CN116775790A - Self-adaptive coordinate conversion algorithm based on accurate point positioning of model - Google Patents
Self-adaptive coordinate conversion algorithm based on accurate point positioning of model Download PDFInfo
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Abstract
The application discloses a self-adaptive coordinate conversion algorithm based on accurate point positioning of a model, which comprises the following steps: A. collecting three-dimensional data of a model; B. analyzing the acquired three-dimensional data to obtain a three-dimensional space data set; C. extracting features of the three-dimensional space data set to obtain three-dimensional space data with feature points; D. then, carrying out coordinate transformation on the three-dimensional space data with the characteristic points; E. finally, outputting the three-dimensional data after coordinate conversion, wherein the coordinate conversion algorithm adopted by the application has simple operation and high data conversion efficiency, can automatically select the conversion parameters of the original three-dimensional data, and improves the conversion efficiency and accuracy; the adopted three-dimensional data analysis method can realize rapid and flexible analysis of complex and various three-dimensional data, and is rapidly supported and reusable through simple configuration; the adopted feature extraction method can reduce the extraction difficulty and improve the feature extraction precision by searching the first keyword and the second keyword.
Description
Technical Field
The application relates to the technical field of coordinate conversion, in particular to a self-adaptive coordinate conversion algorithm based on accurate point positioning of a model.
Background
It is counted that approximately 80% of the total amount of information involved in human activity in today's informative society is associated with spatial location information. With the development of society, the demands of various industries for spatial location information are becoming more and more widespread. Coordinate transformation has become a key element in the determination of spatial location information due to the existence of various coordinate systems.
Currently, coordinate transformation has been related to applications of various industries, such as car navigation positioning, resource investigation, remote sensing image analysis, city construction and management, and the like. In the prior art, the coordinate conversion can only be performed locally, and the operation efficiency is low, so that improvement is needed.
Disclosure of Invention
The application aims to provide a self-adaptive coordinate conversion algorithm based on accurate point positioning of a model so as to solve the problems in the background technology.
In order to achieve the above purpose, the present application provides the following technical solutions: an adaptive coordinate transformation algorithm based on accurate point positioning of a model comprises the following steps:
A. collecting three-dimensional data of a model;
B. analyzing the acquired three-dimensional data to obtain a three-dimensional space data set;
C. extracting features of the three-dimensional space data set to obtain three-dimensional space data with feature points;
D. then, carrying out coordinate transformation on the three-dimensional space data with the characteristic points;
E. and finally outputting the three-dimensional data after coordinate conversion.
Preferably, the application provides a model accurate point positioning-based adaptive coordinate transformation algorithm, wherein in the step A, acquisition is performed through a three-dimensional data acquisition device.
Preferably, the self-adaptive coordinate conversion algorithm based on accurate point positioning of a model provided by the application, wherein the three-dimensional data analysis method in the step B is as follows:
a. defining a basic analysis function for analyzing and processing data for each data according to the storage rule of the data;
b. defining a data resolution identifier for each base resolution function;
c. establishing a mapping relation between the data analysis identifier and the basic analysis function which correspond to each other;
d. organizing a basic analysis function, a data analysis identifier and a mapping relation corresponding to each data in a dynamic link library mode;
e. and configuring a data analysis identifier required by data analysis according to the defined data analysis identifier and the established mapping relation between the data analysis identifier and the basic analysis function, and calling the basic analysis function according to the configured data analysis identifier information to complete analysis of the data.
Preferably, the method for extracting features in the step C according to the present application is as follows:
a. establishing a data set, wherein the data set comprises a plurality of sub-data sets to be extracted by the features;
b. performing feature training on the data set to obtain a training model;
c. extracting a first keyword and a second keyword in the data set;
d. circularly searching each sub-data set in the data set, and searching the sub-data set by taking the first keyword and the second keyword as initial conditions;
e. and searching and matching the first keyword or the second keyword in each sub-data set, and extracting the data.
Preferably, the self-adaptive coordinate conversion algorithm based on accurate point positioning of a model provided by the application, wherein the coordinate conversion method in the step D is as follows:
two coordinate systems A and B are provided, A is a source plane coordinate system, B is a target plane coordinate system, and the space rectangular coordinate of a certain point P under the A coordinate system is [ X ] A y A ] T Let the space rectangular coordinate of the point in the B coordinate system be [ X ] B y B ] T Wherein, the conversion model of the plane four parameters is thatWherein [ x ] A y A ] T Translation parameters for converting the A coordinate system into the B coordinate system; k is a scale factor of converting the A coordinate system into the B coordinate system; alpha is a rotation angle parameter of the a coordinate system converted to the B coordinate system.
Compared with the prior art, the application has the beneficial effects that:
(1) The coordinate conversion algorithm adopted by the application is simple to operate, has high data conversion efficiency, can automatically select the conversion parameters of the original three-dimensional data, and improves the conversion efficiency and accuracy.
(2) The three-dimensional data analysis method adopted by the application can realize rapid and flexible analysis of complex and various three-dimensional data, and is rapidly supported and reusable through simple configuration.
(3) The feature extraction method adopted by the application can reduce the extraction difficulty and improve the feature extraction precision by searching the first keyword and the second keyword.
Drawings
FIG. 1 is a flow chart of a conversion algorithm of the present application;
FIG. 2 is a flow chart of a three-dimensional data parsing method according to the present application;
FIG. 3 is a flow chart of a feature extraction method of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1-3, the present application provides a technical solution: the application provides the following technical scheme: an adaptive coordinate transformation algorithm based on accurate point positioning of a model comprises the following steps:
A. collecting three-dimensional data of a model;
B. analyzing the acquired three-dimensional data to obtain a three-dimensional space data set;
C. extracting features of the three-dimensional space data set to obtain three-dimensional space data with feature points;
D. then, carrying out coordinate transformation on the three-dimensional space data with the characteristic points;
E. and finally outputting the three-dimensional data after coordinate conversion.
The application provides a self-adaptive coordinate conversion algorithm based on accurate point positioning of a model, wherein the self-adaptive coordinate conversion algorithm is acquired by a three-dimensional data acquisition device in the step A.
The application provides a self-adaptive coordinate conversion algorithm based on accurate point positioning of a model, wherein the three-dimensional data analysis method in the step B is as follows:
a. defining a basic analysis function for analyzing and processing data for each data according to the storage rule of the data;
b. defining a data resolution identifier for each base resolution function;
c. establishing a mapping relation between the data analysis identifier and the basic analysis function which correspond to each other;
d. organizing a basic analysis function, a data analysis identifier and a mapping relation corresponding to each data in a dynamic link library mode;
e. and configuring a data analysis identifier required by data analysis according to the defined data analysis identifier and the established mapping relation between the data analysis identifier and the basic analysis function, and calling the basic analysis function according to the configured data analysis identifier information to complete analysis of the data.
The three-dimensional data analysis method adopted by the application can realize rapid and flexible analysis of complex and various three-dimensional data, and is rapidly supported and reusable through simple configuration.
The application provides a self-adaptive coordinate conversion algorithm based on accurate point positioning of a model, wherein the feature extraction method in the step C is as follows:
a. establishing a data set, wherein the data set comprises a plurality of sub-data sets to be extracted by the features;
b. performing feature training on the data set to obtain a training model;
c. extracting a first keyword and a second keyword in the data set;
d. circularly searching each sub-data set in the data set, and searching the sub-data set by taking the first keyword and the second keyword as initial conditions;
e. and searching and matching the first keyword or the second keyword in each sub-data set, and extracting the data.
The feature extraction method adopted by the application can reduce the extraction difficulty and improve the feature extraction precision by searching the first keyword and the second keyword.
The application provides a self-adaptive coordinate conversion algorithm based on accurate point positioning of a model, wherein the coordinate conversion method in the step D is as follows:
two coordinate systems A and B are provided, A is a source plane coordinate system, B is a target plane coordinate system, and the space rectangular coordinate of a certain point P under the A coordinate system is [ x ] A y A ] T Let the space rectangular coordinate of the point in the B coordinate system be [ x ] B y B ] T Wherein, the conversion model of the plane four parameters is thatWherein [ x ] A y A ] T Translation parameters for converting the A coordinate system into the B coordinate system; k is a scale factor of converting the A coordinate system into the B coordinate system; alpha is a rotation angle parameter of the a coordinate system converted to the B coordinate system.
In conclusion, the coordinate conversion algorithm adopted by the application is simple to operate, has high data conversion efficiency, can automatically select the conversion parameters of the original three-dimensional data, and improves the conversion efficiency and accuracy.
In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present application and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application.
In the present application, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art according to the specific circumstances.
In the present application, unless expressly stated or limited otherwise, a first feature "above" or "below" a second feature may include both the first and second features being in direct contact, as well as the first and second features not being in direct contact but being in contact with each other through additional features therebetween. Moreover, a first feature being "above," "over" and "on" a second feature includes the first feature being directly above and obliquely above the second feature, or simply indicating that the first feature is higher in level than the second feature. The first feature being "under", "below" and "beneath" the second feature includes the first feature being directly under and obliquely below the second feature, or simply means that the first feature is less level than the second feature.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (5)
1. The self-adaptive coordinate conversion algorithm based on accurate point positioning of the model is characterized in that: the method comprises the following steps:
A. collecting three-dimensional data of a model;
B. analyzing the acquired three-dimensional data to obtain a three-dimensional space data set;
C. extracting features of the three-dimensional space data set to obtain three-dimensional space data with feature points;
D. then, carrying out coordinate transformation on the three-dimensional space data with the characteristic points;
E. and finally outputting the three-dimensional data after coordinate conversion.
2. The adaptive coordinate transformation algorithm based on model accurate point positioning according to claim 1, wherein: and in the step A, the three-dimensional data is acquired by a three-dimensional data acquisition unit.
3. The adaptive coordinate transformation algorithm based on model accurate point positioning according to claim 1, wherein: the three-dimensional data analysis method in the step B is as follows:
a. defining a basic analysis function for analyzing and processing data for each data according to the storage rule of the data;
b. defining a data resolution identifier for each base resolution function;
c. establishing a mapping relation between the data analysis identifier and the basic analysis function which correspond to each other;
d. organizing a basic analysis function, a data analysis identifier and a mapping relation corresponding to each data in a dynamic link library mode;
e. and configuring a data analysis identifier required by data analysis according to the defined data analysis identifier and the established mapping relation between the data analysis identifier and the basic analysis function, and calling the basic analysis function according to the configured data analysis identifier information to complete analysis of the data.
4. The adaptive coordinate transformation algorithm based on model accurate point positioning according to claim 1, wherein: the feature extraction method in the step C is as follows:
a. establishing a data set, wherein the data set comprises a plurality of sub-data sets to be extracted by the features;
b. performing feature training on the data set to obtain a training model;
c. extracting a first keyword and a second keyword in the data set;
d. circularly searching each sub-data set in the data set, and searching the sub-data set by taking the first keyword and the second keyword as initial conditions;
e. and searching and matching the first keyword or the second keyword in each sub-data set, and extracting the data.
5. The adaptive coordinate transformation algorithm based on model accurate point positioning according to claim 1, wherein: the coordinate conversion method in the step D is as follows:
two coordinate systems A and B are provided, A is a source plane coordinate system, B is a target plane coordinate system, and the space rectangular coordinate of a certain point P under the A coordinate system is [ x ] A y A ] T Let the space rectangular coordinate of the point in the B coordinate system be [ x ] B y B ] T Wherein, the conversion model of the plane four parameters is thatWherein [ x ] A y A ] T Translation parameters for converting the A coordinate system into the B coordinate system; k is a scale factor of converting the A coordinate system into the B coordinate system; alpha is a rotation angle parameter of the a coordinate system converted to the B coordinate system.
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| CN117807434A (en) * | 2023-12-06 | 2024-04-02 | 中国信息通信研究院 | Communication data set processing method and device |
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