Risk investigation application system based on generative image recognition

Abstract

The invention relates to the technical field of risk investigation application systems, and provides a risk investigation application system based on generative image recognition, which comprises an image acquisition module, an image preprocessing module, a generative image recognition module, a risk analysis module, a risk early warning module and a system management module. The invention provides an advanced, efficient and accurate risk investigation solution. The method not only improves the efficiency and accuracy of risk investigation, but also enhances the adaptability to various scenes, can be widely applied to multiple fields, and makes an important contribution to the safety and stability of the society. The method has great practical value and popularization prospect in the aspects of industrial production, public safety, daily life and the like, is expected to play an important role in the field of risk prevention and control in the future, and promotes continuous development and progress of related technologies and applications.

Description

Technical Field

[0001] The present invention relates to the technical field of risk screening application systems, and in particular to a risk screening application system based on generative image recognition. Background Art

[0002] With the development of society and the advancement of technology, the demand for risk investigation in various scenarios is increasing. In many fields, it is crucial to accurately and promptly detect potential risks.

[0003] Traditional risk screening methods often suffer from inefficiency, low accuracy, and reliance on manual experience. For example, relying on manual image observation and judgment is prone to omissions and misjudgments, and struggles to cope with large-scale screening tasks. In complex environments, traditional methods may not effectively adapt to environmental changes and target diversity, resulting in poor image quality, which in turn affects subsequent risk analysis and assessment.

[0004] At the same time, some existing risk screening technologies have limitations in feature extraction and recognition, failing to comprehensively and precisely capture risk information from images, resulting in insufficient accuracy and reliability in risk identification. Furthermore, they lack a comprehensive risk analysis system, making it difficult to effectively combine multiple analytical methods to accurately assess risk. Furthermore, early warning systems lack flexibility, timeliness, and accuracy, failing to meet the demand for rapid risk response in practical applications.

[0005] Therefore, this paper proposes a risk screening application system based on generative image recognition to solve the above problems. Summary of the Invention

[0006] In order to overcome the defects of the prior art, the purpose of the present invention is to provide a risk screening application system based on generative image recognition.

[0007] To achieve the above-mentioned purpose, the technical solution of the present invention is implemented as follows: a risk screening application system based on generative image recognition, comprising: An image acquisition module is used to collect image data of the area to be inspected. The image acquisition module is equipped with an adaptive focus adjustment device that can automatically adjust the focus according to the ambient light and target distance to obtain clearer and more accurate images; An image preprocessing module, connected to the image acquisition module, is used to preprocess the acquired image data, including image enhancement, denoising and normalization operations. It also introduces an intelligent deblurring algorithm based on deep learning to effectively improve the quality of blurred images; A generative image recognition module, connected to the image preprocessing module, is used to extract and identify features from preprocessed image data using a generative adversarial network. This module innovatively adopts a hierarchical feature fusion strategy, fusing image features at different levels to more comprehensively and precisely capture risk information in the image. It also introduces an attention mechanism, enabling the network to focus on key risk-related areas in the image, improving the pertinence and accuracy of feature extraction. Furthermore, it utilizes a dynamic generative adversarial training method to continuously adjust network parameters based on real-time data to adapt to risk identification needs in different scenarios. a risk analysis module, connected to the generative image recognition module, for analyzing the risk characteristic information to determine potential risk types and risk levels; The risk warning module is connected to the risk analysis module and is used to generate corresponding warning information based on the risk type and risk level and send it to relevant personnel. This module has personalized warning settings and can customize the warning method and content according to the needs and permissions of different users; The system management module is used for configuration management, user management and log management of the entire system, and has intelligent system resource monitoring and optimization functions to ensure stable and efficient operation of the system.

[0008] Preferably, the image acquisition module includes but is not limited to a camera, a scanner or an image acquisition device carried by a drone.

[0009] Preferably, the image enhancement algorithm adopted by the image preprocessing module includes but is not limited to histogram equalization, grayscale transformation and filtering processing.

[0010] Preferably, the generative adversarial network in the generative image recognition module includes a generator and a discriminator, wherein the generator is used to generate simulated image features, and the discriminator is used to determine whether the input image features are real or generated, and continuously optimize the network parameters through adversarial training to improve the accuracy of image recognition.

[0011] Preferably, the risk analysis module uses rule-based reasoning, utilizing a pre-set rule base, to logically assess risk characteristics and determine the presence and type of risk. Statistical analysis processes large amounts of risk characteristic data, such as calculating means and variances, to identify potential risk patterns. Machine learning algorithms employ models such as decision trees, random forests, and support vector machines to learn and classify risk characteristics and determine risk types and levels. These methods, combined, form a comprehensive risk analysis system that can more accurately identify and assess various potential risks.

[0012] Preferably, the risk warning module supports multiple warning methods, including but not limited to SMS notifications, email reminders and system pop-ups.

[0013] Preferably, the system management module also includes data backup and recovery functions to ensure the security and reliability of system data.

[0014] Preferably, a risk screening method based on generative image recognition comprises the following steps: Collect image data of the area to be checked through the image acquisition module; The image preprocessing module preprocesses the collected image data; The generative image recognition module uses a generative adversarial network to extract and identify features from pre-processed image data to generate risk feature information; The risk analysis module analyzes the risk characteristic information to determine the potential risk type and risk level; The risk warning module generates corresponding warning information according to the risk type and risk level, and sends it to relevant personnel.

[0015] Preferably, in the risk analysis module, a risk assessment model is established, and the risk characteristic information is input into the model to perform risk analysis and assessment to determine the risk type and risk level.

[0016] Preferably, the warning information includes risk type, risk level, risk location and recommended measures.

[0017] The beneficial effects of the present invention are embodied in: The image acquisition module equipped with an adaptive focal length adjustment device can automatically adapt to different environments and target distances to obtain high-quality, clear and accurate images, providing a reliable data basis for subsequent risk analysis.

[0018] The various processing methods in the image preprocessing module, including image enhancement, denoising, normalization, and intelligent deblurring algorithms, effectively improve image quality and enhance the accuracy of subsequent feature extraction and recognition.

[0019] The generative image recognition module adopts a generative adversarial network, combined with a hierarchical feature fusion strategy, an attention mechanism, and a dynamic generative adversarial training method. It can comprehensively, precisely, and specifically extract and identify risk information in images, thereby improving the accuracy and efficiency of risk identification.

[0020] The risk analysis module uses a variety of analysis methods to form a comprehensive and accurate risk analysis system, which can more effectively identify and evaluate various potential risks and provide a strong basis for decision-making.

[0021] The personalized settings and multiple warning methods of the risk warning module ensure that relevant personnel can receive accurate risk information in a timely manner so that they can take corresponding response measures and ensure the safety of people and property.

[0022] The configuration management, user management, log management, and resource monitoring and optimization functions of the system management module ensure the stable and efficient operation of the system. At the same time, the data backup and recovery functions enhance the security and reliability of system data.

[0023] In summary, this invention provides an advanced, efficient, and accurate risk screening solution. It not only improves the efficiency and accuracy of risk screening but also enhances its adaptability to various scenarios. It can be widely applied in multiple fields, making significant contributions to social security and stability. Whether in industrial production, public safety, or daily life, it has great practical value and widespread application prospects. It is expected to play a significant role in future risk prevention and control, and promote the continuous development and advancement of related technologies and applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In the attached figure: Figure 1 Schematic diagram of the system connection structure of the present invention; Figure 2 This is a flow chart of the steps of the risk investigation method of the present invention. DETAILED DESCRIPTION

[0025] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the embodiments described are only some embodiments of the invention, not all embodiments. In the absence of conflict, the embodiments and features in the embodiments of this application can be combined with each other. Based on the embodiments of the invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the invention.

[0026] In addition, "multiple" means more than two. Furthermore, the technical solutions of the various embodiments may be combined with each other, but this must be based on the premise that they can be implemented by a person of ordinary skill in the art. If the combination of technical solutions is mutually inconsistent or cannot be implemented, it shall be deemed that such combination of technical solutions does not exist and is not within the scope of protection claimed in the invention.

[0027] Please refer to the instruction manual Figure 1-Figure 2 The present invention provides a risk screening application system and method based on generative image recognition, and its specific implementation method will be described in detail below.

[0028] 1. System composition and functions Image acquisition module The module is equipped with a variety of image acquisition devices, including but not limited to cameras, scanners, or drone-mounted image acquisition devices. These devices are used to collect image data of the area to be inspected.

[0029] The adaptive focus adjustment device in the module can automatically adjust the focus according to the ambient light and target distance to obtain clearer and more accurate images.

[0030] Image preprocessing module This module is connected to the image acquisition module and is responsible for preprocessing the acquired image data. Preprocessing operations include image enhancement, denoising, and normalization.

[0031] Image enhancement algorithms include but are not limited to histogram equalization, grayscale transformation, and filtering processing to improve image quality and readability.

[0032] In addition, the module also introduces an intelligent deblurring algorithm based on deep learning, which can effectively improve the quality of blurred images.

[0033] Generative Image Recognition Module This module is connected to the image preprocessing module and uses the generative adversarial network to extract and recognize features of the preprocessed image data.

[0034] Generative adversarial networks (GANs) consist of a generator and a discriminator. The generator generates simulated image features, while the discriminator determines whether input image features are real or generated. Through adversarial training, network parameters are continuously optimized to improve image recognition accuracy.

[0035] The module innovatively adopts a hierarchical feature fusion strategy to fuse image features at different levels to capture risk information in the image more comprehensively and finely.

[0036] At the same time, an attention mechanism is introduced to enable the network to focus on key risk-related areas in the image, improving the pertinence and accuracy of feature extraction.

[0037] In addition, a dynamic generative adversarial training method is used to continuously adjust network parameters based on real-time data to adapt to risk identification needs in different scenarios.

[0038] Risk Analysis Module Connected with the generative image recognition module, it analyzes risk feature information and determines potential risk types and risk levels.

[0039] Risk analysis utilizes a comprehensive system that integrates multiple methods. Rule-based reasoning uses a pre-set rule base to logically analyze risk characteristics, determining the presence and type of risk. Statistical analysis processes large amounts of risk characteristic data, such as calculating means and variances, to identify potential risk patterns. Machine learning algorithms employ models such as decision trees, random forests, and support vector machines to learn and classify risk characteristics and determine risk types and levels.

[0040] Risk warning module Connected with the risk analysis module, it generates corresponding warning information based on risk type and risk level and sends it to relevant personnel.

[0041] This module features personalized alert settings, allowing you to customize alert methods and content based on the needs and permissions of different users. Supported alert methods include but are not limited to SMS notifications, email reminders, and system pop-up windows.

[0042] System Management Module Used for configuration management, user management, and log management of the entire system.

[0043] It has intelligent system resource monitoring and optimization functions to ensure stable and efficient system operation.

[0044] In addition, data backup and recovery functions are included to ensure the security and reliability of system data.

[0045] 2. Risk Investigation Methods Use the image acquisition module to collect image data of the area to be inspected.

[0046] The image preprocessing module preprocesses the collected image data, including image enhancement, denoising and normalization operations, and uses intelligent deblurring algorithms to improve image quality.

[0047] The generative image recognition module uses a generative adversarial network to extract and identify features from preprocessed image data, generating risk signature information. It employs a layered feature fusion strategy, an attention mechanism, and dynamic generative adversarial training to improve the comprehensiveness, pertinence, and accuracy of feature extraction.

[0048] The risk analysis module builds a risk assessment model and inputs risk characteristic information into the model. It uses a variety of methods such as rule-based reasoning, statistical analysis, and machine learning algorithms to conduct risk analysis and assessment to determine the risk type and risk level.

[0049] The risk warning module generates corresponding warning information based on risk type and risk level. The warning information includes risk type, risk level, risk location, and recommended measures. The warning information is then sent to relevant personnel via SMS notifications, email reminders, or system pop-up windows.

[0050] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0051] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

[0052] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A risk screening application system based on generative image recognition, characterized in that: include: An image acquisition module is used to collect image data of the area to be inspected. The image acquisition module is equipped with an adaptive focus adjustment device that can automatically adjust the focus according to the ambient light and target distance to obtain clearer and more accurate images; An image preprocessing module, connected to the image acquisition module, is used to preprocess the acquired image data, including image enhancement, denoising and normalization operations. It also introduces an intelligent deblurring algorithm based on deep learning to effectively improve the quality of blurred images; A generative image recognition module, connected to the image preprocessing module, is used to extract and identify features from preprocessed image data using a generative adversarial network. This module innovatively adopts a hierarchical feature fusion strategy, fusing image features at different levels to more comprehensively and precisely capture risk information in the image. It also introduces an attention mechanism, enabling the network to focus on key risk-related areas in the image, improving the pertinence and accuracy of feature extraction. Furthermore, it utilizes a dynamic generative adversarial training method to continuously adjust network parameters based on real-time data to adapt to risk identification needs in different scenarios. a risk analysis module, connected to the generative image recognition module, for analyzing the risk characteristic information to determine potential risk types and risk levels; The risk warning module is connected to the risk analysis module and is used to generate corresponding warning information based on the risk type and risk level and send it to relevant personnel. This module has personalized warning settings and can customize the warning method and content according to the needs and permissions of different users; The system management module is used for configuration management, user management and log management of the entire system, and has intelligent system resource monitoring and optimization functions to ensure stable and efficient operation of the system.

2. A risk screening application system based on generative image recognition according to claim 1, characterized in that: The image acquisition module includes but is not limited to a camera, a scanner or an image acquisition device carried by a drone.

3. The risk screening application system based on generative image recognition according to claim 1 is characterized in that: The image enhancement algorithm adopted by the image preprocessing module includes but is not limited to histogram equalization, grayscale transformation and filtering processing.

4. The risk screening application system based on generative image recognition according to claim 1, characterized in that: The generative adversarial network in the generative image recognition module includes a generator and a discriminator. The generator is used to generate simulated image features, and the discriminator is used to determine whether the input image features are real or generated. The network parameters are continuously optimized through adversarial training to improve the accuracy of image recognition.

5. The risk screening application system based on generative image recognition according to claim 1 is characterized in that: The risk analysis module uses a preset rule base to perform logical judgment on risk characteristics based on rule-based reasoning to determine whether there is a risk and the type of risk; Statistical analysis performs statistical processing on a large amount of risk characteristic data, such as calculating mean and variance, to discover potential risk patterns; Machine learning algorithms employ models such as decision trees, random forests, and support vector machines to learn and classify risk characteristics and determine risk types and levels. These methods, combined with one another, form a comprehensive risk analysis system that more accurately identifies and assesses various potential risks.

6. The risk screening application system based on generative image recognition according to claim 1 is characterized in that: The risk warning module supports multiple warning methods, including but not limited to SMS notifications, email reminders and system pop-ups.

7. The risk screening application system based on generative image recognition according to claim 1 is characterized in that: The system management module also includes data backup and recovery functions to ensure the security and reliability of system data.

8. A risk screening method based on generative image recognition, applied to the risk screening application system according to any one of claims 1 to 7, characterized in that: The following steps are involved: Collect image data of the area to be checked through the image acquisition module; The image preprocessing module preprocesses the collected image data; The generative image recognition module uses a generative adversarial network to extract and identify features from pre-processed image data to generate risk feature information; The risk analysis module analyzes the risk characteristic information to determine the potential risk type and risk level; The risk warning module generates corresponding warning information according to the risk type and risk level, and sends it to relevant personnel.

9. The risk screening method based on generative image recognition according to claim 8, characterized in that: In the risk analysis module, a risk assessment model is established, and the risk characteristic information is input into the model to perform risk analysis and assessment to determine the risk type and risk level.

10. The risk screening method based on generative image recognition according to claim 1, characterized in that: The warning information includes risk type, risk level, risk location and recommended measures.

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