CN117850784A - Visual equipment scene model building method - Google Patents
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- CN117850784A CN117850784A CN202410172329.XA CN202410172329A CN117850784A CN 117850784 A CN117850784 A CN 117850784A CN 202410172329 A CN202410172329 A CN 202410172329A CN 117850784 A CN117850784 A CN 117850784A
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- 238000000034 method Methods 0.000 title claims abstract description 39
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- 238000004458 analytical method Methods 0.000 claims abstract description 12
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- 238000007781 pre-processing Methods 0.000 claims description 6
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- 238000013528 artificial neural network Methods 0.000 claims description 3
- 230000003190 augmentative effect Effects 0.000 claims description 3
- 238000010224 classification analysis Methods 0.000 claims description 3
- 238000003066 decision tree Methods 0.000 claims description 3
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Abstract
The invention discloses a method for building a scene model of visual equipment, which comprises the following steps: step one, demand analysis; step two, data cleaning; step three, data modeling; step four, optimizing a model; step five, designing a visual interface; step six, testing and verifying; step seven, model application; in the first step, before building the equipment scene model, the requirement analysis needs to be performed and the scene definition needs to be defined; by deeply knowing the user requirements, the use environment and the equipment functions, by utilizing a data driving method, by analyzing a large amount of actual scene data and extracting key features from the actual scene data, more accurate input is provided for an equipment scene model, so that parameters and design targets of the model can be accurately defined; according to the invention, key features are extracted from scene data, so that the accuracy and reliability of a model are improved, and the data is acquired in a real-time mode, so that the efficiency and accuracy of data acquisition are improved.
Description
Technical Field
The invention relates to the technical field of visual equipment scene model building, in particular to a visual equipment scene model building method.
Background
Visualization device scene model building has a wide range of application areas including, but not limited to, the following: the intelligent home system, the industrial automation equipment development, the intelligent agricultural system, the urban intelligent transportation system and the health medical equipment management, but in the use process of the existing built visual equipment scene model, the model cannot be optimized according to real-time data, so that key features cannot be extracted in the subsequent use process, the accuracy and the reliability of the model are reduced, the data acquisition efficiency is low, and therefore, the design of the visual equipment scene model building method is very necessary.
Disclosure of Invention
The invention aims to provide a method for building a scene model of visual equipment, which aims to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: a visual equipment scene model building method comprises the following steps: step one, demand analysis; step two, data cleaning; step three, data modeling; step four, optimizing a model; step five, designing a visual interface; step six, testing and verifying; step seven, model application;
in the first step, before building the equipment scene model, the requirement analysis needs to be performed and the scene definition needs to be defined; by deeply knowing the user requirements, the use environment and the equipment functions, by utilizing a data driving method, by analyzing a large amount of actual scene data and extracting key features from the actual scene data, more accurate input is provided for an equipment scene model, so that parameters and design targets of the model can be accurately defined;
in the second step, an automatic data acquisition technology is used for acquiring data in real time to collect representative data, and data cleaning and preprocessing are performed;
in the third step, a mathematical or statistical model is established based on the data obtained after the processing in the second step;
in the fourth step, a device scene model is designed according to the requirements and the feature extraction results, and model optimization is performed;
in the fifth step, a visual interface of the equipment scene needs to be designed, and a user interaction function is provided, so that a user can conveniently observe and operate the result displayed by the model;
in the sixth step, after the visual equipment scene model is built, the model needs to be tested and verified systematically to ensure the reliability and stability of the model;
in the seventh step, the model is tested and verified, and the expected effect is achieved, and the model is applied to the actual equipment scene.
Preferably, in the first step, key features include, but are not limited to, device status, parameters, and performance.
Preferably, in the second step, the steps of data cleaning and preprocessing are as follows: firstly, denoising data by using a statistical method and a machine learning method, then, processing missing data by using an interpolation method, and finally, normalizing and standardizing the data.
Preferably, in the third step, the step of establishing a mathematical or statistical model includes: regression analysis is used to explore the relationships between variables first and predict the values of dependent variables by building regression equations, then classification analysis is used to classify data samples into different classes or class probabilities, then data samples are divided into groups with similar features, then time series analysis is used to model and predict the data over time, finally hypothesis testing and saliency analysis is used to verify hypotheses in the data, by which the reliability and validity of the model can be evaluated.
Preferably, in the fourth step, in the process of optimizing the model, algorithms of decision trees, support vector machines and neural networks can be adopted to optimize, and parameters are adjusted to optimize the performance of the model.
Preferably, in the fifth step, the interaction between the user and the model is made more vivid and natural by using Virtual Reality (VR) and Augmented Reality (AR).
Preferably, in the sixth step, after the visual equipment scene model is successfully built, the performance and effect of the model can be evaluated through comparison and verification with the actual scene, and necessary adjustment and improvement can be performed according to the feedback result.
Preferably, in the seventh step, in the actual device scene application, the data of the actual application is continuously collected and analyzed, so that the model is further perfected and optimized, and the visual effect and the practical value of the model are improved.
Compared with the prior art, the invention has the beneficial effects that: according to the visual equipment scene model building method, key features are extracted from scene data, accurate input is provided for the equipment scene model, so that the accuracy and reliability of the model are improved, the data are acquired in real time, the data acquisition efficiency and accuracy are improved, manual intervention can be reduced, and the data are more real and reliable.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention 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 invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, an embodiment of the present invention is provided: a visual equipment scene model building method comprises the following steps: step one, demand analysis; step two, data cleaning; step three, data modeling; step four, optimizing a model; step five, designing a visual interface; step six, testing and verifying; step seven, model application;
in the first step, before building the equipment scene model, the requirement analysis needs to be performed and the scene definition needs to be defined; by deeply knowing the user requirements, the use environment and the equipment functions, by utilizing a data driving method, by analyzing a large amount of actual scene data and extracting key features from the actual scene data, more accurate input is provided for an equipment scene model, so that parameters and design targets of the model can be accurately defined; key features include, but are not limited to, device status, parameters, and performance;
in the second step, an automatic data acquisition technology is used for acquiring data in real time to collect representative data, and data cleaning and preprocessing are performed, wherein the steps of data cleaning and preprocessing are as follows: firstly, denoising data by using a statistical method and a machine learning method, then processing missing data by using an interpolation method, and finally, normalizing and standardizing the data;
in the third step, a mathematical or statistical model is built based on the data obtained after the processing in the second step, and the step of building the mathematical or statistical model is as follows: firstly, carrying out regression analysis to explore the relation among variables, predicting the value of the dependent variable by establishing a regression equation, then carrying out classification analysis to divide data samples into different categories or category probabilities, then dividing the data samples into groups with similar characteristics, then carrying out modeling and prediction on the data in time by using time sequence analysis, finally carrying out hypothesis testing and significance analysis to verify the hypothesis in the data, and evaluating the reliability and the effectiveness of the model by adopting the hypothesis testing and the significance analysis;
in the fourth step, a device scene model is designed according to the requirements and the feature extraction results, and the model is optimized, and in the process of optimizing the model, algorithms of decision trees, support vector machines and neural networks can be adopted for optimization, and the performance of the model is optimized through parameter adjustment;
in the fifth step, a visual interface of the device scene needs to be designed, and a user interaction function is provided, so that a user can conveniently observe and operate a result displayed by the model, and interaction between the user and the model is more vivid and natural by using Virtual Reality (VR) and Augmented Reality (AR);
in the sixth step, after the visual equipment scene model is built, the model needs to be tested and verified systematically to ensure the reliability and stability of the model; after the visual equipment scene model is successfully built, the performance and effect of the model can be evaluated through comparison and verification with an actual scene, and necessary adjustment and improvement can be carried out according to a feedback result;
in the seventh step, the model is tested and verified, the expected effect is achieved, the model is applied to an actual equipment scene, and in the actual equipment scene application, the model is further perfected and optimized through continuously collecting and analyzing data of the actual application, so that the visual effect and the practical value of the model are improved.
Based on the above, the invention has the advantages that when the invention is used, the modeling processing is conveniently carried out according to the proposed key features by extracting the features in the data, thereby improving the accuracy and reliability of the model, and in the process of collecting the data, the data is collected in a real-time mode, thereby improving the collection efficiency and ensuring the accuracy of the data.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention 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 invention 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 (8)
1. A visual equipment scene model building method comprises the following steps: step one, demand analysis; step two, data cleaning; step three, data modeling; step four, optimizing a model; step five, designing a visual interface; step six, testing and verifying; step seven, model application; the method is characterized in that:
in the first step, before building the equipment scene model, the requirement analysis needs to be performed and the scene definition needs to be defined; by deeply knowing the user requirements, the use environment and the equipment functions, by utilizing a data driving method, by analyzing a large amount of actual scene data and extracting key features from the actual scene data, more accurate input is provided for an equipment scene model, so that parameters and design targets of the model can be accurately defined;
in the second step, an automatic data acquisition technology is used for acquiring data in real time to collect representative data, and data cleaning and preprocessing are performed;
in the third step, a mathematical or statistical model is established based on the data obtained after the processing in the second step;
in the fourth step, a device scene model is designed according to the requirements and the feature extraction results, and model optimization is performed;
in the fifth step, a visual interface of the equipment scene needs to be designed, and a user interaction function is provided, so that a user can conveniently observe and operate the result displayed by the model;
in the sixth step, after the visual equipment scene model is built, the model needs to be tested and verified systematically to ensure the reliability and stability of the model;
in the seventh step, the model is tested and verified, and the expected effect is achieved, and the model is applied to the actual equipment scene.
2. The method for building the scene model of the visual equipment according to claim 1, wherein the method comprises the following steps: in the first step, key features include, but are not limited to, device status, parameters, and performance.
3. The method for building the scene model of the visual equipment according to claim 1, wherein the method comprises the following steps: in the second step, the steps of data cleaning and preprocessing are as follows: firstly, denoising data by using a statistical method and a machine learning method, then, processing missing data by using an interpolation method, and finally, normalizing and standardizing the data.
4. The method for building the scene model of the visual equipment according to claim 1, wherein the method comprises the following steps: in the third step, the step of establishing a mathematical or statistical model is as follows: regression analysis is used to explore the relationships between variables first and predict the values of dependent variables by building regression equations, then classification analysis is used to classify data samples into different classes or class probabilities, then data samples are divided into groups with similar features, then time series analysis is used to model and predict the data over time, finally hypothesis testing and saliency analysis is used to verify hypotheses in the data, by which the reliability and validity of the model can be evaluated.
5. The method for building the scene model of the visual equipment according to claim 1, wherein the method comprises the following steps: in the fourth step, in the process of optimizing the model, algorithms of decision trees, support vector machines and neural networks can be adopted for optimization, and the performance of the model is optimized through parameter adjustment.
6. The method for building the scene model of the visual equipment according to claim 1, wherein the method comprises the following steps: in the fifth step, the interaction between the user and the model is made more vivid and natural by using Virtual Reality (VR) and Augmented Reality (AR).
7. The method for building the scene model of the visual equipment according to claim 1, wherein the method comprises the following steps: in the sixth step, after the visual equipment scene model is successfully built, the performance and effect of the model can be evaluated through comparison and verification with an actual scene, and necessary adjustment and improvement can be performed according to a feedback result.
8. The method for building the scene model of the visual equipment according to claim 1, wherein the method comprises the following steps: in the seventh step, in the actual equipment scene application, the data of the actual application are continuously collected and analyzed, so that the model is further perfected and optimized, and the visual effect and the practical value of the model are improved.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023093397A1 (en) * | 2021-11-24 | 2023-06-01 | 中国运载火箭技术研究院 | Efficiency evaluation method based on mass adversarial simulation deduction data modeling and analysis |
| CN116958771A (en) * | 2023-07-28 | 2023-10-27 | 北京元境数字科技有限公司 | Computer vision recognition system and method |
| CN117172751A (en) * | 2023-09-08 | 2023-12-05 | 北京红山信息科技研究院有限公司 | Construction method of intelligent operation and maintenance information analysis model |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2023093397A1 (en) * | 2021-11-24 | 2023-06-01 | 中国运载火箭技术研究院 | Efficiency evaluation method based on mass adversarial simulation deduction data modeling and analysis |
| CN116958771A (en) * | 2023-07-28 | 2023-10-27 | 北京元境数字科技有限公司 | Computer vision recognition system and method |
| CN117172751A (en) * | 2023-09-08 | 2023-12-05 | 北京红山信息科技研究院有限公司 | Construction method of intelligent operation and maintenance information analysis model |
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