CN103995951A - Typhoon key parameter extraction method based on half-normal model - Google Patents
Typhoon key parameter extraction method based on half-normal model Download PDFInfo
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Abstract
The invention discloses a typhoon key parameter extraction method based on a half-normal model. The method includes the steps that typhoon historical data are input to a computer, any point in map data corresponding to an area where typhoons occur frequently is selected by the computer to serve as a simulation point, a simulation circle is made, and therefore typhoon key parameters passing through the simulation circle can be obtained; half-normal model characteristic parameters are calculated to obtain half-normal model characteristic parameters of typhoon key parameters of A1 and half-normal model characteristic parameters of typhoon key parameters of AR. The method has the advantages that the typhoon key parameters can be extracted accurately, reliable foundations are laid for further study of influences of typhoon intensity, typhoon tracks and typhoons on life of people, and reliable data foundations are provided for utilization of wind energy of the typhoons.
Description
Technical field
The present invention relates to typhoon studying technological domain, especially relate to a kind of crux parameter that can accurately extract typhoon, for further Research on typhoon intensity, typhoon track and typhoon affect the typhoon key parameter extracting method based on half normal model of having established reliable basis on people's lives.
Background technology
The key parameter of typhoon is directly determining intensity of typhoon, typhoon track, the impact of typhoon on people's lives etc.The key parameter of typhoon comprises central gas pressure reduction, translational speed, moving direction etc.Conventionally according to the histogram of parameter, simulate with certain definite probability distribution function (as normal distribution, lognormal distribution, Weibull distribution etc.), then utilize χ
2matching method of inspection and K-S matching method of inspection carry out matching inspection, finally obtain the typhoon key parameter probability model of simulation points, and according to typhoon key parameter probability model, typhoon key parameter are extracted.But the probability that said method can not simulate key parameter completely changes, therefore, the crux parameter accuracy of extraction is relatively poor.
Chinese patent mandate publication number: CN103177301A, authorize open day on June 26th, 2013, a kind of typhoon disaster risk predictor method is disclosed, carry out statistical study for the lost data of specifying the typhoon disaster of monitored area to cause, selection causes calamity factor danger, pregnant calamity environmental sensitivity, hazard-affected body vulnerability and prevent and reduce natural disasters ability as typhoon disaster Risk Assessment Index System, set up typhoon disaster risk prediction model by blurring mapping theory, entry condition using typhoon forecast result as prediction model and initial conditions, through calculating and the analysis of prediction model, obtaining following a period of time is estimated area and whether causes calamity and cause the disaster risk class of calamity, thereby improve the pre-alerting ability of meteorological disaster.The weak point of this invention is that function singleness, cannot extract typhoon key parameter.
Summary of the invention
Goal of the invention of the present invention is the deficiency in order to overcome the key parameter poor accuracy that method of the prior art cannot extract, provide a kind of key parameter that can accurately extract typhoon, for further Research on typhoon intensity, typhoon track and typhoon affect the typhoon key parameter extracting method based on half normal model of having established reliable basis on people's lives.
A typhoon key parameter extracting method based on half normal model, comprises the steps:
(1-1) typhoon historical data is input in computing machine, any point in the computer selecting map datum corresponding with typhoon region occurred frequently is simulation points, taking simulation points as the center of circle, R is that radius does simulation circle, by central gas pressure reduction Δ p, Typhoon Tracks direction, the Typhoon Tracks speed V of the typhoon through simulation circle
tbe made as the typhoon crux parameter of described simulation points;
Center of typhoon draught head Δ p refers to the poor of center of typhoon air pressure and the peripheral air pressure of typhoon (generally getting 1010hPa), conventionally distributes and describes with lognormal distribution and Weibull;
Typhoon Tracks direction is obtained by the position calculation of center of typhoon longitude and latitude, conventionally describes with normal distribution or two normal distribution;
Typhoon Tracks speed V
tlongitude and latitude by measuring point before and after center of typhoon is tried to achieve, and conventionally describes by normal distribution or lognormal distribution.
(1-2) sample is divided:
Computing machine formula
calculate the poor Δ of typhoon central pressure
p, Typhoon Tracks direction and Typhoon Tracks speed V
tfrequency;
Wherein, x is center of typhoon draught head, Typhoon Tracks direction or Typhoon Tracks speed V
tany sample value, N is center of typhoon draught head, Typhoon Tracks direction or Typhoon Tracks speed V
ttotal sample number amount, f is center of typhoon draught head, Typhoon Tracks direction or Typhoon Tracks speed V
tfrequency;
Computing machine draws center of typhoon draught head Δ p, Typhoon Tracks direction and Typhoon Tracks speed V
tprobability distribution histogram;
(1-2-1) select the histogrammic separation of each probability distribution:
The average θ of second distribution of the two normal distributions of histogram selection of Typhoon Tracks speed is as first separation of half normal model; Taking separation θ as boundary, Typhoon Tracks direction is divided into left half-court and right half-court; By histogrammic left side district peak value V
pmaxas the separation of left side probability distribution, by histogrammic the right district peak value V
pmaxas the separation of the right probability distribution;
Computing machine is selected the peak value V of center of typhoon draught head Δ p, Typhoon Tracks direction histogram
pmaxas the separation of probability distribution;
(1-2-2) each probability distribution histogram is all carried out to following processing:
Taking separation as boundary, the histogram of center of typhoon draught head Δ p, Typhoon Tracks direction is divided into left and right Liang Geban district, obtain left half-court sample set A
l={ x
l| x≤V
pmax, x ∈ A} and right half-court sample set A
r={ x
r| x > V
pmax, x ∈ A}; Typhoon Tracks velocity histogram is divided into the first from left district, the second from left district, You Yiqu, Si Geban district of You Er district;
Wherein, A is center of typhoon draught head sample set, Typhoon Tracks direction sample set or Typhoon Tracks speed V
tsample set, x
lfor A
lany sample value, x
rfor A
rany sample value;
(1-3) construct respectively the symmetry reflection sample set in each halfth district, structure obtains half normal model:
Set A
l'={ x
l' | x
l'=2V
pmax-x
l, x
l∈ A
lbe A
l={ x
l| x≤V
pmax, the symmetry reflection sample set of x ∈ A}, A
lwith A
l' composition the first half normal models;
Set A
r'={ x
r' | x
r'=2V
pmax-x
r, x
r∈ A
rbe A
r={ x
r| x > V
pmax, the symmetry reflection sample set of x ∈ A}, A
r' and A
rform the second half normal models;
Set A
lfor left half-court symmetrization sample probability density collection, A
rright half-court symmetrization sample probability density collection; Wherein, A
r=A
r∪ A
r', A
l=A
l∪ A
l'; ∪ is union operational symbol;
(1-4) calculate half normal model characteristic parameter:
Utilize method for parameter estimation to calculate and obtain A
lthe expectation value μ of air speed data
lwith standard deviation δ
l, A
rthe expectation value μ of air speed data
rwith standard deviation δ
r;
Obtain A
lhalf normal model characteristic parameter of typhoon key parameter
a
rhalf normal model characteristic parameter of typhoon key parameter
Wherein, p is scale-up factor, p=m
l/ (m
l+ m
r), m
lfor left region typhoon key parameter sample size, m
rfor right region typhoon key parameter sample size, N represents normal distribution, (p, 1] and [0, the p] interval that is scale-up factor.
First the present invention is input to typhoon historical data in computing machine, and any point in the computer selecting map datum corresponding with typhoon region occurred frequently is simulation points, makes simulation circle, thereby obtains through simulating round typhoon key parameter;
Computing machine draws center of typhoon draught head Δ p, Typhoon Tracks direction and Typhoon Tracks speed V
tprobability distribution histogram; Taking separation as boundary, the histogram of center of typhoon draught head Δ p, Typhoon Tracks direction is divided into left and right Liang Geban district, obtain left half-court sample set A
l={ x
l| x≤V
pmax, x ∈ A} and right half-court sample set A
r={ x
r| x > V
pmax, x ∈ A}; Typhoon Tracks velocity histogram is divided into the first from left district, the second from left district, You Yiqu, Si Geban district of You Er district; The symmetry reflection sample set of constructing respectively each halfth district, structure obtains half normal model; Calculate half normal model characteristic parameter, obtain A
lhalf normal model characteristic parameter of typhoon key parameter
a
rhalf normal model characteristic parameter of typhoon key parameter
Half normal model of the present invention can accurately reflect ambiguity, the relevance between key parameter, thereby can realize more accurately the extraction to key parameter rule.
Typhoon historical data of the present invention is from " CMA-STI tropical cyclone of northwestern Pacific Ocean optimal path data set ".
As preferably, R is 200 to 500KM.
As preferably, Typhoon Tracks direction is obtained by the position calculation of center of typhoon longitude and latitude, sets 4 reference directions in computing machine, and 4 reference directions are respectively 0 ° of northern row, eastbound 90 °, 180 ° of southern row, head west-90 °.
As preferably, method for parameter estimation comprises least square method, maximum-likelihood method, Maximum Verified Method, minimum risk method and minimization Maximum entropy method.
As preferably, Δ p is 0 to 135hPa; Typhoon Tracks speed V
tfor 2km/h to 65km/h.
To achieve these goals, the present invention is by the following technical solutions:
Therefore, the present invention has following beneficial effect: (1) can accurately extract the key parameter of typhoon, for further Research on typhoon intensity, typhoon track and typhoon affect and established reliable basis people's lives; (2) for the utilization of typhoon wind energy provides authentic data basis.
Brief description of the drawings
Fig. 1 is a kind of structural representation of simulation circle of the present invention;
Fig. 2 is a kind of process flow diagram of embodiments of the invention;
Fig. 3 is the Typhoon Tracks direction fitted figure of conventional method;
Fig. 4 is A of the present invention district Typhoon Tracks direction fitted figure;
Fig. 5 is B of the present invention district Typhoon Tracks direction fitted figure;
Fig. 6 is C of the present invention district Typhoon Tracks direction fitted figure;
Fig. 7 is D of the present invention district Typhoon Tracks direction fitted figure;
Fig. 8 is the Typhoon Tracks speed fitted figure of conventional method;
Fig. 9 is left region of the present invention Typhoon Tracks speed fitted figure;
Figure 10 is right region of the present invention Typhoon Tracks speed fitted figure;
Figure 11 is the center of typhoon draught head fitted figure of conventional method;
Figure 12 is left region of the present invention center of typhoon draught head fitted figure;
Figure 13 is right region of the present invention center of typhoon draught head fitted figure.
In figure: simulation points 1, simulation circle 2, shore line 3, typhoon track 4.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention will be further described.
Embodiment is as shown in Figure 2 a kind of typhoon key parameter extracting method based on half normal model, comprises the steps:
Step 100, typhoon historical data is input in computing machine, any point in the computer selecting map datum corresponding with typhoon region occurred frequently is simulation points 1, taking simulation points as the center of circle, R is that radius does simulation circle 2, by central gas pressure reduction Δ p, Typhoon Tracks direction, the Typhoon Tracks speed V of the typhoon through simulation circle
tbe made as the typhoon key parameter of described simulation points;
As shown in Figure 1, in the present embodiment, any point in the computer selecting map datum corresponding with shore line 3 is simulation points, taking simulation points as the center of circle, R=250KM is that radius does simulation circle, central gas pressure reduction Δ p, Typhoon Tracks direction, Typhoon Tracks speed V by typhoon track 4 through each typhoon of simulation circle
tbe made as the typhoon key parameter of simulation points;
As shown in Figure 2, step 200, sample is divided:
Computing machine formula
calculate the poor Δ p of typhoon central pressure, Typhoon Tracks direction and Typhoon Tracks speed V
tfrequency;
Wherein, x is center of typhoon draught head, Typhoon Tracks direction or Typhoon Tracks speed V
tany sample value, N is center of typhoon draught head, Typhoon Tracks direction or Typhoon Tracks speed V
ttotal sample number amount, f is center of typhoon draught head, Typhoon Tracks direction or Typhoon Tracks speed V
tfrequency;
Computing machine draws center of typhoon draught head Δ p, Typhoon Tracks direction and Typhoon Tracks speed V
tprobability distribution histogram;
The average θ of second distribution of the two normal distributions of histogram selection of computing machine selection Typhoon Tracks speed is as first separation of half normal model; Taking separation θ as boundary, Typhoon Tracks direction is divided into left half-court and right half-court; By histogrammic left side district peak value V
pmaxas the separation of left side probability distribution, by histogrammic the right district peak value V
pmaxas the separation of the right probability distribution;
Computing machine is selected the peak value V of center of typhoon draught head Δ p, Typhoon Tracks direction histogram
pmaxas the separation of probability distribution;
Computing machine, according to different separations, is divided different regions by each straight ten thousand figure, for example, center of typhoon draught head Δ p, Typhoon Tracks direction is divided into left and right Liang Geban district, obtains left half-court sample set A
l={ x
l| x≤V
pmax, x ∈ A} and right half-court sample set A
r={ x
r| x > V
pmax, x ∈ A}; Typhoon Tracks velocity histogram is divided into Si Geban district;
Wherein, A is center of typhoon draught head sample set, Typhoon Tracks direction sample set or Typhoon Tracks speed V
tsample set, x
lfor A
lany sample value, x
rfor A
rany sample value;
Step 300, constructs respectively the symmetry reflection sample set in each halfth district, and structure obtains half normal model:
Set A
l'={ x
l' | x
l'=2V
pmax-x
l, x
l∈ A
lbe A
l={ x
l| x≤V
pmax, the symmetry reflection sample set of x ∈ A}, A
lwith A
l' composition the first half normal models;
Set A
r'={ x
r' | x
r'=2V
pmax-x
r, x
r∈ A
rbe A
r={ x
r| x > V
pmax, the symmetry reflection sample set of x ∈ A}, A
r' and A
rform the second half normal models;
Set A
lfor left half-court symmetrization sample probability density collection, A
rright half-court symmetrization sample probability density collection; Wherein, A
r=A
r∪ A
r', A
l=A
l∪ A
l';
Step 400, calculate half normal model characteristic parameter:
Utilize method for parameter estimation to calculate and obtain A
lthe expectation value μ of air speed data
lwith standard deviation δ
l, A
rthe expectation value μ of air speed data
rwith standard deviation δ
r;
Obtain A
lhalf normal model characteristic parameter of typhoon key parameter
a
rhalf normal model characteristic parameter of typhoon key parameter
Wherein, p is scale-up factor, p=m
l/ (m
l+ m
r), m
lfor left region typhoon crux parameter sample size, m
rfor right region typhoon crux parameter sample size, N represents normal distribution, (p, 1] and [0, the p] interval that is scale-up factor.
L-G simulation test:
One, generate central gas pressure reduction Δ p sample set, Typhoon Tracks direction sample set and the Typhoon Tracks speed V of typhoon
tmixing half cloud wind speed sample set in sample set:
(1) utilize MATLAB to carry randn (1) function and produce at random random number a;
(2) judge the size of a value, if 0≤a≤p utilizes MATLAB to carry normrnd instruction and generates and meet
distribute and be positioned at the random number of left region numerical range; If p < a≤1, utilizes MATLAB to carry normrnd instruction generation and meets
distribute and be positioned at right region numerical range random number;
(3) repeating step (1) (2), obtains N data, and N data are mixed to half cloud wind speed sample set according to sorting from small to large and forming;
Two, to generating central gas pressure reduction Δ p sample set, Typhoon Tracks direction sample set and the Typhoon Tracks speed V of typhoon
thalf normal model of sample set carries out verification:
(1) calculate residual values
By central gas pressure reduction Δ p sample set, Typhoon Tracks direction sample set or the Typhoon Tracks speed V of typhoon
ieach sample value x in sample set
isequence, utilizes formula from small to large
the mixing half cloud wind speed sample set generating is carried out to residual values to be solved;
Wherein, x
i' be and x
ithe sample value of corresponding mixing half cloud wind speed sample set.
(2) test of fitness of fot
The test of fitness of fot is for checking the whether consistent statistical method of certain theoretical distribution and the distribution of former data.
In formula, e
wfor residual values;
for each sample value x in sample set
iaverage; R
2be worth greatlyr, models fitting effect is better.
Three, simulation result contrast
In the present embodiment, certain simulation points (E111.83 °, N21.58 °) the typhoon key parameter contrast that utilizes respectively conventional method, half normal state to extract.
1. Typhoon Tracks direction
Use normal distribution, two normal distribution to distribute and carry out respectively matching Typhoon Tracks Direction Probability, as shown in Figure 3.
By as Fig. 3 fitting effect, can intuitively find out, two normal distribution model fitting effect are better than normal distribution.Probability histogram by Typhoon Tracks direction knows, this distribution has double-hump characteristics, can carry out region division in the trough place between two crests and two crests.Frequency histogram is divided into A, B, C and D region, and builds respectively four-range symmetric data, use normal model to carry out matching to it.Fitting effect is as shown in Fig. 4, Fig. 5, Fig. 6, Fig. 7.
Obtain corresponding half normal model numerical characteristic by the data parameters of four normal distributions, use normal model, two normal model and four and half normal models to generate at random air speed data, degree of fitting result of calculation is as shown in table 1.
The contrast of the different Bogus vortex moving direction of table 1 degree of fitting
| Model | Normal distribution | Two normal distributions | Half normal state |
| Degree of fitting % | 92.480?7 | 99.341?0 | 99.508?3 |
Known by table 1 result, half normal model degree of fitting is the highest, exceeds respectively 7.027 6% and 0.167 3% compared with normal distribution, two normal distribution, shows that half normal model has good fitting effect to Typhoon Tracks direction.
2. typhoon speed
According to statistics, typhoon speed probability distribution has unimodal characteristic, uses normal distribution, the matching respectively of lognormal distribution model, and fitting effect as shown in Figure 8.
Because typhoon speed probability distribution has unimodal characteristic, probability distribution is divided into left and right two regions, and builds the reflection sample in region, left and right, use normal model matching respectively.Fitting effect as shown in Figure 9, Figure 10.
Obtain corresponding half normal model numerical characteristic by the data parameters of two normal distributions, use normal model, lognormal model and two and half normal models to generate at random air speed data, degree of fitting result of calculation is as shown in table 2.
The contrast of the different Bogus vortex translational speed of table 2 degree of fitting
| Model | Normal distribution | Lognormal distribution | Half normal state |
| Degree of fitting % | 92.1175 | 96.1593 | 97.2501 |
Known by table 2 result, half normal model degree of fitting is the highest, exceeds respectively 5.132 6% and 1.090 8% compared with normal distribution, lognormal distribution, shows that half normal model has good fitting effect to Typhoon Tracks speed.
3. center of typhoon draught head
According to statistics, center of typhoon draught head probability distribution has unimodal characteristic, uses lognormal distribution, the matching respectively of Weibull distributed model, and fitting effect as shown in figure 11.
Because center of typhoon draught head probability distribution has unimodal characteristic, probability distribution is divided into left and right two regions, and builds the reflection sample in region, left and right, use normal model matching respectively.Fitting effect as shown in Figure 12 and Figure 13.
Obtain corresponding half normal model numerical characteristic by the data parameters of two normal distributions, use lognormal model, Weibull model and two and half normal models to generate at random air speed data, degree of fitting result of calculation is as shown in table 3.
The contrast of the different Bogus vortex central gas of table 3 pressure reduction degree of fitting
Known by table 3 result, half normal model degree of fitting is the highest, distributes and exceeds respectively 12.455 1% and 0.029% compared with lognormal distribution, Weibull, shows that half normal model has good fitting effect to center of typhoon draught head.
From the result of calculation contrast of above model, data probability distributions is carried out after the division of region, use normal distribution matching respectively, obtain after the parameter of half normal distribution, former data are carried out data fitting and are had the feature of high degree of fitting, highly versatile, therefore the typhoon key parameter accuracy of extracting is higher, for further Research on typhoon intensity, typhoon track and typhoon affect and established reliable basis people's lives; For the utilization of typhoon wind energy provides authentic data basis.
Should be understood that the present embodiment is only not used in and limits the scope of the invention for the present invention is described.In addition should be understood that those skilled in the art can make various changes or modifications the present invention after having read the content of the present invention's instruction, these equivalent form of values fall within the application's appended claims limited range equally.
Claims (5)
1. the typhoon key parameter extracting method based on half normal model, is characterized in that, comprises the steps:
(1-1) typhoon historical data is input in computing machine, any point in the computer selecting map datum corresponding with typhoon region occurred frequently is simulation points (1), taking simulation points as the center of circle, R is that radius does simulation circle (2), by central gas pressure reduction Δ p, Typhoon Tracks direction, the Typhoon Tracks speed V of the typhoon through simulation circle
tbe made as the typhoon crux parameter of described simulation points;
(1-2) sample is divided:
Computing machine formula
calculate the poor Δ p of typhoon central pressure, Typhoon Tracks direction and Typhoon Tracks speed V
tfrequency;
Wherein, x is center of typhoon draught head, Typhoon Tracks direction or Typhoon Tracks speed V
tany sample value, N is center of typhoon draught head, Typhoon Tracks direction or Typhoon Tracks speed V
ttotal sample number amount, f is center of typhoon draught head, Typhoon Tracks direction or Typhoon Tracks speed V
tfrequency;
Computing machine draws center of typhoon draught head Δ p, Typhoon Tracks direction and Typhoon Tracks speed V
tprobability distribution histogram;
(1-2-1) select the histogrammic separation of each probability distribution:
The average θ of second distribution of the two normal distributions of histogram selection of Typhoon Tracks speed is as first separation of half normal model; Taking separation θ as boundary, Typhoon Tracks direction is divided into left half-court and right half-court; By histogrammic left side district peak value v
pmaxas the separation of left side probability distribution, by histogrammic the right district peak value v
pmaxas the separation of the right probability distribution;
Computing machine is selected the peak value v of center of typhoon draught head Δ p, Typhoon Tracks direction histogram
pmaxas the separation of probability distribution;
(1-2-2) each probability distribution histogram is all carried out to following processing:
Taking separation as boundary, the histogram of center of typhoon draught head Δ p, Typhoon Tracks direction is divided into left and right Liang Geban district, obtain left half-court sample set A
l={ x
l| x≤V
pmax, x ∈ A} and right half-court sample set A
r={ x
r| x > V
pmax, x ∈ A}; Typhoon Tracks velocity histogram is divided into the first from left district, the second from left district, You Yiqu, Si Geban district of You Er district;
Wherein, A is center of typhoon draught head sample set, Typhoon Tracks direction sample set or Typhoon Tracks speed V
tsample set, X
lfor A
lany sample value, x
rfor A
rany sample value;
(1-3) construct respectively the symmetry reflection sample set in each halfth district, structure obtains half normal model:
Set A
l'={ x
l' | x
l'=2V
pmax-x
l, x
l∈ A
lbe A
l={ x
l| x≤V
pmax, the symmetry reflection sample set of x ∈ A}, A
lwith A
l' composition the first half normal models;
Set A
r'={ x
r' | x
r'=2V
pmax-x
r, x
r∈ A
rbe A
r={ x
r| x > V
pmax, the symmetry reflection sample set of x ∈ A}, A
r' and A
rform the second half normal models;
Set A
lfor left half-court symmetrization sample probability density collection, A
rright half-court symmetrization sample probability density collection; Wherein, A
r=A
r∪ A
r', A
l=A
l∪ A
l';
(1-4) calculate half normal model characteristic parameter:
Utilize method for parameter estimation to calculate and obtain A
lthe expectation value μ of air speed data
lwith standard deviation δ
l, A
rthe expectation value μ of air speed data
rwith standard deviation δ
r;
Obtain A
lhalf normal model characteristic parameter of typhoon key parameter
a
rhalf normal model characteristic parameter of typhoon key parameter
Wherein, p is scale-up factor, p=m
l/ (m
l+ m
r), m
lfor left region typhoon key parameter sample size, m
rfor right region typhoon key parameter sample size, N represents normal distribution, (p, 1] and [0, the p] interval that is scale-up factor.
2. the typhoon key parameter extracting method based on half normal model according to claim 1, is characterized in that, R is 200 to 500KM.
3. the typhoon key parameter extracting method based on half normal model according to claim 1, Typhoon Tracks direction is obtained by the position calculation of center of typhoon longitude and latitude, in computing machine, set 4 reference directions, 4 reference directions are respectively 0 ° of northern row, eastbound 90 °, 180 ° of southern row, head west-90 °.
4. the typhoon key parameter extracting method based on half normal model according to claim 1, is characterized in that, method for parameter estimation comprises least square method, maximum-likelihood method, Maximum Verified Method, minimum risk method and minimization Maximum entropy method.
5. according to the typhoon key parameter extracting method based on half normal model described in claim 1 or 2 or 3 or 4, it is characterized in that, Δ p is 0 to 135hPa; Typhoon Tracks speed V
tfor 2km/h to 65km/h.
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| CN107330583A (en) * | 2017-06-09 | 2017-11-07 | 哈尔滨工业大学深圳研究生院 | A kind of complete trails typhoon risk analysis method based on statistical dynamics |
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| CN107330583A (en) * | 2017-06-09 | 2017-11-07 | 哈尔滨工业大学深圳研究生院 | A kind of complete trails typhoon risk analysis method based on statistical dynamics |
| CN107330583B (en) * | 2017-06-09 | 2020-06-19 | 哈尔滨工业大学深圳研究生院 | Full-path typhoon risk analysis method based on statistical dynamics |
| CN111921192A (en) * | 2020-08-31 | 2020-11-13 | 网易（杭州）网络有限公司 | Control method and device of virtual object |
| CN111921192B (en) * | 2020-08-31 | 2024-02-23 | 网易（杭州）网络有限公司 | Virtual object control method and device |
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