CN113673617A - Mars dust storm space-time probability prediction method and system based on remote sensing image - Google Patents

Mars dust storm space-time probability prediction method and system based on remote sensing image Download PDF

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CN113673617A
CN113673617A CN202110989708.4A CN202110989708A CN113673617A CN 113673617 A CN113673617 A CN 113673617A CN 202110989708 A CN202110989708 A CN 202110989708A CN 113673617 A CN113673617 A CN 113673617A
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李勃
李晨帆
曲少杰
晋通文
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Abstract

The invention provides a Mars dust storm space-time probability prediction method and a system based on a remote sensing image, which comprise the following steps: identifying and extracting the area of a dust storm object based on the acquired Mars color remote sensing image of the landing area; carrying out regular grid division on the extracted area serving as a research area, extracting dust storm objects at different times in each grid to obtain space-time sequence data containing dust storm occurrence time and space positions, and carrying out statistical calculation on the periodic probability of dust storm occurrence of each grid during landing; and (3) predicting the space-time probability of the occurrence of the dust storm in the landing process by integrating the periodicity and the volatility of the dust storm in each grid of the landing area. The method is suitable for generating the spatio-temporal sequence data set of the Mars surface dust storm event and predicting the spatio-temporal probability of the dust storm event so as to assist the safe realization of Mars landing and patrol tasks.

Description

Mars dust storm space-time probability prediction method and system based on remote sensing image
Technical Field
The invention belongs to the field of planet remote sensing and planet meteorology, and particularly relates to a mars dust storm space-time probability prediction method and system based on remote sensing images.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Dust raised by a mars dust storm event covers the solar sailboard of the mars vehicle, so that the energy of the dust is reduced, and the service life of the mars vehicle is influenced. The first Mars detection task of China "Weeki one" enters a Mars surrounding orbit in 2021 and successfully lands on the surface of a Mars, and the Mars train "congratulate on the fusion number" carries out the detection work on the surface of a fire landing area subsequently. Before landing, the terrain and geological conditions of the surface of the landing area need to be studied in advance, and weather forecast during landing is carried out. Whether the space-time probability of the occurrence of the dust storm in the landing area can be successfully predicted relates to the precision of the Mars landing task and the subsequent normal operation condition of the detector.
Although the atmospheric pressure on Mars is less than 1% of the Earth, it has equally active atmospheric activity. At the end of the 90 s of the 20 th century, a Mars global detector number enters a Mars orbit, a remote sensing image shot by the Mars global detector number observes very frequent Mars dust storm events, and the Mars storm occurs at the same place and the same time with very obvious periodic regularity at very high probability every year. Thereafter, in 2006, "Mars surveys orbital vehicles" continue to develop daily remote sensing mapping of the whole Mars, and extend the time observation scale to more than ten years, reveal the periodic and seasonal trends of the source region and path of the Mars regional scale storm, and perform statistical analysis and prediction on the occurrence probability of the Mars landing zone of the storm. However, the spatiotemporal analysis of dust storm events in the Mars surface landing zone by predecessors has some disadvantages:
(1) and (3) generating a dust storm space-time sequence data set: the predecessors regard the Mars surface or the landing area as a whole to identify the dust storm objects at different times, and then obtain long-time sequence data of the dust storm occurrence in the whole landing area, and a dust storm space-time sequence data set integrating the occurrence time and the spatial position is not generated.
(2) The periodicity and volatility of the dust storm are combined: based on the occurrence time and the occurrence position data of Mars pre-selection landing zone dust storm events, the average daily probability of the dust storms in the landing zone is analyzed by foreigners through counting the periodic law of the dust storms. In fact, with the statistical periodicity of the occurrence of Mars storms, there are also some small fluctuations that require a combination of the statistical periodicity and the real-time volatility of the storms to analyze and predict the spatiotemporal probability of the occurrence of the storms.
(3) And (3) predicting the space-time probability of the dust storm: foreigners mostly carry out statistical analysis on Mars storms of the whole landing area, and further obtain the daily average probability of the occurrence of the Mars storms of the whole landing area. In fact, Mars dust storms have spatial non-uniformity, and different positions of the landing zone should have different dust storm occurrence probabilities, so that the results obtained by the predecessors are difficult to accurately reflect the spatial complexity of the dust storm probability of the research zone.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a Mars dust storm space-time probability prediction method based on remote sensing images, which predicts the space-time probability of occurrence of a dust storm by integrating the statistic periodicity and real-time volatility of the dust storm and improves the accuracy of a prediction result.
In order to achieve the above object, one or more embodiments of the present invention provide the following technical solutions:
in a first aspect, a method for predicting space-time probability of a Mars dust storm based on a remote sensing image is disclosed, which comprises the following steps:
identifying and extracting the area of a dust storm object based on the acquired Mars color remote sensing image of the landing area;
carrying out regular grid division on the extracted area serving as a research area, extracting dust storm objects in different time in each grid, carrying out statistics to obtain the periodicity of dust storm occurrence of each grid dust storm object in the landing period, and obtaining the real-time volatility of the dust storm based on the periodicity;
and predicting the space-time probability of the occurrence of the dust storm in the landing process by integrating the periodicity and the volatility of the dust storm in each grid of the landing area.
According to the further technical scheme, the research area is divided into uniformly distributed square grids, each grid records the dust storm information of each Mars day in a plurality of Mars years, the probability of the dust storm events in each grid occurring at different time is calculated, and the dust storm data of all grids are collected to generate a landing area dust storm time-space sequence data set integrating the dust storm time and space information.
In a further technical scheme, the specific way of calculating the probability of the occurrence of the storm event in each grid at different time is as follows:
for any grid, firstly, the grid is used as a boundary to cut a whole set of storm polygon objects in a landing area to obtain a set of storm polygon objects only appearing in the grid, then the area of each storm object is calculated, and the probability of occurrence of a storm event at different time is calculated based on the area of the storm object and the area of the grid in which the storm object is located.
According to the further technical scheme, the method for predicting the space-time probability of the occurrence of the dust storm in the landing process by integrating the periodicity and the volatility of the dust storm in each grid of the landing area comprises the following steps:
calculating a dust storm period factor;
calculating the real-time fluctuation of the dust storm based on the dust storm period factor;
and multiplying the basic value reflecting the real-time fluctuation of the dust storm by the cycle factor of the Martian day to obtain the prediction probability of the dust storm occurrence on the grid Martian day landing day.
In a further technical scheme, the mode for calculating the period factor of the dust storm is as follows:
calculating the average value of the occurrence probability of the dust storm in any Mars annual cycle interval in the grid;
dividing the occurrence probability of the dust storm of each Mars day in the periodic interval by the mean probability value to obtain the ratio of the dust storm probability of the periodic interval in any Mars year in the grid;
in the grid, the probability ratios of the dust storms appearing in the same Mars day in n Mars years are summed and averaged to obtain the periodic factor of the dust storms in the Mars days in the g grid in the period interval.
In a further preferred technical scheme, the periodic factor of the dust storm is corrected by synthesizing the real-time dust storm data of several Mars days before landing.
According to the further technical scheme, the modification of the periodic factor of the storm by the real-time storm data is specifically as follows:
and dividing the real-time dust storm probability by the dust storm period factor corresponding to Mars day to obtain the periodic real-time dust storm removal probability.
According to the further technical scheme, when the real-time fluctuation of the dust storm is calculated based on the dust storm period factor, the periodic real-time dust storm probability average value is removed and serves as a basic value of dust storm probability prediction, and the fluctuation of real-time data is reflected.
In a second aspect, a system for predicting space-time probability of a Mars dust storm based on remote sensing images is disclosed, which comprises:
an identification module of a dust storm object configured to: identifying and extracting the area of a dust storm object based on the acquired Mars color remote sensing image of the landing area;
a periodicity and volatility calculation module configured to: carrying out regular grid division on the extracted area serving as a research area, extracting dust storm objects in different time in each grid, carrying out statistics to obtain the periodicity of dust storm occurrence of each grid dust storm object in the landing period, and obtaining the real-time volatility of the dust storm based on the periodicity;
a spatio-temporal probability of a storm occurrence calculation module configured to: and predicting the space-time probability of the occurrence of the dust storm in the landing process by integrating the periodicity and the volatility of the dust storm in each grid of the landing area.
The above one or more technical solutions have the following beneficial effects:
the method is used for identifying a dust storm object in a Mars landing area based on the color remote sensing image, generating a dust storm space-time sequence data set by taking a regular grid as a unit, and finally predicting the space-time probability of the occurrence of the dust storm in the landing process by combining the periodicity and the real-time volatility of the dust storm.
The invention carries out regular grid division on the research area, identifies and counts the dust storm objects appearing at different times in each grid to obtain the dust storm space-time data sequence sets which take the grids as units and are at different positions and different times in the research area, thereby solving the problem that the predecessor does not generate a space-time sequence data set integrating the appearance time and the space position of the dust storm by taking the Martian landing area as a whole.
The invention is based on a mars landing area dust storm space-time sequence data set, calculates the space-time periodic rule of the occurrence of the dust storm in the landing area, analyzes the fluctuation of the occurrence of the dust storm according to the actual dust storm observation data before the mars landing task, and predicts the space-time probability of the occurrence of the dust storm by integrating the statistic periodicity and the real-time fluctuation of the dust storm. Therefore, the problem that the prediction result accuracy is influenced by only considering periodicity for the prediction of the landing zone dust storm by the predecessors is solved.
The invention discloses a method for carrying out Mars surface dust storm identification, dust storm space-time data set generation and dust storm space probability prediction based on remote sensing image data. The method is suitable for generating the spatio-temporal sequence data set of the Mars surface dust storm event and predicting the spatio-temporal probability of the dust storm event so as to assist the safe realization of Mars landing and patrol tasks.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a schematic view of a study area according to an embodiment of the present invention;
FIG. 2 is a diagram of MOC images used to identify a dust storm object near the North Pole of a study area in accordance with an embodiment of the present invention;
FIG. 3 is a diagram of the landing zone and its results of identifying storm objects in the North zone according to the present invention.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
The embodiment discloses a mars dust storm space-time probability prediction method based on a remote sensing image, which is used for identifying and extracting a dust storm object and an area according to the reflectivity difference of a dust storm and cloud (water vapor) in red and blue wave bands based on a mars color remote sensing image of a landing area; carrying out regular grid division on a research area, extracting dust storm objects at different times in each grid to obtain space-time sequence data containing dust storm occurrence time and space positions, and carrying out statistical calculation on the periodic probability of dust storm occurrence of each grid during landing; and (3) analyzing the volatility of the occurrence of the dust storm according to the actual dust storm observation result before the Mars mission lands and comparing with the periodic rule of the dust storm, and predicting the space-time probability of the occurrence of the dust storm in the landing process by integrating the periodicity and the volatility of the dust storm in each grid of the landing area. The concrete steps and contents comprise:
the method comprises the following steps: the Mars landing zone dust storm identification method can adopt the specific step of identifying dust storm objects based on RGB (red, green and blue) color remote sensing images in step 1 of patent publication No. CN110930064A, and calculates the area of each dust storm polygon object to generate a dust storm polygon area set A (i, j, ID)ij)。
Step two: and (3) generation of a Mars dust storm space-time sequence data set:
the dust storm events have spatial distribution nonuniformity in a Mars mission landing area, and the occurrence probability of dust storms at different positions is different. The investigation region is thus divided into a uniformly distributed square grid with a side length L (in degrees, for example 0.5 °). Assuming that the landing zone is divided into k grids, the data set is grid (g), g is 1,2, …, k, where the area of the g grid is Sg. Each grid is equivalent to a meteorological station on the surface of a Mars landing area, and records the information of the dust storm of each Mars day in a plurality of Mars years inside, such as whether the dust storm event occurs or not, the area and the state of the dust storm and the like. Thus, the probability of a storm event occurring at different times in each grid is calculated, and a composite storm time and space message is generated by aggregating the storm data for all gridsLanding zone storm spatio-temporal sequence datasets of information.
Referring to FIG. 1, the study area is the "day one" pre-selected landing zone, and the dust storm objects in each Mars day landing zone are identified and their areas calculated. Then, the landing area is divided into grids, and the occurrence of the dust storm in each grid is counted. Namely, whether a dust storm occurs in each grid in a certain Mars day, and the area of the dust storm which occurs accounts for the percentage of the area of the grid.
For example, in fig. 1, a black line box is a research area, a white grid is a divided 0.5-degree grid, and a fine gray line in the black line box is a dust storm boundary line identified in a plurality of Mars years in a landing zone.
Taking the g grid in the landing zone as an example, the probability of the dust storm of the j Mars day of the ith Mars year is calculated statistically. First, a whole set D (i, j, ID) of polygon objects of the landing zone is cut out by using a grid g as a boundaryij) Obtaining a set D of storm polygon objects that appear only within the mesh gg(i,j,IDijg) Wherein the number of the dust storm objects IDijg＝1,2,…,mijgThen calculate the area A of each of the dust storm objectsg(i,j,IDijg). Then the g grid in the ith Mars year, the jth Mars day, the probability of the occurrence of the dust storm event is:
Figure BDA0003231865560000071
similarly, calculating the occurrence probability of the dust storm events in any Mars year and any Mars day of all grids in the Mars landing area, and finally generating a landing area dust storm spatio-temporal sequence data set P with the grids as the unitg(i, j), wherein g is 1,2, …, k, i is 1,2, …, n, j is 1,2, …, 360.
Step three: dust storm prediction integrating statistics of periodicity and real-time volatility
(1) Dust storm cycle factor calculation
According to previous researches, the Mars dust storm events have space-time continuity and repeatability, so that the Mars dust storm space-time sequence data set can predict the occurrence probability of the Mars dust storm in the future. Suppose the Mars landing place is grid g, the landing time is the a-th Mars day of the (n + 1) -th Mars year (Ls ═ a), a is an integer, and a ∈ [0,360 ]. A period I (week, month, quarter, etc.) is taken from Ls ═ a forward, the period interval length is an integer L, and the period interval time range is Ls ═ a-L, a.
First, in the grid g, the ith Mars year, the period interval Ls ═ a-L, a]Mean value of probability of occurrence of medium dust storm
Figure BDA0003231865560000072
Then using g grid, i-th Mars year, period interval Ls ═ a-L, a]Divided by the mean of the probabilities of occurrence of a dust storm for each Mars day
Figure BDA0003231865560000073
Obtaining the ratio R of the probability of the dust storm in the period intervalg(i, j) where j ∈ [ a-L, a]. Finally, in the grid g, the probability ratios of the dust storms appearing in the same Mars day j in the n Mars years are summed and averaged to obtain the periodic factor of the dust storms in the j-th Mars day in the period interval in the g grid
Figure BDA0003231865560000074
The storm periodicity factor is based on statistics of the landing zone storm spatio-temporal sequence data set.
(2) Storm real-time volatility calculation
In order to ensure the accuracy of the prediction of the dust storm in the landing area when the Mars mission lands, the periodic factor of the dust storm needs to be corrected by synthesizing the real-time dust storm data of several Mars days before the Mars mission lands. Suppose that d Mars days before landing (d) in the grid g can be obtained<L) and calculates a real-time interval I' (whose time range is Ls ═ a-d, a)]) Probability of dust storm in Pg(n+1,I')。
Firstly, in order to eliminate the periodicity of the dust storm and reflect the real-time dust storm probability characteristic, the real-time dust storm probability is divided by the dust storm period factor corresponding to Mars day to obtain the real-time dust storm probability P 'without periodicity'g(n +1, I'), wherein the jth fireThe real-time dust storm probability of the stars and the days is as follows:
P'g(n+1,j)＝Pg(n+1,j)/Rg(j)。
then, the average value of the real-time storm probability in the interval I' is calculated to be used as a basic value B of the prediction of the storm probability, and the volatility of real-time data is embodied as follows:
Figure BDA0003231865560000081
finally, multiplying the basic value B by the periodic factor of the Mars day of Ls ═ a to obtain the g grid, and the forecast probability P of the occurrence of the storm on the day of landing of Ls ═ ag(n+1,a):
Pg(n+1,a)＝B×Rg(a)。
The method is applied to all grids in the landing area, and the prediction probability of the dust storm space in the whole landing area on the same landing day is obtained. And according to the information such as the morphology, the composition and the bearing capacity of the landing area and the combination of the dust storm space prediction probability, the Mars landing task is assisted to select a proper landing place in the landing area.
Verification example
In order to verify the validity of the above scheme, specific calculations are performed:
fig. 2MOC images are used to identify the study area and its north dust storm objects. The first row is the red band image of the MOC image, and the second row is the blue band image of the corresponding MOC image. Wherein the black arrows indicate the object of the dust storm and the white arrows indicate the condensation cloud. FIG. 3 illustrates the results of identifying a storm object in the landing zone and the north area. Wherein the black line is the boundary line of the dust storm, and the gray line is the pre-selected landing area with the number one in the day.
Example mesh and mars remote sensing images: according to the research on the landing zone dust storm of 'Tianhao I', the dust storm object mainly originates from the north pole moving backwards to the south of the Utoban plain, and almost no primary dust storm exists in the landing zone. Therefore, the invention selects a 0.5-degree grid in the north direction of the landing area of the 'Tian Yi', the latitude and longitude range of the grid is 110-110.5-degree E and 56-56.5-degree N.
The images used in the example of the invention were MOC and MARCI data taken with an american satellite camera with a spatial resolution of 6km/pixel and a temporal resolution of Ls 0.5 °. A total of 5 Mars Years (MY), 25 to 29 are obtained. Because the "question one" landed in the spring of the northern hemisphere of mars, the study period for the mars dust storm event of this example was only the spring (Ls 0-90 °).
And (3) generating a dust storm space-time sequence data set: and carrying out dust storm identification based on remote sensing images of different Mars days in the research area. As shown in fig. 2, the red and blue band images are compared to distinguish between a dust storm and cloud (water vapor). And then, parameters such as the shape, the area and the like of the dust storm and the action track thereof are determined by using the color remote sensing image. As shown in FIG. 3, the "Tian Yi" landing zone and its north areas identify storms represented by black polygons. And intersecting the example grid with the identified polygon of the dust storm object to obtain the dust storm object in the example grid, and generating a dust storm space-time sequence data set.
And (3) predicting the spatial probability of the dust storm:
the invention synthesizes the periodicity and real-time fluctuation of the dust storm to predict the land area dust storm space-time probability. The first 4 Mars years (MY-25-28) are used as input dust storm period data, and the 5 th Mar year (MY-29) is used as real-time dust storm data to predict the probability of occurrence of a dust storm at the landing time (assuming that Ls is 40 °) in the example grid. Assuming that the dust storm data for 40 mars days onward, the time range of period I is Ls ═ 1, 40.
(1) Dust storm periodicity factor calculation
As shown in table 1, is the corresponding dust storm spatio-temporal sequence data set for the first 4 mars years (MY25-28), Ls [ [1,40], and the last column is the mean of the probability of occurrence of a dust storm for each mars year Ls [ [1,40 ]. The ratio of the probability of a storm over the period interval and the periodicity factor of the storm are calculated as shown in tables 2 and 3.
Table 1 MY25-28, Ls ═ 1,40], corresponding dust storm occurrence probabilities in the example grids
Figure BDA0003231865560000091
Figure BDA0003231865560000101
Figure BDA0003231865560000102
Table 2 MY25-28, Ls ═ 1,40], example grid cycle interval sandstorm probability ratios
Figure BDA0003231865560000103
Figure BDA0003231865560000104
Table 3 example grid Ls ═ 1,40] sandstorm periodicity factor
Figure BDA0003231865560000105
Figure BDA0003231865560000111
(2) Storm real-time volatility calculation
The cycle interval storm probability in the example grid at 5 Mars year (MY-29) and Ls-1, 40 is shown in Table 4. Using the real-time ratio of the probability of a storm (0 and 1) on two days before landing (i.e., Ls 38 ° and 39 °), dividing by the periodic factors of the storm corresponding to Ls 38 ° and 39 ° in table 3 (2 and 2), averaging to obtain the base value B0.25.
The base value B is multiplied by a period factor (3.43) for a Mars day, Ls 40 °, to obtain an example grid, with a predicted probability of 0.86 for the occurrence of a storm on the day of landing Ls 40 °. And finally, calculating the occurrence prediction probability of the dust storm of each grid in the landing area on the same landing day, so as to obtain the dust storm prediction probability of the whole landing area, thereby assisting the successful implementation of the Mars landing task.
Table 4 probability of occurrence of a dust storm in an example grid when MY is 29 and Ls is [1,40]
Figure BDA0003231865560000112
Figure BDA0003231865560000113
The invention obtains the dust storm space-time data sequence sets at different positions and different times of the research area by carrying out regular grid division and dust storm object identification on the research area. Therefore, the problem that a space-time sequence data set integrating the occurrence time and the spatial position of the dust storm is not generated by the predecessor who uses the Mars landing area as a whole is solved.
And predicting the space-time probability of the occurrence of the dust storm in the research area by integrating the statistical periodicity and the real-time volatility of the dust storm based on the previous dust storm space-time sequence data set of the Mars landing area and the actual dust storm observation data before the landing task. Therefore, the problem that the prediction result precision is influenced by considering periodicity and neglecting volatility of the previous person for the landing zone storm prediction is solved.
Example two
It is an object of this embodiment to provide a computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the program.
EXAMPLE III
An object of the present embodiment is to provide a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
Example four
The purpose of this embodiment is to provide a mars dust storm space-time probability prediction system based on remote sensing image, includes:
an identification module of a dust storm object configured to: identifying and extracting the area of a dust storm object based on the acquired Mars color remote sensing image of the landing area;
a periodicity probability calculation module configured to: carrying out regular grid division on the extracted area serving as a research area, extracting dust storm objects at different times in each grid to obtain space-time sequence data containing dust storm occurrence time and space positions, and carrying out statistical calculation on the periodic probability of dust storm occurrence of each grid during landing;
a spatio-temporal probability of a storm occurrence calculation module configured to: and (3) predicting the space-time probability of the occurrence of the dust storm in the landing process by integrating the periodicity and the volatility of the dust storm in each grid of the landing area.
The steps involved in the apparatuses of the above second, third and fourth embodiments correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.
Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code that is executable by computing means, such that they are stored in memory means for execution by the computing means, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps of them are fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (10)

1. A mars dust storm space-time probability prediction method based on remote sensing images is characterized by comprising the following steps:
identifying and extracting the area of a dust storm object based on the acquired Mars color remote sensing image of the landing area;
carrying out regular grid division on the extracted area serving as a research area, extracting dust storm objects in different time in each grid, carrying out statistics to obtain the periodicity of dust storm occurrence of each grid dust storm object in the landing period, and obtaining the real-time volatility of the dust storm based on the periodicity;
and predicting the space-time probability of the occurrence of the dust storm in the landing process by integrating the periodicity and the volatility of the dust storm in each grid of the landing area.
2. The method as claimed in claim 1, wherein the research area is divided into square grids which are uniformly distributed, each grid records the information of the dust storm of each Mars day of a plurality of Mars years inside, the probability of the occurrence of the dust storm event in each grid at different time is calculated, and the landing area dust storm spatio-temporal sequence data set integrating the dust storm time and the space information is generated by summarizing the dust storm data of all grids.
3. The method for predicting the Mars dust storm space-time probability based on the remote sensing image as claimed in claim 2, wherein the specific way of calculating the probability of occurrence of the dust storm events in each grid at different times is as follows:
for any grid, firstly, the grid is used as a boundary to cut a whole set of storm polygon objects in a landing area to obtain a set of storm polygon objects only appearing in the grid, then the area of each storm object is calculated, and the probability of occurrence of a storm event at different time is calculated based on the area of the storm object and the area of the grid in which the storm object is located.
4. The method for predicting the spatiotemporal probability of the Mars dust storm based on the remote sensing image as claimed in claim 1, wherein the spatiotemporal probability of the occurrence of the dust storm in the landing process is predicted by integrating the periodicity and the volatility of the dust storm in each grid of the landing area, and the method comprises the following steps:
calculating a dust storm period factor;
calculating the real-time fluctuation of the dust storm based on the dust storm period factor;
and multiplying the basic value reflecting the real-time fluctuation of the dust storm by the cycle factor of the Martian day to obtain the prediction probability of the dust storm occurrence on the grid Martian day landing day.
5. The method for forecasting the Mars dust storm space-time probability based on the remote sensing image as claimed in claim 4, wherein the mode of calculating the period factor of the dust storm is as follows:
calculating the average value of the occurrence probability of the dust storm in any Mars annual cycle interval in the grid;
dividing the occurrence probability of the dust storm of each Mars day in the periodic interval by the mean probability value to obtain the ratio of the dust storm probability of the periodic interval in any Mars year in the grid;
in the grid, the probability ratios of the dust storms appearing in the same Mars day in n Mars years are summed and averaged to obtain the periodic factor of the dust storms in the Mars days in the g grid in the period interval.
6. The remote sensing image-based Mars dust storm space-time probability prediction method as claimed in claim 5, characterized in that the real-time dust storm data of several Mars days before landing are synthesized to correct the periodic factor of the dust storm;
preferably, the modification of the periodic factor of the storm by the real-time storm data is specifically as follows:
and dividing the real-time dust storm probability by the dust storm period factor corresponding to Mars day to obtain the periodic real-time dust storm removal probability.
7. The method as claimed in claim 4, wherein the method for forecasting the Mars dust storm space-time probability based on the remote sensing image is characterized in that when the real-time fluctuation of the dust storm is calculated based on the dust storm period factor, the periodic real-time dust storm probability average value is removed to be used as a base value for forecasting the dust storm probability, and the base value is used for reflecting the fluctuation of real-time data.
8. A mars dust storm space-time probability prediction system based on remote sensing images is characterized by comprising:
an identification module of a dust storm object configured to: identifying and extracting the area of a dust storm object based on the acquired Mars color remote sensing image of the landing area;
a periodicity and volatility calculation module configured to: carrying out regular grid division on the extracted area serving as a research area, extracting dust storm objects in different time in each grid, carrying out statistics to obtain the periodicity of dust storm occurrence of each grid dust storm object in the landing period, and obtaining the real-time volatility of the dust storm based on the periodicity;
a spatio-temporal probability of a storm occurrence calculation module configured to: and predicting the space-time probability of the occurrence of the dust storm in the landing process by integrating the periodicity and the volatility of the dust storm in each grid of the landing area.
9. A computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method of any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, is adapted to carry out the steps of the method according to any one of the preceding claims 1 to 7.
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