CN114066225A - Space-time cognition method for urban group surface element observation capability and storage medium - Google Patents
Space-time cognition method for urban group surface element observation capability and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of intelligent city geographic information service, in particular to a space-time cognitive method and a storage medium for urban group surface element observation capability. The invention solves the problems that the existing urban group ground surface element monitoring lacks an air-space-ground collaborative planning layout mechanism and lacks a comprehensive cognition method for evaluating the observation capability and the observation credibility of the sensor, constructs an observation capability space-time cognition model and provides an urban group ground surface element observation capability space-time cognition method. Compared with the existing ground surface element monitoring mode, the method sets the cognitive accuracy rate of the ground surface element monitoring task, adopts multi-platform sensors such as a satellite, an unmanned aerial vehicle and a ground monitoring station for cooperative observation, carries out all-dimensional and space-time seamless perception on the ground surface element, evaluates the matching capability of observation resources to the monitoring task according to an observation capability space-time cognitive model, and finally obtains the urban group ground surface element monitoring scheme with the cognitive accuracy rate of the observation capability reaching the expected target.
Description
Technical Field
The invention relates to the technical field of intelligent city geographic information service, in particular to a space-time cognitive method and a storage medium for urban group surface element observation capability.
Background
The urban natural surface elements refer to various natural geographic elements covering the urban surface, such as water systems, soil, vegetation and the like. With the continuous development of earth observation technology, earth surface element monitoring is developed from a traditional single ground station network to a new stage of a heaven and earth integrated combined observation sensor network.
Nowadays, the number of various observation resources is increasing, and the cooperative observation among different platforms becomes a development trend. However, the Multi-sensor resource collaborative Observation problem [ e.g. the documents Zhang B, Xu P D, Wang J, et al. optimization development Deployment of Multi-Platform Earth Observation Resources for emergences [ C ]// Advanced Materials research. trans. transport technologies publishing Ltd,2013,765: 552) relates to a variety of Observation Resources, strong sensor Platform heterogeneity, difficulty in using a single operational mathematical Model to uniformly Model the problem, a need for an Association collaborative method between the research platforms according to the Observation characteristics of different platforms, a certain complementary, competitive and enhanced relation between the Observation capabilities of different Sensors [ e.g. the documents Hu C, titanium L, J, et al. an Observation Capability Information Model for the Observation Management Model, environment software, environment, 2019,19(23):11510-11525]. For example, an author Herold [ Thomas Michael Herold. Asynchronous, Distributed Optimization for the Coordinated Planning of air and Space Assets [ J ].2010 ]) studies the cooperative observation problem of unmanned aerial vehicles and satellite sensors, and the idea of adopting a layered framework converts the cooperative observation problem into a top-down observation demand distribution problem.
Similar to the work in this study, for example, the literature [ Wei M, Chen G, blast E, et al. Game the biological multiple mobile sensor management under addition environmental aspects [ C ]// 200811 th International Conference on Information fusion. IEEE,2008:1-8] has also studied the problem of cooperative observation of satellites, drones and ground station networks, but the observation task of this study was to accomplish the tracking of the target. The urban group earth surface element observation capability space-time cognition method obtains an observation scheme meeting an expected target by carrying out combined optimization on a multi-sensor platform, and belongs to one of optimization problems which are generally divided into two categories: one is a variable with a continuous type, and the other is a variable with a discrete type, and the problem formed by the discrete variables is called combinatorial optimization, which is sometimes called discrete optimization problem [ for example, documents: liu vibra, cai michigan. combined optimization algorithm and complexity [ M ] qing hua university press, 19885 ]. When the ground monitoring station is optimized, relevant layout standards need to be considered, and basic requirements of site selection and layout of the hydrological station are also specified by the national hydrological station network planning technical guide (for example, documents: water conservancy department of the people's republic of China 2013.SL 34-2013: water conservancy project technical guide of the people's republic of China water conservancy industry standard ].
The main problems faced by current surface element observation are:
(1) most of current sensor network researches only consider the space-time coverage of the satellite sensor, neglect factors influencing the observation capability of the sensor, such as theme, precision and the like, and lack a comprehensive cognitive method for evaluating the observation capability and the observation credibility of the satellite sensor.
(2) Due to the lack of an air-space-ground sensor collaborative planning mechanism, the current ground station and satellite often play a role in isolation, the observation efficiency is low, the requirements of sensor network collaborative observation and resource optimization configuration cannot be met, and meanwhile, the problem of space-time discontinuity of the satellite-ground sensor to ground coverage exists.
Disclosure of Invention
In order to solve the technical problem, the invention provides a space-time cognition method and a storage medium for urban group surface element observation capability.
According to one aspect of the invention, the invention provides a space-time cognition method for the observation capability of the earth surface elements of the urban area group, which comprises the following steps:
acquiring space-time and theme parameters of an urban group earth surface element observation task, and setting the space-time cognition accuracy of the expected observation capability;
according to the space-time and theme parameters, observation resource query is carried out to obtain an urban group earth surface element basic observation scheme;
constructing an observation capability space-time cognition model, performing basic association observation capability space-time cognition on the urban group ground surface element basic observation scheme based on the observation capability space-time cognition model, and calculating to obtain observation capability satisfaction and observation capability reliability of the urban group ground surface element basic observation scheme;
calculating to obtain the space-time cognition accuracy rate of the basic associated observation capability of the urban group surface element observation scheme according to the observation capability satisfaction and the observation capability credibility;
judging whether the basic association observation capability space-time cognition accuracy rate of the urban group earth surface element observation scheme meets the expected observation capability space-time cognition accuracy rate or not;
if so, outputting the basic observation scheme of the urban group ground surface elements as a final space-time cognition scheme of the ground surface element observation capability;
otherwise, optimizing a satellite sensor side-sway mode for the basic observation scheme of the urban group ground surface elements;
performing optimization association observation capability space-time cognition on the earth surface element observation scheme optimized by the side swing mode through the observation capability space-time cognition model to obtain the accuracy rate of the optimization association observation capability space-time cognition;
judging whether the optimization associated observation capability space-time cognition accuracy rate meets the expected observation capability space-time cognition accuracy rate or not;
if so, outputting the earth surface element observation scheme optimized by the side sway mode as a final earth surface element observation capability space-time cognition scheme;
otherwise, the unmanned aerial vehicle and ground station resource supplement is carried out on the earth surface element observation scheme after the sidesway mode optimization;
performing supplementary association observation capability space-time cognition on the supplemented earth surface element observation scheme through the observation capability space-time cognition model to obtain supplementary association observation capability space-time cognition accuracy;
judging whether the supplementary associated observation capability space-time cognition accuracy rate meets the expected observation capability space-time cognition accuracy rate or not;
if so, outputting the supplemented earth surface element observation scheme as a final earth surface element observation capability space-time cognition scheme;
otherwise, continuing to optimize the satellite sensor side-sway mode, and/or supplementing the unmanned aerial vehicle and the ground station resources until the expected observation capability space-time cognition accuracy is achieved.
Preferably, the calculation of the observation capability satisfaction degree of the urban group surface element basic observation scheme includes:
calculating the theme matching capability of each satellite sensor in the basic observation scheme of the urban group ground surface elements;
calculating the resolution satisfying capacity of each satellite sensor in the basic observation scheme of the urban group ground surface elements;
calculating the space coverage satisfaction capacity of the basic observation scheme of the urban group surface elements;
and calculating to obtain the observation capability satisfaction degree of the urban group earth surface element basic observation scheme according to the theme matching capability, the resolution satisfaction capability and the space coverage satisfaction capability.
Preferably, the calculating of the theme matching capability of each satellite sensor in the urban group ground surface element basic observation scheme includes the following specific calculation formula:
preferably, the calculation of the resolution satisfaction capacity of each satellite sensor in the urban group surface element basic observation scheme has the following specific calculation formula:
resolution satisfaction index is evaluated by resolutionIs divided into PiExpressed, in the formula, a represents the resolution of the satellite sensor.
Preferably, the calculating of the space coverage satisfaction capability of the basic observation scheme of the urban group surface elements includes:
in the formula, CiRepresenting the space coverage satisfaction capacity of the ith satellite sensor in a certain time period, S represents the area of the research area, SiRepresenting the area of the investigation region covered by the ith satellite sensor over a period of time.
Preferably, the observation capability satisfaction degree of the urban group surface element basic observation scheme is obtained by calculation according to the theme matching capability, the resolution satisfaction capability and the space coverage satisfaction capability, and a specific calculation formula is as follows:
wherein, OCSI represents the observation ability satisfaction degree of the basic observation scheme of the city group ground surface elements, i is 1,2iIndicating the subject matching capability, P, of each sensor in the satellite sensor combinationiIndicating the resolution satisfaction capability of each sensor in the satellite sensor combination, CiIndicating the spatial coverage satisfaction capability of each sensor in the satellite sensor combination over a period of time.
Preferably, the calculation of the observation capability credibility of the urban group surface element basic observation scheme includes:
calculating the environmental uncertainty U based on terrain conditions, the characteristics of the satellite sensor itself and different calculation methodsESatellite sensor self-uncertainty UpyAnd model method uncertainty UM；
Calculating the observation capability credibility of the urban group earth surface element basic observation scheme according to the environment uncertainty, the satellite sensor self uncertainty and the model method uncertainty, wherein a specific calculation formula is as follows:
wherein MOPR is the reliability of observation ability, UEFor environmental uncertainty, UpyFor the self-uncertainty of the sensor, UMIs the model method uncertainty.
Preferably, the specific calculation formula of the accuracy of the spatiotemporal cognition of the basic associated observation capability is as follows:
F＝OCSI*MOPR*100％
wherein, F is the accuracy rate of space-time cognition of the basic associated observation capability, OCSI is the satisfaction degree of the observation capability, and the credibility of the MOPR observation capability.
According to another aspect of the present invention, the present invention further provides a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and the computer program is used for implementing any one of the methods for space-time cognition of the city group surface element observation capability when the computer program runs.
The invention has the following beneficial effects:
(1) the space-time continuous coverage capability of the urban surface element observation network is enhanced. The space-time cognitive method for the observation capability of the earth surface elements fully exerts the observation complementarity of the discontinuous space continuous time of the satellite sensor and the discontinuous space continuous time of the ground station, and can obviously enhance the space-time continuous coverage capability of the earth surface element observation network by combining the real-time maneuvering observation capability of the unmanned aerial vehicle.
(2) The observation capability of the limited observation resources is improved. The observation capacity space-time cognitive model constructed by the invention adopts an observation mode adaptive to different observation requirements, and effectively improves the utilization rate and monitoring efficiency of limited observation resources through space-sky-ground cooperative observation.
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The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a general flow chart of a method for space-time cognition of earth surface elements of an urban area in accordance with the present invention;
FIG. 2 shows the result of a 5/9/month satellite band query of soil moisture according to the present invention;
FIG. 3 is a soil moisture optimization associated observation coverage of the present invention day 5, month 9;
FIG. 4 shows the results of soil moisture supplement on 5/9 days of the present invention.
Detailed Description
In recent years, the problem of frequent heavy rainfall in Wuhan city and increasingly obvious 'city sea-seeing' is increasingly highlighted, and the inland inundation in the whole city causes huge economic and social losses. As one of the first sponge cities in China, the development of the management work of waterlogging in Wuhan cities is not slow as the core of the midstream city group in the Yangtze river economic zone. In the afternoon of May and Ten days in 2021, Wuhan suddenly rainstorms, waterlogging is formed in partial areas due to waterlogging traffic control in multiple road sections, and certain economic loss is caused. In the rainfall process, the soil humidity reflects the water content of the soil, the larger the soil humidity in the early stage of rainfall is, the worse the storage and seepage capability of the soil to rainwater is, and the more easily the waterlogging is caused during rainstorm.
Therefore, the Wuhan city group is selected as a scheme experiment scene, the soil humidity of typical surface element related to rainstorm waterlogging caused by 1+8 city groups in Wuhan city in 5-month and 9-day days is seamlessly sensed, the space-sky-ground sensor observation capability satisfaction degree and the collaborative observation scheme credibility are calculated, a multi-sensor collaborative planning scheme that the space-time cognition accuracy of the observation capability meets the requirement is solved, and scientific scheme guidance is provided for rainstorm waterlogging monitoring.
The space-time cognitive method for the urban group surface element observation capability provided by the invention is described in detail below by combining the application scene and the attached drawings, and the general flow is shown in fig. 1.
Step S1: and (3) observing resource query:
and acquiring space-time and theme parameters of the urban group ground surface element observation task, setting the space-time cognition accuracy of the expected observation capability, and inquiring resources according to the space-time and theme parameters to obtain an urban group ground surface element basic observation scheme.
In this embodiment, step S1 specifically includes:
acquiring a Wuhan city group soil humidity observation task demand parameter, carrying out parameter discretization to obtain a time-space and theme parameter of an observation task, setting the time-space cognition accuracy of an expected observation capability to be 90%, searching and matching observation satellite resources from available resources according to the time-space and theme parameter to obtain a city group earth surface element basic observation scheme, and calculating a coverage strip of an available satellite sensor of an inquired satellite in a target time-space range. Four satellite resources including CartoSat 2, CartoSat 2B, Superview 1-04, and Sentiniel 2B were found at 5, month and 9, and the detailed parameters are shown in Table 1.
TABLE 1. inquiry results of soil moisture satellite bands
Step S2: basic associated observation ability space-time cognition:
constructing an observation capability space-time cognition model, performing basic association observation capability space-time cognition on the urban group ground surface element basic observation scheme based on the observation capability space-time cognition model, and calculating to obtain observation capability satisfaction and observation capability reliability of the urban group ground surface element basic observation scheme;
and calculating to obtain the space-time cognition accuracy rate of the basic associated observation capability of the urban group surface element observation scheme according to the observation capability satisfaction and the observation capability credibility.
In this embodiment, step S2 specifically includes:
s2-1: the soil humidity foundation association observation capability satisfaction degree is as follows:
and (3) solving the cognitive accuracy of the observation capability of the existing observation resources (the basic observation scheme of the earth surface elements of the urban group) of the soil humidity. The observation capability satisfaction degree of the daily scheme is calculated according to the observation capability satisfaction degree calculation method and the theme precision and the space-time coverage as shown in the table 2.
TABLE 2 satisfaction degree of soil humidity basic correlation observation capability
S2-2: reliability of soil humidity observation capability
And calculating the credibility of the observation scheme by using an observation capability credibility calculation formula according to the inquired satellites, wherein the result is shown in a table 3.
TABLE 3 confidence of soil moisture observation capability
S2-3: soil moisture base correlation results
And finally, calculating to obtain the cognitive accuracy rate of the soil humidity observation capability by combining the satisfaction degree of the soil humidity basic association observation capability and the credibility of the soil humidity observation capability, wherein the cognitive result of the soil humidity basic association result in 1+8 city circle of Wuhan city in 5-month-9-year 2021 is shown in table 4, the satellite coverage of the soil humidity basic association result is shown in fig. 2, the calculation result of the observation cognitive accuracy rate is 71.1%, the observation requirement is not met, and the optimization association is required to be continuously carried out in order to achieve the cognitive accuracy rate of the observation capability of 90%.
TABLE 4 results of soil moisture base correlation
Step S3: optimizing the space-time cognition of the associated observation ability:
and optimizing a satellite sensor side sway mode of the urban group ground surface element basic observation scheme, and optimizing the associated observation capability space-time cognition of the ground surface element observation scheme after the side sway mode is optimized through the observation capability space-time cognition model to obtain the accuracy rate of the optimized associated observation capability space-time cognition.
In this embodiment, step S3 specifically includes:
s3-1: optimizing a soil humidity satellite sensor side sway mode;
the optimization association is mainly to adjust the sidesway angle of the satellite sensor, and the unreasonable configuration of the satellite sensor is mainly embodied in that different observation effects are brought by the selection of different covering strip combinations, so that the strip covering effect can be optimized by optimizing the sidesway of the covering strips.
And importing a soil humidity basic observation scheme, optimizing the satellite sensor with the sidesway mode in the scheme, generating satellite sensor observation strips under different sidesway angles, and obtaining an optimal strip combination through a genetic algorithm to obtain the optimized scheme.
S3-2: optimizing the space-time cognition of the associated observation capability;
through an observation capacity space-time cognition model, observation capacity cognition accuracy rate evaluation is carried out on the satellite sensor after side swing optimization, the result after soil humidity optimization association is shown in table 5, soil humidity optimization association observation coverage is shown in fig. 3, after the result of basic association is optimized and associated, cognition accuracy rate is improved to a certain extent, the optimization association cognition accuracy rate is 72.3%, but the cognition accuracy rate requirement of 90 is not met, and supplementary association is required to be carried out continuously.
TABLE 5 soil moisture optimization correlation results
Step S4: augmenting associated observational capacity spatiotemporal cognition:
performing unmanned aerial vehicle and ground station resource supplement on the earth surface element observation scheme after the sidesway mode optimization; and performing supplementary association observation capability space-time cognition on the supplemented earth surface element observation scheme through the observation capability space-time cognition model to obtain supplementary association observation capability space-time cognition accuracy.
In this embodiment, through the operation of the satellite sensor sidesway mode optimization association stage, the observation capability of the current sensing network has already reached the maximum, if the task index requirement is not yet met at this time, and there is a part of observation capability deficiency, the sidesway mode optimization needs to be continued, and/or resources are additionally added to complement the observation capability of the sensing network, wherein the addition can be performed through the ground station and the unmanned aerial vehicle.
S4-1: unmanned aerial vehicle resource augmentation
The unmanned aerial vehicle resource supplement is firstly carried out, after the unmanned aerial vehicle is subjected to path planning, the coverage of a space area can be realized by carrying a sensor to scan on a path, and the problem of path planning is solved by adopting the unmanned aerial vehicle resource supplement missing observation capability. And after the unmanned aerial vehicle resource supplement is completed, cognitive accuracy evaluation is carried out on the observation scheme obtained in the supplement association stage.
S4-2: resource supplement for ground station
Supplement through unmanned aerial vehicle, make the space cover reach more than 90%, nevertheless because the observation characteristic of soil humidity key element, need ground station data to provide inversion input, to the demand of soil humidity meter level perception, need supplement suitable ground station equally and improve the precision. Therefore, the ground station needs to be supplemented, and the supplementary result is shown in table 6.
TABLE 6 location information added to the ground station
S4-3: augmenting associative observational capacity spatiotemporal cognition
Through satellite sidesway optimization and unmanned aerial vehicle and ground station resource supplementation, the cognitive accuracy rate evaluation result of the scheme reaches 93.5%, the soil humidity supplementation correlation result of 5 months and 9 days is shown in figure 4, the soil humidity observation task requirement of Wuhan city groups is met, and the finally output soil humidity observation capability space-time cognitive scheme result is shown in table 7.
TABLE 7 soil moisture supplement correlation results
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third and the like do not denote any order, but rather the words first, second and the like may be interpreted as indicating any order.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
1. A space-time cognition method for the observation capability of urban group surface elements is characterized by comprising the following steps:
acquiring space-time and theme parameters of an urban group earth surface element observation task, and setting the space-time cognition accuracy of the expected observation capability;
according to the space-time and theme parameters, observation resource query is carried out to obtain an urban group earth surface element basic observation scheme;
constructing an observation capability space-time cognition model, performing basic association observation capability space-time cognition on the urban group ground surface element basic observation scheme based on the observation capability space-time cognition model, and calculating to obtain observation capability satisfaction and observation capability reliability of the urban group ground surface element basic observation scheme;
calculating to obtain the space-time cognition accuracy rate of the basic associated observation capability of the urban group surface element observation scheme according to the observation capability satisfaction and the observation capability credibility;
judging whether the basic association observation capability space-time cognition accuracy rate of the urban group earth surface element observation scheme meets the expected observation capability space-time cognition accuracy rate or not;
if so, outputting the basic observation scheme of the urban group ground surface elements as a final space-time cognition scheme of the ground surface element observation capability;
otherwise, optimizing a satellite sensor side-sway mode for the basic observation scheme of the urban group ground surface elements;
performing optimization association observation capability space-time cognition on the earth surface element observation scheme optimized by the side swing mode through the observation capability space-time cognition model to obtain the accuracy rate of the optimization association observation capability space-time cognition;
judging whether the optimization associated observation capability space-time cognition accuracy rate meets the expected observation capability space-time cognition accuracy rate or not;
if so, outputting the earth surface element observation scheme optimized by the side sway mode as a final earth surface element observation capability space-time cognition scheme;
otherwise, the unmanned aerial vehicle and ground station resource supplement is carried out on the earth surface element observation scheme after the sidesway mode optimization;
performing supplementary association observation capability space-time cognition on the supplemented earth surface element observation scheme through the observation capability space-time cognition model to obtain supplementary association observation capability space-time cognition accuracy;
judging whether the supplementary associated observation capability space-time cognition accuracy rate meets the expected observation capability space-time cognition accuracy rate or not;
if so, outputting the supplemented earth surface element observation scheme as a final earth surface element observation capability space-time cognition scheme;
otherwise, continuing to optimize the satellite sensor side-sway mode, and/or supplementing the unmanned aerial vehicle and the ground station resources until the expected observation capability space-time cognition accuracy is achieved.
2. The method for spatiotemporal cognition of the observation capability of the urban grouping surface element as claimed in claim 1, wherein the calculation process of the satisfaction degree of the observation capability of the basic observation scheme of the urban grouping surface element comprises the following steps:
calculating the theme matching capability of each satellite sensor in the basic observation scheme of the urban group ground surface elements;
calculating the resolution satisfying capacity of each satellite sensor in the basic observation scheme of the urban group ground surface elements;
calculating the space coverage satisfaction capacity of the basic observation scheme of the urban group surface elements;
and calculating to obtain the observation capability satisfaction degree of the urban group earth surface element basic observation scheme according to the theme matching capability, the resolution satisfaction capability and the space coverage satisfaction capability.
3. The method for space-time cognition of the observation capability of the urban grouping surface element as claimed in claim 2, wherein the specific calculation formula for calculating the topic matching capability of each satellite sensor in the basic observation scheme of the urban grouping surface element is as follows:
4. the method for spatiotemporal cognition of the observation capability of the urban grouping surface elements according to claim 2, characterized in that the resolution of each satellite sensor in the basic observation scheme for calculating the urban grouping surface elements meets the capability, and the specific calculation formula is as follows:
resolution satisfies the capability index by a resolution score PiExpressed, in the formula, a represents the resolution of the satellite sensor.
5. The method for spatiotemporal cognition of the observation capability of the urban grouping surface element as claimed in claim 2, wherein the space coverage satisfaction capability of the basic observation scheme of the urban grouping surface element is calculated by the following specific calculation formula:
in the formula, CiRepresenting the space coverage satisfaction capacity of the ith satellite sensor in a certain time period, S represents the area of the research area, SiRepresenting the area of the investigation region covered by the ith satellite sensor over a period of time.
6. The method for spatiotemporal cognition of the observation capability of the urban group surface elements according to the claim 2, wherein the observation capability satisfaction degree of the urban group surface element basic observation scheme is obtained by the calculation according to the theme matching capability, the resolution satisfaction capability and the spatial coverage satisfaction capability, and the specific calculation formula is as follows:
wherein, OCSI represents the observation ability satisfaction degree of the basic observation scheme of the city group ground surface elements, i is 1,2iIndicating the subject matching capability, P, of each sensor in the satellite sensor combinationiIndicating the resolution satisfaction capability of each sensor in the satellite sensor combination, CiIndicating deviceThe spatial coverage of each sensor in the star sensor combination over a period of time satisfies the capability.
7. The method for space-time cognition of the observation capability of the urban grouping surface element according to claim 1, wherein the calculation process of the observation capability credibility of the basic observation scheme of the urban grouping surface element comprises the following steps:
calculating the environmental uncertainty U based on terrain conditions, the characteristics of the satellite sensor itself and different calculation methodsESatellite sensor self-uncertainty UpyAnd model method uncertainty UM；
Calculating the observation capability credibility of the urban group earth surface element basic observation scheme according to the environment uncertainty, the satellite sensor self uncertainty and the model method uncertainty, wherein a specific calculation formula is as follows:
wherein MOPR is the reliability of observation ability, UEFor environmental uncertainty, UpyFor the self-uncertainty of the sensor, UMIs the model method uncertainty.
8. The method for space-time cognition of the observation capability of the urban grouping surface element as claimed in claim 1, characterized in that the concrete calculation formula of the accuracy rate of the space-time cognition of the basic associated observation capability of the urban grouping surface element observation scheme is as follows:
F＝OCSI*MOPR*100％
wherein, F is the accuracy rate of space-time cognition of the basic associated observation capability, OCSI is the satisfaction degree of the observation capability, and the credibility of the MOPR observation capability.
9. A storage medium, characterized in that the storage medium is a computer-readable storage medium, on which a computer program is stored, the computer program being executed to implement the method for space-time cognition of the observational capability of the surface elements of the urban area according to any of the claims 1 to 8.
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