Integrated visual intelligent platform of wisdom store deployed locally under brain-like technology

Abstract

The application discloses an integrated visual intelligent platform of WisdomStore local deployment under a brain-like technology, and the platform main body is composed of a user interface layer, a basic service layer, a language framework layer and a bottom service layer; the user interface layer comprises four modules, namely, a data labeling interface, a model training interface, a model reasoning section and a result display interface; the basic service layer comprises two modules, namely, data processing and model management; the language framework layer is based on a Python language and an ecological system thereof; and the bottom service layer combines public data sets and internal customized data sets in the data source part. The application improves the intelligent level of the industry, improves market competitiveness, promotes digital transformation and promotes scientific and technological innovation.

Description

Technical Field

[0001] This invention relates to the fields of computer vision and artificial intelligence, specifically to an integrated visual intelligence platform deployed locally on WisdomStore using brain-like technology. Background Technology

[0002] With the growth of China's core artificial intelligence industry, the demand for high-precision image analysis is increasing. In fields such as medicine, materials science, industrial manufacturing, agriculture, and security monitoring, high-precision image analysis has become a key factor in improving decision-making quality, optimizing operational processes, and enhancing product quality. For example, in the medical field, computer vision is becoming a crucial technology for improving diagnostic accuracy. It assists doctors in disease diagnosis by accurately analyzing medical images, such as identifying tumor boundaries and assessing lesions, and also shows great potential in surgical navigation and robot-assisted surgery. In pathology, it helps pathologists make more accurate cancer staging and treatment decisions by analyzing microscopic images. However, when faced with large-scale data analysis, especially high-resolution medical images, the accuracy and versatility of traditional image processing methods remain insufficient. In industrial manufacturing, computer vision is crucial for automated quality control and defect detection, improving production efficiency and product quality. In the automotive and electronics industries, it can detect welding, painting, and assembly defects, as well as problems on tiny circuit boards. However, these traditional methods are limited in high-speed production environments due to insufficient processing speed and accuracy.

[0003] Therefore, to address the aforementioned problems, this invention designs and develops a zero-code, one-stop intelligent image analysis platform that supports local offline deployment. Utilizing artificial intelligence and deep learning technologies, it provides a highly accurate and automated image recognition and segmentation service platform. The platform lowers the technical barrier for image analysis for professionals in the medical, materials science, and industrial manufacturing fields. Its zero-code development environment allows non-professionals to easily perform image analysis without programming skills. The platform's data project management and model management functions, along with its offline deployment support, ensure efficient and secure data processing. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To overcome the shortcomings of existing technologies, an integrated visual intelligence platform based on brain-like technology and deployed locally in WisdomStore is proposed to solve the problems mentioned in the background.

[0006] (II) Technical Solution

[0007] This invention is achieved through the following technical solution: This invention proposes an integrated visual intelligence platform for local deployment on WisdomStore under brain-like technology, comprising a platform body, characterized in that: the platform body consists of a user interface layer, a basic service layer, a language framework layer, and a bottom service layer. The user interface layer includes four modules: data annotation interface, model training interface, model inference interface, and result display interface. The basic service layer includes two modules: data processing and model management. The language framework layer is based on the Python language and its ecosystem. The bottom service layer: the data source part combines public datasets and internally customized datasets.

[0008] Furthermore, the user interface layer provides an easy-to-use operation interface, enabling users to efficiently complete tasks such as data uploading, feature extraction, report generation, and result visualization.

[0009] Furthermore, the main task of the data processing module is to load, preprocess, and convert the data to ensure that the original data can be adapted to subsequent model training. The model management module supports functions such as model training, algorithm selection, and network structure design, covering key tasks such as model loading, configuration, segmentation, and result saving, and providing support for the dynamic adjustment and deployment of the model.

[0010] Furthermore, the public dataset provides a foundation for validating the model's generality, while the internal dataset supports performance optimization for specific application scenarios.

[0011] (III) Beneficial Effects

[0012] Compared with the prior art, the present invention has the following advantages:

[0013] Enhancing Industry Intelligence: Locally deployed visual intelligence computing platforms, by integrating advanced technologies such as deep learning, image segmentation, and object detection, significantly improve the data processing capabilities of various industries. The application of this technology not only promotes the popularization and application of artificial intelligence but also drives the overall improvement of societal intelligence, particularly in industries such as healthcare, security, and transportation, where increased intelligence is crucial for improving service quality and efficiency.

[0014] Enhancing Market Competitiveness: By leveraging intelligent visual computing platforms, businesses can improve the quality of their products and services, thereby strengthening their market competitiveness. The application of this technology can help businesses explore new markets, create more business opportunities, and ultimately increase revenue and market share.

[0015] Promoting Digital Transformation: The application of intelligent vision technology has helped traditional industries improve productivity and efficiency, reduce manual intervention, and drive the digital and automated transformation of these industries. This transformation not only enhances the competitiveness of enterprises but also provides new impetus for the upgrading of the entire industry.

[0016] Promoting Technological Innovation: The platform's application will drive the use of artificial intelligence (AI) technology across various fields, fostering its innovative development. This innovation will not only improve the efficiency and accuracy of existing technologies but also spawn new technologies and applications, propelling technological progress. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] This invention proposes an integrated visual intelligence platform for local deployment on WisdomStore using neuromorphic technology. The platform consists of a main body, characterized by the following features: the main body comprises a user interface layer, a basic service layer, a language framework layer, and a bottom service layer. The user interface layer includes four modules: a data annotation interface, a model training interface, a model inference interface, and a result display interface. The basic service layer includes two modules: data processing and model management. The language framework layer is based on the Python language and its ecosystem. The bottom service layer combines public datasets and internally customized datasets in its data source section.

[0019] Furthermore, the user interface layer provides an easy-to-use operation interface, enabling users to efficiently complete tasks such as data uploading, feature extraction, report generation, and result visualization.

[0020] Furthermore, the main task of the data processing module is to load, preprocess, and convert the data to ensure that the original data can be adapted to subsequent model training. The model management module supports functions such as model training, algorithm selection, and network structure design, covering key tasks such as model loading, configuration, segmentation, and result saving, and providing support for the dynamic adjustment and deployment of the model.

[0021] Furthermore, the public dataset provides a foundation for validating the model's generality, while the internal dataset supports performance optimization for specific application scenarios.

[0022] This invention discloses an integrated visual intelligence platform deployed locally on WisdomStore using neuromorphic technology. Its overall architecture is clearly layered and modularized. Through hierarchical design, a highly integrated architecture is constructed, from underlying hardware support to high-level user functions, embodying the design principles of modularity, flexibility, and performance optimization. Each layer has clearly defined responsibilities, operating independently yet closely collaborating, achieving full-process coverage from data annotation to result generation. This architectural design not only adapts to modern information processing needs but also provides ample flexibility for future functional expansion and system optimization. Specific implementations cover graphical interface design, data annotation, model training, and inference. By integrating multiple core algorithms and providing rich functionality, it efficiently supports the entire process from data acquisition to model application. It provides a convenient, efficient, and accurate interactive operating experience for non-computer professionals in industries such as materials science and medicine that require data annotation, helping users quickly complete various image processing tasks and enabling them to benefit from the rapid development of artificial intelligence.

[0023] Specifically, based on user needs analysis and software positioning, in order to ensure that the final software can meet user needs, including user management, data warehousing, image annotation, model inference, training, and statistical representation, the overall software functional module structure is divided into six parts: user management, project management, image annotation, model training, model inference, and statistical representation.

[0024] The intelligent platform's technology covers the entire technology stack, from the underlying hardware to the user interface. It is divided into four main layers:

[0025] (1) User Interface Layer: This layer is the top layer of the system and mainly undertakes the user's interaction with the system. It includes four main modules: data annotation interface, model training interface, model inference interface, and result display interface. Its main function is to provide an easy-to-use operation interface, enabling users to efficiently complete tasks such as data uploading, feature extraction, report generation, and result visualization. By designing an intuitive and user-friendly interface, this layer significantly lowers the technical threshold, allowing non-professional users to operate the system easily. This user-oriented design approach conforms to the trend of modern information system development, namely, pursuing a balance between functionality and ease of use.

[0026] (2) Basic Service Layer: As the core computing component of the system, the basic service layer comprises two main modules: data processing and model management. The main task of the data processing module is to load, preprocess, and convert data formats, ensuring that the raw data is suitable for subsequent model training. The model management module supports functions such as model training, algorithm selection, and network structure design, covering key tasks such as model loading, configuration, segmentation, and result saving, providing support for dynamic adjustment and deployment of the model. In addition, the core algorithm section integrates a variety of cutting-edge algorithms, such as Grabcut, Graphcut, and RITM, providing computational support for intelligent image annotation and model training inference.

[0027] (3) Language Framework Layer: This layer is developed based on the Python language and its ecosystem, supplemented by the PyQt5 framework. Python, with its extensive library support and flexibility, has become the preferred programming language for data processing and algorithm development, while PyQt5 simplifies graphical interface development, making user interface construction more efficient. Furthermore, the server-side is also developed based on Python. This unified technology stack design not only simplifies system maintenance but also ensures efficient collaboration between different modules.

[0028] (4) Underlying Service Layer: The data source combines public datasets (such as Berkeley, Grabcut, LVIS, COCO, etc.) with internally customized datasets, providing rich data support for basic training and testing. Public datasets provide the foundation for the general validation of the model, while internal datasets support performance optimization for specific application scenarios. The system hardware configuration can meet the high computational demands of large-scale data processing and complex model training, ensuring the reliability and efficiency of system performance.

[0029] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the concept and scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the inventive concept should fall within the protection scope of the present invention. All technical contents for which protection is sought in this invention are fully described in the claims.

Claims

1. An integrated visual intelligence platform under the WisdomStore local deployment of brain-like technology, comprising a platform main body, characterized in that: The platform body is composed of a user interface layer, a basic service layer, a language framework layer, and a bottom service layer. The user interface layer includes four modules: data labeling interface, model training interface, model reasoning interface, and result display interface. The basic service layer includes two modules: data processing and model management. The language framework layer is based on Python language and its ecosystem. The bottom service layer combines public data sets and internal customized data sets.

2. The integrated visual intelligence platform deployed locally by WisdomStore under the brain-like technology according to claim 1, characterized in that: The user interface layer provides an easy-to-use operation interface, enabling users to efficiently complete tasks such as data uploading, feature extraction, report generation, and result visualization.

3. The integrated visual intelligence platform deployed locally by WisdomStore under the brain-like technology according to claim 1, characterized in that: The main task of the data processing module is to realize data loading, preprocessing, and format conversion, ensuring that the original data can adapt to subsequent model training. The model management module supports functions such as model training, algorithm selection, and network structure design, covering key tasks such as model loading, configuration, segmentation, and result saving, providing support for dynamic adjustment and deployment of models.

4. The integrated visual intelligence platform deployed locally by WisdomStore under the brain-like technology according to claim 1, characterized in that: The public data sets provide a foundation for the generality verification of the model, while the internal data sets support the performance optimization of the system for specific application scenarios.