Intelligent environment color hiding system based on mobile offshore single cabin

Abstract

The invention relates to an intelligent environment color hiding system based on a mobile offshore single cabin, the hiding system realizes intelligent perception and accurate control of environment colors under the operation condition of a sea-land dual-purpose sound and temperature hiding and transferring device of the offshore single cabin, and the hiding system comprises an intelligent environment color identification system, an environment control conversion module and connection conversion between the intelligent environment color identification system and the environment control conversion module; the environment color intelligent identification system comprises an image acquisition module, a preprocessing module, a feature extraction module, a deep learning model module, a color identification module and a result output module. The environment control conversion module comprises a surface hidden layer, a third control module conversion layer, an environment color difference verification module and a data output terminal device. According to the system, through a deep learning algorithm and a multi-dimensional color feature model, the color recognition accuracy and adaptability are improved, accurate and real-time data and stability and reliability of the system are ensured, the system can be applied to the fields of image analysis, environment monitoring, intelligent security and protection and the like, and intelligent sensing and precise control of environment colors are achieved in a sea-land dual-purpose sound and temperature hidden transfer device.

Description

Technical Field

[0001] The invention relates to the field of image analysis based on special environment monitoring operations and intelligent security technology, and in particular to an environment color intelligent hiding system based on a mobile offshore single cabin. Background Art

[0002] In many application scenarios, accurate identification of color information in the environment is of great significance. For example, in smart home systems, lighting effects are automatically adjusted according to the color of the environment; in industrial production, product colors are detected and classified; in artistic creation, inspiration for environmental colors is obtained, etc. However, existing color recognition technologies often have problems such as low accuracy and poor adaptability when faced with complex and changeable ambient lighting conditions and diverse color distributions.

[0003] Meanwhile, in traditional color recognition technology, methods based on fixed thresholds are mostly used. For example, in some simple image color recognition software, a specific RGB (red, green, blue) color threshold range is set to determine the color category. However, this method has poor accuracy in complex environments, because factors such as changes in light in the environment and the surface material of objects can cause visual changes in color, and fixed thresholds are difficult to adapt to these changes. In addition, although some existing intelligent recognition technologies take some influencing factors into consideration, they lack a comprehensive analysis of the overall characteristics of the environment. For example, some color recognition algorithms based on machine learning may only target a limited set of samples during training, and insufficient consideration is given to the diversity and complexity of colors in different environmental scenes, resulting in insufficient recognition accuracy in practical applications, especially in environmental scenes with multiple colors mixed or color gradients.

[0004] Moreover, the existing color recognition technology cannot adapt well to environmental changes, which makes it difficult to guarantee the accuracy of the recognition results. In practical applications, misjudgments may occur, such as misclassifying similar colors or failing to correctly recognize colors under complex lighting conditions.

[0005] The lack of wide adaptability to different environmental scenarios (such as indoors, outdoors, at sea, in strong light, weak light, etc.) limits the application of these technologies in a variety of environments and cannot meet diverse needs, such as application in complex industrial environments or natural environments. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides an intelligent hiding system of environmental color based on a mobile marine single cabin. The hiding system realizes intelligent perception and precise control of environmental color when the marine and amphibious acoustic and temperature concealed transfer device of the marine single cabin is in operation, and includes an environmental color intelligent recognition system, an environmental control conversion module, and the connection and conversion between them;

[0007] The described environment color intelligent hiding system based on a mobile offshore single cabin, the mobile offshore single cabin here refers to a amphibious sound and temperature concealed transfer device, its structure is designed as a single cabin for a single person in the cabin, and the cabin can be moved intelligently in the sea area; the environment color intelligent hiding system here needs to be embedded in the third control conversion layer inside the mobile offshore single cabin equipment for connection.

[0008] The environmental color intelligent recognition system includes an image acquisition module, an image preprocessing module, a color feature extraction module, a deep learning model module, a color recognition module, a feedback optimization module and a result output module;

[0009] The connection conversion of the environmental control conversion module is based on the execution hidden module design in the land-sea acoustic temperature concealment, including the surface concealment layer, the third layer control module conversion layer, the environmental color difference verification module, and the data output terminal equipment;

[0010] Its deep learning model module adopts the convolutional neural network (CNN) architecture, which is trained with a large amount of image data with annotated color information to learn the mapping relationship between color features and actual color categories, and output category labels, probability values ​​or feature vectors;

[0011] At the same time, the connection and conversion between the deep learning model module and the environmental control conversion module is used for the conversion communication between the third-layer control module conversion layer and the surface hidden layer, and the environmental color intelligent recognition system is embedded in the third-layer control module conversion layer. The inner side of the other surface hidden layer is covered with an explosion-proof crystal layer, forming the outermost interface of the land-sea dual-purpose acoustic temperature hidden transfer device.

[0012] The environmental color intelligent recognition system is connected to the environmental control conversion module through a computing platform and its deep learning model module using a convolutional neural network (CNN); the image acquisition module collects data from different environmental scenes, the image preprocessing module performs data preprocessing, the color feature extraction module uses a deep learning algorithm to extract features, the color recognition module recognizes colors based on features, and the feedback optimization module compares the recognition results with the upper optimization system.

[0013] In one embodiment of the present invention, the image acquisition module is used to obtain image data of the environment, which may be a static image or a dynamic video frame.

[0014] In one embodiment of the present invention, the image preprocessing module performs operations such as denoising, enhancement, cropping, scaling, etc. on the collected image, and normalizes the original color data.

[0015] In one embodiment of the present invention, the color feature extraction module converts the preprocessed image into a suitable color space and extracts color histogram, color moment, and color texture features.

[0016] In one embodiment of the present invention, the color recognition module inputs the extracted color features into a trained deep learning model to perform color category prediction, and can perform feedback adjustment based on the confidence level of the recognition result.

[0017] In one embodiment of the present invention, the result output module outputs the color recognition result in an intuitive manner, such as displaying the color name, RGB value or marking different color areas on the original image.

[0018] In one embodiment of the present invention, the third-layer control module conversion layer is located at the third layer of the device, and different application control modules are distributed and their functions are interconnected, and the environmental color intelligent recognition system is embedded therein.

[0019] In one embodiment of the present invention, the environmental color difference verification module is used to capture the environmental color difference for comparison, and output the comparison information to the terminal device.

[0020] In one embodiment of the present invention, the data in the data output terminal device is output to the core environment color intelligent recognition system of the device.

[0021] The above technical solution of the present invention has the following advantages over the prior art: the environment color intelligent hiding system of the present invention, through the deep learning model module in the computer vision and intelligent recognition system, adopts the convolutional neural network (CNN) to show powerful image recognition and data analysis capabilities, while the environment control conversion module is responsible for converting these analysis results into actual environment control actions. Realizing the effective connection and conversion between the two is crucial for building an intelligent environment control system.

[0022] Improve the accuracy of color recognition and be able to cope with complex environmental lighting and diverse color combinations. Its core feature is the use of an adaptive algorithm based on deep learning in the environmental color intelligent recognition system. The algorithm can automatically learn the relationship between environmental factors (such as light intensity, object material reflectivity, etc.) and color features. Through training with a large amount of sample data in different environmental scenarios, the algorithm can dynamically adjust the recognition strategy according to the input image or environmental data, thereby improving the accuracy of color recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings.

[0024] Figure 1 It is a schematic structural diagram of the environment color intelligent hiding system based on a mobile offshore single cabin of the present invention;

[0025] Figure 2It is a schematic diagram of the installation position of the environment color intelligent recognition system and the environment control conversion module in the amphibious sound and temperature concealed transfer device of the present invention;

[0026] Figure 3 It is a flow chart of the connection between the environment color intelligent recognition system and the environment control conversion module of the present invention;

[0027] Figure 4 It is a diagram of the data flow and interaction relationship between the modules of the intelligent environment color recognition system of the present invention;

[0028] Figure 5 It is a schematic diagram of method data processing and model training in the intelligent environment color recognition system of the present invention.

[0029] As shown in the figure, 1. Environmental color intelligent recognition system; 2. Environmental control conversion module; 3. Third-layer control module conversion layer; 4. Explosion-proof crystal layer; 5. Computing platform of this equipment; 6. Display end; 7. User terminal; 8. Computing system end; 9. Intelligent recognition and collection of environmental data; 10. Establish training module; 11. System input; 12. Color recognition; 13. Matching algorithm; 14. Data transmission; 15. Instruction execution; 16. Terminal device; 17. Image acquisition device; 18. Image preprocessing; 19. Feature extraction; 20. Deep learning model training. DETAILED DESCRIPTION

[0030] This embodiment provides an environment color intelligent hiding system based on a mobile marine single cabin. The hiding system realizes intelligent perception and precise control of the environment color when the marine and amphibious acoustic and temperature concealed transfer device of the marine single cabin is in operation, and includes an environment color intelligent recognition system, an environment control conversion module, and the connection conversion between them.

[0031] like Figure 1 and Figure 2 As shown, the installation position of the environment color intelligent recognition system and the environment control conversion module is in the third-layer control module conversion layer 3 inside the amphibious sound and temperature concealed transfer device, including the environment color intelligent recognition system 1 and the environment control conversion module 2; the environment color intelligent recognition system is embedded in the third-layer control module conversion layer 3 and is connected;

[0032] like Figure 3 As shown, the connection process between the environment color intelligent recognition system and the environment control conversion module includes:

[0033] First, the amphibious acoustic and temperature concealed transfer device is started, and then the third-layer control module conversion layer 3 of the equipment simultaneously starts the environment color intelligent recognition system 1 and the environment control conversion module 2 in the third-layer control module conversion layer, and connects the environment control conversion module 2 with the explosion-proof crystal layer 4;

[0034] Start the explosion-proof crystal layer 4 and connect it to the environment color intelligent recognition system 1 through the third layer control module conversion layer 3; intelligently recognize and collect the environment data 9 and analyze it;

[0035] Execute the acquisition and establish the deep learning of the training module 10 for the environment, capture the difference of the environment color and compare it with the environment color difference verification module,

[0036] System input 11, the environment color intelligent recognition system continuously obtains image data of the environment as input.

[0037] Color recognition and analysis 12. The environmental color intelligent recognition system uses image processing technology and algorithms to extract and analyze color features of input images.

[0038] Through machine learning models or pattern matching algorithms13, the main color distribution, hue, saturation and other information in the environment are determined.

[0039] Data transmission 14, the recognition system encapsulates the analyzed color data in a standardized data format.

[0040] The packaged data is transmitted to the environment control conversion module 2 via a high-speed data interface (such as Ethernet, USB, etc.).

[0041] The conversion module processes and the environment control conversion module receives data from the color intelligent recognition system.

[0042] According to preset rules and mapping relationships, the color data is converted into corresponding control instructions.

[0043] Control instruction generation, based on the correspondence between color data and control strategy, generates specific environmental control instructions, such as instructions for adjusting the surface color and brightness of the explosion-proof crystal layer 4;

[0044] Verify the validity and security of the generated control instructions;

[0045] Instruction execution 15, the control instruction that passes the verification is sent to the corresponding user-side environmental control explosion-proof crystal layer surface device;

[0046] The environmental control device receives instructions and performs corresponding actions to adjust the environment.

[0047] Feedback and optimization,After the environmental control device performs an action, it feeds back the execution result to the environmental control conversion module.

[0048] The conversion module evaluates the control effect based on the feedback information and optimizes and adjusts the control strategies and rules if necessary to improve the performance and accuracy of the system.

[0049] The feedback information data is output to the terminal device 16.

[0050] Through the above connection process, the environmental color intelligent recognition system can work together with the environmental control conversion module to achieve intelligent perception and analysis of the environmental color and to accurately and effectively control the environment.

[0051] Furthermore, if Figure 4 As shown in the figure, the technical solution of the intelligent environment color recognition system architecture includes:

[0052] 1. Environmental color intelligent recognition system and through the computing platform of this device, the deep learning model module in the computer vision and intelligent recognition system adopts convolutional neural network (CNN) to show powerful image recognition and data analysis capabilities; among them,

[0053] 2. The environmental color intelligent recognition system includes an environmental control conversion module to achieve effective connection and conversion between the two to build an intelligent environmental control system;

[0054] 3. The environmental control conversion module is responsible for converting these analysis results into actual environmental control actions.

[0055] 4. The data acquisition module 17 (image acquisition device): is used to acquire image data in different environmental scenes or directly acquire color data in the environment. The acquired data includes but is not limited to color information of different lighting conditions (strong light, weak light, different color temperatures, etc.), different object material surfaces, and environmental color data in different seasons and different geographical locations.

[0056] 5. The preprocessing module 18 (image preprocessing): preprocesses the collected data, including operations such as image denoising and contrast enhancement, and normalizes the collected original color data to make it within a uniform numerical range for subsequent analysis and processing.

[0057] 6. The feature extraction module 19 (color feature extraction): uses a deep learning algorithm to extract multi-dimensional color features from the preprocessed data. This module uses advanced neural network structures such as convolutional neural networks (CNNs), which can automatically learn the deep-level features of color and features related to the environment, such as extracting local features of color through convolutional layers, and performing feature compression and dimensionality reduction through pooling layers to improve the efficiency of feature extraction. Convolutional neural networks extract valuable feature information by performing multi-layer convolution and pooling operations on input images and data, and classify or predict through fully connected layers. These output results are usually category labels, probability values, or feature vectors expressed in digital form.

[0058] In order to connect the output of CNN with the environmental control conversion module, it is necessary to first define clear conversion rules and mapping relationships. For example, if the output of CNN is the classification results of different environmental states (such as "bright", "dim", and "moderate"), then in the conversion module, these classifications can be mapped to specific environmental control actions (such as "bright" corresponds to dimming the lights, "dim" corresponds to brightening the lights, and "moderate" maintains the current state).

[0059] 7. The recognition module: performs color recognition using a pre-trained adaptive recognition model based on the extracted features. This recognition model is trained based on a large amount of labeled sample data and can accurately determine the color category based on the input features. At the same time, the recognition module can also make feedback adjustments based on the confidence level of the recognition results. If the confidence level is lower than the set threshold, the data acquisition module can be required to re-collect data or adjust the parameters of preprocessing and feature extraction.

[0060] 8. The feedback optimization module: Based on the comparison between the actual recognition results and the real results, the entire recognition system is optimized. If there is a recognition error or the accuracy rate drops, this module will adjust the parameters of the deep learning algorithm and retrain the recognition model to continuously adapt to the new environment and data changes.

[0061] Furthermore, if Figure 5 As shown, the intelligent environment color recognition method is run on the computing platform of the device, including the following steps:

[0062] 1. The image acquisition: In order to protect the hidden security of the device, the image acquisition device 17 of the device computing platform 5 is responsible for acquiring the image in the environment.

[0063] 2. The image preprocessing 18: performing operations such as denoising and enhancement on the collected images to improve image quality.

[0064] 3. The color space conversion: convert the image from the common RGB color space to the HSV or Lab color space that is more suitable for color analysis.

[0065] 4. The feature extraction 19: The computing system 8 calculates feature vectors such as color histograms and color moments to describe the color distribution in the image.

[0066] 5. The deep learning model training 20: using a large amount of image data with labeled color information to train the convolutional neural network model, and optimizing the model parameters is performed on the computing system end 8 of the device.

[0067] 6. The color recognition 12: input the features of the image to be recognized into the trained model to obtain the color category prediction result.

[0068] 7. The result output: output to the user terminal 7 device surface hidden layer display terminal 6 in a user-friendly form to display the color recognition result.

[0069] The environmental color intelligent hiding system described in this embodiment improves the accuracy of color recognition and can cope with complex environmental lighting and diverse color combinations. Its core feature is that an adaptive algorithm based on deep learning is adopted in the environmental color intelligent recognition system. The algorithm can automatically learn the relationship between environmental factors (such as light intensity, object material reflectivity, etc.) and color features. Through training with a large amount of sample data in different environmental scenes, the algorithm can dynamically adjust the recognition strategy according to the input image or environmental data, thereby improving the accuracy of color recognition.

[0070] It has good adaptability and can be applied to different scenarios and fields. Its multi-dimensional analysis improves the accuracy and adaptability of intelligent recognition, and builds a multi-dimensional environmental color feature model in its environmental color intelligent recognition system. This model not only takes into account the basic properties of color (such as RGB value, hue, saturation, brightness, etc.), but also integrates the spatial characteristics of the environment (such as the position of the object, the color distribution of surrounding objects, etc.) and time characteristics (such as the changing rules of light in different time periods, etc.). Through this multi-dimensional comprehensive analysis, the environmental color can be identified more accurately, especially in complex color mixing and gradient scenes.

[0071] To ensure data accuracy, in terms of data transmission, the output data of CNN needs to be passed to the environmental control conversion module in a standardized and parseable format. This can be achieved through common data interfaces and communication protocols to ensure data accuracy and timeliness.

[0072] To ensure real-time performance and response speed, environmental control often requires timely response. Therefore, during the connection process, it is necessary to optimize the data processing and conversion process and reduce delays to ensure that environmental control actions can be quickly adjusted according to the analysis results of CNN.

[0073] To improve the stability and reliability of the connection, error handling and abnormal situation response mechanisms should also be added to the system. For example, if the output of the CNN is abnormal or unrecognizable, the conversion module should have a default processing method to avoid confusion in environmental control.

[0074] It helps improve user experience and work efficiency, and its intelligent applications provide reliable color information support.

[0075] In short, the connection and conversion of the deep learning model module using convolutional neural network and the environmental control conversion module in the environmental color intelligent recognition system is a comprehensive process involving data format standardization, conversion rule definition, real-time optimization and exception handling. Through careful design and effective implementation, the intelligent analysis ability of CNN can be fully utilized to achieve accurate and efficient environmental control.

[0076] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the protection scope of the invention.

Claims

1. An intelligent hiding system for environmental color based on a mobile marine single cabin, wherein the hiding system realizes intelligent perception and precise control of environmental color when the marine and amphibious acoustic and temperature concealed transfer device of the marine single cabin is in operation, and is characterized in that: It includes an environment color intelligent recognition system, an environment control conversion module, and the connection and conversion between them; The environmental color intelligent recognition system includes an image acquisition module, an image preprocessing module, a color feature extraction module, a deep learning model module, a color recognition module, a feedback optimization module and a result output module; The connection conversion of the environmental control conversion module is based on the execution hidden module design in the land-sea acoustic temperature concealment, including the surface concealment layer, the third layer control module conversion layer, the environmental color difference verification module, and the data output terminal equipment; Its deep learning model module adopts a convolutional neural network architecture, which is trained with a large amount of image data with annotated color information to learn the mapping relationship between color features and actual color categories, and output category labels, probability values ​​or feature vectors; At the same time, the connection and conversion between the deep learning model module and the environmental control conversion module is used for the conversion communication between the third-layer control module conversion layer and the surface hidden layer, and the environmental color intelligent recognition system is embedded in the third-layer control module conversion layer. The inner side of the other surface hidden layer is covered with an explosion-proof crystal layer, forming the outermost interface of the land-sea dual-purpose acoustic temperature hidden transfer device. The environmental color intelligent recognition system is connected to the environmental control conversion module through a computing platform and its deep learning model module using a convolutional neural network; the image acquisition module collects data from different environmental scenes, the image preprocessing module performs data preprocessing, the color feature extraction module uses a deep learning algorithm to extract features, the color recognition module recognizes colors based on features, and the feedback optimization module compares the recognition results with the upper optimization system.

2. The environment color intelligent hiding system according to claim 1 is characterized in that: The image acquisition module is used to obtain image data of the environment, which is a static image or a dynamic video frame.

3. The environment color intelligent hiding system according to claim 1 is characterized in that: The image preprocessing module performs denoising, enhancement, cropping, and scaling operations on the collected images, and normalizes the original color data.

4. The environment color intelligent hiding system according to claim 1 is characterized in that: The color feature extraction module converts the preprocessed image into a suitable color space and extracts color histogram, color moment, and color texture features.

5. The environment color intelligent hiding system according to claim 1 is characterized in that: The color recognition module inputs the extracted color features into a trained deep learning model to predict color categories, and can make feedback adjustments based on the confidence level of the recognition results.

6. The environment color intelligent hiding system according to claim 1, characterized in that: The result output module outputs the color recognition result in an intuitive manner, such as displaying the color name, RGB value or marking different color areas on the original image.

7. The environment color intelligent hiding system according to claim 1 is characterized by: The third-layer control module conversion layer is located at the third layer of the device, where different application control modules are distributed and their functions are interconnected, and the environmental color intelligent recognition system is embedded therein.

8. The environment color intelligent hiding system according to claim 1 is characterized by: The environmental color difference verification module is used to capture the environmental color difference for comparison and output the comparison information to the terminal device.

9. The environment color intelligent hiding system according to claim 1, characterized in that: The data in the data output terminal device is output to the core environment color intelligent recognition system of the device.

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