CN107679167A - Weather Risk appraisal procedure and analysis and early warning platform based on lattice point meteorological data - Google Patents
Weather Risk appraisal procedure and analysis and early warning platform based on lattice point meteorological data Download PDFInfo
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Abstract
The present invention discloses a kind of Weather Risk appraisal procedure and analysis and early warning platform based on lattice point meteorological data, including：Data acquisition module, data cleansing module, data fusion module, weather risk evaluation module.The Weather Risk appraisal procedure and analysis and early warning platform, the history meteorological data obtained by multiple channel is fully cleaned, correct mistake present in history meteorological data, the preciseness in the source of the data of database based on guarantee.To combining become more meticulous terrain data and Satellite checking after the history meteorological data interpolation after cleaning, lattice point meteorological data is formed, all related datas are uniformly arrived into same physical space, by spatial field algorithm fusion, to obtain the numerical value closest to fact.The Weather Risk appraisal procedure and analysis and early warning platform, existing weather forecast data are included into Weather Risk assessment models, realize the report of real-time weather disaster, and can be assessed exactly for the risk class of targeted gas phase disaster.
Description
Technical field
The present invention relates to weather risk evaluation areas, more particularly to the Weather Risk assessment side based on lattice point meteorological data
Method and analysis and early warning platform.
Background technology
Meteorological disaster risk be to the regular period risk area by varying strength meteorological disaster possibility shape and its may make
Into consequence carry out quantitative analysis and assessment, meteorological disaster risk management is commented by risk identification, risk estimation, risk
Valency, and the various risk management technologies of optimum organization on this basis, meteorological disaster risk is implemented to efficiently control and properly
The consequence that processing risk is caused, to reach knowing the real situation with the minimum old maximum safety guarantee of acquisition.Calamity source is assessed
It is the key link in management of hazard risk, is the scientific basis for carrying out the activity such as effective disaster prevention, emergency management and rescue.
The appraisal procedure of weather risk, is mainly analyzed historical data, draws Flood inducing factors and its weight coefficient,
Meteorological disaster risk index is established, and weather risk assessment models are established with this, finally, day is carried out by the data for loading forecast
Gas risk assessment.
Therefore, the prior art is defective, it is necessary to improve.
The content of the invention
The purpose of the present invention is overcome the deficiencies in the prior art, there is provided a kind of Weather Risk based on lattice point meteorological data
Appraisal procedure, comprise the following steps：
S1, collect history meteorological data：Weather Risk is collected by multiple channel and mode and assesses each of required various regions
The history meteorological data of class weather；
S2, clean history meteorological data：Compare the uniformity of history meteorological data collected in several ways, detect
Comprising the dirty data including invalid value and missing values and handled in history meteorological data, again to history meteorological data after processing
Repeated examination and verification are carried out, corrects mistake present in history meteorological data；
S3：Lattice point meteorological data：Row interpolation is entered to the history meteorological data of all kinds of scarce surveys, forms complete meteorological data
Gather, the meteorological detection data for the independent website that script is laid out according to certain distribution density, be converted into the face of lattice point
Data, realize that meteorological data compartmentalization covers, and verified to obtain the numerical value closest to fact；
S4：Establish basic database：All kinds of lattice point meteorological datas, which is combined, includes the time and geographical area information exists
Interior data establish basic database；
S5：Weather Risk is assessed：The risk class of each regional all kinds of meteorological disasters is carried out according to basic database
Assess.
Further, the approach of the historical data needed for Weather Risk assessment is obtained in step S1 to be included：National weather is seen
Survey station data, other Professional Meteorological observation station data, satellite remote sensing date and radar observation data.
Further, the dirty data in history meteorological data is cleaned using quality control algorithm in step S2.
Further, step S3 comprises the following steps：
S301：With dual kriging interpolation method, all kinds of history meteorological datas are entered with row interpolation, is formed complete meteorological
Data acquisition system, the meteorological detection data for the independent website that script is laid out according to certain distribution density, is converted into lattice point
Face data, realize meteorological data compartmentalization cover；
S302：Reveal data using including topographic(al) data assimilation technique, Satellite card technology plaid matching and verified；
Step S303：All related datas are uniformly arrived into same physical space, by spatial field algorithm fusion, to obtain most
Close to live numerical value.
Further, step S4 comprises the following steps：
S401：According to time, the table structure of geographical area information component basic database；
S402：The form of all kinds of meteorological elements can be read according to time, longitude and latitude by making；
S403：Synthetic standards SQL, and insert basic database.
Further, step S5 comprises the following steps：
S501：By way of threshold values is set, the risk class of all kinds of weather of objective is divided；
S502：Risk evaluation model is established for the targeted gas phase disaster on objective；
S503：Using the master of objective, objective weather for forcasting data as risk evaluation model input data, according to wind
Dangerous assessment models assess the probability that targeted gas phase disaster occurs, and risk class is divided according to the probability of happening of targeted gas phase disaster.
The present invention also provides a kind of Meteorological Risk Analysis early warning platform based on lattice point meteorological data, the Weather Risk
Analysis and early warning platform includes：
Data acquisition module, all kinds of weather of the various regions needed for Weather Risk assessment are collected by multiple channel and mode
History meteorological data；
Data cleansing module, for correcting mistake present in history meteorological data；
Data fusion module, the data obtained to data cleansing module carry out interpolation processing and verified, closest to obtain
Live numerical value；
Weather risk evaluation module, the weather risk evaluation module includes overview display unit and target risk assesses list
Member, the overview display unit are used for the assessed value for showing all kinds of Weather Elements of objective, and target risk assessment unit is used
In the risk class of all kinds of meteorological disasters of displaying objective.
Using such scheme, the present invention provides a kind of based on the Weather Risk appraisal procedure of lattice point meteorological data and analysis
Early warning platform, has the advantages that：
1st, the Weather Risk appraisal procedure and analysis and early warning platform, the history meteorological data obtained by multiple channel is entered
Row fully cleaning, comprising the dirty data including invalid value and missing values and handled in detection history meteorological data, after processing
Repeated examination and verification are carried out to history meteorological data again, correct mistake present in history meteorological data, using ensure as
The preciseness in the source of the data of basic database.
2nd, row interpolation is entered to the history meteorological data after cleaning with dual kriging method interpolation method.Kriging technique utilizes change
Different function weighs the spatial coherence between data, and emphasizes local optimum and unbiased esti-mator, is most widely used.Gold side in gram
General kriging method in method assumes the tendency that there is regionalized variable certain to be characterized with multinomial, without stationary hypothesis,
It is suitable for non-stationary situation, is widely used in the field of earth sciences such as geology, geography, ocean and meteorology.In meteorological data grid
When changing processing, for the shortcomings that universal Kriging is computationally intensive, time-consuming, by equivalence replacement, general Kriging regression method is turned
Change dual kriging interpolation method into, realize the global difference of regionalized variable, while precision is not influenceed, greatly improve meter
Efficiency is calculated, has the characteristics that amount of calculation is small, easily realizes.
The 3rd, history meteorological data after interpolation is combined to become more meticulous terrain data and Satellite checking, form lattice point
Change meteorological data, all related datas are uniformly arrived into same physical space, by spatial field algorithm fusion, to obtain closest to real
The numerical value of condition.
4th, the Weather Risk appraisal procedure and analysis and early warning platform are applied to a variety of meteorological disasters, by existing weather forecast
Data include Weather Risk assessment models, realize the report of real-time weather disaster, and can be exactly for targeted gas phase disaster
Risk class is assessed.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the required accompanying drawing used in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with
Structure according to these accompanying drawings obtains other accompanying drawings.
Fig. 1 is the analysis and early warning platform structure schematic diagram of the present invention；
Fig. 2 is the schematic flow sheet of the Weather Risk appraisal procedure of the present invention.
The realization, functional characteristics and advantage of the object of the invention will be described further referring to the drawings in conjunction with the embodiments.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention is described in detail.
Shown referring to Figures 1 and 2, the present invention provides a kind of Meteorological Risk Analysis early warning based on lattice point meteorological data and put down
Platform, the Meteorological Risk Analysis early warning platform include：
Data acquisition module, all kinds of weather of the various regions needed for Weather Risk assessment are collected by multiple channel and mode
History meteorological data, wherein from the mode such as national weather observation station, other Professional Meteorological observation station, satellite remote sensing and radar observations
The data obtained with channel.
Data cleansing module, compares the uniformity of history meteorological data, in detection history meteorological data comprising invalid value and
Dirty data including missing values is simultaneously handled, and is carried out repeated examination and verification after processing to history meteorological data again, is corrected
Mistake present in history meteorological data.
Data fusion module, the data obtained to data cleansing module carry out interpolation processing and verified, closest to obtain
Live numerical value；
Weather risk evaluation module, the weather risk evaluation module includes overview display unit and target risk assesses list
Member, the overview display unit are used for the assessed value for showing all kinds of Weather Elements of objective, and target risk assessment unit is used
In the risk class of all kinds of meteorological disasters of displaying objective.
The present invention also provides a kind of Weather Risk appraisal procedure based on lattice point meteorological data, comprises the following steps：
S1, collect history meteorological data：Weather Risk is collected by multiple channel and mode and assesses each of required various regions
The history meteorological data of class weather；
S2, clean history meteorological data：Compare the uniformity of history meteorological data collected in several ways, detect
Comprising the dirty data including invalid value and missing values and handled in history meteorological data, again to history meteorological data after processing
Repeated examination and verification are carried out, corrects mistake present in history meteorological data；
S3：Lattice point meteorological data：Row interpolation is entered to all kinds of history meteorological datas, forms complete meteorological data set,
The meteorological detection data for the independent website that script is laid out according to certain distribution density, the face data of lattice point are converted into,
Realize that meteorological data compartmentalization covers, and verified to obtain the numerical value closest to fact；
S4：Establish basic database：All kinds of lattice point meteorological datas, which is combined, includes the time and geographical area information exists
Interior data establish basic database；
S5：Weather Risk is assessed：The risk class of each regional all kinds of meteorological disasters is carried out according to basic database
Assess.
Wherein, Weather Risk is obtained in step S1 and assesses the approach of required historical data including being observed from national weather
Stand, the data that the mode such as other Professional Meteorological observation station, satellite remote sensing and radar observations obtains.
As a kind of embodiment, the dirty data in history meteorological data is carried out clearly using quality control algorithm in step S2
Wash.The basic ideas of meteorological data quality control diagnostic method are with time (a weather forecast) data during an observation or one day
Data are unit, and the result (quality control data) for carrying out various individual event quality inspections is established into a quality control data table,
Table data are progressively confirmed with the superseded method analyzed again using high suspicious data, is completed to the result of various individual event quality inspections
Comprehensive distinguishing, mainly include the following steps that：
First, key element quality inspection comprehensive distinguishing table and key element quality inspection code table are established
Assuming that m items will be known as by carrying out the meteorological data of quality control, carrying out more key element test of inner consistencys has k items, then
Initially set up two tables：Key element quality inspection comprehensive distinguishing table and key element quality inspection code table.
(1) key element quality inspection comprehensive distinguishing table
Key element quality inspection comprehensive distinguishing table is recording the inspection of each single item extreme value, internal consistency inspection, time consistency
The result of property inspection, and it is used as the statistical disposition worksheet (being shown in Table a) of key element quality inspection comprehensive distinguishing.
Table a key element quality inspection comprehensive distinguishing tables
Wherein, table element aI, jFor the result of individual event quality inspection, j=1~m, corresponding m item meteorological data key elements, i=1~
N (n=m+k+L), i=1~m correspond to m item meteorological data key elements, and i=m+1~m+k corresponds to the more key element test of inner consistencys of k items
Project, i=m+1+1~m+k+L corresponds to L classes time consistency and examines project.
As i=j, aI, jFor key element permissible value scope, the result that climatology boundary value is examined and extreme value is examined；
As i ≠ j and i≤m, aI, jFor the result of two key element consistency checks；
As m+1≤i≤m+k, aI, jFor the result of more key elements (corresponding j key elements) test of inner consistency；
As i=m+k+1~m+k+L, aI, jThe result examined for key element time consistency；
Setting：
aI, j=0, initial value (quality inspection for not carrying out respective item)；
aI, j=-1, represent the quality inspection by respective item；
aI, j=1, expression does not pass through the quality inspection of respective item；
(2) key element quality inspection code table
Key element quality inspection code table (is shown in Table to record the intermediate result of key element quality inspection comprehensive distinguishing and final result
b)：
Table b key element quality inspection code tables
Wherein, table element bjFor key element quality inspection code, j=1~m, corresponding m item meteorological data key elements
Setting：
bj=9, quality inspection is not done in initial value, expression；
bj=0, represent that data are correct；
bj=1, represent that data are suspicious；
bj=2, represent error in data；
bj=8, represent that data lack and survey；
(3) each element to key element quality inspection comprehensive distinguishing table assigns initial value 0.Each element to key element quality inspection code table
Initial value 9 is assigned, data lack assignment 8 when surveying.
2nd, individual event assay is recorded
(1) record of key element permissible value scope and weather educational circles limit value assay
The inspection of key element permissible value scope and weather educational circles limit value is carried out, by the key element of inspection, puts corresponding bj=0,
Not verified key element, put corresponding bj=2.
(2) record of key element extreme value assay
Key element extreme value inspection is carried out, by the key element of inspection, puts corresponding aI, j=-1, not verified key element, puts phase
The a answeredI, j=1.
The record of (3) two key element consistency check results
Two key element consistency checks are carried out, when passing through inspection, to a corresponding to corresponding two key elementI, j=-1, aj, i=-1, not
When passing through inspection, to a corresponding to corresponding two key elementI, j=1, aI, j=1.
(4) record of more key element consistency check results
Carry out more key element consistency checks.When passing through inspection, in i rows corresponding to inspection project, to corresponding to relevant key element
Each column element assignment aI, j=-1, when not verified, to relevant key element in i (i=m+1~m+k) corresponding to inspection project OK
Corresponding each column element assignment aI, j=1
(5) record of key element time consistency assay
The inspection of key element time consistency can be divided into the polytypes such as first 24 hours change, the change of latter 24 hours.Will
The result that plain time consistency is examined records in lines by type.When passing through inspection, i (i=m+k+1~m corresponding to type are being examined
+ k+L) OK, to column element assignment a corresponding to key elementI, j=-1, when not verified, in (i (i=m+k+ corresponding to inspection project
1~m+k+L) OK, to column element assignment a corresponding to key elementI, j=1.
3rd, comprehensive statistics differentiates quality inspection
Individual event assay record finishes, that is, the comprehensive statistics differentiation for carrying out quality inspection is as follows：
(1) for bj=9 (j=1~m) situation, count respectively in key element quality inspection comprehensive distinguishing table+corresponding each row
aI, j=1 and aI, j=-1 quantity axj and ayj.The picking maximum max x in each axj,
If max x >=2,：
If there was only an axd, the corresponding bd=2 of assignment equal to max x in axj；Simultaneously to ad, j ≠ 0 (j=1~
M) situation assignment ad, j=0 (j=1~m), ai, d=0；To ai, the situation of d ≠ 0 (i=m+1~m+k), the assignment row (i
OK) all elements as, j=0 (j=1~m).Assignment ai, d=0 (i=1~n) simultaneously.
If being equal to max x in each axj has two or more, therefrom the picking minimum value max y in each ayj, for each
All situations (being expressed as ayd) equal to max y in ayj, assignment each bd=2 accordingly；Simultaneously to ad, j ≠ 0 (j=1~m)
Situation assignment ad, j=0 (j=1~m), ai, d=0；To ai, the situation of d ≠ 0 (i=m+1~m+k), the assignment row (i rows)
All elements as, j=0 (j=1~m).Assignment ai, d=0 (i=1~n) simultaneously.
If not emphasizing the concept by inspection, ayj situation can also be ignored, the situation equal to axj is put on an equal footing.
Max x≤1, then for all axj=1, the corresponding bj=1 of assignment；For all axj=0, therefrom picking ayj
>=1 all row, the corresponding bj=0 of assignment.Quality control is completed.
(2) circulation performs said process, until max x≤1.
Step S3 comprises the following steps：
S301：With interpolation method, row interpolation is entered to the history meteorological data of all kinds of scarce surveys, forms complete meteorological data
Gather, the meteorological detection data for the independent website that script is laid out according to certain distribution density, be converted into the face of lattice point
Data, realize that meteorological data compartmentalization covers；
S302：Reveal data using including topographic(al) data assimilation technique, Satellite card technology plaid matching and verified；
S303：All related datas are uniformly arrived into same physical space, it is closest to obtain by spatial field algorithm fusion
Live numerical value.
When weather risk is assessed, it is necessary to according to the history meteorological data of certain point as reference, and because locality is not built
The reason such as weather station or meteorological data missing, it is necessary to using interpolation method by irregular website Data Interpolation to regular grid,
To obtain the data estimate of the point and intuitively area data distribution situation.
Existing meteorological data lattice point interpolation method has common Ke Lijin (Kriging) method, inverse distance weighted interpolation method
(IDW), gradient is apart from inverse ratio interpolation method (GIDW), polynomial interpolation, Spline interpolation method (Spline), trend surface interpolation
Method etc..Wherein Kriging technique is using the spatial coherence between variation function measurement data, and emphasizes that local optimum and unbiased are estimated
Meter, is most widely used.
Kriging method includes common kriging method and general kriging method.Common kriging method assumes that compartmentalization becomes
Amount meets second-order stationary and assumed with intrinsic it is assumed that general kriging method assumes that regionalized variable has what certain was characterized with multinomial
Tendency, without stationary hypothesis, it is suitable for non-stationary situation, the non-dual kriging method in general kriging method extensively should
For field of earth sciences such as geology, geography, ocean and meteorologies.When meteorological data gridding is handled, for universal Kriging
Computationally intensive, the shortcomings that time-consuming, by equivalence replacement, general Kriging regression method is converted into dual kriging interpolation method, it is real
The global difference of existing regionalized variable, while precision is not influenceed, greatly improves computational efficiency, has that amount of calculation is small, holds
The features such as easily realizing.
It is assumed that a space physics variable can be represented with a random function F (X), wherein X is locus vector, false
It is fi such as in N number of different locus Xi physical quantity observation value, wherein 1≤i≤N.Kriging method is exactly to construct F (X)
An approximate function f (X) so that f (Xi)=fi.
Wherein, f (X) simplest form can be taken as fi different weight θ i linear combination：
General kriging method assumes that regionalized variable is single order non-stationary, and therefore, F (X) can be expressed as region tendency item d
(X) with residual error item e (X) sum：F (X)=d (X)+e (X).
Tendency item d (X) is typically expressed as locus vector X multinomial：
C (h) is the covariance of residual error item, and in the case of assuming that residual error item e (X) isotropic, covariance is characterized as a little
To the function apart from correlative, point is adjusted the distance, and only mutual distance between observation field is relevant for correlative, the absolute position with them
Put unrelated.
The equation group of general Kriging method：
Wherein, Cij=C (hi (Xj)), Ci (X)=C (hi (X)), μ is Lagrange multiplier.
Because the right-hand vector of this formula depends on the locus vector X of interpolation point；Therefore, will for each interpolation point X
Again equation group is solved, obtains weight θ i, can just obtain interpolation point X value f (X).
By equivalence transformation, nonallelic general Kriging regression method is converted into dual kriging interpolation method,
Because above formula is independent of interpolation point X, therefore only demand solution once obtains factor alpha j and β k, you can calculates different insert
It is worth point X value f (X).
Step S4 is optimized to database and comprised the following steps to establish basic database：
S401：According to time, the table structure of geographical area information component basic database；
S402：The form of all kinds of meteorological elements can be read according to time, longitude and latitude by making；
S403：Synthetic standards SQL, and insert basic database.
Step S5 Weather Risks, which are assessed, to use the exact algorithm of insurance field to make risk assessment, implemented as one kind
Example, herein Weather Risk assessment comprise the following steps：
S501：By way of threshold values is set, the risk class of all kinds of weather of objective is divided；
S502：Risk evaluation model is established for the targeted gas phase disaster on objective；
S503：Using the master of objective, objective weather for forcasting data as risk evaluation model input data, according to wind
Dangerous assessment models assess the probability that targeted gas phase disaster occurs, and risk class is divided according to the probability of happening of targeted gas phase disaster.
In summary, the present invention provides a kind of Weather Risk appraisal procedure and analysis and early warning based on lattice point meteorological data
Platform, have the advantages that：
The Weather Risk appraisal procedure and analysis and early warning platform, the history meteorological data obtained by multiple channel is carried out
Fully cleaning, comprising the dirty data including invalid value and missing values and handled in detection history meteorological data, after processing again
Repeated examination and verification are carried out to history meteorological data, mistake present in history meteorological data is corrected, to ensure to be used as base
The preciseness in the source of the data of plinth database.
Row interpolation is entered to the history meteorological data after cleaning with dual kriging method interpolation method.Kriging technique utilizes variation
Function weighs the spatial coherence between data, and emphasizes local optimum and unbiased esti-mator, is most widely used.Kriging method
In general kriging method assume the tendency that there is regionalized variable certain to be characterized with multinomial, without stationary hypothesis, fit
Together in non-stationary situation, it is widely used in the field of earth sciences such as geology, geography, ocean and meteorology.In meteorological data gridding
During processing, for the shortcomings that universal Kriging is computationally intensive, time-consuming, by equivalence replacement, general Kriging regression method is changed
Into antithesis Kriging regression method, the global difference of regionalized variable is realized, while precision is not influenceed, greatly improves calculating
Efficiency, have the characteristics that amount of calculation is small, easily realizes.
History meteorological data after interpolation is combined to become more meticulous terrain data and Satellite checking, forms lattice point
Meteorological data, all related datas are uniformly arrived into same physical space, by spatial field algorithm fusion, to obtain closest to fact
Numerical value.
The Weather Risk appraisal procedure and analysis and early warning platform are applied to a variety of meteorological disasters, by existing weather forecast number
According to Weather Risk assessment models are included, the report of real-time weather disaster is realized, and the wind of targeted gas phase disaster can be directed to exactly
Dangerous grade is assessed.
These are only presently preferred embodiments of the present invention, be not intended to limit the invention, it is all the present invention spirit and
All any modification, equivalent and improvement made within principle etc., should be included in the scope of the protection.
Claims (7)
1. a kind of Weather Risk appraisal procedure based on lattice point meteorological data, it is characterised in that comprise the following steps：
S1, collect history meteorological data：All kinds of days of the various regions needed for Weather Risk assessment are collected by multiple channel and mode
The history meteorological data of gas；
S2, cleaning history meteorological data：Compare the uniformity by the history meteorological data collected by multiple channel and mode, examine
Survey in history meteorological data comprising the dirty data including invalid value and missing values and handled, again to history meteorology number after processing
According to repeated examination and verification is carried out, mistake present in history meteorological data is corrected；
S3, lattice point meteorological data：Row interpolation is entered to history meteorological data, forms complete meteorological data set, will be pressed originally
The meteorological detection data for the independent website being laid out according to certain distribution density, the face data of lattice point are converted into, realized meteorological
Data areaization covers, and is verified to obtain the numerical value closest to fact；
S4, establish basic database：All kinds of lattice point meteorological datas was combined including time and geographical area information
Data establish basic database；
S5, Weather Risk are assessed：The risk class of each regional all kinds of meteorological disasters is assessed according to basic database.
2. the Weather Risk appraisal procedure according to claim 1 based on lattice point meteorological data, it is characterised in that step
The approach of the historical data needed for Weather Risk assessment is obtained in S1 to be included：National weather observation station data, other Professional Meteorologicals
Observation station data, satellite remote sensing date and radar observation data.
3. the Weather Risk appraisal procedure according to claim 1 based on lattice point meteorological data, it is characterised in that step
The dirty data in history meteorological data is cleaned using quality control algorithm in S2.
4. the Weather Risk appraisal procedure according to claim 1 based on lattice point meteorological data, it is characterised in that step
S3 comprises the following steps：
S301, with dual kriging interpolation method, row interpolation is entered to all kinds of history meteorological datas, forms complete meteorological data
Gather, the meteorological detection data for the independent website that script is laid out according to certain distribution density, be converted into the face of lattice point
Data, realize that meteorological data compartmentalization covers；
S302, using including topographic(al) data assimilation technique, Satellite card technology plaid matching reveal data carry out inverting checking；
S303, all related datas are uniformly arrived into same physical space, by spatial field algorithm fusion, to obtain closest to fact
Numerical value.
5. the Weather Risk appraisal procedure according to claim 1 based on lattice point meteorological data, it is characterised in that step
S4 comprises the following steps：
S401, according to time, the table structure of geographical area information component basic database；
S402, making can read the form of all kinds of meteorological elements according to time, longitude and latitude；
S403, synthetic standards SQL, and insert basic database.
6. the Weather Risk appraisal procedure according to claim 1 based on lattice point meteorological data, it is characterised in that step
S5 comprises the following steps：
S501, by way of threshold values is set, divide all kinds of weather of objective risk class；
S502, for the targeted gas phase disaster on objective establish risk evaluation model；
S503, using the master of objective, objective weather for forcasting data as the input data of risk evaluation model, commented according to risk
Estimate the probability of model evaluation targeted gas phase disaster generation, risk class is divided according to the probability of happening of targeted gas phase disaster.
A kind of 7. Meteorological Risk Analysis early warning platform based on lattice point meteorological data, it is characterised in that the Weather Risk point
Analysis early warning platform includes：
Data acquisition module, the history of all kinds of weather of the various regions needed for Weather Risk assessment is collected by multiple channel and mode
Meteorological data；
Data cleansing module, for correcting mistake present in history meteorological data；
Data fusion module, the data obtained to data cleansing module carry out interpolation processing and verified, to obtain closest to fact
Numerical value；
Weather risk evaluation module, the weather risk evaluation module include overview display unit and target risk assessment unit,
The overview display unit is used for the assessed value for showing all kinds of Weather Elements of objective, and target risk assessment unit is used to open up
Show the risk class of all kinds of meteorological disasters of objective.
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