CN116305703A - Environmental parameter space consistency characterization method for simulation system - Google Patents
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Abstract
The invention relates to a simulation system-oriented environmental parameter space consistency characterization method, which comprises the following steps: the simulation system comprises five parts, namely, simulation system environment parameter input, real environment parameter numerical value space construction, manifold space representation, simulation environment parameter space calculation and space consistency representation. The invention establishes a numerical subspace of a real environment parameter aiming at a large amount of historical meteorological data of a target area, establishes geometric distribution of the real environment parameter space of the target area on Riemann manifold in different time periods, calculates a subspace model of the simulation environment parameter by a Riemann optimization method, defines the consistency measurement of the environment input parameter vector and manifold parameter space of the simulation system, and provides a consistency characterization method of environment simulation parameters and environment numerical space.
Description
Technical Field
The invention relates to the technical field of environment simulation and pattern recognition, and particularly provides an environment parameter space consistency characterization method for a simulation system.
Background
For most simulation software systems (such as GRM), the input environment simulation numerical parameters are manually input, and how to judge the rationality of the simulation parameters, so that a parameter rationality evaluation method is provided for the environment input parameters of the simulation software system, and the simulation image result output by the simulation system is directly influenced.
Because the real environment parameters in the nature have continuously changing properties, the real environment parameters show continuity in the parameter space, and the continuous micro-properties of manifold space can be used for carrying out space modeling on the real environment parameters by combining the topological properties of differential geometry. The real environment parameter data is actually measured by a precision instrument, the simulation environment parameter is manually input by a human, and if the parameter space is directly constructed based on the real environment parameter due to the difference of the data source acquisition modes of the real environment parameter data and the simulation environment parameter data, the geometric position of the simulation environment parameter in the real environment numerical value space may fall on the periphery of the parameter space.
In summary, the key of judging the rationality of the simulation input parameters is that: (1) How to construct manifold value space of real environment parameters according to the history data of the real environment parameters; (2) How to measure and evaluate the deviation degree of the input simulation environment parameter and the real environment parameter space, and the deviation degree is used as a judgment standard for the rationality of the simulation parameter. Therefore, an environment parameter space consistency characterization method is lacked, technical support is provided for high fidelity image output of a simulation system, and the fidelity of the simulation image output by the simulation system is improved.
Disclosure of Invention
In view of the above, the present invention aims to provide a simulation system-oriented method for characterizing spatial consistency of environmental parameters, so as to solve the problem of low fidelity of a simulation image output by a simulation system. According to the method, a geometric model of a simulation environment parameter space is constructed according to the geometric property of the environment parameter, an environment parameter space consistency characterization method is provided, a consistency measure of simulation environment input parameters and the environment parameter space of a simulation system is calculated, and finally technical support is provided for high-fidelity image output of the simulation system.
The technical scheme adopted by the invention for achieving the purpose is as follows: a simulation system-oriented environmental parameter space consistency characterization method comprises the following steps:
1) Preprocessing historical meteorological data of a target area, and establishing an environment data sample library of the target area;
2) For the input simulation environment parameters of the simulation system, constructing the environment parameter geometric distribution between the simulation environment parameters and the real environment history data;
3) Establishing subspaces of a real environment parameter space according to the constructed geometric distribution of the environment parameters, and carrying out manifold space representation on the real environment parameters;
4) Calculating a relevant real environment parameter space in which the simulation environment parameters are located according to subspaces of the real environment parameter space;
5) And calculating projection points of the input simulation environment parameters in a real environment parameter space, and evaluating the rationality of the simulation environment parameters through the consistency measurement of the simulation environment parameters and the manifold parameter space.
Preprocessing historical meteorological data of a target area, and establishing an environment data sample base of the target area, wherein the preprocessing comprises the following steps:
acquiring historical environment data of a target area, establishing an environment data sample base of the target area, wherein the environment data sample base comprises environment parameter samples of n groups of time points, and the environment parameter sample space of the target area is expressed as A= { V 1 ,V 2 ,…,V n }, wherein V i ∈R d ，i＝1,2,…,n，V i And d-dimensional environmental parameter data vector representing actual measurement of the current period of the region.
The method for constructing the geometrical distribution between the simulation environment parameters and the real environment history data for the input environment parameters of the simulation system comprises the following steps:
extracting a plurality of d-dimensional environmental parameter data vectors from an environmental parameter sample space A of a target area to form a simulation environmental parameterNumber X n ；
For simulation environment parameter X n ∈R d Find X n R neighbor points c of (2) ni ∈R d ，i∈{1,2,…,r}；
For each adjacent point c ni Find c again ni Q neighbor points c of (2) nij ∈R d J e {1,2, …, q } and i e {1,2, …, r }; c nij Representing simulation environment parameter X n The j-th neighbor point, c, of the i-th neighbor point of (c) ni And c nij Representing real environment history data.
According to the constructed geometric distribution of the environmental parameters, a subspace model of a real environmental parameter numerical space is established, manifold space representation is carried out on the real environmental parameters, and the method comprises the following steps:
for simulation environment parameter X n Every real environment parameter subspace S of the surroundings i ＝{S 1 ,S 2 ,…,S N }，X n The numerical parameter subspace model S is the surrounding subspace S i Is defined by a geometric center position of the lens.
The related real environment parameter space where the simulation environment parameter is located is calculated according to the subspace of the real environment parameter space, and the method specifically comprises the following steps:
the real environment parameter space is characterized on the Grassmann manifold as { S ] i } i＝1,2,…,N The geometric center position S of these points is calculated as:
where d (·) represents the geodesic distance on the Grassmann manifold, N is the selected environmental parameter sample S i The number of (3);
for geodesic distance on Grassmann manifold, the expression is
In the above equation, |·| represents the Frobenius norm of the matrix.
The calculation of the projection point of the input simulation environment parameter in the real environment parameter space evaluates the rationality of the simulation environment parameter through the consistency measurement of the simulation environment parameter and the manifold parameter space, and comprises the following steps:
for input simulation environment parameter X n ，X n ∈R d ，X 1 ，X 2 And X 3 Respectively X n Three nearest neighbors of (a), point X n The mapping point to the geometric center position S is c p ：
c p ＝SS + X n
Wherein S is + ＝(S T S) -1 S T A pseudo-inverse matrix representing S;
for an ambient input vector X in peripheral Euclidean space n ∈R d The consistency of the environment parameter space to the environment parameter space is characterized by the calculation mode of distance dis
dis＝||X n -c p ||
τ is a confidence threshold;
if the consistency characterization metric dis of the input simulation environment parameters and the environment parameter space is less than or equal to tau, the input simulation environment parameters are considered to be reasonable;
otherwise, the input simulation environment parameters are considered unreasonable.
The invention has the following beneficial effects and advantages:
aiming at environment input parameters of a simulation system, a large amount of historical meteorological data of a target area are combined, a numerical subspace of real environment parameters is built by utilizing the geometric structure and the spatial characteristics of a Riemann manifold, geometric distribution of the real environment parameter space of the target area on the Riemann manifold in different time periods is built, consistency measurement of environment input parameter vectors and manifold parameter numerical space of the simulation system is defined, a consistency characterization method of environment simulation parameters and environment numerical space is provided, and technical support is provided for high-reality image output of a simulation software system finally.
Drawings
FIG. 1 is a schematic flow diagram of a simulation system-oriented environmental parameter space consistency characterization method provided by the invention;
FIG. 2 is a sample diagram of historical meteorological data of a real environment in the simulation system-oriented environmental parameter space consistency characterization method provided by the invention.
FIG. 3 is a schematic diagram of a geometric distribution construction between input simulation environment parameters and real environment history data.
FIG. 4 is a schematic diagram of simulation environment parametric space model construction.
FIG. 5 is a schematic representation of an environmental parameter spatial consistency characterization model.
Fig. 6 is a diagram of experimental test samples of the simulation system.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The invention discloses an environmental parameter space consistency characterization method based on Grassmann manifold, which comprises the following steps: the simulation system comprises five parts, namely, input of environment parameters, construction of a real environment parameter numerical value space, manifold space representation, simulation environment parameter space calculation and space consistency representation. Firstly, according to the environmental parameters input by a simulation system, constructing the geometric distribution of the simulation environmental parameters, and for the historical meteorological data of a specific area, constructing a historical data sample base of the real environmental parameters of the area;
then, utilizing differential geometry and Riemann manifold theory, according to the mathematical characteristics of subspaces, establishing geometrical distribution of a plurality of high-dimensional subspace models in the Grassmann manifold space, further expressing a plurality of Gao Weizi space models as point sets on the Grassmann manifold, and finally converting geometrical center position solving problems of a plurality of Gao Weizi spaces into geometrical center point solving problems in the Grassmann manifold space through geometrical characterization of Gao Weizi spaces on the Riemann manifold.
The invention establishes a numerical subspace of a real environment parameter aiming at a large amount of historical meteorological data of a target area, establishes geometric distribution of the real environment parameter space of the target area on Riemann manifold in different time periods, calculates a subspace model of the simulation environment parameter by a Riemann optimization method, defines the consistency measurement of the environment input parameter vector and manifold parameter space of the simulation system, and provides a consistency characterization method of environment simulation parameters and environment numerical space.
A simulation system-oriented environmental parameter space consistency characterization method comprises the following steps:
step one, inputting a simulation environment parameter vector of a simulation system, and constructing a historical meteorological data sample base of a target area.
And secondly, for the input environment parameters of the simulation system, constructing the geometric distribution between the simulation environment parameters and the real environment calendar history data.
And thirdly, establishing a subspace model of a real environment parameter numerical space according to the constructed environment parameter geometric distribution, and carrying out manifold space representation on the real environment parameter.
And step four, calculating a relevant real environment parameter space in which the simulation environment parameters are located according to the real environment parameter subspace.
And fifthly, calculating projection points of the input simulation environment parameters in a real environment parameter space, and taking the consistency measurement of the simulation parameter vector and the manifold parameter space as a measurement criterion for judging the rationality of the simulation parameters.
And step six, outputting consistency measurement of simulation environment input parameters and environment parameter space of the simulation system, and finally evaluating the rationality of the environment parameters of the simulation system.
The second step of constructing geometrical distribution between simulation environment parameters and real environment history data for the input environment parameters of the simulation system comprises the following steps:
step 2-1, for the input environmental parameter X of the simulation system n Calculating the neighbor point { c } of the input parameter 1 ,c 2 ,c 3 }。
And 2-2, constructing geometrical distribution between simulation environment parameters and real environment historical data.
And step three, establishing a subspace model of a real environment parameter numerical space by utilizing the geometric structure and the spatial characteristics of the Riemann manifold according to the constructed geometric distribution of the environment parameters, and carrying out manifold space characterization on the actually measured real environment parameters through an orthogonal subspace. Based on the mathematical properties of the subspaces, the geometric distribution of the plurality of high-dimensional subspace models is built in the Grassmann manifold space, and the plurality of Gao Weizi space models are expressed as point sets on the Grassmann manifold.
And step four, calculating a relevant real environment parameter space in which the simulation environment parameters are located according to the real environment parameter subspace. Through the geometric representation of Gao Weizi space on the Riemann manifold, the plurality of Gao Weizi space models are expressed as a point set on the Grassmann manifold, and finally, the geometric center position solving problem of the plurality of Gao Weizi spaces is converted into the geometric center point solving problem in the Grassmann manifold space.
And step five, calculating the real environment parameter space in which the simulation parameters are located according to the real environment parameter space around the input parameters of the simulation system. According to the real environment parameter space where the simulation parameters are located, calculating projection points of the input simulation environment parameters in the real environment parameter space, and taking the consistency measurement of the simulation parameter vector and the manifold parameter space as a measurement criterion for judging the rationality of the simulation parameters.
And step six, outputting the consistency measurement of the simulation environment input parameters and the environment parameter space of the simulation system, setting a threshold parameter, judging the relation between the threshold value and the consistency characterization measurement, and finally evaluating the rationality of the environment parameters of the simulation system.
As shown in FIG. 1, the invention provides a simulation system-oriented environmental parameter space consistency characterization method, which comprises the following steps:
step one: environmental parameter data of the simulation system is input, and the environmental parameter data is represented in a vectorization mode. For a specific target area, a large amount of real historical environment data can be acquired through a weather station, an astronomical platform and the like, the time range covers decades, and the time is momentThe degree can be accurate to hour, and the corresponding environment parameter samples containing n groups of time points in a large amount of environment databases, namely the environment parameter sample space of the region can be expressed as A= { V 1 ,V 2 ,…,V n }, wherein V i ∈R d R represents real space, i=1, 2, …, n, V i And d-dimensional environmental parameter data vectors which are actually measured in the current period of the region are represented, and environmental parameters comprise solar illumination, average temperature, wind speed, rainfall, air relative humidity, longitude, latitude and other environmental parameter information, so that a historical meteorological data sample base of the target region is constructed.
As shown in fig. 2, the actual measurement environmental parameter sample library of the historic meteorological data of the east tower airport includes:
1) Station name;
2) A station number;
3) A date;
4) Measuring time;
5) Average wind speed (knots);
6) Maximum sustained wind speed (knots);
7) Maximum instantaneous wind speed (knots);
8) Wind direction;
9) Latitude, longitude;
10 Visibility (mi);
11 Precipitation (in);
12 Dew point temperature (F);
13 Height (m);
14 Average air temperature (DEG C), daily maximum air temperature (DEG C), daily minimum air temperature (DEG C);
15 Average relative humidity (%), minimum relative humidity (%);
16 Small evaporation capacity (mm);
17 Average barometric pressure (hPa), daily maximum barometric pressure (hPa), daily minimum barometric pressure (hPa).
For an environmental parameter model in a real space, actual measurement historical environmental meteorological data of the east tower airport in the Shenyang city of Liaoning, 2010 to 2020, in the last ten years are selected as a real sample database for constructing the real environmental parameter space. The actual measurement environment parameter sample library of the historical meteorological data of the east tower airport is shown in fig. 2, and the data of the environment sample library are divided into four seasons of spring, summer, autumn and winter according to different types of real environment parameters. 8770 sets of actual environmental parameter actual measurement data in different time periods of the day and the night are selected according to the seasonal characteristics. In the 8770 set of real history data, a corresponding environment parameter subspace is constructed.
And secondly, for the input environment parameters of the simulation system, constructing the geometric distribution between the simulation environment parameters and the real environment calendar history data.
Input vector X for simulation environment n ∈R d Find X n R adjacent points of (c). Let these neighboring points be denoted as c ni ∈R d (i ε {1,2, …, r }) for each neighbor point c ni Find c again ni Q neighbor points c of (2) nij ∈R d (j ε {1,2, …, q } and i ε {1,2, …, r }). In other words, c nij Representing simulation environment parameter X n The j-th neighbor of the i-th neighbor of (c) in the set.
As shown in FIG. 3, X n For the newly input simulation environment parameters, { c 1 ,c 2 ,c 3 The } is the neighbor point of the input parameter, the red triangle point c 1 Is { c } 11 ,c 12 ,c 13 As shown by the red circular dots in the figure, the blue triangle dots c 2 Is { c } 21 ,c 22 ,c 23 The green triangle point c is shown as blue circular point 3 Is { c } 31 ,c 32 ,c 33 As indicated by the green circles in the figure, the environment parameter subspace can be built next by the structural distribution between the simulation environment parameters constructed by the method described above and the real historical environment data.
Thirdly, establishing a subspace model of a real environment parameter numerical space according to the constructed environment parameter geometric distribution, and carrying out manifold space representation on the real environment parameter:
S i ＝svd([V 1 ,V 2 ,V 3 ,…,V i ])
wherein V is i Environmental parameter calendar representing different periods of timeThe smith measurement data svd represents the singular value decomposition function of the matrix, S i Representing a real environment parameter space.
For simulation environment parameter X n Every real environment parameter subspace S of the surroundings i ＝{S 1 ,S 2 ,…,S N }，X n The numerical parameter subspace model S is the surrounding subspace S i Is defined by a geometric center position of the lens. Because of the high-dimensional characteristics and structural characteristics of subspaces, the traditional method for vector data is difficult to solve the geometric centers of a plurality of Gao Weizi spaces. Therefore, according to the mathematical characteristics of subspaces, the geometric distribution of the plurality of Gao Weizi space models is built in the Grassmann manifold space, the plurality of Gao Weizi space models are expressed as point sets on the Grassmann manifold, and the geometric center position solving problem of the plurality of Gao Weizi spaces is finally converted into the geometric center point solving problem in the Grassmann manifold space through the geometric characterization of the Gao Weizi spaces on the Rissmann manifold. As shown in fig. 4.
And step four, calculating a relevant real environment parameter space in which the simulation environment parameters are located according to the real environment parameter subspace.
The known real environment parameter space is characterized on the Grassmann manifold as { S ] i } i＝1,2,…,N The representation of the geometric mean S of these points is calculated as:
where d (·) represents the geodesic distance on the Grassmann manifold and N is the number of sample data.
For geodesic distance on Grassmann manifold, its computational expression is
In the above equation, |·| represents the Frobenius norm of the matrix.
Calculating projection points of the input simulation environment parameters in a real environment parameter space, taking the consistency measurement of the simulation parameter vector and the manifold parameter space as a measurement criterion for judging the rationality of the simulation parameters, and comprising the following steps:
for input vector X n ，X n ∈R d ，X 1 ，X 2 And X 3 Respectively X n (corresponding to the red, green, blue dots in fig. 5), { S 1 ,S 2 ,S 3 Respectively is X n Three subspaces around, point X n The mapping point to subspace is c p ，c p The calculated expression of (2) is
c p ＝SS + X n
Wherein S is + ＝(S T S) -1 S T Representing the pseudo-inverse of S.
For an ambient input vector X in peripheral Euclidean space n ∈R d The calculation mode of the consistency characterization distance from the environment parameter space is that
dis＝||X n -c p ||
And step six, outputting consistency measurement of simulation environment input parameters and environment parameter space of the simulation system, and finally evaluating the rationality of the environment parameters of the simulation system.
Specifically, a reliability threshold value is set as tau, and if the consistency characterization metric dis of the input environment parameter vector of the simulation system and the environment parameter space is calculated to be less than or equal to tau, the simulation environment parameter input by the simulation system is considered to be reasonable; otherwise, if the consistency characterization metric dis > tau of the input environment parameter vector and the environment parameter space of the simulation system is calculated, the simulation environment parameter input by the simulation system is considered to be unreasonable.
The schematic diagram of a sample library used by the simulation system-oriented environmental parameter space consistency characterization method is shown in fig. 2, and experimental test data is shown in fig. 6.
For input parameters of the simulation system, two main categories are: the simulation environment parameters are generated by a Gaussian distribution model according to historical meteorological data of a target area as a reference; the other is unreasonable simulation input parameters, so that the validity of the environment parameter space consistency characterization model is tested. The simulation environment parameters are used as test data sets and are divided into two types, namely reasonable simulation environment parameters and unreasonable simulation environment parameters. The tested simulation environment parameters comprise 1800 groups, including a reasonable simulation environment parameter 1000 group and an unreasonable simulation environment parameter 800 group.
The experimental test method is as follows:
(1) When the test data are reasonable simulation data, calculating the consistency characterization measurement of the test data and the environment parameter space according to the input simulation environment parameters, and if the test data are smaller than a threshold value, determining that the simulation parameters are reasonable;
(2) Similarly, for unreasonable simulation parameter data, according to the input simulation environment parameters, calculating the consistency characterization measurement of the unreasonable simulation parameter data and the environment parameter space, and if the consistency characterization measurement is larger than a threshold value, recognizing the simulation parameters as unreasonable;
the simulation environment parameter data for the experimental test are shown in fig. 6, and the evaluation indexes used for the experiment are as follows:
the experimental results are as follows:
through multiple experimental tests, the average recognition rate is 88.78%, and the effectiveness of the method is verified.
While the embodiments of the present invention have been described in detail with reference to the drawings, the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present invention.
Claims (6)
1. The environment parameter space consistency characterization method facing the simulation system is characterized by comprising the following steps of:
1) Preprocessing historical meteorological data of a target area, and establishing an environment data sample base of the target area;
2) For the input simulation environment parameters of the simulation system, constructing the environment parameter geometric distribution between the simulation environment parameters and the real environment history data;
3) Establishing subspaces of a real environment parameter space according to the constructed geometric distribution of the environment parameters, and carrying out manifold space representation on the real environment parameters;
4) Calculating a relevant real environment parameter space in which the simulation environment parameters are located according to subspaces of the real environment parameter space;
5) And calculating projection points of the input simulation environment parameters in a real environment parameter space, and evaluating the rationality of the simulation environment parameters through the consistency measurement of the simulation environment parameters and the manifold parameter space.
2. The simulation system-oriented environmental parameter spatial consistency characterization method as set forth in claim 1, wherein: preprocessing historical meteorological data of a target area, and establishing an environment data sample base of the target area, wherein the preprocessing comprises the following steps:
acquiring historical environment data of a target area, establishing an environment data sample base of the target area, wherein the environment data sample base comprises environment parameter samples of n groups of time points, and the environment parameter sample space of the target area is expressed as A= { V 1 ,V 2 ,…,V n }, wherein V i ∈R d ，i＝1,2,…,n，V i And d-dimensional environmental parameter data vector representing actual measurement of the current period of the region.
3. The simulation system-oriented environmental parameter spatial consistency characterization method as set forth in claim 1, wherein: the method for constructing the geometrical distribution between the simulation environment parameters and the real environment historical data for the input environment parameters of the simulation system comprises the following steps:
extracting a plurality of d-dimensional environmental parameter data vectors from an environmental parameter sample space A of a target area to form a simulation environmental parameter X n ；
For simulation environment parameter X n ∈R d Find X n R neighbor points c of (2) ni ∈R d ，i∈{1,2,…,r}；
For each adjacent point c ni Find c again ni Q neighbor points c of (2) nij ∈R d J e {1,2, …, q } and i e {1,2, …, r }; c nij Representing simulation environment parameter X n The j-th neighbor point, c, of the i-th neighbor point of (c) ni And c nij Representing real environment history data.
4. The simulation system-oriented environmental parameter spatial consistency characterization method as set forth in claim 1, wherein: according to the constructed geometric distribution of the environmental parameters, a subspace model of a real environmental parameter numerical space is established, manifold space representation is carried out on the real environmental parameters, and the method comprises the following steps:
for simulation environment parameter X n Every real environment parameter subspace S of the surroundings i ＝{S 1 ,S 2 ,…,S N }，X n The numerical parameter subspace model S is the surrounding subspace S i Is defined by a geometric center position of the lens.
5. The simulation system-oriented environmental parameter spatial consistency characterization method as set forth in claim 1, wherein: the related real environment parameter space where the simulation environment parameter is located is calculated according to the subspace of the real environment parameter space, and the method specifically comprises the following steps:
the real environment parameter space is characterized on the Grassmann manifold as { S ] i } i＝1,2,…,N Calculation ofThe geometric center position S of these points is expressed as:
where d (·) represents the geodesic distance on the Grassmann manifold and N is the selected environmental parameter sample S i The number of (3);
for geodesic distance on Grassmann manifold, the expression is
In the above equation, |·| represents the Frobenius norm of the matrix.
6. The simulation system-oriented environmental parameter spatial consistency characterization method as set forth in claim 1, wherein: the calculation of the projection point of the input simulation environment parameter in the real environment parameter space evaluates the rationality of the simulation environment parameter through the consistency measurement of the simulation environment parameter and the manifold parameter space, and comprises the following steps:
for input simulation environment parameter X n ，X n ∈R d ，X 1 ，X 2 And X 3 Respectively X n Three nearest neighbors of (a), point X n The mapping point to the geometric center position S is c p ：
c p ＝SS + X n
Wherein S is + ＝(S T S) -1 S T A pseudo-inverse matrix representing S;
for an ambient input vector X in peripheral Euclidean space n ∈R d The consistency of the environment parameter space to the environment parameter space is characterized by the calculation mode of distance dis
dis＝||X n -c p ||
τ is a confidence threshold;
if the consistency characterization metric dis of the input simulation environment parameters and the environment parameter space is less than or equal to tau, the input simulation environment parameters are considered to be reasonable;
otherwise, the input simulation environment parameters are considered unreasonable.
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