Power plant violation behavior analysis system fusing STC coding and AI identification

Abstract

The invention discloses a power plant violation behavior analysis system fusing STC coding and AI recognition. The power plant violation behavior analysis system comprises a video acquisition module, an STC coding enhancement module, an AI behavior recognition module, a violation analysis engine, an alarm response module and an evidence management module. According to the invention, through deep fusion of STC coding and AI identification, a violation prevention and control system integrating real-time monitoring, behavior analysis and evidence management is constructed, and the system uses STC coding to enhance the video anti-interference capability and combines a deep learning model to improve the behavior identification accuracy in a complex scene; full-process automation from data acquisition to alarm response is realized through cooperation of multiple modules, and the omission ratio and the false alarm rate are remarkably reduced. The system also has an adaptive learning capability, can optimize an identification strategy along with environmental changes, improves the violation evidence retrieval efficiency through structured storage, and provides technical support for power plant safety management.

Description

Technical Field

[0001] This invention relates to the field of computer system technology, and specifically to a power plant violation analysis system that integrates STC coding and AI recognition. Background Technology

[0002] In the production environment of power plants, personnel violations are considered a key contributing factor to safety accidents. Traditional monitoring methods rely primarily on manual on-site inspections and post-incident video playback checks. This approach is not only extremely inefficient but also slow to respond to emergencies. Current technologies based on ordinary video analysis suffer from interference factors such as frequent changes in lighting conditions and object obstruction, resulting in low accuracy in identifying violations. Furthermore, they lack effective dynamic tracking capabilities for the entire continuous process of violations from occurrence to completion. In addition, while Spatio-Temporal Context (STC) encoding offers advantages in improving video compression efficiency, it has not yet been deeply integrated with behavior recognition technology, failing to achieve intelligent early warning of personnel violations and effective evidence preservation. Summary of the Invention

[0003] To address this, the present invention provides a power plant violation analysis system that integrates STC encoding and AI recognition, in order to solve the problem that existing ordinary video analysis technologies are difficult to achieve an ideal level of recognition accuracy due to interference factors such as frequent changes in lighting conditions and object occlusion. At the same time, they lack effective dynamic tracking capabilities for the entire continuous process of violation from occurrence to end.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A power plant violation analysis system integrating STC coding and AI recognition includes a video acquisition module, an STC coding enhancement module, an AI behavior recognition module, a violation analysis engine, an alarm response module, and an evidence management module.

[0006] The video acquisition module is used to acquire raw video streams in real time through multiple high-definition cameras deployed in the power plant's operating area, and to preprocess the video signals to eliminate environmental noise;

[0007] The STC encoding enhancement module is connected to the video acquisition module and is used to perform spatiotemporal context encoding on the preprocessed video stream. It extracts dynamic features through inter-frame difference and motion vector analysis to generate compressed enhanced video data.

[0008] The AI ​​behavior recognition module communicates with the STC encoding enhancement module to perform multi-scale detection of target behaviors in enhanced video data based on a deep learning model. The deep learning model adopts a hybrid architecture that integrates a YOLOv5 skeleton network and a Transformer encoder. It locates the human pose sequence through an attention mechanism and uses a temporal convolutional network to determine the behavior category.

[0009] The violation analysis engine receives the output of the AI ​​behavior recognition module and performs behavior matching according to the preset power plant safety rule library. If a violation is detected, an event label and its risk level are generated. The rule library includes violation scenarios such as not wearing a safety belt while working at height, not wearing a safety helmet, and entering a restricted area.

[0010] The alarm response module is connected to the violation analysis engine to trigger a multi-level response mechanism based on the risk level. The multi-level response mechanism includes on-site audible and visual alarms, monitoring center pop-up prompts, and remote mobile terminal push alarm information.

[0011] The evidence management module stores video clips, timestamps, behavior types, and associated metadata of violations, and ensures data integrity through hash verification. It supports structured retrieval and export by time, region, or behavior type.

[0012] Preferably, the STC encoding enhancement module includes:

[0013] The motion sensing unit calculates the motion trajectory of people in consecutive frames using optical flow and separates dynamic targets from static scenes by combining background modeling.

[0014] The spatiotemporal context modeling unit performs spatiotemporal context enhancement processing on the preprocessed video stream: first, the video is compressed and encoded based on the H.265 standard; at the same time, it extracts the spatiotemporal feature information of dynamic targets by combining motion perception analysis, and uses the spatiotemporal feature information as a context descriptor, which is associated with the corresponding keyframe in the form of metadata;

[0015] The intelligent hierarchical coding unit compresses and encodes the preprocessed video stream based on the H.265 standard, and embeds spatiotemporal context descriptors in key frames. The descriptors contain the target position, motion velocity and direction vectors obtained from motion analysis, which are used to assist in subsequent behavior recognition.

[0016] The anti-interference unit employs adaptive histogram equalization and local contrast enhancement techniques to suppress the impact of sudden changes in illumination and dust interference on video quality.

[0017] Preferably, the AI ​​behavior recognition module includes:

[0018] The feature extraction unit uses a dilated convolutional network to extract multi-scale spatial features and models behavioral temporal dependencies through a bidirectional long short-term memory network.

[0019] The behavior classification unit jointly optimizes the classification and localization tasks based on an improved loss function, which is composed of a weighted sum of focus loss and IoU loss, and is used to solve the problems of sample class imbalance and bounding box regression error.

[0020] The adaptive learning unit dynamically adjusts the weights of training samples through an online hard example mining strategy and uses incremental learning to update model parameters to adapt to new violation scenarios in the power plant.

[0021] Preferably, the violation analysis engine is also connected to a rule configuration interface, which allows users to customize the violation judgment threshold and related logical relationships according to the power plant safety regulations.

[0022] Preferably, the alarm response module integrates an alarm priority scheduling algorithm to dynamically adjust the response strategy based on the frequency of occurrence, duration, and risk factor of the associated area of ​​the violation.

[0023] Preferably, the feature extraction unit uses a channel attention mechanism and a spatial pyramid pooling layer to fuse global contextual information, thereby improving the detection sensitivity for small-scale targets.

[0024] Preferably, the evidence management module also includes a data encryption and access control subsystem, which encrypts the stored evidence of violations using AES-256 and manages data access operations based on role-based access permissions.

[0025] Preferably, it also includes a performance monitoring dashboard, which is used to display statistical charts of violations, model recognition accuracy curves, and system operation status indicators in real time.

[0026] Preferably, the system deploys video acquisition and STC encoding enhancement modules through edge computing nodes and centrally runs an AI behavior recognition and violation analysis engine on a cloud server.

[0027] This invention offers the following advantages: By deeply integrating STC encoding and AI recognition, it constructs a violation prevention and control system that combines real-time monitoring, behavior analysis, and evidence management. The system utilizes STC encoding to enhance video anti-interference capabilities and combines deep learning models to improve the accuracy of behavior recognition in complex scenarios. Through multi-module collaboration, it achieves full-process automation from data acquisition to alarm response, significantly reducing the missed detection rate and false alarm rate. The system also possesses adaptive learning capabilities, optimizing recognition strategies according to environmental changes, and improves the efficiency of violation evidence retrieval through structured storage, providing technical support for power plant safety management. Attached Figure Description

[0028] To more intuitively illustrate the prior art and this application, exemplary drawings are provided below. It should be understood that the specific shapes and structures shown in the drawings should not generally be regarded as limiting conditions for implementing this application; for example, based on the technical concept disclosed in this application and the exemplary drawings, those skilled in the art are able to easily make conventional adjustments or further optimizations to the addition / reduction / classification, specific shapes, positional relationships, connection methods, size ratios, etc. of certain units (components).

[0029] Figure 1 A module diagram of a power plant violation analysis system that integrates STC coding and AI recognition, provided for an embodiment of this application. Detailed Implementation

[0030] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these embodiments are merely for further explanation of the present invention and should not be construed as limiting the scope of protection of the present invention. Technical engineers in the field can make some non-essential improvements and adjustments to the present invention based on the above-described content. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Please see Figure 1 A power plant violation analysis system integrating STC coding and AI recognition includes:

[0032] The video acquisition module is used to acquire raw video streams in real time through multiple high-definition cameras deployed in the power plant's operating area, and to preprocess the video signals to eliminate environmental noise;

[0033] The STC encoding enhancement module, connected to the video acquisition module, is used to perform spatiotemporal context encoding on the preprocessed video stream, extract dynamic features through inter-frame difference and motion vector analysis, and generate compressed enhanced video data.

[0034] The AI ​​behavior recognition module communicates with the STC encoding enhancement module. Based on a deep learning model, it performs multi-scale detection of target behaviors in enhanced video data. The deep learning model adopts a hybrid architecture that integrates a YOLOv5 skeleton network and a Transformer encoder. It locates the human pose sequence through an attention mechanism and uses a temporal convolutional network to determine the behavior category.

[0035] The violation analysis engine receives the output results of the AI ​​behavior recognition module and performs behavior matching according to the preset power plant safety rule library. If a violation is detected, an event label and its risk level are generated. The rule library includes typical violation scenarios such as not wearing a safety belt while working at height, not wearing a safety helmet, and entering a restricted area.

[0036] The alarm response module connects to the violation analysis engine and is used to trigger a multi-level response mechanism based on the risk level, including on-site audible and visual alarms, pop-up notifications in the monitoring center, and remote mobile terminal push alarm information.

[0037] The evidence management module stores video clips, timestamps, behavior types, and associated metadata of violations, and ensures data integrity through hash verification. It supports structured retrieval and export by time, region, or behavior type.

[0038] During implementation, the video acquisition module utilizes high-definition cameras deployed throughout the power plant to acquire real-time video of the operation. Through noise reduction and image enhancement preprocessing, it effectively overcomes challenges such as varying lighting conditions and dust interference inherent in the complex power plant environment, providing a high-quality visual data foundation for subsequent analysis. Next, the STC encoding enhancement module performs spatiotemporal context encoding on the preprocessed video, employs inter-frame difference technology to extract motion features, and combines this with motion vector analysis to achieve efficient compression of the video data. This process not only reduces storage and transmission burdens but also significantly improves the accuracy of subsequent behavior recognition by preserving key motion information.

[0039] The AI ​​behavior recognition module analyzes the compressed video stream based on an improved deep learning architecture. The YOLOv5 skeleton network quickly locates personnel targets, while the Transformer encoder captures long-distance dependencies through an attention mechanism. This hybrid architecture design ensures both real-time performance and accurate recognition of complex pose changes. The introduction of a temporal convolutional network further enhances the system's ability to recognize continuous violations (such as the entire process of not wearing a seatbelt), effectively avoiding the missed detection problems caused by inter-frame breaks in traditional methods.

[0040] The violation analysis engine intelligently matches the identification results with the power plant's safety rule base, supporting parallel analysis of various typical violation scenarios. This rule-based judgment mechanism ensures the professionalism and configurability of behavioral assessments. When a violation is detected, the alarm response module activates differentiated alarm strategies based on preset risk levels, achieving a comprehensive response from on-site warnings to remote notifications, significantly shortening the time interval from violation discovery to intervention. The evidence management module, through structured storage and hash verification technology, completely preserves video evidence of the violation process and its metadata. This design not only ensures the integrity of the evidence chain but also greatly facilitates subsequent querying and tracing.

[0041] The STC encoding enhancement module specifically includes:

[0042] The motion sensing unit calculates the motion trajectory of people in consecutive frames using optical flow and separates dynamic targets from static scenes by combining background modeling.

[0043] The spatiotemporal context modeling unit performs spatiotemporal context enhancement processing on the preprocessed video stream: First, it performs efficient compression encoding of the video based on the H.265 standard; simultaneously, it extracts the spatiotemporal features of dynamic targets (including target position, motion velocity, and direction vector) by combining motion perception analysis, and uses this information as a context descriptor, which is associated with the corresponding keyframe in the form of metadata. This descriptor does not participate in video pixel reconstruction, but it can be used for temporal modeling and target tracking in the subsequent AI behavior recognition module, thereby improving the accuracy of behavior analysis in complex scenes while ensuring transmission efficiency.

[0044] The intelligent hierarchical coding unit compresses and encodes the preprocessed video stream based on the H.265 standard, and embeds spatiotemporal context descriptors in keyframes. The descriptors contain the target position, motion velocity, and direction vectors obtained from motion analysis, which are used to assist in subsequent behavior recognition.

[0045] The anti-interference unit employs adaptive histogram equalization and local contrast enhancement techniques to suppress the impact of sudden changes in illumination and dust interference on video quality.

[0046] The motion sensing unit uses optical flow to calculate pixel-level motion vectors, enabling precise tracking of personnel movement trajectories in complex backgrounds. For example, in a boiler room area, it can effectively distinguish between normal walking and abnormal running behavior. The anti-interference unit is specifically designed for the high-dust environment of power plants, effectively improving image visibility under hazy conditions through local contrast enhancement technology, ensuring clear monitoring images even under harsh operating conditions.

[0047] The AI ​​behavior recognition module further includes:

[0048] The feature extraction unit uses a dilated convolutional network to extract multi-scale spatial features and models behavioral temporal dependencies through a bidirectional long short-term memory network.

[0049] The behavior classification unit jointly optimizes the classification and localization tasks based on an improved loss function, which is composed of a weighted sum of focus loss and IoU loss, and is used to solve the problems of sample class imbalance and bounding box regression error.

[0050] The adaptive learning unit dynamically adjusts the weights of training samples through an online hard example mining strategy and uses incremental learning to update model parameters to adapt to new violation scenarios in the power plant.

[0051] The feature extraction unit employs dilated convolution to expand the receptive field and utilizes a Bi-LSTM network to model long-term sequence dependencies. This design is particularly suitable for identifying multi-step violations such as "climbing first and then not wearing a safety belt." The behavior classification unit alleviates the imbalance between positive and negative samples in power plant scenarios (e.g., normal operation samples far outnumber violation samples) through a focus loss function and optimizes bounding box localization accuracy by combining IoU loss, thereby reducing the false alarm rate of safety helmet wearing recognition in actual tests. The adaptive learning unit employs an online hard example mining strategy to continuously learn new features from failed recognition cases. For example, when a new type of protective clothing is added, the system can quickly adapt through incremental learning without affecting existing recognition capabilities.

[0052] The violation analysis engine is also connected to a rule configuration interface, which allows users to customize the threshold for judging violations and the associated logical relationships according to the power plant safety regulations.

[0053] Employing a graphical design, safety administrators can combine various judgment conditions through drag-and-drop, such as setting a composite rule for "being in a high-voltage area without wearing insulated shoes for more than 10 seconds". The interface supports dynamic adjustment of threshold parameters, such as modifying the allowable duration of high-temperature operations according to seasonal changes. This design enables the system to adapt to the different safety management standards of various power plants, significantly improving the system's practicality and scalability.

[0054] The alarm response module integrates an alarm priority scheduling algorithm, which dynamically adjusts the response strategy based on the frequency of violations, duration, and risk factor of associated areas.

[0055] The algorithm comprehensively considers the frequency, duration, and regional hazard level of violations to establish a three-tiered early warning mechanism. For example, in high-risk areas such as turbine rooms, the first instance of not wearing a safety helmet triggers a medium-level alarm; while in office areas, the same violation triggers a low-level warning. This differentiated strategy ensures both the intensity of safety supervision in key areas and avoids regulatory fatigue caused by excessive alarms, thereby improving the effective alarm response rate in practical applications.

[0056] The feature extraction unit employs a channel attention mechanism and a spatial pyramid pooling layer to fuse global contextual information, thereby improving the detection sensitivity for small-scale targets.

[0057] The evidence management module also includes a data encryption and access control subsystem, which encrypts the stored evidence of violations using AES-256 and manages data access operations based on role-based access permissions.

[0058] The stored videos are encrypted using the AES-256 algorithm, combined with a role-based access control mechanism to ensure that only authorized personnel can view the relevant evidence. For example, ordinary safety officers can only query data within their designated area, while the safety supervisor has plant-wide access. This hierarchical management not only ensures data security but also complies with the power plant's safety management hierarchy, while digital signature technology prevents evidence tampering.

[0059] It also includes a performance monitoring dashboard, which displays real-time statistics of violations, model recognition accuracy curves, and system operation status indicators.

[0060] The dashboard integrates multiple visualization components, displaying real-time distribution of violation types, accuracy trends, and system load status. For example, heatmaps show high-violation areas, helping managers to strengthen targeted supervision; model accuracy curves monitor AI performance degradation, triggering timely model retraining. This design upgrades the system from a simple monitoring tool to a safety management decision support platform, helping power plants achieve preventative safety management.

[0061] The system deploys video acquisition and STC encoding enhancement modules on edge computing nodes, and centrally runs an AI behavior recognition and violation analysis engine on cloud servers to reduce network transmission load and improve real-time response. By deploying edge computing nodes on-site, video acquisition and preliminary processing are localized, while key video features, rather than the raw video stream, are uploaded to the cloud. This design reduces network bandwidth consumption. Simultaneously, the centralized operation of complex AI analysis models in the cloud ensures consistent algorithm updates and cutting-edge analytical capabilities.

[0062] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A power plant violation analysis system integrating STC coding and AI recognition, characterized in that, Includes a video capture module, an STC encoding enhancement module, an AI behavior recognition module, a violation analysis engine, an alarm response module, and an evidence management module. The video acquisition module is used to acquire raw video streams in real time through multiple high-definition cameras deployed in the power plant's operating area, and to preprocess the video signals to eliminate environmental noise; The STC encoding enhancement module is connected to the video acquisition module and is used to perform spatiotemporal context encoding on the preprocessed video stream. It extracts dynamic features through inter-frame difference and motion vector analysis to generate compressed enhanced video data. The AI ​​behavior recognition module communicates with the STC encoding enhancement module to perform multi-scale detection of target behaviors in enhanced video data based on a deep learning model. The deep learning model adopts a hybrid architecture that integrates a YOLOv5 skeleton network and a Transformer encoder. It locates the human posture sequence through an attention mechanism and uses a temporal convolutional network to determine the behavior category. The violation analysis engine receives the output results of the AI ​​behavior recognition module and performs behavior matching according to the preset power plant safety rule library. If a violation is detected, an event label and its risk level are generated. The rule library includes violation scenarios such as not wearing a safety belt while working at height, not wearing a safety helmet, and entering a restricted area. The alarm response module is connected to the violation analysis engine and is used to trigger a multi-level response mechanism based on the risk level. The multi-level response mechanism includes on-site audible and visual alarms, monitoring center pop-up prompts, and remote mobile terminal push alarm information. The evidence management module stores video clips, timestamps, behavior types, and associated metadata of violations, and ensures data integrity through hash verification. It supports structured retrieval and export by time, region, or behavior type.

2. The power plant violation analysis system integrating STC coding and AI recognition as described in claim 1, characterized in that, The STC encoding enhancement module includes: The motion sensing unit calculates the motion trajectory of people in consecutive frames using optical flow and separates dynamic targets from static scenes by combining background modeling. The spatiotemporal context modeling unit performs spatiotemporal context enhancement processing on the preprocessed video stream: first, the video is compressed and encoded based on the H.265 standard; at the same time, it extracts the spatiotemporal feature information of dynamic targets by combining motion perception analysis, and uses the spatiotemporal feature information as a context descriptor, which is associated with the corresponding keyframe in the form of metadata; The intelligent hierarchical coding unit compresses and encodes the preprocessed video stream based on the H.265 standard, and embeds spatiotemporal context descriptors in key frames. The descriptors contain the target position, motion velocity and direction vectors obtained from motion analysis, which are used to assist in subsequent behavior recognition. The anti-interference unit employs adaptive histogram equalization and local contrast enhancement techniques to suppress the impact of sudden changes in illumination and dust interference on video quality.

3. The power plant violation analysis system integrating STC coding and AI recognition as described in claim 2, characterized in that, The AI ​​behavior recognition module includes: The feature extraction unit uses a dilated convolutional network to extract multi-scale spatial features and models behavioral temporal dependencies through a bidirectional long short-term memory network. The behavior classification unit jointly optimizes the classification and localization tasks based on an improved loss function, which is composed of a weighted sum of focus loss and IoU loss, and is used to solve the problems of sample class imbalance and bounding box regression error. The adaptive learning unit dynamically adjusts the weights of training samples through an online hard example mining strategy and uses incremental learning to update model parameters to adapt to new violation scenarios in the power plant.

4. The power plant violation analysis system integrating STC coding and AI recognition as described in claim 1, characterized in that, The violation analysis engine is also connected to a rule configuration interface, which allows users to customize the threshold for judging violations and the associated logical relationships according to the power plant safety regulations.

5. The power plant violation analysis system integrating STC coding and AI recognition according to claim 4, characterized in that, The alarm response module integrates an alarm priority scheduling algorithm, which dynamically adjusts the response strategy based on the frequency of violations, duration, and risk factor of associated areas.

6. The power plant violation analysis system integrating STC coding and AI recognition according to claim 3, characterized in that, The feature extraction unit employs a channel attention mechanism and a spatial pyramid pooling layer to fuse global contextual information, thereby improving the detection sensitivity for small-scale targets.

7. The power plant violation analysis system integrating STC coding and AI recognition as described in claim 1, characterized in that, The evidence management module also includes a data encryption and access control subsystem, which encrypts the stored evidence of violations using AES-256 and manages data access operations based on role-based access permissions.

8. The power plant violation analysis system integrating STC coding and AI recognition according to claim 1, characterized in that, It also includes a performance monitoring dashboard, which is used to display statistical charts of violations, model recognition accuracy curves, and system operation status indicators in real time.

9. A power plant violation analysis system integrating STC coding and AI recognition as described in claim 1, characterized in that, The system deploys video acquisition and STC encoding enhancement modules through edge computing nodes, and centrally runs an AI behavior recognition and violation analysis engine on cloud servers.

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