysis database, lightning data screening sample condition is set in database, portray the basic parameter of thunder and lightning density, obtain in chronological order thunder and lightning sample data and classify according to the time period; By lightning data, put and distance value, construct lightning data spatial relation table; Determine the position feature point that different thunderstorms are rolled into a ball; According to the position feature point of thunderstorm group, along with the change in displacement in passage of time, calculate its direction of motion and speed, and predict the position that next one time period thunderstorm group will occur.But, after research, find, there is following problem in the method: if when certain time period thunder cloud division occurs or merges, the aggregate of data number forming after this time period ground thunderbolt point cluster analysis will change with a upper time segment data bunch number, and the variation of aggregate of data number will directly cause the calculating of next time period thunder cloud position feature point to carry out.Therefore, how that single thunder cloud and independent data is bunch corresponding one by one, thunder cloud division and the physical phenomenon merging to be identified when the cluster analysis, the logical relation of setting up between adjacent time period aggregate of data is problem anxious to be resolved in thundercloud trajectory predictions.
Summary of the invention
The present invention is intended to solve at least to a certain extent one of technical matters in above-mentioned correlation technique.
For this reason, one object of the present invention is to propose a kind of Forecasting Methodology of the thunder cloud mobile route based on lightning location system, and the method can be predicted thunder cloud position more accurately.
Another object of the present invention is to provide a kind of prognoses system of the thunder cloud mobile route based on lightning location system.
To achieve these goals, the embodiment of first aspect present invention has proposed a kind of Forecasting Methodology of the thunder cloud mobile route based on lightning location system, comprises the following steps: set up cluster analysis database; Obtain the lightning data in the same time period, and calculate the distance between each thunder and lightning point periphery thunder and lightning and described thunder and lightning point according to each thunder and lightning point, and put and described distance structure lightning data spatial relation table by thunder and lightning; Calculate the density relationship between any two thunder and lightning points; According to the density relationship between described any two thunder and lightning points, described lightning data is carried out to cluster, to generate thunderstorm group, and obtain the position feature point that described thunderstorm is rolled into a ball; According to the position feature point of described thunderstorm group along with the change in displacement in passage of time, the position feature point of thunderstorm group described in linear fit, and the prediction next one time period thunderstorm group position that will occur.
According to the Forecasting Methodology of the thunder cloud mobile route based on lightning location system of the embodiment of the present invention, single thunder cloud and independent data is bunch corresponding one by one, in cluster analysis, identify thunder cloud division and the physical phenomenon merging, set up the logical relation between adjacent time period aggregate of data, obtain thunder cloud position prediction result more accurately, and utilize linear fit to determine that thunderstorm cumularsharolith puts unique point, make result more reasonable, can be power transmission line lightning shielding early warning effective reference frame is provided.
In addition, the Forecasting Methodology of the thunder cloud mobile route based on lightning location system according to the above embodiment of the present invention can also have following additional technical characterictic:
In some instances, also comprise: judge in each thunderstorm group of adjacent time period whether have the thunderstorm group of thundercloud position feature point drift in preset range, if existed, two time period states do not change in front and back to judge this thundercloud, further judge that whether other thunderstorms roll into a ball in adjacent time period thundercloud position feature point drift in preset range, until residue thunderstorm is rolled into a ball in adjacent time period thundercloud position feature point drift all not in preset range, and, when the number of a rear time period residue thunderstorm group is 0, after keeping, the residue thunderstorm group of a time period is constant, and the identifier L that revises previous time period residue thunderstorm group is 0, otherwise, the thundercloud position feature point of a rear time period residue thunderstorm being rolled into a ball is as new thundercloud position feature point, and the identifier L that revises previous time period residue thunderstorm group is 0, and the identifier L that after revising, a time period residue thunderstorm is rolled into a ball is 1.
In some instances, by determining the density relationship between described any two thunder and lightning points to give a definition: if thunder and lightning point p exists another thunder and lightning point q in radius ε spatial dimension, defining between thunder and lightning point q and thunder and lightning point p is that direct density arrives relation; If there is the thunder and lightning point p of a sequence
1, p
2..., p
n, wherein, p
1=p, and p
1=q, if for each p
i+1with p
1be all that direct density arrives relation, claim q density to arrive p.
In some instances, described position of predicting that next one time period thunderstorm group will occur specifically comprises: the position feature point that a plurality of time period thunderstorms that are connected are rolled into a ball carries out linear fit, and generates fitting formula; By described fitting formula, calculate the position feature point that next time period thunderstorm is rolled into a ball.
The embodiment of second aspect present invention provides a kind of prognoses system of the thunder cloud mobile route based on lightning location system, comprising: set up module, the described module of setting up is for setting up cluster analysis database; Build module, described structure module is for obtaining the lightning data in the same time period, and calculate the distance between each thunder and lightning point periphery thunder and lightning and described thunder and lightning point according to each thunder and lightning point, and put and described distance structure lightning data spatial relation table by thunder and lightning; Computing module, described computing module is for calculating the density relationship between any two thunder and lightning points; Acquisition module, described acquisition module, for described lightning data being carried out to cluster according to the density relationship between described any two thunder and lightning points, to generate thunderstorm group, and obtains the position feature point that described thunderstorm is rolled into a ball; Prediction module, described prediction module for according to the position feature point of described thunderstorm group along with the change in displacement in passage of time, the position feature point of thunderstorm group described in linear fit, and the prediction next one time period thunderstorm group position that will occur.
According to the prognoses system of the thunder cloud mobile route based on lightning location system of the embodiment of the present invention, single thunder cloud and independent data is bunch corresponding one by one, in cluster analysis, identify thunder cloud division and the physical phenomenon merging, set up the logical relation between adjacent time period aggregate of data, obtain thunder cloud position prediction result more accurately, and utilize linear fit to determine that thunderstorm cumularsharolith puts unique point, make result more reasonable, can be power transmission line lightning shielding early warning effective reference frame is provided.
In addition, the prognoses system of the thunder cloud mobile route based on lightning location system according to the above embodiment of the present invention can also have following additional technical characterictic:
In some instances, also comprise: adjusting module, described adjusting module is for judging each thunderstorm of adjacent time period rolls into a ball whether there be the thunderstorm group of thundercloud position feature point drift in preset range, if existed, two time period states do not change in front and back to judge this thundercloud, further judge that whether other thunderstorms roll into a ball in adjacent time period thundercloud position feature point drift in preset range, until residue thunderstorm is rolled into a ball in adjacent time period thundercloud position feature point drift all not in preset range, and the number of rolling into a ball at a rear time period residue thunderstorm is 0 o'clock, after keeping, the residue thunderstorm of time period group is constant, and the identifier L that revises previous time period residue thunderstorm group is 0, otherwise, the thundercloud position feature point that a rear time period residue thunderstorm is rolled into a ball is as new thundercloud position feature point, and the identifier L that revises previous time period residue thunderstorm group is 0, and the identifier L that after revising, a time period residue thunderstorm is rolled into a ball is 1.
In some instances, by determining the density relationship between described any two thunder and lightning points to give a definition: if thunder and lightning point p exists another thunder and lightning point q in radius ε spatial dimension, defining between thunder and lightning point q and thunder and lightning point p is that direct density arrives relation; If there is the thunder and lightning point p of a sequence
1, p
2..., p
n, wherein, p
1=p, and p
1=q, if for each p
i+1with p
1be all that direct density arrives relation, claim q density to arrive p.
In some instances, described prediction module is carried out linear fit for the position feature point that a plurality of time period thunderstorms that are connected are rolled into a ball, and generates fitting formula, and by the position feature point of described fitting formula calculating thunderstorm of next time period group.
Additional aspect of the present invention and advantage in the following description part provide, and part will become obviously from the following description, or recognize by practice of the present invention.
Accompanying drawing explanation
Above-mentioned and/or additional aspect of the present invention and advantage accompanying drawing below combination obviously and is easily understood becoming the description of embodiment, wherein:
Fig. 1 is the process flow diagram of the Forecasting Methodology of the thunder cloud mobile route based on lightning location system according to an embodiment of the invention;
Fig. 2 is the process flow diagram of the Forecasting Methodology of the thunder cloud mobile route based on lightning location system in accordance with another embodiment of the present invention; And
Fig. 3 is the structured flowchart of the prognoses system of the thunder cloud mobile route based on lightning location system according to an embodiment of the invention.
Embodiment
Describe embodiments of the invention below in detail, the example of described embodiment is shown in the drawings, and wherein same or similar label represents same or similar element or has the element of identical or similar functions from start to finish.Below by the embodiment being described with reference to the drawings, be exemplary, only for explaining the present invention, and can not be interpreted as limitation of the present invention.
Below in conjunction with accompanying drawing, describe according to Forecasting Methodology and the system of the thunder cloud mobile route based on lightning location system of the embodiment of the present invention.
Fig. 1 is the process flow diagram of the Forecasting Methodology of the thunder cloud mobile route based on lightning location system according to an embodiment of the invention.As shown in Figure 1, the Forecasting Methodology of the thunder cloud mobile route based on lightning location system according to an embodiment of the invention, comprises the following steps:
Step S101, sets up cluster analysis database.
More specifically, shown in Fig. 2, lightning data screening sample condition is set in database, and the basic parameter of portraying thunder and lightning density, comprise: distance, for example be denoted as ε, minimum thunder and lightning is counted, for example, be denoted as minPts, and obtain in chronological order thunder and lightning sample data and classify according to the time period.
Step S102, obtains the lightning data in the same time period, and calculates the distance between each thunder and lightning point periphery thunder and lightning and described thunder and lightning point according to each thunder and lightning point, and puts and described distance structure lightning data spatial relation table by thunder and lightning.
Step S103, calculates the density relationship between any two thunder and lightning points.
Particularly, in some instances, by determine the density relationship between any two thunder and lightning points to give a definition:
Definition one: if thunder and lightning point p exists another thunder and lightning point q in default radius ε spatial dimension, defining between thunder and lightning point q and thunder and lightning point p is that direct density arrives relation.
Definition two: if there is the thunder and lightning point p of a sequence
1, p
2..., p
n, wherein, p
1=p, and p
1=q, if for each p
i+1with p
1be all that direct density arrives relation, claim q density to arrive p.
And then, by defining one or definition two density relationship that can calculate any two thunder and lightning points.
Step S104, carries out cluster according to the density relationship between any two thunder and lightning points to lightning data, to generate thunderstorm group, and obtains the position feature point that thunderstorm is rolled into a ball.
Particularly, from any thunder and lightning point, if counting out, the thunder and lightning comprising do not reach minPts in its radius ε spatial dimension, this thunder and lightning point is labeled as noise spot, an otherwise newly-generated clustering cluster, by all, are all grouped into this type of and mark with the thunder and lightning point of this thunder and lightning point for direct density arrival relation or density arrival relation, and then look for the next one not make the point of any mark, repeat aforesaid operations, generate new clustering cluster, until all thunder and lightning sample datas are all labeled, after to be clustered completing, the identifier L initial value of giving each clustering cluster (being thunderstorm group) is 1, L span is 0 or 1, determine the position feature point that different thunderstorms are rolled into a ball.
Further, shown in Fig. 2, in some instances, after this step, also comprise: judge in each thunderstorm group of adjacent time period whether have the thunderstorm group of thundercloud position feature point drift in preset range, if existed, two time period states do not change in front and back to judge this thundercloud, further judge that whether other thunderstorms roll into a ball in adjacent time period thundercloud position feature point drift in preset range, until residue thunderstorm is rolled into a ball in adjacent time period thundercloud position feature point drift all not in preset range, and, when the number of a rear time period residue thunderstorm group is 0, after keeping, the residue thunderstorm group of a time period is constant, and the identifier L that revises previous time period residue thunderstorm group is 0, otherwise, the thundercloud position feature point of a rear time period residue thunderstorm being rolled into a ball is as new thundercloud position feature point, and the identifier L that revises previous time period residue thunderstorm group is 0, and the identifier L that after revising, a time period residue thunderstorm is rolled into a ball is 1.
In other words, whether each clustering cluster of more adjacent time period (thunderstorm group) there is the clustering cluster of thundercloud position feature point drift in preset range, if exist, this thundercloud is described, and in front and back, two time period states do not change, and further judge other clustering cluster in adjacent time period thundercloud position feature point drift whether in preset range, until residue clustering cluster in adjacent time period thundercloud position feature point drift all not in preset range, now, if two groups of front and back time period residue clustering cluster number is 1, illustrating in a rear time period has the new thundercloud thundercloud that occurs simultaneously haveing been friends in the past to dissipate just, should be using a rear time period residue clustering cluster thundercloud position feature point as new thundercloud position feature point, revising this residue clustering cluster identifier of previous time period L is 0, and after revising, a time period this residue clustering cluster identifier L is 1, if one of the time period residue clustering cluster number in two groups of front and back is 0, one is 1, explanation has new thundercloud to generate or has been friends in the past thundercloud dissipation, if a rear time period residue clustering cluster number is 0, there is thundercloud dissipation in explanation, after keeping, a time period clustering cluster is constant, revising this residue clustering cluster identifier L of the upper time period is 0, if a rear time period residue clustering cluster number is 1, explanation has new thundercloud to generate, should be using a rear time period residue clustering cluster thundercloud position feature point as new thundercloud position feature point, and after revising, a time period this residue clustering cluster identifier L is 1, if two groups of front and back time period residue clustering cluster number is respectively 1 and 2, there is thundercloud fusion or thundercloud division in explanation, now, should be using a rear time period residue clustering cluster thundercloud position feature point as new thundercloud position feature point, and to revise this residue clustering cluster identifier of previous time period L be 0, after revising, a time period this residue clustering cluster identifier L is 1 simultaneously.
Step S105, according to the position feature point of thunderstorm group along with the change in displacement in passage of time, the position feature point of linear fit thunderstorm group, and the prediction next one time period thunderstorm group position that will occur.In some instances, shown in Fig. 2, this step specifically comprises: the position feature point that a plurality of time period thunderstorms that are connected are rolled into a ball carries out linear fit, and generates fitting formula, and by this fitting formula, calculates the position feature point of next one time period thunderstorm group.
According to the Forecasting Methodology of the thunder cloud mobile route based on lightning location system of the embodiment of the present invention, single thunder cloud and independent data is bunch corresponding one by one, in cluster analysis, identify thunder cloud division and the physical phenomenon merging, set up the logical relation between adjacent time period aggregate of data, obtain thunder cloud position prediction result more accurately, and utilize linear fit to determine that thunderstorm cumularsharolith puts unique point, make result more reasonable, can be power transmission line lightning shielding early warning effective reference frame is provided.
Further embodiment of the present invention also provides a kind of prognoses system of the thunder cloud mobile route based on lightning location system.
Fig. 3 is the structured flowchart of the prognoses system of the thunder cloud mobile route based on lightning location system according to an embodiment of the invention.As shown in Figure 3, the prognoses system 300 of the thunder cloud mobile route based on lightning location system, comprising: set up module 310, build module 320, computing module 330, acquisition module 340 and prediction module 350 according to an embodiment of the invention.
Wherein, set up module 310 for setting up cluster analysis database.More specifically, in some instances, lightning data screening sample condition is set in database, and the basic parameter of portraying thunder and lightning density, comprise: distance, for example be denoted as ε, minimum thunder and lightning is counted, for example, be denoted as minPts, and obtain in chronological order thunder and lightning sample data and classify according to the time period.
Build module 320 for obtaining the lightning data in the same time period, and calculate the distance between each thunder and lightning point periphery thunder and lightning and thunder and lightning point according to each thunder and lightning point, and put and distance structure lightning data spatial relation table by thunder and lightning.
Computing module 330 is for calculating the density relationship between any two thunder and lightning points.
Particularly, in some instances, by determine the density relationship between any two thunder and lightning points to give a definition:
Definition one: if thunder and lightning point p exists another thunder and lightning point q in default radius ε spatial dimension, defining between thunder and lightning point q and thunder and lightning point p is that direct density arrives relation.
Definition two: if there is the thunder and lightning point p of a sequence
1, p
2..., p
n, wherein, p
1=p, and p
1=q, if for each p
i+1with p
1be all that direct density arrives relation, claim q density to arrive p.
And then computing module 330 is by defining one or definition two density relationship that can calculate any two thunder and lightning points.
Acquisition module 340, for according to the density relationship between any two thunder and lightning points, lightning data being carried out to cluster, to generate thunderstorm group, and obtains the position feature point that thunderstorm is rolled into a ball.
Particularly, from any thunder and lightning point, if counting out, the thunder and lightning comprising do not reach minPts in its radius ε spatial dimension, this thunder and lightning point is labeled as noise spot, an otherwise newly-generated clustering cluster, by all, are all grouped into this type of and mark with the thunder and lightning point of this thunder and lightning point for direct density arrival relation or density arrival relation, and then look for the next one not make the point of any mark, repeat aforesaid operations, generate new clustering cluster, until all thunder and lightning sample datas are all labeled, after to be clustered completing, the identifier L initial value of giving each clustering cluster (being thunderstorm group) is 1, L span is 0 or 1, determine the position feature point that different thunderstorms are rolled into a ball.
In one embodiment of the present of invention, exist, this system 300 also comprises adjusting module 360 (not shown)s.Adjusting module 360 is for judging each thunderstorm of adjacent time period rolls into a ball whether there be the thunderstorm group of thundercloud position feature point drift in preset range, if existed, two time period states do not change in front and back to judge this thundercloud, further judge that whether other thunderstorms roll into a ball in adjacent time period thundercloud position feature point drift in preset range, until residue thunderstorm is rolled into a ball in adjacent time period thundercloud position feature point drift all not in preset range, and, when the number of a rear time period residue thunderstorm group is 0, after keeping, the residue thunderstorm group of a time period is constant, and the identifier L that revises previous time period residue thunderstorm group is 0, otherwise, the thundercloud position feature point of a rear time period residue thunderstorm being rolled into a ball is as new thundercloud position feature point, and the identifier L that revises previous time period residue thunderstorm group is 0, and the identifier L that after revising, a time period residue thunderstorm is rolled into a ball is 1.
In other words, whether each clustering cluster of more adjacent time period (thunderstorm group) there is the clustering cluster of thundercloud position feature point drift in preset range, if exist, this thundercloud is described, and in front and back, two time period states do not change, and further judge other clustering cluster in adjacent time period thundercloud position feature point drift whether in preset range, until residue clustering cluster in adjacent time period thundercloud position feature point drift all not in preset range, now, if two groups of front and back time period residue clustering cluster number is 1, illustrating in a rear time period has the new thundercloud thundercloud that occurs simultaneously haveing been friends in the past to dissipate just, should be using a rear time period residue clustering cluster thundercloud position feature point as new thundercloud position feature point, revising this residue clustering cluster identifier of previous time period L is 0, and after revising, a time period this residue clustering cluster identifier L is 1, if one of the time period residue clustering cluster number in two groups of front and back is 0, one is 1, explanation has new thundercloud to generate or has been friends in the past thundercloud dissipation, if a rear time period residue clustering cluster number is 0, there is thundercloud dissipation in explanation, after keeping, a time period clustering cluster is constant, revising this residue clustering cluster identifier L of the upper time period is 0, if a rear time period residue clustering cluster number is 1, explanation has new thundercloud to generate, should be using a rear time period residue clustering cluster thundercloud position feature point as new thundercloud position feature point, and after revising, a time period this residue clustering cluster identifier L is 1, if two groups of front and back time period residue clustering cluster number is respectively 1 and 2, there is thundercloud fusion or thundercloud division in explanation, now, should be using a rear time period residue clustering cluster thundercloud position feature point as new thundercloud position feature point, and to revise this residue clustering cluster identifier of previous time period L be 0, after revising, a time period this residue clustering cluster identifier L is 1 simultaneously.
Prediction module 350 for according to the position feature point of thunderstorm group along with the change in displacement in passage of time, the position feature point of thunderstorm group described in linear fit, and the prediction next one time period thunderstorm group position that will occur.Specifically comprise: the position feature point that 350 pairs of connected a plurality of time period thunderstorms of prediction module are rolled into a ball carries out linear fit, and generates fitting formula, and calculates by this fitting formula the position feature point that next time period thunderstorm is rolled into a ball.
According to the prognoses system of the thunder cloud mobile route based on lightning location system of the embodiment of the present invention, single thunder cloud and independent data is bunch corresponding one by one, in cluster analysis, identify thunder cloud division and the physical phenomenon merging, set up the logical relation between adjacent time period aggregate of data, obtain thunder cloud position prediction result more accurately, and utilize linear fit to determine that thunderstorm cumularsharolith puts unique point, make result more reasonable, can be power transmission line lightning shielding early warning effective reference frame is provided.
In description of the invention, it will be appreciated that, term " " center ", " longitudinally ", " laterally ", " length ", " width ", " thickness ", " on ", D score, " front ", " afterwards ", " left side ", " right side ", " vertically ", " level ", " top ", " end " " interior ", " outward ", " clockwise ", " counterclockwise ", " axially ", " radially ", orientation or the position relationship of indications such as " circumferentially " are based on orientation shown in the drawings or position relationship, only the present invention for convenience of description and simplified characterization, rather than device or the element of indication or hint indication must have specific orientation, with specific orientation structure and operation, therefore can not be interpreted as limitation of the present invention.
In addition, term " first ", " second " be only for describing object, and can not be interpreted as indication or hint relative importance or the implicit quantity that indicates indicated technical characterictic.Thus, at least one this feature can be expressed or impliedly be comprised to the feature that is limited with " first ", " second ".In description of the invention, the implication of " a plurality of " is at least two, for example two, and three etc., unless otherwise expressly limited specifically.
In the present invention, unless otherwise clearly defined and limited, the terms such as term " installation ", " being connected ", " connection ", " fixing " should be interpreted broadly, and for example, can be to be fixedly connected with, and can be also to removably connect, or be integral; Can be mechanical connection, can be to be also electrically connected to; Can be to be directly connected, also can indirectly be connected by intermediary, can be the connection of two element internals or the interaction relationship of two elements, unless separately there is clear and definite restriction.For the ordinary skill in the art, can understand as the case may be above-mentioned term concrete meaning in the present invention.
In the present invention, unless otherwise clearly defined and limited, First Characteristic Second Characteristic " on " or D score can be that the first and second features directly contact, or the first and second features are by intermediary indirect contact.And, First Characteristic Second Characteristic " on ", " top " and " above " but First Characteristic directly over Second Characteristic or oblique upper, or only represent that First Characteristic level height is higher than Second Characteristic.First Characteristic Second Characteristic " under ", " below " and " below " can be First Characteristic under Second Characteristic or tiltedly, or only represent that First Characteristic level height is less than Second Characteristic.
In the description of this instructions, the description of reference term " embodiment ", " some embodiment ", " example ", " concrete example " or " some examples " etc. means to be contained at least one embodiment of the present invention or example in conjunction with specific features, structure, material or the feature of this embodiment or example description.In this manual, to the schematic statement of above-mentioned term not must for be identical embodiment or example.And, the specific features of description, structure, material or feature can one or more embodiment in office or example in suitable mode combination.In addition,, not conflicting in the situation that, those skilled in the art can carry out combination and combination by the feature of the different embodiment that describe in this instructions or example and different embodiment or example.
Although illustrated and described embodiments of the invention above, be understandable that, above-described embodiment is exemplary, can not be interpreted as limitation of the present invention, and those of ordinary skill in the art can change above-described embodiment within the scope of the invention, modification, replacement and modification.
Claims (8)
1. a Forecasting Methodology for the thunder cloud mobile route based on lightning location system, is characterized in that, comprises the following steps:
Set up cluster analysis database;
Obtain the lightning data in the same time period, and calculate the distance between each thunder and lightning point periphery thunder and lightning and described thunder and lightning point according to each thunder and lightning point, and put and described distance structure lightning data spatial relation table by thunder and lightning;
Calculate the density relationship between any two thunder and lightning points;
According to the density relationship between described any two thunder and lightning points, described lightning data is carried out to cluster, to generate thunderstorm group, and obtain the position feature point that described thunderstorm is rolled into a ball;
According to the position feature point of described thunderstorm group along with the change in displacement in passage of time, the position feature point of thunderstorm group described in linear fit, and the prediction next one time period thunderstorm group position that will occur.
2. the Forecasting Methodology of the thunder cloud mobile route based on lightning location system as claimed in claim 1, is characterized in that, also comprises:
Judge in each thunderstorm group of adjacent time period and whether have the thunderstorm group of thundercloud position feature point drift in preset range, if existed, two time period states do not change in front and back to judge this thundercloud, further judge that whether other thunderstorms roll into a ball in adjacent time period thundercloud position feature point drift in preset range, until residue thunderstorm is rolled into a ball in adjacent time period thundercloud position feature point drift all not in preset range, and, when the number of a rear time period residue thunderstorm group is 0, after keeping, the residue thunderstorm group of a time period is constant, and the identifier L that revises previous time period residue thunderstorm group is 0, otherwise, the thundercloud position feature point of a rear time period residue thunderstorm being rolled into a ball is as new thundercloud position feature point, and the identifier L that revises previous time period residue thunderstorm group is 0, and the identifier L that after revising, a time period residue thunderstorm is rolled into a ball is 1.
3. the Forecasting Methodology of the thunder cloud mobile route based on lightning location system as claimed in claim 1, is characterized in that, by determine the density relationship between described any two thunder and lightning points to give a definition:
If thunder and lightning point p exists another thunder and lightning point q in radius ε spatial dimension, defining between thunder and lightning point q and thunder and lightning point p is that direct density arrives relation;
If there is the thunder and lightning point p of a sequence
1, p
2..., p
n, wherein, p
1=p, and p
1=q, if for each p
i+1with p
1be all that direct density arrives relation, claim q density to arrive p.
4. the Forecasting Methodology of the thunder cloud mobile route based on lightning location system as claimed in claim 1, is characterized in that, described position of predicting that next one time period thunderstorm group will occur specifically comprises:
The position feature point that a plurality of time period thunderstorms that are connected are rolled into a ball carries out linear fit, and generates fitting formula;
By described fitting formula, calculate the position feature point that next time period thunderstorm is rolled into a ball.
5. a prognoses system for the thunder cloud mobile route based on lightning location system, is characterized in that, comprising:
Set up module, the described module of setting up is for setting up cluster analysis database;
Build module, described structure module is for obtaining the lightning data in the same time period, and calculate the distance between each thunder and lightning point periphery thunder and lightning and described thunder and lightning point according to each thunder and lightning point, and put and described distance structure lightning data spatial relation table by thunder and lightning;
Computing module, described computing module is for calculating the density relationship between any two thunder and lightning points;
Acquisition module, described acquisition module, for described lightning data being carried out to cluster according to the density relationship between described any two thunder and lightning points, to generate thunderstorm group, and obtains the position feature point that described thunderstorm is rolled into a ball;
Prediction module, described prediction module for according to the position feature point of described thunderstorm group along with the change in displacement in passage of time, the position feature point of thunderstorm group described in linear fit, and the prediction next one time period thunderstorm group position that will occur.
6. the prognoses system of the thunder cloud mobile route based on lightning location system according to claim 5, is characterized in that, also comprises:
Adjusting module, described adjusting module is for judging each thunderstorm of adjacent time period rolls into a ball whether there be the thunderstorm group of thundercloud position feature point drift in preset range, if existed, two time period states do not change in front and back to judge this thundercloud, further judge that whether other thunderstorms roll into a ball in adjacent time period thundercloud position feature point drift in preset range, until residue thunderstorm is rolled into a ball in adjacent time period thundercloud position feature point drift all not in preset range, and the number of rolling into a ball at a rear time period residue thunderstorm is 0 o'clock, after keeping, the residue thunderstorm of time period group is constant, and the identifier L that revises previous time period residue thunderstorm group is 0, otherwise, the thundercloud position feature point that a rear time period residue thunderstorm is rolled into a ball is as new thundercloud position feature point, and the identifier L that revises previous time period residue thunderstorm group is 0, and the identifier L that after revising, a time period residue thunderstorm is rolled into a ball is 1.
7. the prognoses system of the thunder cloud mobile route based on lightning location system according to claim 5, is characterized in that, by determine the density relationship between described any two thunder and lightning points to give a definition:
If thunder and lightning point p exists another thunder and lightning point q in radius ε spatial dimension, defining between thunder and lightning point q and thunder and lightning point p is that direct density arrives relation;
If there is the thunder and lightning point p of a sequence
1, p
2..., p
n, wherein, p
1=p, and p
1=q, if for each p
i+1with p
1be all that direct density arrives relation, claim q density to arrive p.
8. the prognoses system of the thunder cloud mobile route based on lightning location system according to claim 5, it is characterized in that, described prediction module is carried out linear fit for the position feature point that a plurality of time period thunderstorms that are connected are rolled into a ball, and generate fitting formula, and calculate by described fitting formula the position feature point that next time period thunderstorm is rolled into a ball.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410422077.8A CN104200281B (en) | 2014-08-25 | 2014-08-25 | The Forecasting Methodology and system of thunder cloud mobile route based on lightning location system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410422077.8A CN104200281B (en) | 2014-08-25 | 2014-08-25 | The Forecasting Methodology and system of thunder cloud mobile route based on lightning location system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104200281A true CN104200281A (en) | 2014-12-10 |
| CN104200281B CN104200281B (en) | 2017-09-22 |
Family
ID=52085570
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410422077.8A Active CN104200281B (en) | 2014-08-25 | 2014-08-25 | The Forecasting Methodology and system of thunder cloud mobile route based on lightning location system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104200281B (en) |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104898186A (en) * | 2015-06-09 | 2015-09-09 | 天津大学 | Method for extracting and automatically drawing characteristic points of groove and ridge lines |
| CN104991145A (en) * | 2015-07-17 | 2015-10-21 | 云南电力试验研究院(集团)有限公司 | Lightning early warning method based on atmospheric electric field instrument and thunder cloud movement |
| CN107271793A (en) * | 2017-07-07 | 2017-10-20 | 曹发祥 | One kind automation Lightning Warning system |
| CN107957597A (en) * | 2017-12-12 | 2018-04-24 | 国网新疆电力公司电力科学研究院 | A kind of method for extracting thunder and lightning cluster movement locus and trend |
| CN108879644A (en) * | 2018-07-28 | 2018-11-23 | 安徽诚意电气科技有限公司 | A kind of intelligent lightning protection regulator control system based on region division |
| CN108899884A (en) * | 2018-07-28 | 2018-11-27 | 安徽诚意电气科技有限公司 | A kind of lightning protection Intelligentized regulating and controlling method suitable for subregion detection |
| CN106157540B (en) * | 2016-07-12 | 2019-01-08 | 中国石油化工股份有限公司 | A kind of Lightning Warning and thundercloud trajectory predictions method based on Lightning Warning system |
| CN109738970A (en) * | 2018-12-07 | 2019-05-10 | 国网江苏省电力有限公司电力科学研究院 | The method, apparatus and storage medium for realizing Lightning Warning are excavated based on lightning data |
| CN110487294A (en) * | 2019-08-29 | 2019-11-22 | 绍兴文理学院 | Intelligent path planning system and method based on weather cloud atlas |
| CN112630597A (en) * | 2020-12-25 | 2021-04-09 | 广东电网有限责任公司 | Fault determination method, device, equipment and storage medium |
| CN113011663A (en) * | 2021-03-26 | 2021-06-22 | 云南电网有限责任公司电力科学研究院 | Method and device for predicting lightning activity of power transmission line area |
| CN115201938A (en) * | 2022-07-26 | 2022-10-18 | 保定市气象局 | Strong convection weather nowcasting method and system based on thunderstorm high-pressure analysis |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101650444A (en) * | 2008-08-11 | 2010-02-17 | 华东电力试验研究院有限公司 | Method for calculating thunderbolt density |
| CN102141587A (en) * | 2010-12-03 | 2011-08-03 | 湖州电力局 | Lightening locating data imaging method |
| CN102253426A (en) * | 2011-04-29 | 2011-11-23 | 国网电力科学研究院武汉南瑞有限责任公司 | Method for predicting lightning motion by space density clustering |
| US20130268232A1 (en) * | 2012-04-10 | 2013-10-10 | Qualcomm Incorporated | Informative Display for Statistical Positioning System |
-
2014
- 2014-08-25 CN CN201410422077.8A patent/CN104200281B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101650444A (en) * | 2008-08-11 | 2010-02-17 | 华东电力试验研究院有限公司 | Method for calculating thunderbolt density |
| CN102141587A (en) * | 2010-12-03 | 2011-08-03 | 湖州电力局 | Lightening locating data imaging method |
| CN102253426A (en) * | 2011-04-29 | 2011-11-23 | 国网电力科学研究院武汉南瑞有限责任公司 | Method for predicting lightning motion by space density clustering |
| US20130268232A1 (en) * | 2012-04-10 | 2013-10-10 | Qualcomm Incorporated | Informative Display for Statistical Positioning System |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104898186A (en) * | 2015-06-09 | 2015-09-09 | 天津大学 | Method for extracting and automatically drawing characteristic points of groove and ridge lines |
| CN104898186B (en) * | 2015-06-09 | 2017-05-03 | 天津大学 | Method for extracting and automatically drawing characteristic points of groove and ridge lines |
| CN104991145A (en) * | 2015-07-17 | 2015-10-21 | 云南电力试验研究院(集团)有限公司 | Lightning early warning method based on atmospheric electric field instrument and thunder cloud movement |
| CN106157540B (en) * | 2016-07-12 | 2019-01-08 | 中国石油化工股份有限公司 | A kind of Lightning Warning and thundercloud trajectory predictions method based on Lightning Warning system |
| CN107271793A (en) * | 2017-07-07 | 2017-10-20 | 曹发祥 | One kind automation Lightning Warning system |
| CN107957597A (en) * | 2017-12-12 | 2018-04-24 | 国网新疆电力公司电力科学研究院 | A kind of method for extracting thunder and lightning cluster movement locus and trend |
| CN108899884A (en) * | 2018-07-28 | 2018-11-27 | 安徽诚意电气科技有限公司 | A kind of lightning protection Intelligentized regulating and controlling method suitable for subregion detection |
| CN108879644A (en) * | 2018-07-28 | 2018-11-23 | 安徽诚意电气科技有限公司 | A kind of intelligent lightning protection regulator control system based on region division |
| CN109738970A (en) * | 2018-12-07 | 2019-05-10 | 国网江苏省电力有限公司电力科学研究院 | The method, apparatus and storage medium for realizing Lightning Warning are excavated based on lightning data |
| CN109738970B (en) * | 2018-12-07 | 2021-02-02 | 国网江苏省电力有限公司电力科学研究院 | Method, device and storage medium for realizing lightning early warning based on lightning data mining |
| CN110487294A (en) * | 2019-08-29 | 2019-11-22 | 绍兴文理学院 | Intelligent path planning system and method based on weather cloud atlas |
| CN112630597A (en) * | 2020-12-25 | 2021-04-09 | 广东电网有限责任公司 | Fault determination method, device, equipment and storage medium |
| CN113011663A (en) * | 2021-03-26 | 2021-06-22 | 云南电网有限责任公司电力科学研究院 | Method and device for predicting lightning activity of power transmission line area |
| CN113011663B (en) * | 2021-03-26 | 2023-01-20 | 云南电网有限责任公司电力科学研究院 | Method and device for predicting lightning activity of power transmission line area |
| CN115201938A (en) * | 2022-07-26 | 2022-10-18 | 保定市气象局 | Strong convection weather nowcasting method and system based on thunderstorm high-pressure analysis |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104200281B (en) | 2017-09-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104200281A (en) | Method and system for predicting thunder cloud moving path based on lightning location system | |
| CN104166171A (en) | Method and system for predicating thundercloud cover area based on lightning locating system | |
| CN107168342B (en) | Pedestrian trajectory prediction method for robot path planning | |
| CN110677859B (en) | Method and device for determining weak coverage area and computer readable storage medium | |
| CN103218675B (en) | A kind of based on the short-term load forecasting method of cluster and moving window | |
| CN102253426B (en) | Method for predicting lightning motion by space density clustering | |
| CN104200288A (en) | Equipment fault prediction method based on factor-event correlation recognition | |
| CN110579823A (en) | method and system for forecasting short-term rainfall | |
| CN104200081A (en) | Method and system for forecasting landed typhoon characterization factors based on historical data | |
| US20140365103A1 (en) | Method and system for collecting traffic data | |
| CN103426121A (en) | Method for calculating power grid operation evaluation index dimensionless | |
| CN104794927B (en) | Bus arrival time forecasting method | |
| CN106710316A (en) | Method and device of airspace capacity determination based on bad weather condition | |
| CN105005822A (en) | Optimal step length and dynamic model selection based ultrahigh arch dam response prediction method | |
| Nie et al. | Optimizing actuated traffic signal control using license plate recognition data: Methods for modeling and algorithm development | |
| CN114596709B (en) | Data processing method, device, equipment and storage medium | |
| CN106980874B (en) | A kind of multi-time Scales dimension data fusion method towards distribution big data | |
| Zha et al. | Selection of time window for wind power ramp prediction based on risk model | |
| CN106408936B (en) | A kind of highway anomalous event real-time detection method based on data in mobile phone | |
| US20170039306A1 (en) | Traffic System Optimization Device | |
| CN107479112B (en) | Dense fog prediction method, device and system | |
| CN113705693A (en) | Power grid lightning early warning method, device, recording medium and system | |
| CN111160747B (en) | Scheduling method and device for unmanned robot taxi and electronic equipment | |
| CN108055639A (en) | A kind of density of stream of people Forecasting Methodology and system based on signaling data analysis | |
| CN102142192B (en) | Method and system for forewarning pedestrian traffic congestion based on combined multi-step prediction |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |