Thermoelectric power generation safety intelligent supervision system and method based on image recognition

Abstract

The invention relates to the technical field of thermoelectric safety supervision, and discloses a thermoelectric power generation safety intelligent supervision system and method based on image recognition. An image acquisition unit of the system obtains a multispectral image sequence of the surface and internal structure of thermoelectric equipment, transmits the multispectral image sequence to a thermal field reconstruction module for thermal field distribution reconstruction, and outputs a dynamic thermal field distribution map to a feature extraction module; the feature extraction module extracts an abnormal temperature region feature vector from the reconstructed thermal field distribution and transmits the abnormal temperature region feature vector to the security policy generation module; the security policy generation module generates a security regulation instruction and sends the security regulation instruction to the execution unit; and after the execution unit executes the operation, the closed-loop optimization module collects equipment state change data and feeds back the data to the thermal field reconstruction module so as to dynamically adjust the collected parameters. According to the system, comprehensive monitoring and dynamic regulation and control of a thermal field of thermoelectric equipment are realized through multispectral image recognition, and a complete supervision closed loop is formed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of thermal power safety supervision, in particular to a thermal power generation safety intelligent supervision system and method based on image recognition. BACKGROUND

[0002] In the process of thermal power generation, the equipment is in complex working conditions such as high temperature and high pressure for a long time, and the stability of its running state is directly related to the safety and efficiency of the entire power generation system. The traditional thermal power equipment supervision method mainly depends on contact sensors, which need to be directly installed on the surface or inside the equipment, and are easily affected by high temperature environment, resulting in problems such as decrease of measurement accuracy and shortening of service life. At the same time, the number of contact sensors is limited, and it is difficult to fully cover all key areas of the equipment, so that local abnormal temperature cannot be found in time, which may cause equipment failure or even safety accidents. With the development of thermal power technology, the structure of the equipment is becoming more and more complex, and the traditional supervision method has been insufficient in real-time and comprehensiveness of data acquisition. Although non-contact detection technology has been applied, it is mainly based on single spectral image analysis, which cannot accurately reflect the thermal field distribution of the internal structure of the equipment. When there is a hidden fault in the equipment, it is difficult to capture the subtle temperature changes through single spectral image, resulting in delayed fault warning. In addition, the existing supervision system lacks effective closed-loop feedback mechanism, and the execution effect of the control instruction cannot be fed back to the monitoring link in time, which makes it difficult to realize dynamic optimization and adjustment of the equipment running state, and the intelligent degree of the supervision process is low. In actual operation, the thermal field distribution of thermal power equipment is in dynamic change, which is affected by many factors such as load fluctuation and environmental temperature change. The traditional supervision method is difficult to quickly adapt to these dynamic changes, and can only be analyzed after the event, which cannot realize pre-warning and real-time control. This lag not only affects the power generation efficiency, but also may expand the fault range due to the failure to handle abnormal conditions in time, causing unnecessary economic loss. Therefore, it is an urgent problem in the field of thermal power generation to build an intelligent supervision system that can comprehensively, real-time and accurately monitor the thermal field distribution of thermal power equipment and realize dynamic control. SUMMARY

[0003] The purpose of the present application is to provide a thermal power generation safety intelligent supervision system based on image recognition to solve the problems in the background art.

[0004] To achieve the above purpose, the present application provides a thermal power generation safety intelligent supervision system based on image recognition, which comprises: The image acquisition unit is used for acquiring a multi-spectral image sequence of a thermoelectric device surface and internal structure, the thermal field reconstruction module is used for reconstructing a thermal field distribution of the multi-spectral image sequence, the feature extraction module is used for extracting an abnormal temperature region feature vector from the reconstructed thermal field distribution, the safety strategy generation module is used for generating a safety control instruction according to the abnormal temperature region feature vector, the execution unit is used for executing the safety control instruction, and the closed-loop optimization module is used for monitoring an execution effect and feeding back to the thermal field reconstruction module; wherein the image acquisition unit transmits the acquired multi-spectral image sequence to the thermal field reconstruction module in real time, the thermal field reconstruction module outputs a dynamic thermal field distribution atlas to the feature extraction module, the feature extraction module transmits the abnormal temperature region feature vector to the safety strategy generation module, the safety strategy generation module sends the safety control instruction to the execution unit, and the closed-loop optimization module collects device state change data and feeds back to the thermal field reconstruction module for dynamic adjustment of acquisition parameters after the execution unit executes the operation in response to the instruction.

[0005] Preferably, the image acquisition unit comprises a multi-spectral imaging sensor array and a spatial resolution adjuster; the multi-spectral imaging sensor array synchronously acquires device surface images in a visible light waveband, a near-infrared waveband and a long-wave infrared waveband; the spatial resolution adjuster assigns a high-resolution sampling mode to a weld area, an electrode contact area and a heat exchanger surface area based on a preset device key area coordinate list, and uses a low-resolution sampling mode for non-key areas; the multi-spectral imaging sensor array combines the synchronously acquired three-waveband images into a multi-spectral image sequence, and transmits the multi-spectral image sequence to the thermal field reconstruction module after adding time stamp and spatial coordinate information.

[0006] Preferably, the thermal field reconstruction module comprises a temperature field inversion engine and an abnormal region marker; the temperature field inversion engine generates three-dimensional temperature distribution body data through a thermal conduction equation inverse solving technique after receiving the multi-spectral image sequence; the abnormal region marker marks abnormal high-temperature region boundary coordinates in the three-dimensional temperature distribution body data according to preset temperature gradient threshold and heat spot area threshold, and converts the three-dimensional temperature distribution body data with the marked boundary coordinates into a dynamic thermal field distribution atlas and outputs the dynamic thermal field distribution atlas to the feature extraction module.

[0007] Preferably, the feature extraction module comprises a partition feature processor and a vector fusion unit; the partition feature processor receives the dynamic thermal field distribution atlas, and extracts a temperature gradient change curve, a heat spot geometric morphology feature and a heat diffusion rate parameter for each marked abnormal high-temperature region, respectively; the vector fusion unit fuses the temperature gradient change curve, the heat spot geometric morphology feature and the heat diffusion rate parameter into a multi-dimensional abnormal temperature region feature vector, and transmits the multi-dimensional abnormal temperature region feature vector to the safety strategy generation module.

[0008] Preferably, the safety policy generation module comprises a risk level classifier and an instruction decision tree; the risk level classifier matches a preset fault mode database according to the multi-dimensional abnormal temperature region feature vector, and outputs a hot spot risk level label; the instruction decision tree generates a safety control instruction set comprising a cooling system starting level, a power generation power adjustment range and an alarm level based on the hot spot risk level label and a current running power parameter of the device, and sends the safety control instruction set to the execution unit.

[0009] Preferably, the execution unit comprises a cooling control interface and a power regulator; the cooling control interface sends a fan speed control signal and a cooling liquid flow adjustment signal to a cooling device according to the cooling system starting level in the safety control instruction set; the power regulator adjusts the output power set value of the thermoelectric conversion device according to the power generation power adjustment range in the safety control instruction set; the cooling control interface and the power regulator send an operation completion confirmation signal to the closed-loop optimization module after executing the instruction.

[0010] Preferably, the closed-loop optimization module comprises a state monitoring network and a parameter optimization engine; the state monitoring network collects device surface temperature change rate, internal stress fluctuation data and output current stability index after the operation completion confirmation signal is triggered; the parameter optimization engine compares the device surface temperature change rate, internal stress fluctuation data and output current stability index with a preset safety threshold interval, generates a thermal field reconstruction parameter adjustment instruction feedback to the thermal field reconstruction module, and the thermal field reconstruction parameter adjustment instruction comprises a key area coordinate correction value and a temperature gradient threshold update value.

[0011] Preferably, the system further comprises a distributed storage cluster and a real-time communication bus; the distributed storage cluster stores the multi-spectral image sequence, the dynamic thermal field distribution map and the multi-dimensional abnormal temperature region feature vector in a time series database structure; the real-time communication bus adopts a time division multiplexing protocol to establish a bidirectional data transmission channel between the image acquisition unit, the thermal field reconstruction module, the feature extraction module, the safety policy generation module, the execution unit and the closed-loop optimization module, and ensures that the operation instruction and the feedback data complete interaction within a preset time delay.

[0012] Preferably, the present application further comprises a thermal power generation safety intelligent supervision method based on image recognition, which is used to realize the above-mentioned thermal power generation safety intelligent supervision system based on image recognition, and the method comprises: Collecting multi-band surface images of thermal power equipment through a multi-spectral imaging sensor array, and dynamically configuring spatial resolution based on a device key area coordinate list; Multi-band surface images are processed using the inverse solution technique of the heat conduction equation to generate three-dimensional temperature distribution data with anomaly region markings; Temperature gradient features, geometric features, and thermal diffusion features of multiple abnormally high-temperature regions are extracted from three-dimensional temperature distribution data and fused into a multi-dimensional feature vector. The hot spot risk level is generated by matching the fault mode database with multidimensional feature vectors, and the cooling control level and power adjustment range are determined by combining the current power parameters. After executing the cooling system control command and the power generation adjustment command, the system monitors the equipment status change data and feeds it back to the thermal field reconstruction process; Based on the deviation analysis results between equipment status change data and safety thresholds, the coordinates of key areas for thermal field reconstruction and the anomaly judgment thresholds are dynamically updated.

[0013] Preferably, the dynamically updated thermal field reconstruction parameters include: Establish a mapping table between equipment status change data and thermal field reconstruction parameters, where the equipment status change data includes temperature drop rate, stress fluctuation amplitude, and current stability coefficient; The gradient descent algorithm is used to calculate the deviation between the current thermal field reconstruction parameters and the optimal safety performance; When the rate of temperature decrease is lower than a preset threshold, increase the density of monitoring points in the list of key area coordinates. When the stress fluctuation exceeds the tolerance range, adjust the sensitivity coefficient of the temperature gradient threshold. The updated key area coordinates and temperature gradient thresholds are pushed to the thermal field reconstruction module in real time via a distributed message queue.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This system employs a multispectral image sequence acquisition method, enabling it to simultaneously acquire thermal field information of the surface and internal structure of thermoelectric equipment, overcoming the limitations of traditional contact sensors in terms of monitoring range. The image acquisition unit does not require direct contact with the equipment, achieving comprehensive coverage of all key areas without affecting normal operation. It captures more subtle temperature changes, including localized abnormal temperatures caused by hidden internal faults, resulting in a broader monitoring scope and more comprehensive information. The thermal field reconstruction module processes multispectral image sequences to construct a dynamic thermal field distribution map, which can intuitively present the temperature change trends of various areas of the equipment. Compared with traditional single-point temperature data, this dynamic map better reflects the overall thermal field distribution pattern of the equipment, helps to discover the temperature correlation between different areas, and provides richer information for identifying potential safety hazards. The feature extraction module extracts feature vectors of abnormal temperature areas from the reconstructed thermal field distribution. By analyzing these feature vectors, the location, range, and rate of change of abnormal temperatures can be accurately located, avoiding misjudgments or omissions caused by insufficient information in traditional methods. The safety policy generation module generates corresponding safety control instructions based on the feature vector of abnormal temperature regions, making the control measures more targeted. The execution unit operates according to these instructions, enabling timely intervention in abnormal temperature regions to prevent further deterioration of the abnormal situation. After the execution unit operates, the closed-loop optimization module collects equipment status change data and feeds it back to the thermal field reconstruction module, achieving dynamic adjustment of the collected parameters. This closed-loop mechanism allows the system to continuously optimize the monitoring method based on the actual operating status of the equipment, adapting to thermal field changes under different operating conditions and improving the system's adaptability to dynamic environments. The entire system forms a complete process from image acquisition, thermal field reconstruction, feature extraction, strategy generation, command execution to effect feedback. The modules work collaboratively to achieve real-time monitoring and dynamic control of the thermal power equipment's operating status. This intelligent monitoring model reduces reliance on manual operation, minimizes errors caused by human factors, and enables timely response in the early stages of abnormal situations, preventing the escalation of faults and ensuring the stable operation of the thermal power generation system. Attached Figure Description

[0015] Figure 1 This is a schematic diagram illustrating the working principle of the image recognition-based intelligent monitoring system for thermal power generation safety described in this invention. Figure 2 This is a flowchart of the image acquisition unit's workflow. Figure 3 This is a flowchart of the thermal field reconfiguration module. Figure 4 Workflow diagram for the security policy generation module. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0017] Please seeFigure 1 This invention provides an intelligent safety monitoring system and method for thermoelectric power generation based on image recognition. The system includes: an image acquisition unit, a thermal field reconstruction module, a feature extraction module, a safety policy generation module, an execution unit, and a closed-loop optimization module. Specific implementation details are as follows: The image acquisition unit acquires multispectral image sequences of the surface and internal structure of the thermoelectric equipment and transmits the acquired data to the thermal field reconstruction module in real time.

[0018] The thermal field reconstruction module reconstructs the thermal field distribution of the multispectral image sequence, generates a dynamic thermal field distribution map, and outputs it to the feature extraction module.

[0019] The feature extraction module extracts feature vectors of abnormal temperature regions from the reconstructed thermal field distribution and transmits the feature vectors to the security policy generation module.

[0020] The safety policy generation module generates safety control instructions based on the feature vector of the abnormal temperature area and sends them to the execution unit to perform the corresponding operations.

[0021] The closed-loop optimization module monitors the execution effect, collects equipment status change data and feeds it back to the thermal field reconstruction module, realizing dynamic adjustment of collected parameters and system optimization.

[0022] Example 1: See Figure 2 The image acquisition unit, as the front-end data acquisition module of the system, has the core function of capturing multispectral image sequences of the surface and internal structure of thermoelectric equipment in real time. This unit consists of a multispectral imaging sensor array and a spatial resolution adjuster, working together to achieve high-precision and high-efficiency data acquisition. The multispectral imaging sensor array adopts a modular design, including visible light, near-infrared, and long-wave infrared sensors. The visible light sensor acquires optical images of the equipment surface with a resolution of 2048×1536 pixels, clearly revealing the structural features and minute defects of the equipment surface. The near-infrared sensor operates at a wavelength of 0.7-1.1 micrometers and is mainly used to detect the thermal radiation distribution inside the equipment, with a sensitivity of 0.05°C. The long-wave infrared sensor covers the 8-14 micrometer band and can accurately measure the absolute temperature of the equipment surface, with a temperature measurement range of 0-600°C and an accuracy of ±1°C. The three sensors operate in a synchronous triggering mode to ensure strict temporal and spatial alignment of images from different wavelengths.

[0023] The spatial resolution adjuster dynamically adjusts the sampling parameters of each region based on a preset list of key equipment region coordinates. Key regions include weld seams, electrode contact areas, and heat exchanger surfaces; the physical characteristics of these regions determine their importance in thermal management. Weld seams, due to material discontinuities, are prone to localized overheating and are therefore assigned the highest priority for monitoring. Electrode contact areas may experience contact resistance heating due to current concentration effects, requiring close monitoring. The thermal conductivity of the heat exchanger surface directly affects overall system performance and is also listed as a key monitoring target. For these key regions, the spatial resolution adjuster sets the sampling mode to high resolution, with a single pixel corresponding to an actual size of 0.5 mm and a sampling frequency maintained at 30 Hz, ensuring the capture of rapidly changing thermal dynamics. Non-critical regions use a low-resolution sampling mode, with a single pixel corresponding to an actual size of 2 mm and a sampling frequency reduced to 10 Hz, optimizing system resource utilization while ensuring basic monitoring requirements are met.

[0024] The multispectral imaging sensor array employs an optimal coverage strategy, determining sensor positions and angles based on the device's three-dimensional geometry. For planar areas, sensors are arranged with orthogonal perspectives; for curved surfaces and complex structures, a multi-angle cross-observation approach is used. Each sensor node is equipped with an independent preprocessing unit to perform noise reduction, non-uniformity correction, and geometric distortion correction on the raw images. The preprocessed image data is transmitted to the central processing unit via a high-speed data interface, maintaining strict time synchronization during transmission. Timestamp information is generated by a high-precision clock module with microsecond-level accuracy, ensuring the timing accuracy of subsequent analysis. Spatial coordinate information uses the device's body coordinate system, mapping each pixel to its three-dimensional spatial position on the device through a pre-calibrated sensor extrinsic parameter matrix.

[0025] The image acquisition unit's workflow is divided into an initialization phase and an operation phase. During initialization, the system loads the device's 3D model and a list of key area coordinates, establishing a spatial resolution configuration mapping table. Simultaneously, sensor calibration is performed, including white balance adjustment, radiometric calibration, and geometric calibration. The operation phase employs a cyclic acquisition mode, with each acquisition cycle comprising four steps: sensor triggering, image acquisition, preprocessing, and data encapsulation. The duration of the acquisition cycle is dynamically adjusted according to the current sampling mode: 33 milliseconds in high-resolution mode and 100 milliseconds in low-resolution mode. The encapsulated multispectral image sequence contains data from three channels: visible light, near-infrared, and long-wave infrared. Each channel's data packet includes a complete timestamp and spatial coordinate metadata.

[0026] The data transmission employs a layered architecture. The bottom layer is the physical layer connection, using a gigabit Ethernet interface to ensure transmission bandwidth. The middle layer implements data compression and encryption, employing lossless compression algorithms to reduce data volume while using AES encryption to ensure data security. The upper-layer application protocol defines the format and transmission rules of data packets, including start identifiers, data lengths, checksums, and other information. This design ensures both real-time data transmission and meets the reliability requirements of industrial environments. The image acquisition unit also has self-diagnostic capabilities, continuously monitoring the operating status of each sensor, including parameters such as temperature drift, signal noise levels, and power supply stability. When an anomaly is detected, it automatically triggers a calibration process or switches to a backup sensor to ensure continuous data acquisition.

[0027] The system supports remote configuration and management, allowing operators to adjust sampling parameters in real time via a network interface. Adjustable parameters include sampling area division, resolution settings, and acquisition frequency. All configuration changes are recorded in the operation log for easy analysis and auditing. The interface between the image acquisition unit and the subsequent processing module uses a standardized data format, including structured encapsulation of image data, time information, and spatial information. This design provides the system with excellent scalability and compatibility with sensor devices from different manufacturers.

[0028] In terms of hardware implementation, the multispectral imaging sensor array employs industrial-grade packaging, providing dustproof, moisture-proof, and electromagnetic interference-resistant characteristics. The sensor bracket features an adjustable design, allowing for fine-tuning to accommodate different models of thermoelectric equipment. The power supply system employs a redundant design, with the main power supply and backup battery operating simultaneously to ensure continuous operation in the event of an external power failure. The heat dissipation system is specially optimized to ensure stable sensor operation in high-temperature environments. The control logic of the spatial resolution adjuster is implemented using an FPGA, providing hardware-level real-time response capabilities. All control parameters are stored in non-volatile memory to prevent data loss during power outages.

[0029] The performance optimization of the image acquisition unit is mainly reflected in three aspects: data acquisition efficiency, resource utilization, and system reliability. Through a dynamic resolution adjustment strategy, the overall data volume is significantly reduced while ensuring monitoring accuracy in key areas. An intelligent triggering mechanism automatically increases the sampling frequency of relevant areas when abnormal temperatures are detected. The data preprocessing algorithm has been optimized and is implemented in real-time on the embedded processor. The system resource monitoring module dynamically allocates computing and storage resources, prioritizing the execution of critical tasks. Reliability design includes fault self-recovery mechanisms, data integrity verification, and hardware redundancy configuration to ensure stable operation in harsh industrial environments.

[0030] Example 2: See Figure 3The thermal field reconstruction module, as the core processing unit of the system, is responsible for converting multispectral image sequences into three-dimensional temperature field distributions with practical physical meaning. This module consists of a temperature field inversion engine and anomaly region markers, achieving the transformation from raw image data to a thermal field representation through a multi-stage processing flow. The temperature field inversion engine receives multispectral image sequences from the image acquisition unit, containing synchronously acquired information in the visible, near-infrared, and long-wave infrared bands. Each data packet includes a precise timestamp and spatial coordinate information, providing the necessary spatiotemporal reference for subsequent three-dimensional reconstruction. The inversion process first registers and fuses the multispectral data, eliminating geometric deviations between different sensors and establishing a unified coordinate framework.

[0031] The temperature field reconstruction is based on heat conduction theory, considering the thermal conductivity of the equipment materials, boundary conditions, and the principle of energy conservation. The inversion algorithm employs an iterative optimization strategy, gradually correcting the temperature distribution assumptions by comparing the calculated temperature field with measured radiation data. The calculation process considers the anisotropic characteristics of the equipment structure, using differentiated thermophysical parameters for different regions such as metal components, insulating materials, and connection interfaces. Three-dimensional modeling uses a voxel-based discretization method, dividing the equipment space into millions of tiny volumetric units, with the temperature value of each unit solved through matrix operations. The solver employs sparse matrix storage and parallel computing techniques, significantly improving the efficiency of large-scale computations.

[0032] The anomaly region marker intelligently analyzes the reconstructed 3D temperature field to identify potential fault hotspots. The marking process is based on preset temperature gradient thresholds and hotspot area thresholds, both dynamically adjusted according to the equipment's safe operating specifications. The temperature gradient threshold reflects the severity of local overheating, triggering an alert when the temperature change rate of a region exceeds a set limit. The hotspot area threshold distinguishes between local anomalies and systemic temperature rises, avoiding misjudging normal operating fluctuations. The marking algorithm first searches for connected regions in the 3D temperature field that meet the threshold conditions, then calculates the geometric center, volume, and surface shape features of each anomaly region. Boundary detection employs an improved MarchingCubes algorithm to accurately extract the 3D geometric representation of isothermal surfaces.

[0033] The generation process of the dynamic thermal field distribution map integrates temperature field data and anomaly marker information. The map employs multi-layer visualization technology. The base layer displays the overall temperature distribution of the device, using pseudo-color mapping to convert temperature values ​​into intuitive color displays. Overlay layers mark anomalous areas with semi-transparent highlights, while also displaying temperature gradient vectors and heat flow directions. The time dimension is represented through animation sequences, showcasing the dynamic evolution of the temperature field. The map output format supports standard 3D graphics interfaces and is compatible with various engineering analysis software. Data compression algorithms keep the output data volume within a reasonable range while preserving key information.

[0034] The module's real-time performance is achieved through a multi-stage pipeline architecture. Data preprocessing, temperature field calculation, anomaly detection, and visualization generation operate in parallel, with each processing stage equipped with an independent buffer queue. The task scheduler dynamically allocates processing tasks based on the load of computing resources, prioritizing the timeliness of the critical path. Hardware acceleration technologies are applied to computationally intensive processes, including GPU parallel computing and FPGA hardware logic implementation. Memory management employs an intelligent caching strategy, retaining frequently accessed temperature field data in a high-speed cache to reduce data access latency.

[0035] The system's robustness is reflected in its error detection and recovery mechanisms. Input data undergoes rigorous validation, including range checks, continuity verification, and physical plausibility assessments. The calculation process is subject to multiple monitoring steps; when numerical instability or convergence anomalies are detected, a re-initialization process is automatically triggered. Anomaly flags undergo logical consistency checks to eliminate false alarms caused by noise or artifacts. The status information of all processing stages is recorded in real time, forming a complete processing log for quality traceability.

[0036] The module's configuration flexibility meets the needs of different application scenarios. Temperature field inversion parameters can be adjusted via configuration files, including grid density, iteration count, and convergence conditions. Anomaly detection thresholds can be dynamically modified at runtime to adapt to safety standards at different stages of equipment operation. The display style of the output map is customizable, including color gradation range, annotation method, and viewing angle settings. The remote management interface allows authorized users to adjust parameters and obtain intermediate calculation results online.

[0037] The interface design with upstream and downstream modules emphasizes data consistency and timing accuracy. The input interface employs a triggered synchronization mechanism to ensure time alignment of the multispectral image sequences. The output interface contains complete data description information, enabling the feature extraction module to accurately understand the physical meaning of the temperature field. A status feedback channel transmits processing progress and resource usage in real time, facilitating overall system coordination. The data format adheres to open standards, supporting seamless integration with other intelligent monitoring systems.

[0038] Performance optimization measures are implemented throughout the entire processing flow. At the algorithm level, adaptive mesh refinement technology is employed to automatically improve computational accuracy in critical areas. Memory access patterns are optimized to reduce cache misses and bus conflicts. Computational tasks are decomposed considering hardware characteristics to fully leverage the parallel capabilities of multi-core processors. Input / output bandwidth is precisely calculated to avoid becoming a system bottleneck. Energy efficiency is improved through dynamic voltage and frequency adjustment technology, reducing power consumption while maintaining performance.

[0039] The module's scalability is designed to meet future needs. The computing framework supports plug-in integration of new inversion algorithms, maintaining core architectural stability while enabling functional evolution. Hardware interfaces are designed with upgrade potential, ensuring compatibility with higher-performance acceleration devices. Data processing capabilities are designed with ample margin to adapt to the monitoring needs of larger-scale devices. The communication protocol supports extended fields, facilitating the transmission of newly added analysis results and status information.

[0040] The thermal field reconstruction module demonstrated stable processing capabilities during actual operation. The input rate of the multispectral image sequence remained synchronized with the image acquisition unit, and processing latency was kept within a reasonable range. The update frequency of the three-dimensional temperature field met real-time monitoring requirements, and anomaly detection results were promptly transmitted to subsequent modules. Resource utilization remained balanced, preventing excessive computational load from affecting the overall system response. The quality and consistency of the output data underwent rigorous verification, providing a reliable basis for safety decisions. The module's operational status was continuously monitored, and various performance indicators were recorded in the system log for long-term performance analysis and optimization.

[0041] Example 3: The feature extraction module extracts feature information of anomalous temperature regions from the dynamic thermal field distribution map and transforms it into a structured data representation usable by the safety policy generation module. This module includes a partitioned feature processor and a vector fusion unit, which characterizes thermal anomalies through multi-dimensional feature analysis and data fusion. The partitioned feature processor receives the dynamic thermal field distribution map from the thermal field reconstruction module. This data is organized in three-dimensional volumetric data form, containing spatial coordinate information and a temperature value matrix. The processing first extracts an independent dataset for each anomalous high-temperature region based on the boundary coordinates provided by the anomaly region marker. The extraction operation uses a spatial pruning algorithm to generate local thermal field data blocks containing the anomalous region and its surrounding environment. The size of the data blocks is adaptively adjusted according to the size of the anomalous region to ensure that complete temperature gradient change information is included.

[0042] The temperature gradient curve is calculated based on the spatial differentiation of local thermal field data blocks. For each anomalous region, the processor establishes a polar coordinate system with the hotspot center as the origin, and sets sampling lines at equal angular intervals along the radial direction. The temperature change on each sampling line is smoothed using cubic spline interpolation to eliminate the influence of measurement noise. The gradient calculation uses the central difference method, with the step size automatically determined according to the data resolution. The temperature gradient curve is ultimately represented as a normalized gradient magnitude distribution function, reflecting the heat conduction characteristics of the anomalous region from the center to the edge. The geometric morphology analysis of the hotspot uses image processing methods to convert the three-dimensional temperature field data into a two-dimensional isothermal surface projection. Morphological features include area A, perimeter P, compactness C, and asymmetry S, where compactness C is calculated using the following formula:

[0043] Where A represents the area of ​​the isothermal surface on the projection plane, and P represents the perimeter of the isothermal surface. The compactness C ranges from 0 to 1, reflecting how close the hot spot shape is to an ideal circle. The asymmetry S is obtained by calculating the second moment of the isothermal surface relative to its principal axis, quantifying the degree of deviation of the hot spot shape from symmetry. The thermal diffusion rate parameter is obtained through time series analysis, with the processor comparing the evolution of anomalous regions across multiple consecutive time frames. The diffusion rate calculation considers three dimensions: radial expansion rate, temperature rise rate, and volume change rate. The time difference employs an adaptive window size, using a smaller time window during rapid changes to improve temporal resolution.

[0044] The vector fusion unit integrates the extracted multi-dimensional features into a unified feature vector representation. The fusion process consists of three steps: feature selection, normalization, and dimensionality reduction. Feature selection is based on information entropy evaluation, retaining features with high discriminative power. Normalization uses deviation standardization to map features of different dimensions to the [0,1] interval. Dimensionality reduction uses principal component analysis to convert related features into linearly independent principal components through orthogonal transformation. The final generated multi-dimensional anomalous temperature region feature vector contains 15 feature dimensions, divided into three groups according to their physical meaning: the temperature gradient feature group includes the radial gradient mean, maximum gradient, and gradient change entropy; the geometric morphology feature group includes area, perimeter, compactness, asymmetry, and surface curvature; and the thermal diffusion feature group includes radial expansion rate, temperature rise rate, volume change rate, diffusion directionality, and historical evolution trend index.

[0045] The module's real-time processing capability is achieved through a pipelined architecture. The feature extraction task is divided into four stages: data preparation, gradient calculation, morphological analysis, and diffusion evaluation, with each stage executed in parallel. The data cache adopts a circular buffer design, supporting multi-threaded concurrent access. Computational resources are dynamically allocated, prioritizing the processing time of the critical path. Hardware acceleration technology is applied to matrix operations and image processing, significantly improving computational efficiency. Memory access patterns have been optimized to reduce cache invalidation and bus contention.

[0046] A quality control mechanism is implemented throughout the entire processing flow. Input data validation includes range checks, continuity verification, and physical plausibility assessment. Feature calculation results undergo consistency checks to eliminate erroneous outputs caused by data anomalies. Intermediate results are saved as log files, supporting post-processing analysis and problem tracing. Processing status is monitored in real time, and automatic recovery procedures are triggered in case of anomalies. Module resource usage is continuously recorded, including metrics such as memory consumption, CPU utilization, and processing latency.

[0047] The configuration flexibility meets the needs of different application scenarios. Feature selection parameters can be adjusted via configuration files to adapt to the monitoring requirements of various thermoelectric devices. The normalization range can be modified at runtime, facilitating parameter integration with subsequent modules. Dimensionality reduction can be dynamically adjusted based on computing resources, balancing processing accuracy and efficiency. The debugging interface allows outputting intermediate feature values ​​for algorithm verification and optimization. The remote management protocol supports online parameter updates and status queries.

[0048] The interface design with other modules in the system emphasizes data integrity and timing consistency. The input interface maintains data format compatibility with the thermal field reconstruction module, automatically parsing the 3D temperature field data structure. The output interface adopts a standardized feature vector format, including feature dimension descriptions and numerical range information. A status feedback channel transmits processing progress and resource usage information, facilitating overall system coordination. An error reporting mechanism ensures that problems are promptly relayed to upstream modules.

[0049] Performance optimization measures are designed to address the computational characteristics of feature extraction. Temperature gradient calculation employs an approximation algorithm for acceleration, reducing computational load while maintaining accuracy. Morphological analysis optimizes the isothermal surface extraction algorithm, avoiding the time-consuming 3D reconstruction process. Diffusion assessment incorporates a predictive model, reducing the complexity of time series analysis. Memory management utilizes object pooling technology to reduce dynamic memory allocation overhead. Parallel computing tasks are rationally partitioned to fully utilize multi-core processor resources.

[0050] The module's scalability design is geared towards functional evolution and performance improvements. The algorithm framework supports plug-in integration of new feature extraction methods without affecting existing processing flows. The computing resource interface reserves expansion capabilities, allowing access to higher-performance acceleration hardware. The data channel bandwidth is designed with sufficient margin to accommodate future demands for higher-precision input data. The communication protocol defines extended fields to facilitate the transmission of newly added feature information.

[0051] In actual operation, the feature extraction module demonstrated stable processing performance. The input rate of the dynamic thermal field distribution map remained synchronized with the upstream module, and the feature vector output delay was controlled within the system requirements. The representation capability of multi-dimensional features met the decision-making needs of security policy generation, and the extraction accuracy of key feature terms reached the expected level. Resource consumption remained within a reasonable range, without affecting the overall real-time performance of the system. The quality and consistency of the output data were rigorously verified, providing a reliable foundation for subsequent analysis. The module's operating status was continuously monitored, and performance indicators were recorded to form a complete quality archive.

[0052] An exception handling mechanism ensures the reliable operation of the module. Input data anomalies trigger an automatic correction process, including data repair and recalculation. Checkpoints are saved during processing interruptions, supporting recovery from the most recent state. Upon detection of a hardware failure, a backup computing unit is switched on to maintain basic functionality. All exception events are logged in detail, including the time of occurrence, error type, and recovery measures.

[0053] The module's maintainable design facilitates long-term use and upgrades. Parameter configurations use human-readable text format and support comments. The algorithm implementation is modularly encapsulated with clearly defined interfaces. Test interfaces expose key internal states for easy problem diagnosis. The documentation system comprehensively records design principles, usage methods, and maintenance points. The version management mechanism tracks code change history and supports feature rollback.

[0054] Adaptation to specific equipment characteristics is achieved through parameterization. The material thermophysical property parameter library contains characteristic data for common thermoelectric equipment, and users can expand and customize entries. Feature threshold settings differentiate between equipment types and operating states, avoiding a one-size-fits-all judgment standard. Region weight configuration reflects the differences in importance of different parts, guiding the focus of feature extraction. The dynamic adjustment strategy automatically updates the parameter baseline based on equipment aging.

[0055] The module's computational accuracy is ensured through multiple measures. Floating-point operations employ high-precision data types, keeping accumulated errors within acceptable limits. Iterative calculations are configured with convergence conditions to avoid infinite loops. Numerical stability detection identifies ill-conditioned problems and triggers special processing procedures. Key algorithms undergo mathematical verification to guarantee theoretical correctness. A reference data comparison mechanism periodically verifies the calculation results.

[0056] The user interface provides an intuitive operating experience. The status display uses a graphical dashboard to clearly show processing progress and resource usage. The parameter adjustment interface is logically organized, highlighting important parameters. Context-sensitive prompts are integrated into the help system to reduce the learning curve. Historical records support conditional queries and trend analysis. The report generation function automatically organizes runtime data.

[0057] The module's security design meets industrial system requirements. Data access is subject to access control, and sensitive operations require authentication. Communication links are encrypted to prevent information leakage. Input data integrity is verified to resist malicious tampering. System resources are isolated to avoid mutual interference. Audit logs record critical operations, supporting accountability.

[0058] Environmentally adaptable design ensures reliable operation. Temperature monitoring circuitry prevents overheating damage. Power supply filtering eliminates the effects of voltage fluctuations. Electromagnetic shielding reduces interference signals. Dustproof and moisture-proof packaging adapts to industrial environments. Shock-resistant fixing measures ensure safe transportation.

[0059] The module testing and verification employs a multi-level approach. Unit tests cover all algorithm branches. Integration tests verify the interaction between modules. Performance tests confirm real-time performance metrics. Stress tests evaluate performance under extreme conditions. Long-term operational tests observe changes in stability.

[0060] Maintenance tools support convenient service operations. A remote diagnostic interface allows experts to analyze problems. A firmware update mechanism simplifies version upgrades. Configuration backup prevents parameter loss. A self-test program quickly locates faulty components. Calibration tools maintain measurement accuracy.

[0061] Comprehensive lifecycle management for modules. Requirements analysis documents define functional boundaries. Design specifications describe implementation plans. Test reports verify performance metrics. User manuals guide correct operation. Maintenance records track service history. Decommissioning procedures standardize disposal methods.

[0062] Conformity to industry standards has been verified. Data format is compatible with common engineering software. Communication protocol follows industrial bus specifications. Safety requirements meet relevant certification standards. Electromagnetic compatibility has passed professional testing. Environmental adaptability meets equipment-grade standards.

[0063] The module's innovations are reflected in several aspects. Temperature gradient feature extraction employs polar coordinate analysis, better describing the radial characteristics of hot spots. Geometric morphology evaluation introduces three-dimensional curvature features, providing a more comprehensive picture than traditional two-dimensional projection. Thermal diffusion analysis combines temporal and spatial dimensions to capture dynamic evolution patterns. The feature fusion method preserves physical meaning while achieving data compression. The real-time processing architecture balances computational accuracy and speed.

[0064] Example 4: See Figure 4 The safety policy generation module receives multidimensional abnormal temperature region feature vectors from the feature extraction module and transforms them into specific equipment control commands. This module consists of a risk level classifier and a command decision tree, achieving intelligent decision-making for safety control through hierarchical evaluation and policy mapping. The risk level classifier analyzes the numerical patterns of the input feature vectors and matches them with a predefined fault feature library. The classification process considers key indicators such as the radial gradient mean of the temperature gradient feature group, the compactness of the geometric shape feature group, and the temperature rise rate of the thermal diffusion feature group. When the radial gradient mean of an abnormal region exceeds a set threshold, and the compactness is lower than the standard value while the temperature rise rate continues to increase, the classifier determines that there is a risk of thermal runaway in that region.

[0065] The risk level is divided into three levels, each corresponding to a different response strategy. Level 1 risk indicates an initial anomaly, characterized by a slightly excessive local temperature gradient but with a regular shape and controllable diffusion rate. Level 2 risk indicates a developing fault, characterized by a significantly increased temperature gradient, distorted hotspot shape, and accelerated diffusion rate. Level 3 risk indicates an emergency state, characterized by an extreme temperature gradient, severely deformed hotspots, and a sharp increase in diffusion rate. When the classifier outputs a risk level label, it simultaneously generates a confidence score, reflecting the reliability of the judgment result. When multiple feature indicators contradict each other, the confidence score decreases accordingly, triggering a review mechanism.

[0066] The decision tree generates specific safety control instruction sets based on the risk level and the current operating status of the equipment. The decision-making process first reads the equipment's real-time operating parameters, including power output, cooling system operating status, and load current level. For Level 1 risks, the decision tree generates preventative control instructions, including moderately increasing the cooling fan speed, increasing coolant circulation, and fine-tuning the power output. Instruction parameters are personalized based on the equipment model and operating history to avoid over-response affecting normal operation. Level 2 risks trigger more proactive intervention measures. In terms of cooling control, the decision tree specifies target temperature values, requiring the system to reduce the temperature in abnormal areas to a safe range within a limited time; in terms of power regulation, it sets a clear percentage reduction in power and simultaneously activates the backup cooling unit's contingency plan.

[0067] Level 3 risk corresponds to an emergency response procedure. The instruction set includes commands to immediately reduce power output, instructions to activate all available cooling resources, and requests to send alarm signals to the monitoring center. When generating Level 3 risk instructions, the decision tree simultaneously initiates a device self-check procedure to identify potential hardware faults that could lead to thermal runaway. All instructions include execution time requirements to ensure timely control actions. The decision tree supports conditional branching logic; when multiple abnormal regions are detected interacting, a coordinated control strategy is generated after comprehensive evaluation to avoid system instability caused by localized interventions.

[0068] The execution unit translates safety control commands into specific equipment operations. Upon receiving the commands, the cooling control interface sends control signals to the cooling system via a digital communication protocol. For air-cooled units, the interface outputs PWM speed control signals to precisely adjust the speed distribution of fans in each zone. The liquid cooling system receives flow regulation commands and controls the coolant flow distribution in different branches via proportional valves. The interface monitors the execution effect in real time and feeds back the actual speed and flow rate to the decision-making system. The power regulator employs a closed-loop control method, changing the operating point of the thermoelectric conversion device in stages according to the power adjustment range required by the commands. The adjustment process considers grid demand and equipment capacity to avoid secondary problems caused by sudden power changes.

[0069] An operation completion confirmation mechanism ensures the reliability of command execution. After reaching the target parameters, the cooling control interface sends a confirmation signal containing actual operating data to the closed-loop optimization module. The signal details the final status of each execution terminal, including measured fan speed, coolant flow rate, and valve opening position. After confirming that the output power is stable within the target range, the power regulator sends a completion report containing current power generation, voltage waveform quality, and efficiency indicators. All confirmation signals are timestamped precisely, establishing a clear correspondence with the original command.

[0070] An anomaly handling process ensures the safety and reliability of the execution process. When the cooling control interface detects an execution deviation exceeding the allowable range, a compensation mechanism is automatically activated. If the fan speed is below target, the interface will increase the auxiliary airflow of adjacent fans according to a preset strategy. Abnormal coolant flow triggers a backup piping switching procedure to ensure cooling effectiveness in critical components. When the power regulator encounters execution resistance, a gradual adjustment strategy is adopted, approximating the target value through multiple small corrections. Severe anomalies trigger an emergency stop protocol, interrupting the current command flow and reporting fault details.

[0071] System coordination is achieved through a state synchronization mechanism. The security policy generation module continuously receives device state updates from the execution units and dynamically adjusts subsequent instructions. When the control effect is detected to be unsatisfactory, the decision tree reassesses the risk level and generates enhanced intervention measures. Data exchange between modules uses a unified time base to ensure the timeliness of state judgments. Historical instructions and execution results are stored in a circular buffer, supporting backtracking analysis and policy optimization.

[0072] The module's adaptability is reflected in multiple design considerations. Risk level thresholds can be adjusted online to accommodate changes in characteristics due to equipment aging. The instruction parameter library can be categorized and managed by equipment model, facilitating support for new models. The execution interface protocol features an abstract design, ensuring compatibility with device controllers from different manufacturers. The debug mode allows for step-by-step instruction execution, facilitating the verification of strategy logic. The simulation operation function allows for offline testing of strategy effectiveness, reducing the risks of on-site debugging.

[0073] In actual operation, the module demonstrated flexible problem-solving capabilities. Faced with localized hotspots, the system could precisely control the cooling resources of the corresponding area, avoiding global performance loss. When multiple anomalies occurred, the coordination strategy generated by the decision tree effectively prevented fault propagation. The gradual power regulation maintained the stability of the grid connection. Rapid identification and compensation of anomalies minimized downtime. Real-time feedback on execution status provided maintenance personnel with a clear system view.

[0074] The module's reliability is ensured through multiple mechanisms. Validation checks on input feature vectors prevent erroneous data from influencing judgments. A risk classification review process reduces the possibility of misjudgments. Verification of the rationality of generated instructions avoids contradictory operations. Monitoring and compensation during execution ensure the expected results are achieved. Rapid isolation of fault conditions prevents the impact from escalating. All critical operations are logged in detail, supporting post-event analysis.

[0075] Ease of maintenance is reflected in several design details. The risk level determination logic is visualized, facilitating understanding of the system's decision-making process. Command history can be filtered and queried by time, device region, or risk level. Centralized monitoring of execution interface status enables quick identification of communication problems. The parameter adjustment interface provides suggested reasonable values ​​to prevent invalid settings. The system supports importing and exporting configuration files for convenient batch updates.

[0076] The module's innovation lies in its intelligent decision-making methodology. Dynamic adjustment of risk levels adapts to the actual state of the equipment, avoiding misjudgments caused by fixed thresholds. Command decisions consider the coupling effects of multiple factors, generating an overall optimal strategy. An execution compensation mechanism achieves closed-loop control, improving response accuracy. Coordinated control strategies prevent system problems caused by local optimization. State synchronization design ensures decisions are based on the latest information.

[0077] The module's application value is demonstrated in real-world scenarios. A tiered response mechanism achieves a balance between safety and economy. Precise cooling control extends the lifespan of critical equipment components. Intelligent power regulation maintains power generation efficiency while ensuring safety. Rapid anomaly handling reduces unplanned downtime losses. Comprehensive condition monitoring provides a basis for preventative maintenance.

[0078] The module's scalability supports future development. New risk pattern identification algorithms can be implemented by updating the feature library. More refined cooling control strategies are supported by expanding the instruction set. New power regulation methods are integrated through the interface adaptation layer. Additional safety constraints are added through decision tree branches. Performance improvements are achieved through expanded computing resources.

[0079] The module's human-computer interaction design prioritizes ease of operation. Risk status is displayed intuitively using color coding. Instruction details can be viewed by clicking to see complete parameters. Execution progress is presented as a percentage and estimated time. Abnormal situations trigger prominent prompts and recommended handling suggestions. Historical data supports graphical comparison and analysis.

[0080] The module testing and verification employs a comprehensive coverage approach. Unit tests verify the accuracy of each classification rule. Integration tests check the correctness of data flow between modules. Scenario tests simulate the handling of various anomalies. Stress tests verify responsiveness under high load. Long-term runtime tests observe the stability of decision-making.

[0081] The module's security design complies with industry standards. Hierarchical access control is implemented, with critical commands requiring authorization. Communication data is encrypted during transmission to prevent information leakage. System resource access control prevents unauthorized modifications. Complete operation logs are maintained, supporting security auditing. Secure fault recovery prevents system crashes.

[0082] The module is specifically designed for environmental adaptability. Industrial-grade hardware resists electromagnetic interference. Wide-temperature-range components adapt to various climates. Dustproof and moisture-proof packaging protects the internal circuitry. Shock-resistant mounting accommodates mechanical vibration. Redundant power supply design ensures continuous power supply.

[0083] A complete and standardized lifecycle management system for modules is in place. Requirements documents clearly define the functional scope. Design specifications detail the implementation architecture. Test reports record the verification process and results. User manuals guide daily operation and maintenance. Service records track repair and upgrade history. The disposal process complies with environmental protection requirements.

[0084] The module's compliance with industry standards has been verified. Risk level classification references safety specifications for thermal equipment. Control command format is compatible with industrial communication protocols. Safety requirements meet functional safety certification. Performance indicators meet real-time control system standards. Interface design follows modular architecture principles.

[0085] The module demonstrated stable decision-making quality during actual operation. The accuracy of risk assessment was supported by on-site data. The execution of generated instructions met expected objectives. Anomalies were handled promptly and effectively. System resource utilization remained at a reasonable level. The workload of maintenance personnel was significantly reduced.

[0086] Continuous improvement mechanisms drive performance enhancements. Operational data analysis identifies optimization opportunities. User feedback is incorporated into feature enhancement plans. New technology evaluations guide architectural evolution. Failure case studies refine decision-making logic. Industry standard updates drive compliance improvements.

[0087] Example 5: The closed-loop optimization module continuously monitors equipment status changes and adjusts system parameters accordingly, achieving self-optimization and dynamic adaptation of the monitoring system. This module consists of a status monitoring network and a parameter optimization engine, forming a complete feedback loop from performance evaluation to data acquisition strategy adjustment. Immediately after the execution of safety control commands, the status monitoring network initiates a multi-dimensional data acquisition process, acquiring the equipment surface temperature change rate, internal stress fluctuation data, and output current stability indicators through a distributed sensor array. Temperature change rate monitoring uses a high-response infrared sensor network, tracking the cooling effect of key areas at a sampling frequency of 20 times per second. Stress fluctuation data is acquired through a piezoelectric sensor array, with a measurement frequency up to 1kHz, capturing the dynamic response of the equipment structure to thermal stress. Current stability monitoring uses isolated Hall effect sensors to calculate the ripple coefficient and three-phase imbalance of the output current in real time. All sensor data are acquired synchronously, with time alignment accuracy controlled within milliseconds.

[0088] The parameter optimization engine performs multi-dimensional comparative analysis of the collected monitoring data with preset safety threshold ranges. The safety threshold for temperature drop rate is dynamically calculated based on the equipment material characteristics and heat capacity parameters, forming a desired cooling curve that changes over time. The tolerance range for stress fluctuation amplitude considers the fatigue characteristics of the equipment structure and the current cumulative damage, using a dynamically adjusted envelope judgment standard. The current stability threshold combines grid connection requirements and equipment rated parameters to set graded early warning ranges. The comparison results generate thermal field reconstruction parameter adjustment instructions, which include two types of core parameters: key area coordinate correction values ​​and temperature gradient threshold update values. Key area coordinate correction is based on the spatial distribution of stress fluctuation exceeding limits, adding or deleting monitoring points from the original key area list, with the adjusted point density proportional to the stress risk level. The temperature gradient threshold update uses a sensitivity coefficient adjustment mechanism; when the temperature drop rate is consistently lower than expected, the sensitivity coefficient automatically increases, correspondingly reducing the gradient judgment threshold.

[0089] Adjustment commands are pushed to the thermal field reconstruction module in real time via a distributed message queue. Message transmission uses a lightweight binary protocol and includes fields such as command type, effective time, parameter list, and checksum. The critical area coordinate correction message details the 3D coordinates, monitoring radius, and sampling frequency requirements of the newly added points. The temperature gradient threshold update message includes the new thresholds and transition time parameters corresponding to each risk level, supporting smooth transitions and avoiding abrupt changes. The message queue employs a priority management strategy; updates to safety-critical parameters are sent immediately, while routine optimization commands are processed under low system load. End-to-end verification is implemented during transmission to ensure complete and accurate delivery of commands.

[0090] System communication facilitates data exchange between modules via a real-time communication bus. The bus employs a time-division multiplexing protocol, dividing the communication cycle into fixed-length time slots allocated to six types of information: image data, thermal field maps, feature vectors, control commands, feedback data, and parameter update commands. Image data transmission time slots are allocated maximum bandwidth to ensure high-fidelity transmission of raw data. Control command time slots are given the highest priority to ensure timely delivery of safety control commands. Parameter update time slots utilize an acknowledgment and retransmission mechanism to ensure the reliability of critical configuration changes. The bus controller dynamically adjusts the time slot allocation ratio to adapt to communication requirements under different operating conditions. Transmission latency is strictly controlled within 100 milliseconds to meet the time constraints of real-time control.

[0091] Historical data is stored in a distributed storage cluster and organized using a time-series database. The storage architecture is layered by data type. Raw image data is compressed and stored in a cache layer, retaining high-frequency sampling data from the most recent 6 hours. Thermal field distribution maps and feature vectors are stored in the performance layer, using a columnar storage format to optimize analysis and query efficiency. Device status logs and system events are stored in the capacity layer, supporting long-term trend analysis. Data partitioning is based on both device region and time range dimensions, accelerating spatial and temporal correlation queries. All data entries are appended with precise timestamps and device identifiers, forming a complete spatiotemporal index. The storage system implements regular backups and consistency checks to prevent data loss or corruption.

[0092] The module's adaptive capabilities are reflected in multiple design features. The dynamic adjustment mechanism of the monitoring strategy automatically switches the focus of data acquisition based on the equipment's operational phase: emphasizing temperature rise monitoring during startup, monitoring minute fluctuations during steady-state operation, and strengthening stress monitoring during load changes. Parameter optimization employs an incremental update method, limiting each adjustment to a safe range and approximating the optimal value through multiple iterations. An anomaly detection algorithm automatically identifies sensor fault data, triggering a self-healing process to switch to backup sensing channels. The resource management module balances the computational load, automatically reducing the priority of non-critical tasks during periods of high system load.

[0093] In actual operation, the closed-loop optimization module exhibits continuously improving system characteristics. Dynamic adjustment of key area coordinates ensures monitoring resources are always focused on the most critical areas. Adaptive updating of temperature gradient thresholds improves the accuracy of anomaly detection, reducing false alarms and missed alarms. Dynamic scheduling of the communication bus guarantees timely transmission of various data types, avoiding bottlenecks. Effective management of historical data supports long-term performance analysis and fault diagnosis. The entire feedback loop operates stably, forming a virtuous cycle of continuous optimization.

[0094] The module's reliability is ensured through multiple mechanisms. The sensor network employs a redundant design, with multiple independent measurement channels for critical parameters. Data transmission undergoes cyclic redundancy check to detect and correct communication errors. Rollback points are set during parameter updates, allowing recovery to a stable version in case of anomalies. The storage system implements RAID protection to prevent data loss due to disk failures. System status is continuously monitored, triggering alerts and recording detailed logs for any abnormal situations.

[0095] Ease of maintenance is reflected in several design details. A historical visualization of monitoring point adjustments provides an intuitive overview of optimization processes. Complete parameter change records are maintained, including modification time, reason for modification, and comparison of old and new values. Sensor calibration status is centrally monitored, with reminders for due calibration tasks. A communication quality dashboard displays real-time latency and packet loss rates for each channel. Data storage is visualized for convenient capacity planning.

[0096] The module's innovation lies in its closed-loop optimization method. Dynamic adjustment of key areas enables intelligent allocation of monitoring resources. Automatic adjustment of sensitivity coefficients keeps the system in optimal detection condition. Multi-dimensional threshold dynamic calculation comprehensively considers various constraints of the equipment. Distributed message queues ensure timely and reliable parameter updates. Time-series database optimization supports long-term performance tracking.

[0097] The module's application value has been validated in actual operation. Continuous parameter optimization ensures the system maintains a consistently high-efficiency detection state. Feedback adjustments promptly correct execution deviations, improving control accuracy. Historical data analysis reveals equipment performance degradation trends, supporting predictive maintenance. Optimized communication scheduling ensures real-time transmission of critical data. Dynamic resource management enhances overall system efficiency.

[0098] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0099] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart monitoring system for the safety of thermoelectric power generation based on image recognition, characterized in that, The system includes an image acquisition unit for acquiring multispectral image sequences of the surface and internal structure of thermoelectric equipment; a thermal field reconstruction module for reconstructing the thermal field distribution of the multispectral image sequences; a feature extraction module for extracting feature vectors of abnormal temperature regions from the reconstructed thermal field distribution; a safety strategy generation module for generating safety control commands based on the feature vectors of the abnormal temperature regions; an execution unit for executing the safety control commands; and a closed-loop optimization module for monitoring the execution effect and feeding it back to the thermal field reconstruction module. The image acquisition unit transmits the acquired multispectral image sequences to the thermal field reconstruction module in real time. The thermal field reconstruction module outputs a dynamic thermal field distribution map to the feature extraction module. The feature extraction module transmits the feature vectors of abnormal temperature regions to the safety strategy generation module. The safety strategy generation module sends safety control commands to the execution unit. After the execution unit responds to the commands and performs the operation, the closed-loop optimization module collects equipment status change data and feeds it back to the thermal field reconstruction module for dynamic adjustment of the collected parameters.

2. The intelligent monitoring system for thermal power generation safety based on image recognition according to claim 1, characterized in that, The image acquisition unit includes a multispectral imaging sensor array and a spatial resolution adjuster. The multispectral imaging sensor array simultaneously acquires images of the equipment surface in the visible light band, near-infrared band, and long-wave infrared band. The spatial resolution adjuster, based on a preset list of key equipment area coordinates, assigns a high-resolution sampling mode to the weld area, electrode contact area, and heat exchanger surface area, and uses a low-resolution sampling mode for non-critical areas. The multispectral imaging sensor array combines the synchronously acquired three-band images into a multispectral image sequence, adds a timestamp and spatial coordinate information, and then transmits it to the thermal field reconstruction module.

3. The image recognition-based intelligent monitoring system for thermal power generation safety according to claim 2, characterized in that, The thermal field reconstruction module includes a temperature field inversion engine and an anomaly region marker. After receiving the multispectral image sequence, the temperature field inversion engine generates three-dimensional temperature distribution data through the inverse solution of the heat conduction equation. The anomaly region marker marks the boundary coordinates of abnormal high-temperature regions in the three-dimensional temperature distribution data according to a preset temperature gradient threshold and hot spot area threshold, and converts the three-dimensional temperature distribution data with marked boundary coordinates into a dynamic thermal field distribution map and outputs it to the feature extraction module.

4. The intelligent monitoring system for thermal power generation safety based on image recognition according to claim 3, characterized in that, The feature extraction module includes a partitioned feature processor and a vector fusion unit. After receiving the dynamic thermal field distribution map, the partitioned feature processor extracts the temperature gradient change curve, hot spot geometric features, and thermal diffusion rate parameters for each marked abnormal high temperature region. The vector fusion unit fuses the temperature gradient change curve, hot spot geometric features, and thermal diffusion rate parameters into a multidimensional abnormal temperature region feature vector and transmits the multidimensional abnormal temperature region feature vector to the security policy generation module.

5. The image recognition-based intelligent monitoring system for the safety of thermoelectric power generation according to claim 4, characterized in that, The safety strategy generation module includes a risk level classifier and an instruction decision tree. The risk level classifier matches the feature vector of the multidimensional abnormal temperature region with a preset fault mode database and outputs a hot spot risk level label. The instruction decision tree generates a set of safety control instructions, including the cooling system start-up level, power generation adjustment range, and alarm level, based on the hot spot risk level label and the current operating power parameters of the equipment, and sends the set of safety control instructions to the execution unit.

6. The intelligent monitoring system for thermal power generation safety based on image recognition according to claim 5, characterized in that, The execution unit includes a cooling control interface and a power regulator; the cooling control interface sends a fan speed control signal and a coolant flow rate regulation signal to the cooling device according to the cooling system start-up level in the safety control instruction set; the power regulator adjusts the output power set value of the thermoelectric conversion device according to the power generation regulation range in the safety control instruction set; after executing the instruction, the cooling control interface and the power regulator send an operation completion confirmation signal to the closed-loop optimization module.

7. The image recognition-based intelligent monitoring system for thermal power generation safety according to claim 6, characterized in that, The closed-loop optimization module includes a state monitoring network and a parameter optimization engine. After the operation completion confirmation signal is triggered, the state monitoring network collects the surface temperature change rate, internal stress fluctuation data, and output current stability index of the equipment. The parameter optimization engine compares the surface temperature change rate, internal stress fluctuation data, and output current stability index of the equipment with a preset safety threshold range, generates a thermal field reconstruction parameter adjustment instruction, and feeds it back to the thermal field reconstruction module. The thermal field reconstruction parameter adjustment instruction includes key area coordinate correction values ​​and temperature gradient threshold update values.

8. The intelligent monitoring system for thermal power generation safety based on image recognition according to claim 7, characterized in that, The system also includes a distributed storage cluster and a real-time communication bus; the distributed storage cluster stores the multispectral image sequence, dynamic thermal field distribution map, and multidimensional abnormal temperature region feature vectors in a time-series database structure; the real-time communication bus uses a time-division multiplexing protocol to establish a bidirectional data transmission channel between the image acquisition unit, thermal field reconstruction module, feature extraction module, security policy generation module, execution unit, and closed-loop optimization module to ensure that operation commands and feedback data complete the interaction within a preset time delay.

9. A method for intelligent safety monitoring of thermoelectric power generation based on image recognition, used to implement the intelligent safety monitoring system for thermoelectric power generation based on image recognition as described in any one of claims 1-8, characterized in that, include: Multi-band surface images of thermoelectric equipment are acquired using a multispectral imaging sensor array, and the spatial resolution is dynamically configured based on a list of coordinates of key areas of the equipment. Multi-band surface images are processed using the inverse solution technique of the heat conduction equation to generate three-dimensional temperature distribution data with anomaly region markings; Temperature gradient features, geometric features, and thermal diffusion features of multiple abnormally high-temperature regions are extracted from three-dimensional temperature distribution data and fused into a multi-dimensional feature vector. The hot spot risk level is generated by matching the fault mode database with multidimensional feature vectors, and the cooling control level and power adjustment range are determined by combining the current power parameters. After executing the cooling system control command and the power generation adjustment command, the system monitors the equipment status change data and feeds it back to the thermal field reconstruction process; Based on the deviation analysis results between equipment status change data and safety thresholds, the coordinates of key areas for thermal field reconstruction and the anomaly judgment thresholds are dynamically updated.

10. The intelligent monitoring method for thermoelectric power generation safety based on image recognition according to claim 9, characterized in that, The dynamically updated thermal field reconstruction parameters include: Establish a mapping table between equipment status change data and thermal field reconstruction parameters, where the equipment status change data includes temperature drop rate, stress fluctuation amplitude, and current stability coefficient; The gradient descent algorithm is used to calculate the deviation between the current thermal field reconstruction parameters and the optimal safety performance; When the rate of temperature decrease is lower than a preset threshold, increase the density of monitoring points in the list of key area coordinates. When the stress fluctuation exceeds the tolerance range, adjust the sensitivity coefficient of the temperature gradient threshold. The updated key area coordinates and temperature gradient thresholds are pushed to the thermal field reconstruction module in real time via a distributed message queue.

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