CN112231929B - Evaluation scene large sample generation method based on track parameters - Google Patents
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Abstract
According to the method for generating the large sample of the evaluation scene based on the track parameters, the large sample parameters of the evaluation scene and the value range of the large sample parameters of the evaluation scene are determined based on the evaluation task, and the large sample parameters of the evaluation scene comprise track preset parameters and weather parameters; determining a sampling value set of each evaluation scene large sample parameter according to the evaluation scene large sample parameter and the parameter value range; based on the full factor design model, generating an evaluation scene large sample parameter matrix according to the number of the evaluation scene large sample parameters and the sampling value set; determining and supplementing track element supplementing parameters of each evaluation scene large sample parameter in the evaluation scene large sample parameter matrix according to track preset parameters based on constraint relation of track numbers; and combining the track preset parameters, the weather parameters and the track element supplementary parameters according to the large sample parameter matrix of the evaluation scene to obtain a large sample of the evaluation scene. The method can simplify the dimension of the large sample parameter of the evaluation scene, comprehensively and effectively cover the sample, and reduce the scale of the sample parameter matrix.
Description
Technical Field
The disclosure belongs to the technical field of spacecraft system efficiency evaluation, and particularly relates to an evaluation scene large sample generation method based on orbit parameters.
Background
The performance evaluation of the spacecraft system is based on various technical indexes and comprehensive indexes thereof, describes the capacity of the spacecraft system to complete tasks according to a certain weighting algorithm, predicts and evaluates the satisfaction degree of the spacecraft system to user demands or tasks, and provides decision basis for reasonable utilization of resources.
The general flow for realizing the performance evaluation task of the spacecraft system by using the simulation method is that an evaluation scene is firstly created, and then evaluation index calculation and evaluation analysis are carried out according to a certain evaluation criterion according to a simulation calculation result under the scene. The effect of the completion of the assessment task is related to the configuration selection of the scene in addition to the rationality of the index selection and the comprehensive calculation. In the research, the evaluation calculation is carried out by using the results of multiple simulation through a method for increasing the scene sample size, and the evaluation results are subjected to statistical analysis, so that the confidence of the evaluation results is improved, and the purpose of scientific and objective evaluation results is achieved.
And a large sample mode of the scene is introduced to improve the efficiency evaluation confidence of the spacecraft system, designable factors in the evaluation scene are firstly selected, the factor level is determined, and then the sample design is carried out by utilizing a test design method. The existing spacecraft system level simulation scene is generally in the existing satellite system scene, namely, limited satellite combinations must be determined first and orbit element data of a given satellite are given, and the sample design mainly considers meteorological environments in environmental factors. The design method is to design factors by taking weather such as cloud, rain, fog and the like as samples, the occurrence probability of various weather is a factor level number, and the influence probability of various weather on satellite and load work is considered in subsequent simulation calculation. The evaluation scene sample designed by the method solves the evaluation problem that satellite and combined undersea point coverage is basically determined, and the evaluation task cannot be independently operated and the coverage of the evaluation sample is insufficient depending on the input of the design result of a satellite system; secondly, by adopting the method to design environmental factors, factor parameters are difficult to act on load influence effect calculation, and cannot be applied to application efficiency evaluation statistical analysis such as seasonal and regional systems, so that the evaluation result is difficult to characterize the defect of actual application effect.
To overcome the above drawbacks, it is not preferable to directly use the orbit design parameters of the satellite and the weather values as sample design factors. Because of the orbit extrapolation model selected by the common satellite orbit design method, a plurality of parameters such as 6 orbit parameters, time and the like need to be designed. Parameters that may be used to characterize weather are a range of parameters of various types, such as temperature, humidity, wind, cloud, rain, sea conditions, etc. The method directly uses the track design parameters and weather parameters as large sample design factors to bring the defects of huge sample size and overlarge workload of system design simulation calculation analysis.
Disclosure of Invention
In view of this, the disclosure provides an evaluation scene large sample generation method based on orbit parameters, which can simplify the dimension of the evaluation scene large sample parameters, and not only ensure the comprehensive and effective coverage of samples, but also reduce the scale of a sample parameter matrix under the condition that new satellites and constellation schemes are not required to be designed in advance, and perform scientific statistical analysis on system efficiency evaluation.
According to an aspect of the present disclosure, there is provided a method for generating a large sample of an evaluation scene based on track parameters, the method including:
determining an evaluation scene large sample parameter and an evaluation scene large sample parameter value range based on an evaluation task, wherein the evaluation scene large sample parameter comprises an evaluation scene track preset parameter and an evaluation scene weather parameter;
determining a sampling value set of each evaluation scene large sample parameter according to the evaluation scene large sample parameter and the evaluation scene large sample parameter value range;
generating an evaluation scene large sample parameter matrix according to the number of the evaluation scene large sample parameters and the sampling value set of the evaluation scene large sample parameters based on a full factor design model;
determining and supplementing track element supplementing parameters of each evaluation scene large sample parameter in the evaluation scene large sample parameter matrix according to the track preset parameters based on the constraint relation of the track number;
and combining the evaluation scene track preset parameters, the evaluation scene weather parameters and the evaluation scene track element supplementary parameters into the evaluation scene large sample according to the evaluation scene large sample parameter matrix.
In one possible implementation manner, the determining the sampling value set of each evaluation scene large sample parameter according to the evaluation scene large sample parameter and the evaluation scene large sample parameter value range includes:
for each parameter of the large sample of the evaluation scene, determining the sampling value of the large sample parameter of the evaluation scene according to the sampling number and the value range of the large sample parameter of the evaluation scene, and combining the sampling value of the large sample parameter of the evaluation scene to be the sampling value set of the large sample parameter of the evaluation scene.
In one possible implementation, the determining the sample value of the large sample parameter of the evaluation scene includes:
sampling the large sample parameters of the evaluation scene by adopting a uniform distribution method to obtain sampling values of the large sample parameters of the evaluation scene.
In one possible implementation, the track preset parameter includes a track semi-major axis and a track ascending intersection point, which are right ascent; the weather parameters include: wind-level samples and cloud categories.
In one possible implementation, the track element supplementation parameters include: track inclination, eccentricity, perigee argument and perigee argument.
Determining an evaluation scene large sample parameter and an evaluation scene large sample parameter value range based on an evaluation task, wherein the evaluation scene large sample parameter comprises an evaluation scene track preset parameter and an evaluation scene weather parameter; determining a sampling value set of each evaluation scene large sample parameter according to the evaluation scene large sample parameter and the evaluation scene large sample parameter value range; generating an evaluation scene large sample parameter matrix according to the number of the evaluation scene large sample parameters and the sampling value set of the evaluation scene large sample parameters based on a full factor design model; determining and supplementing track element supplementing parameters of each evaluation scene large sample parameter in the evaluation scene large sample parameter matrix according to the track preset parameters based on the constraint relation of the track number; and combining the evaluation scene track preset parameters, the evaluation scene weather parameters and the evaluation scene track element supplementary parameters into the evaluation scene large sample according to the evaluation scene large sample parameter matrix. The method can simplify the dimension of large sample parameters of the evaluation scene, ensure the comprehensive and effective coverage of the sample, reduce the scale of a sample parameter matrix and carry out scientific statistical analysis on the evaluation of the system efficiency under the condition that new satellites and constellation schemes are not required to be designed in advance.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features and aspects of the present disclosure and together with the description, serve to explain the principles of the disclosure.
FIG. 1 illustrates a flowchart of an evaluation scenario large sample generation method based on track parameters according to an embodiment of the present disclosure;
Detailed Description
Various exemplary embodiments, features and aspects of the disclosure will be described in detail below with reference to the drawings. In the drawings, like reference numbers indicate identical or functionally similar elements. Although various aspects of the embodiments are illustrated in the accompanying drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
In addition, numerous specific details are set forth in the following detailed description in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art have not been described in detail in order not to obscure the present disclosure.
Fig. 1 illustrates a flowchart of an evaluation scenario large sample generation method based on track parameters according to an embodiment of the present disclosure. The method can be applied to spacecraft system efficiency evaluation, and as shown in fig. 1, the method can comprise the following steps:
step S1: and determining an evaluation scene large sample parameter and an evaluation scene large sample parameter value range based on the evaluation task, wherein the evaluation scene large sample parameter comprises an evaluation scene track preset parameter and an evaluation scene weather parameter.
The orbit preset parameters, the weather parameters or both may be selected independently based on the task of evaluating the performance of the spacecraft system, which is not limited herein. When the solar synchronous regression orbit is used as a sample design object for estimating the large sample of the estimating scene based on the efficiency of the spacecraft system, therefore, the orbit preset parameter and the weather parameter are selected as the estimating scene orbit preset parameter.
The evaluation scene track preset parameter may include a track semi-major axis and a track ascending intersection point right ascent. For example, the track semi-major axis a in kilometers; the intersection point of the track rise is right through omega, and the unit is degree.
Weather parameters may include wind level samples and cloud categories, such as weather parameters (α, β), α being wind level sample parameter samples and β being cloud category parameter samples. The wind level sample may include a sample representing the wind power intensity in the atmosphere, and may be, for example, a wind level such as level 0, level 1, level 2, level 3, level 4, level 5, level 6, level 7, level 8, level 9, level 10, level 11, and level 12. The cloud class is used for representing the shape and thickness of the cloud in the atmosphere, and can be, for example, the cloud, the rain cloud, the layer cloud, the rain cloud, the broken rain cloud and the like.
Step S2: and determining a sampling value set of each evaluation scene large sample parameter according to the evaluation scene large sample parameter and the evaluation scene large sample parameter value range, and obtaining the sampling value set of each evaluation scene large sample parameter.
Wherein, the solar synchronous regression orbit observed by the earth is taken as a sample design object, and the value range of the orbit semi-long axis a is taken as (a down ，a up ) The right angle can be 300-1000 m, and the right angle of the track rising intersection point is as the value range of (omega) down ,Ω up ) I.e. 0 to 360 degrees.
When the main factors of the weather effect on earth observation are considered to be the corresponding relations between wind and cloud, rain, snow, fog, sea conditions and the like, the wind level sample parameter α may be an enumerated value of wind level, and as the corresponding relation between the wind level sample parameter and sea condition weather shown in table 1 is known, the enumerated value of the wind level sample parameter α may be {0,1, …,11,12}. The cloud class parameter β may be an enumerated value of a cloud class, and table 2 shows that the enumerated value of the cloud class parameter β may be {1,2, …,11}, as known from the correspondence between the cloud class parameter and weather.
TABLE 1
| Wind stage | Name of the name | Wind speed (m/s) | Sea condition | Accompanying weather |
| 0 | Windless | 0～0.2 | Without any means for | Evaporation fog |
| 1 | Soft wind | 0.3～1.5 | Without any means for | Radiation fog |
| 2 | Breeze cleaning device | 1.6～3.3 | Wavelet | Radiation fog |
| 3 | Breeze of breeze | 3.4～5.4 | Larger wavelets | Advection fog |
| 4 | Wind and wind | 5.5～7.9 | Small wave | Advection fog |
| 5 | Clear and strengthen wind | 8.0～10.7 | Middle wave | Possibly accompanied by precipitation |
| 6 | Strong wind | 10.8～13.8 | High waves | Possibly accompanied by precipitation |
| 7 | High wind | 13.9～11.7 | High waves | Possibly accompanied by precipitation |
| 8 | Strong wind | 17.2～20.7 | Big wave to huge wave | Possibly accompanied by precipitation |
| 9 | Strong wind | 20.8～24.4 | High waves | Possibly accompanied by loweringWater and its preparation method |
| 10 | Gust of wind | 24.5～28.4 | Furt | Possibly accompanied by precipitation |
| 11 | Storm wind | 28.5～32.6 | Abnormal billows | Possibly accompanied by precipitation |
| 12 | Hurricane (Hurricane) | 32.7～36.9 | Visibility 0 | Sea surface is filled with water foam |
As can be seen from table 1, the wind force includes 0 level (no wind), the sea state is none, accompanied by "evaporation fog" weather, 1 level (soft wind), the sea state is none, accompanied by "radiation fog" weather, …,12 level (hurricane), the sea state is visibility 0, accompanied by "sea surface full of water foam" weather.
TABLE 2
As can be seen from table 2, cloud families are classified into three cloud families of low cloud, medium cloud and high cloud. The low cloud group corresponds to 6 cloud categories of clouds, rain clouds, layer clouds, rain layer clouds and broken rain clouds. The middle cloud group corresponds to 2 cloud categories of high-rise clouds and high-accumulation clouds. Gao Yunzu corresponds to a volume cloud, a layer cloud, and a volume cloud for a total of 3 cloud categories. Wherein, when the cloud is accumulated, there is no accompanying weather, the accumulated rain cloud is accompanied with 'thundergust rain, hail' weather, …, and the accumulated cloud is free from accompanying weather.
The contents in table 1 and table 2 are merely examples, and are not limited thereto. When the actual evaluation task of the spacecraft system efficiency is performed, the important attention is paid to sample parameters in the evaluation scene of the spacecraft system efficiency and the adaptability adjustment is performed on the value range according to the needs.
Step S2: and determining a sampling value set of each evaluation scene large sample parameter according to the evaluation scene large sample parameter and the evaluation scene large sample parameter value range.
In an example, the determining the sampling value set of each evaluation scene large sample parameter according to the evaluation scene large sample parameter and the evaluation scene large sample parameter value range includes:
for each parameter of the large sample of the evaluation scene, determining the sampling value of the large sample parameter of the evaluation scene according to the sampling number and the value range of the large sample parameter of the evaluation scene, and combining the sampling value of the large sample parameter of the evaluation scene to be the sampling value set of the large sample parameter of the evaluation scene. And sampling the large sample parameters of the evaluation scene by adopting a uniform distribution method to obtain sampling values of the large sample parameters of the evaluation scene.
For example, if the number of samples of one sample parameter in the large sample parameter of the evaluation scene is q, the range of values of the sample parameter is (m, N), and if the sample parameter is uniformly distributed, the sample values of the sample parameter may be { m+ (i-1) · (N-m)/q|1 +.i+.q, i∈n }, where i gives different values to obtain all sample values of the sample parameter, and all sample values of the sample are combined to obtain the sample value set of the sample parameter, where m, N are real numbers, i and q are positive integers, and N is a natural number.
For example, as shown in step S1, the solar synchronous regression orbit observation spacecraft system performance evaluation scene is a large sample parameter and a range of values of each sample parameter. Taking orbit sample parameters as an example, the sun observed by the earth is the sameTrack height fluctuation range caused by track perturbation is set to sample set { a } of semi-long axis a of track i Sample set { a } having 70 sample values, i.e., the orbit half-major axis a i The number of samples of the track half major axis a is 70, the value range of the track half major axis a is 300-1000 meters, namely m=300, n=1000, and the sample value of the track half major axis a is a i =m+ (i-1) × (N-m)/70, substituting m=300, n=1000 to obtain the sampling value {300+10×i i 1 +.ltoreq.i.ltoreq.70, i e N }, traversing the value of i step by step can obtain all the sampling values of the track semi-major axis a, and combining all the sampling values of the track semi-major axis a to obtain the sampling value set of the track semi-major axis a. Similarly, a track rising intersection point right-hand warp sample set { omega } is set according to the load breadth distribution range j Sample set { Ω } having 360 sample values, i.e., track-rise intersection point right through Ω j The number of samples is 360, and if the right ascent point of the track is set to be 1 DEG at the right ascent point, omega j And j is larger than or equal to 1 and smaller than or equal to 360, j is smaller than or equal to 360, the value of j is gradually traversed to obtain all the sampling values of the track intersection point right ascent omega, and all the sampling values of the track intersection point right ascent omega are combined to obtain the sampling value set of the track intersection point right ascent omega.
According to the principle, the sampling value set of each sample parameter in the large sample parameters of the evaluation scene can be calculated in turn. For example, the sample set of the wind level sample parameter alpha is {0,1, …,11,12}, the sampling number is 13, and the sampling value set of the wind level sample parameter can be obtained according to { m+ (i-1) · (N-m)/q|1. Ltoreq.i.ltoreq.q, i.epsilon.N }. Similarly, the sample set of the cloud class parameter beta is {1,2, …,11}, and the sampling number is 11, so that the sample value set of the wind level sample parameter is obtained.
Step S3: based on a full factor design model, generating the evaluation scene large sample parameter matrix according to the number of the evaluation scene large sample parameters and the sampling value set of the evaluation scene large sample parameters.
Since the sampling numbers of the sample parameters of the large sample parameter of the evaluation scene may be different, the test design method generally refers to test design with different sampling intervals of the large sample parameter of the evaluation scene, and no obvious main effect sample parameter and no obvious interactive effect sample parameter exist among the sample parameters of the large sample parameter of the evaluation scene. Of course, the test design method model may be designed uniformly, orthogonally, or in latin square, and is not limited herein.
If the evaluation scene large sample parameter has j sample parameters, according to the actual requirement, the sampling value set of each sample parameter of the evaluation scene large sample parameter contains q sampling values respectively 1 ，q 2 ，…，q j The total of the sampling value combinations of all the sample parameters of the large sample parameters of the evaluation scene is Q, and q=q 1 *q 2 …*q j The sample parameter matrix for evaluating the scene large sample parameters is a matrix in dimension Q x j, where j and Q are positive integers. Taking the solar earth synchronous orbit observation spacecraft system efficiency evaluation scene large sample parameter as an example, the sampling number of the orbit semi-long axis a is 70, the sampling number of the orbit ascending intersection point right angle omega is 360, the wind level sample sampling number is 13, the cloud class sampling number is 11, the total sampling number of the evaluation scene large sample parameter when the solar earth observation is influenced is S,there are 603600 samples, and each sample includes 4 parameters of orbit semi-long axis, orbit ascending intersection point right ascent, wind level sample and cloud class, so the evaluation scene large sample parameter matrix is a 4 x 603600 dimension matrix.
Step S4: and determining and supplementing track element supplementing parameters of each evaluation scene large sample parameter in the evaluation scene large sample parameter matrix according to the preset parameters of the evaluation scene track based on the constraint relation of the track number.
Wherein the track element supplementation parameters may include: track inclination, eccentricity, perigee argument and perigee argument. For example, the orbit of a satellite is determined by 6 elements including an orbit inclination i, a near-point argument ω, an ascending intersection point right ascent angle Ω, an eccentricity e, an orbit semi-major axis a, and a plane near-point angle M at which the satellite passes through the near point.
The track semimajor axis a and the track ascending intersection point of the known sun-to-earth observation synchronous track are right through omega, and can be obtained according to the track root relation of the sun synchronous track of the circular track: track inclination angleIn degrees, where R e The equatorial radius is 6378.137 km.
Assuming that the number of satellites on the solar synchronous orbit surface is d and the phases among the satellites are uniformly distributed, the angle of the closest point isd is a positive integer, and p is a natural number.
Due to the sun synchronous orbit, the perigee amplitude angle omega and the eccentricity e of the satellite can be set to 0 in the system simulation evaluation task. Other four track element replenishment parameters for each large sample of the evaluation scene can be calculated and replenished according to the principles described above.
Step S5: and combining the evaluation scene track preset parameters, the evaluation scene weather parameters and the evaluation scene track element supplementary parameters into the evaluation scene large sample according to the evaluation scene large sample parameter matrix.
Supplementing the other four track element supplementing parameters of the large sample of the evaluation scene calculated in the step S4 into each sample parameter in the parameter matrix of the large sample of the evaluation scene to obtain the track parameters (namely the combination of the track preset parameters and the track element supplementing parameters) of the large sample of the evaluation scene, and combining the weather parameters of the large sample of the evaluation scene to obtain the large sample { (S) Track preset parameter lambda ，S Track element supplement parameter lambda ，S Weather parameter lambda ) Lambda is not less than 1 and not more than N, lambda is epsilon N. Of course, the load parameter or the supplementary element parameter of the evaluation scene is based on the supplementary parameter of the track element of the evaluation scene, and therefore, the large sample of the evaluation scene can be { (S) Track preset parameter lambda ，S Track element supplement parameter lambda ，S Weather parameter lambda )1≤λ≤N，λ∈N}。
Determining an evaluation scene large sample parameter and an evaluation scene large sample parameter value range based on an evaluation task, wherein the evaluation scene large sample parameter comprises an evaluation scene track preset parameter and an evaluation scene weather parameter; determining a sampling value set of each evaluation scene large sample parameter according to the evaluation scene large sample parameter and the evaluation scene large sample parameter value range; generating an evaluation scene large sample parameter matrix according to the number of the evaluation scene large sample parameters and the sampling value set of the evaluation scene large sample parameters based on a full factor design model; determining and supplementing track element supplementing parameters of each evaluation scene large sample parameter in the evaluation scene large sample parameter matrix according to the track preset parameters based on the constraint relation of the track number; and combining the evaluation scene track preset parameters, the evaluation scene weather parameters and the evaluation scene track element supplementary parameters into the evaluation scene large sample according to the evaluation scene large sample parameter matrix. The method can simplify the dimension of large sample parameters of the evaluation scene, ensure the comprehensive and effective coverage of the sample, reduce the scale of a sample parameter matrix and carry out scientific statistical analysis on the evaluation of the system efficiency under the condition that new satellites and constellation schemes are not required to be designed in advance.
The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvements in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (5)
1. An evaluation scene large sample generation method based on track parameters, which is characterized by comprising the following steps:
determining an evaluation scene large sample parameter and an evaluation scene large sample parameter value range based on an evaluation task, wherein the evaluation scene large sample parameter comprises an evaluation scene track preset parameter and an evaluation scene weather parameter;
determining a sampling value set of each evaluation scene large sample parameter according to the evaluation scene large sample parameter and the evaluation scene large sample parameter value range;
generating an evaluation scene large sample parameter matrix according to the number of the evaluation scene large sample parameters and the sampling value set of the evaluation scene large sample parameters based on a full factor design model;
determining and supplementing track element supplementing parameters of each evaluation scene large sample parameter in the evaluation scene large sample parameter matrix according to the track preset parameters based on the constraint relation of the track number;
and combining the evaluation scene track preset parameters, the evaluation scene weather parameters and the evaluation scene track element supplementary parameters into the evaluation scene large sample according to the evaluation scene large sample parameter matrix.
2. The method for generating a large sample of an evaluation scene according to claim 1, wherein determining a set of sample values for each large sample parameter of the evaluation scene based on the large sample parameter of the evaluation scene and the range of values of the large sample parameter of the evaluation scene comprises:
for each parameter of the large sample of the evaluation scene, determining the sampling value of the large sample parameter of the evaluation scene according to the sampling number and the value range of the large sample parameter of the evaluation scene, and combining the sampling value of the large sample parameter of the evaluation scene to be the sampling value set of the large sample parameter of the evaluation scene.
3. The evaluation scene large sample generation method according to claim 2, wherein the determining the sampling value of the evaluation scene large sample parameter comprises:
sampling the large sample parameters of the evaluation scene by adopting a uniform distribution method to obtain sampling values of the large sample parameters of the evaluation scene.
4. The evaluation scene large sample generation method according to claim 1, wherein the orbit preset parameters include an orbit semi-major axis and an orbit ascending intersection point barefoot; the weather parameters include: wind-level samples and cloud categories.
5. The evaluation scene large sample generation method according to claim 1, wherein the track element supplementing parameters include: track inclination, eccentricity, perigee argument and perigee argument.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002015010A (en) * | 2000-06-29 | 2002-01-18 | Yokohama Rubber Co Ltd:The | Method for designing product form and pneumatic tire designed with it |
| CN106411587A (en) * | 2016-09-26 | 2017-02-15 | 中国空间技术研究院 | Simulation architecture suitable for performance evaluation of satellite communications network |
| CN107844462A (en) * | 2017-10-26 | 2018-03-27 | 北京理工大学 | A kind of interplanetary continuous thrust transfer orbit appraisal procedure |
| WO2020042795A1 (en) * | 2018-08-31 | 2020-03-05 | 阿里巴巴集团控股有限公司 | Sample attribute evaluation model training method and apparatus, and server |
| CN111684721A (en) * | 2018-01-15 | 2020-09-18 | 量子数公司 | Method and system for generating random bit samples |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9989634B2 (en) * | 2014-04-22 | 2018-06-05 | Specialized Arrays, Inc. | System and method for detection and orbit determination of earth orbiting objects |
| US11205028B2 (en) * | 2018-09-06 | 2021-12-21 | Terrafuse, Inc. | Estimating physical parameters of a physical system based on a spatial-temporal emulator |
-
2020
- 2020-11-02 CN CN202011205458.2A patent/CN112231929B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002015010A (en) * | 2000-06-29 | 2002-01-18 | Yokohama Rubber Co Ltd:The | Method for designing product form and pneumatic tire designed with it |
| CN106411587A (en) * | 2016-09-26 | 2017-02-15 | 中国空间技术研究院 | Simulation architecture suitable for performance evaluation of satellite communications network |
| CN107844462A (en) * | 2017-10-26 | 2018-03-27 | 北京理工大学 | A kind of interplanetary continuous thrust transfer orbit appraisal procedure |
| CN111684721A (en) * | 2018-01-15 | 2020-09-18 | 量子数公司 | Method and system for generating random bit samples |
| WO2020042795A1 (en) * | 2018-08-31 | 2020-03-05 | 阿里巴巴集团控股有限公司 | Sample attribute evaluation model training method and apparatus, and server |
Non-Patent Citations (1)
| Title |
|---|
| 基于典型应用场景的北斗导航效能评估方法;刘婕;王玲;黄文德;张利云;;大地测量与地球动力学;20181015(第10期);1027-1032 * |
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