A control system for infrared image transmission and reproduction

Abstract

The application discloses the technical field of infrared image and is specifically a control system for infrared image transmission and reproduction, which comprises an image acquisition module, a data transmission module and an intelligent resource scheduling module; the image acquisition module is used for collecting infrared image data; the data transmission module is used for compressing and transmitting the infrared image data by using an image compression algorithm, and the reliability and safety of transmission can be improved by using a transmission protocol during the transmission process; and the intelligent resource scheduling module is used for sensing data flow changes and dynamically allocating transmission and processing data; by using the JPEG2000 compression algorithm, the application can greatly compress the data volume under the premise of ensuring certain image quality, reduce the data volume of transmission, thereby reducing the transmission time; by using the network adaptive technology, the frame rate and compression ratio can be dynamically adjusted according to the bandwidth, the transmission strategy can be actively optimized when the bandwidth is insufficient, data accumulation caused delay can be avoided, and the fast image transmission can be ensured.

Description

Technical Field

[0001] This invention relates to the field of infrared imaging technology, specifically to a control system for infrared image transmission and reproduction. Background Technology

[0002] With the continuous advancement of technology, infrared imaging technology has been widely applied in numerous fields, such as security monitoring, industrial inspection, medical diagnosis, military defense, and forest fire prevention. In security monitoring, infrared images can clearly capture target objects at night or in low-light environments, effectively ensuring public safety. In industrial inspection, infrared images can detect thermal anomalies in equipment, preventing malfunctions in advance. In medical diagnosis, it can assist doctors in detecting the body's temperature distribution, aiding in disease diagnosis. In the military defense field, infrared images play a crucial role in target recognition and night vision navigation. In forest fire prevention, infrared images can be used to promptly detect potential fire sources. However, the effective application of infrared images relies on accurate transmission and high-fidelity reproduction, which places stringent demands on related control systems.

[0003] The existing control system has the following problems:

[0004] 1. Regarding the transmission stage:

[0005] 1.1 Large Data Volume Leads to Transmission Delay: Infrared images typically contain much more detailed information than ordinary images, resulting in extremely large data volumes. For example, a single frame of a high-resolution infrared camera can generate several megabytes of data. In network environments with limited bandwidth, such as remote areas or areas covered by outdated network infrastructure (where bandwidth may only be tens of Mbps), the transmission of large amounts of infrared image data can lead to severe latency issues. In forest fire monitoring scenarios, excessively long delays in infrared image data transmission can prevent the timely detection of initial fire sources, missing the optimal firefighting window and causing significant losses.

[0006] 1.2 Network instability leading to packet loss: In practical applications, network environments are complex and changeable, with signal interference and network congestion occurring frequently. This can easily lead to packet loss during the transmission of infrared image data. For example, in industrial plants, the presence of numerous electronic devices can strongly interfere with wireless network signals. When inspection robots use wireless networks to transmit infrared images, packet loss due to signal interference can easily occur, preventing the receiving end from obtaining complete image information and affecting the accurate judgment of equipment operating status.

[0007] 1.3 Insufficient Transmission Security: Infrared images contain sensitive information in some applications, such as infrared images of military facilities and infrared images of industrial equipment involving core enterprise technologies. However, the encryption mechanisms of some existing transmission systems are inadequate, making them vulnerable to hacker attacks or data leaks. For example, in some security monitoring systems, if the encryption strength of the transmission channel is insufficient, hackers may intercept and tamper with infrared image data, interfering with normal monitoring operations and even causing serious security risks.

[0008] 2. Regarding the reproduction stage:

[0009] 2.1 Image Distortion Issue: During the reconstruction of infrared images after transmission, image distortion often occurs due to factors such as data loss during transmission, differences in color spaces between different devices, and limitations of image compression algorithms. For example, in medical diagnosis, if distortion occurs during the reconstruction of infrared images, it may lead to misjudgment of the body's temperature distribution by doctors, thereby affecting the accurate diagnosis of diseases.

[0010] 2.2 Resolution Reduction: To accommodate transmission bandwidth or storage requirements, infrared images may undergo resolution reduction during processing. However, this results in the loss of detail in the reproduced image, affecting the identification and analysis of target objects. In the field of security monitoring, low-resolution infrared images may not clearly display the facial features or behavioral details of suspicious individuals, posing difficulties for subsequent investigations.

[0011] 2.3 Display Device Compatibility: Different types of display devices have differences in parameters such as color gamut, brightness, and contrast, which may lead to inconsistent reproduction of infrared images on different devices. For example, portable outdoor display devices may not accurately reproduce subtle temperature differences in infrared images due to their limited brightness and contrast compared to professional indoor monitoring screens, thus affecting users' acquisition of image information.

[0012] Based on the above, a control system for infrared image transmission and reproduction is invented. Summary of the Invention

[0013] To address the aforementioned technical problems, according to one aspect of the present invention, the present invention provides the following technical solution:

[0014] A control system for infrared image transmission and reproduction, comprising:

[0015] The image acquisition module is used to acquire infrared image data;

[0016] The data transmission module is used to compress and transmit infrared image data using an image compression algorithm, and during the transmission process, it can improve the reliability and security of the transmission through a transmission protocol.

[0017] The intelligent resource scheduling module is used to sense changes in data traffic and dynamically allocate data for transmission and processing.

[0018] The data processing module is used to perform noise reduction, non-uniformity correction, and image enhancement processing on infrared image data;

[0019] The dynamic adaptive fusion module is used to automatically adjust the fusion strategy according to environmental changes and task requirements using a dynamic adaptive algorithm;

[0020] The image reproduction module is used to perform color mapping, resolution adaptation, and display device calibration on infrared image data.

[0021] The image reproduction module includes:

[0022] The color mapping conversion module is used to first analyze the infrared image data, then select a color mapping algorithm, and finally use the selected algorithm to convert the infrared image data into colors.

[0023] The resolution adaptation processing module is used to first determine the display capabilities of the device, then calculate the image size, and then use bilinear interpolation or bicubic interpolation algorithms to scale the image.

[0024] The calibration and output module is used to first calibrate the display device and then output and display the image.

[0025] As a preferred embodiment of the control system for infrared image transmission and reproduction described in this invention, the image acquisition module includes:

[0026] The infrared camera module is used to acquire infrared image data using a high-sensitivity, high-resolution infrared camera, while ensuring that the camera has good stability and adaptability and can work normally under different ambient temperature and humidity conditions.

[0027] The acquisition parameter optimization module is used to set the camera's acquisition parameters according to specific application scenarios.

[0028] As a preferred embodiment of the control system for infrared image transmission and reproduction described in this invention, the data transmission module includes:

[0029] The compression algorithm module is used to compress and transmit infrared image data using the JPEG2000 image compression algorithm;

[0030] The transmission protocol module uses TCP / IP as the transport and network layer protocol, responsible for end-to-end transmission and routing of infrared image data. At the application layer, it uses AES encryption to encrypt the infrared image data, converting the original data into ciphertext to prevent data theft or tampering during transmission. At the data link layer, it uses CRC checksum algorithm to verify the encrypted data packets. The checksum is calculated and added to the data packet before data encapsulation. The receiving end verifies data integrity by recalculating the checksum, promptly detects transmission errors and triggers a retransmission mechanism, ensuring the reliability and security of data transmission.

[0031] The network optimization module is used to dynamically adjust the transmission strategy based on network conditions using network adaptive technology.

[0032] As a preferred embodiment of the control system for infrared image transmission and reproduction described in this invention, the intelligent resource scheduling module includes:

[0033] The data monitoring and acquisition module is used to monitor data at key nodes in real time during the transmission and processing of infrared image data. In the data transmission module, it monitors the bandwidth usage, latency, and packet loss rate of the network link. In the data processing module, it collects information on the utilization rate, memory usage, and task queue length of CPU and GPU computing devices. At the same time, it can also obtain the traffic volume, priority label, and current processing progress data of infrared image data, providing a comprehensive and accurate basis for subsequent resource scheduling.

[0034] The resource requirements analysis module is used to perform in-depth analysis of the resource requirements for the transmission and processing of infrared image data based on the collected data. For transmission tasks, it assesses the required transmission bandwidth resources based on the size of the image data, real-time requirements, and network bandwidth conditions. For processing tasks, it analyzes the requirements for CPU, GPU computing resources, and memory resources based on the complexity of the image algorithm, the amount of data, and the urgency of the task. In addition, it also considers storage resource requirements to determine whether it is necessary to temporarily store a large amount of intermediate data.

[0035] The resource assessment and allocation strategy formulation module is used to assess the network bandwidth, computing resources, and storage resources within the system based on the current available resource status and the results of task resource requirement analysis. If resources are sufficient, resources are allocated directly according to task priority and requirements; if resources are scarce, an intelligent allocation strategy is adopted.

[0036] The resource dynamic scheduling execution module is used to send resource scheduling instructions to the data transmission module and the data processing module according to the established allocation strategy; in the data transmission module, it adjusts the network bandwidth allocation; in the data processing module, it reallocates computing tasks to the corresponding CPU cores or GPU processing units and allocates memory resources reasonably.

[0037] The feedback and optimization module is used to collect actual resource usage data and execution results after the task is completed, compare and analyze them with expected resource requirements and scheduling goals, and then optimize resource scheduling strategies and algorithm parameters based on the feedback results, providing more accurate and efficient solutions for subsequent resource scheduling and continuously improving the overall performance of the system.

[0038] As a preferred embodiment of the control system for infrared image transmission and reproduction described in this invention, the data processing module includes:

[0039] The noise reduction module is used to perform noise reduction processing on the image using mean filtering and median filtering algorithms;

[0040] The non-uniformity correction module is used to perform non-uniformity correction on images using two-point correction or multi-point correction methods.

[0041] The image enhancement module is used to improve the contrast and clarity of images using image enhancement algorithms.

[0042] As a preferred embodiment of the control system for infrared image transmission and reproduction described in this invention, the dynamic adaptive fusion module includes:

[0043] The data access and preprocessing module is used to first receive the infrared image data processed by the data processing module, and then perform standardized preprocessing on the infrared image data, including unifying the data format, aligning timestamps, and transforming coordinate systems, to ensure that data from different sources are consistent in the spatiotemporal dimension, laying the foundation for subsequent fusion.

[0044] The environment and task parameter perception module is used to perceive the current environmental conditions and task requirements in real time through sensor feedback, user input or system preset parameters.

[0045] The adaptive fusion strategy generation module is used to dynamically generate the optimal fusion strategy based on the perceived environment and task parameters, combined with a preset fusion strategy library and machine learning algorithms.

[0046] The data fusion execution module is used to perform fusion operations on the preprocessed infrared image data according to the generated fusion strategy. During the fusion process, the data processing progress and quality are monitored in real time. If an anomaly is found, the strategy is adjusted in time or the error handling mechanism is triggered.

[0047] The fusion result quality assessment and feedback module is used to assess the quality of the fused image and then feed the assessment results back to the strategy generation stage as the basis for the next strategy adjustment.

[0048] As a preferred embodiment of the control system for infrared image transmission and reproduction described in this invention, the color mapping conversion module includes:

[0049] The data receiving and analysis module is used to first receive infrared image data processed by the dynamic adaptive fusion module, and then analyze the grayscale range and temperature distribution characteristics of the infrared image data to provide a basis for color mapping.

[0050] The color mapping algorithm selection module is used to select the corresponding color mapping algorithm based on the characteristics of the display device and user needs;

[0051] The color conversion execution module is used to convert the grayscale values ​​of an infrared image pixel by pixel into RGB color values ​​according to the selected algorithm.

[0052] In a preferred embodiment of the control system for infrared image transmission and reproduction described in this invention, the resolution adaptation processing module includes:

[0053] The display device parameter acquisition module is used to acquire the resolution and aspect ratio parameters of the display device to determine the display capabilities of the device.

[0054] The image size calculation module is used to calculate the scaling ratio and cropping area based on the display device resolution and the original image resolution;

[0055] The scaling and interpolation module is used to scale images using a bilinear interpolation algorithm. When shrinking an image, the interpolation algorithm is used to calculate the color value of new pixels to preserve image details. When enlarging an image, the interpolation algorithm is also used to supplement missing pixel information, making the image transition smooth and ensuring that a clear and undistorted image is presented on the display device.

[0056] As a preferred embodiment of the control system for infrared image transmission and reproduction described in this invention, the calibration and output module includes:

[0057] The display device calibration module is used to periodically calibrate the display device using calibration tools.

[0058] The image output display module is used to transmit image data, which has undergone color mapping and resolution adaptation processing, to a display device for display.

[0059] Compared with existing technologies:

[0060] 1. Addressing transmission delays caused by large data volumes: The JPEG2000 compression algorithm can significantly compress data volume while maintaining a certain image quality, reducing the size of transmitted data and thus reducing transmission time; the network adaptive technology can dynamically adjust the frame rate and compression ratio according to bandwidth, and actively optimize the transmission strategy when bandwidth is insufficient, avoiding data accumulation and delays, and ensuring fast image transmission.

[0061] 2. To address packet loss caused by network instability: By adding AES encryption algorithm and verification mechanism to the TCP / IP protocol, not only can data be prevented from being tampered with and affecting transmission, but also erroneous data can be detected in time and retransmission can be arranged. Thus, network adaptive technology can increase the number of retransmissions or adopt redundant transmission when the packet loss rate is high, ensuring that the data is delivered completely and reducing information loss caused by network problems.

[0062] 3. Addressing insufficient transmission security: Encrypting transmitted data using the AES encryption algorithm not only defends against hacker attacks and prevents data leaks, but also enables strict user access control, allowing different users to have different permissions to prevent unauthorized access to sensitive data by internal personnel; furthermore, it allows for detailed log recording and analysis, timely detection of potential security threats, and further ensures data transmission security.

[0063] 4. Addressing image distortion issues: By employing algorithms such as mean filtering and median filtering to remove noise generated during acquisition and transmission, the original image information can be preserved; by performing non-uniformity correction on the image, the inherent problems of infrared focal plane arrays can be eliminated, making the image grayscale more accurate; by processing the image through image enhancement algorithms, the contrast and clarity of the image can be improved, ensuring the realism and accuracy of the reproduced image.

[0064] 5. Regarding resolution reduction: Before image reproduction, the image is first scaled using a bilinear interpolation algorithm, and then adjusted according to the display device resolution. This ensures image quality while avoiding the loss of detail caused by resolution reduction, thus ensuring clear image presentation.

[0065] 6. Regarding display device compatibility: First, select the color mapping algorithm based on the device characteristics, then regularly calibrate the display devices to unify parameters such as color gamut and brightness. This enables infrared images to accurately present key information such as temperature differences on different devices, improving the accuracy of information obtained by users. Attached Figure Description

[0066] Figure 1 This is a schematic diagram of the overall framework of the present invention;

[0067] Figure 2 This is a schematic diagram of the intelligent resource scheduling module framework of the present invention;

[0068] Figure 3 This is a schematic diagram of the dynamic adaptive fusion module framework of the present invention;

[0069] Figure 4 This is a schematic diagram of the image reproduction module framework of the present invention. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0071] This invention provides a control system for infrared image transmission and reproduction. Please refer to [link / reference]. Figures 1-4 ;

[0072] It includes: an image acquisition module for acquiring infrared image data; a data transmission module for compressing and transmitting infrared image data using image compression algorithms, and improving the reliability and security of transmission through transmission protocols; an intelligent resource scheduling module for sensing changes in data flow and dynamically allocating transmitted and processed data; a data processing module for denoising, non-uniformity correction, and image enhancement of infrared image data; a dynamic adaptive fusion module for automatically adjusting the fusion strategy based on environmental changes and task requirements using dynamic adaptive algorithms; and an image reproduction module for color mapping, resolution adaptation, and display device calibration of infrared image data.

[0073] The image acquisition module includes: an infrared camera module, used to acquire infrared image data using a high-sensitivity, high-resolution infrared camera, while ensuring that the camera has good stability and adaptability and can work normally under different ambient temperature and humidity conditions; and an acquisition parameter optimization module, used to set the acquisition parameters of the camera according to specific application scenarios.

[0074] The data transmission module includes: a compression algorithm module, used to compress and transmit infrared image data using the JPEG2000 image compression algorithm; a transmission protocol module, used to use the TCP / IP protocol as the transport and network layer protocol, responsible for end-to-end transmission and routing of infrared image data; at the application layer, the infrared image data is encrypted using the AES encryption algorithm, converting the original data into ciphertext to prevent data from being stolen or tampered with during transmission; at the data link layer, the encrypted data packet is verified using the CRC check algorithm, calculating the checksum and adding it to the data packet before data encapsulation, and the receiving end verifies data integrity by recalculating the checksum, promptly detecting transmission errors and triggering a retransmission mechanism to ensure the reliability and security of data transmission; and a network optimization module, used to dynamically adjust the transmission strategy according to network conditions using network adaptive technology.

[0075] The intelligent resource scheduling module includes: a data monitoring and acquisition module, used to monitor data at key nodes in real time during infrared image data transmission and processing; in the data transmission module, it monitors network link bandwidth usage, latency, and packet loss rate; in the data processing module, it collects CPU and GPU computing device utilization, memory usage, and task queue length information; simultaneously, it can also obtain infrared image data traffic volume, priority tags, and current processing progress data, providing a comprehensive and accurate basis for subsequent resource scheduling; a resource demand analysis module, used to perform in-depth analysis of the resource requirements for infrared image data transmission and processing tasks based on the collected data; for transmission tasks, it assesses the required transmission bandwidth resources based on the image data volume, real-time requirements, and network bandwidth status; for processing tasks, it analyzes the demand for CPU, GPU computing resources, and memory resources based on the complexity of the image algorithm, data volume, and the urgency of the task; in addition, it considers storage resource requirements to determine whether temporary storage of large amounts of intermediate data is needed; and a resource assessment and allocation strategy formulation module, used to combine the current available resource status and task resource demand analysis results to formulate system... The system assesses network bandwidth, computing resources, and storage resources. If resources are sufficient, they are allocated directly according to task priority and requirements. If resources are scarce, an intelligent allocation strategy is adopted (e.g., using a priority queue algorithm to prioritize the resource needs of high-priority tasks (such as infrared image transmission and processing related to fire alarms); for low-priority tasks, resource allocation is appropriately delayed or reduced. Simultaneously, a dynamic load balancing algorithm is used to rationally distribute computing tasks across different computing devices, avoiding overload of individual devices and improving overall processing efficiency). A resource dynamic scheduling execution module sends resource scheduling instructions to the data transmission module and data processing module according to the established allocation strategy. In the data transmission module, network bandwidth allocation is adjusted. In the data processing module, computing tasks are reallocated to corresponding CPU cores or GPU processing units, and memory resources are rationally allocated. A feedback and optimization module collects actual resource usage data and execution results after task completion, compares and analyzes them with expected resource requirements and scheduling goals, and then optimizes resource scheduling strategies and algorithm parameters based on feedback results, providing more accurate and efficient solutions for subsequent resource scheduling and continuously improving overall system performance.

[0076] By incorporating an intelligent resource scheduling module, the system can monitor the load of data transmission and processing in real time, and intelligently allocate network bandwidth, computing resources, and storage resources based on device performance parameters and task priorities. When a large amount of infrared image data is transmitted concurrently, priority is given to ensuring the bandwidth requirements of critical tasks (such as fire alarm image transmission). During data processing, computing resources such as CPU and GPU are rationally allocated according to the complexity and real-time requirements of different algorithms, avoiding resource waste or uneven allocation that leads to low processing efficiency, thereby improving the overall system's operating efficiency and response speed.

[0077] The data processing module includes: a denoising module for denoising the image using mean filtering and median filtering algorithms; a non-uniformity correction module for non-uniformity correction of the image using two-point correction or multi-point correction methods; and an image enhancement module for improving the contrast and clarity of the image using image enhancement algorithms.

[0078] The dynamic adaptive fusion module includes: a data access and preprocessing module, used to first receive infrared image data processed by the data processing module, and then perform standardized preprocessing on the infrared image data, including unifying the data format, aligning timestamps, and transforming coordinate systems, to ensure that data from different sources have consistency in the spatiotemporal dimension, laying the foundation for subsequent fusion; an environment and task parameter perception module, used to perceive the current environmental conditions and task requirements in real time through sensor feedback, user input, or system preset parameters; an adaptive fusion strategy generation module, used to dynamically generate the optimal fusion strategy based on the perceived environment and task parameters, combined with a preset fusion strategy library and machine learning algorithms; a data fusion execution module, used to perform fusion operations on the preprocessed infrared image data according to the generated fusion strategy, and to monitor the data processing progress and quality in real time during the fusion process, and to adjust the strategy or trigger an error handling mechanism if an anomaly is detected; and a fusion result quality evaluation and feedback module, used to evaluate the quality of the fused image, and then feed the evaluation results back to the strategy generation stage as the basis for the next strategy adjustment.

[0079] By incorporating a dynamic adaptive fusion module, the system can receive data in real time from multiple different types of sensors (such as infrared and visible light cameras). Utilizing a dynamic adaptive algorithm, it automatically adjusts the fusion strategy based on environmental changes and task requirements. In complex lighting environments, it intelligently balances the advantages of infrared and visible light images, achieving complementary fusion and outputting clearer, more information-rich images. For example, in nighttime security scenarios, it fuses the thermal information of infrared images with the texture details of visible light images, allowing security personnel to quickly detect targets and clearly see their features, improving monitoring accuracy and efficiency, and expanding the system's applicability to multiple scenarios.

[0080] The image reproduction module includes: a color mapping conversion module, used to first analyze the infrared image data, then select a color mapping algorithm, and then use the selected algorithm to perform color conversion on the infrared image data; a resolution adaptation processing module, used to first determine the display capability of the device, then calculate the image size, and then use bilinear interpolation or bicubic interpolation algorithms to scale the image; and a calibration and output module, used to first calibrate the display device, and then output and display the image.

[0081] The color mapping conversion module includes: a data receiving and analysis module, used to first receive infrared image data processed by the dynamic adaptive fusion module, and then analyze the grayscale range and temperature distribution characteristics of the infrared image data to provide a basis for color mapping; a color mapping algorithm selection module, used to select the corresponding color mapping algorithm according to the characteristics of the display device and user needs; and a color conversion execution module, used to convert the grayscale values ​​of the infrared image pixel by pixel into RGB color values ​​according to the selected algorithm.

[0082] The resolution adaptation processing module includes: a display device parameter acquisition module, used to acquire the resolution and display ratio parameters of the display device to determine the display capability of the device; an image size calculation module, used to calculate the scaling ratio and cropping area based on the display device resolution and the original image resolution; and a scaling and interpolation operation module, used to scale the image using a bilinear interpolation algorithm. When shrinking the image, the color value of the new pixel is calculated using the interpolation algorithm to preserve image details. When enlarging the image, the missing pixel information is supplemented using the interpolation algorithm to make the image transition smoothly and ensure that a clear and undistorted image is presented on the display device.

[0083] The calibration and output module includes: a display device calibration module, used to periodically calibrate the display device using calibration tools; and an image output display module, used to transmit image data after color mapping and resolution adaptation processing to the display device for display.

[0084] In practical use, the specific steps of the control system are as follows:

[0085] S1: The infrared camera module uses a high-sensitivity, high-resolution infrared camera to collect infrared image data. At the same time, the acquisition parameter optimization module will set the camera's acquisition parameters according to the specific application scenario.

[0086] S2: The infrared image data is compressed and transmitted using the JPEG2000 image compression algorithm through the compression algorithm module. During transmission, the TCP / IP protocol is used as the transport and network layer protocol through the transmission protocol module, which is responsible for end-to-end transmission and routing of the infrared image data. At the application layer, the AES encryption algorithm is used to encrypt the infrared image data, converting the original data into ciphertext to prevent the data from being stolen or tampered with during transmission. At the data link layer, the CRC check algorithm is used to verify the encrypted data packets. The checksum is calculated and added to the data packet before data encapsulation. The receiving end verifies the data integrity by recalculating the checksum, promptly detects transmission errors, and triggers a retransmission mechanism to ensure the reliability and security of data transmission. At the same time, the network optimization module uses network adaptive technology to dynamically adjust the transmission strategy according to the network conditions.

[0087] S3: Through the data monitoring and acquisition module, it can monitor the data of each key node in real time during the transmission and processing of infrared image data. It can also acquire the data volume, priority label, and current processing progress of the infrared image data, providing a comprehensive and accurate basis for subsequent resource scheduling. After monitoring, the resource requirement analysis module performs in-depth analysis of the resource requirements for the transmission and processing tasks of infrared image data based on the acquired data. For transmission tasks, it assesses the required transmission bandwidth resources based on the image data volume, real-time requirements, and network bandwidth conditions. For processing tasks, it analyzes the requirements for CPU, GPU computing resources, and memory resources based on the complexity of the image algorithm, the data volume, and the urgency of the task. Furthermore, it also considers… The system assesses storage resource requirements, determining whether temporary storage of large amounts of intermediate data is necessary. After analysis, the resource evaluation and allocation strategy formulation module evaluates the network bandwidth, computing resources, and storage resources within the system, taking into account the current available resources and the task resource requirements analysis results. Following the evaluation, the resource dynamic scheduling execution module sends resource scheduling instructions to the data transmission module and data processing module according to the formulated allocation strategy. After implementation, the feedback and optimization module collects the actual resource usage data and execution effect of the task execution after completion, compares and analyzes them with the expected resource requirements and scheduling goals, and then optimizes the resource scheduling strategy and algorithm parameters based on the feedback results. This provides a more accurate and efficient solution for subsequent resource scheduling, continuously improving the overall system performance.

[0088] S4: The image is denoised by the denoising module using mean filtering and median filtering algorithms. After denoising, the image is corrected by the non-uniformity correction module using two-point correction or multi-point correction. After non-uniformity correction, the image is improved by the image enhancement module using image enhancement algorithms.

[0089] S5: The data access and preprocessing module first receives the infrared image data processed by the data processing module, and then performs standardized preprocessing on the infrared image data. After preprocessing, the environment and task parameter perception module perceives the current environmental conditions and task requirements in real time based on sensor feedback, user input, or system preset parameters. After perception, the adaptive fusion strategy generation module dynamically generates the optimal fusion strategy based on the perceived environment and task parameters, combined with the preset fusion strategy library and machine learning algorithm. After generation, the data fusion execution module performs fusion operation on the preprocessed infrared image data according to the generated fusion strategy. During the fusion process, the data processing progress and quality are monitored in real time. If an anomaly is found, the strategy is adjusted in time or the error handling mechanism is triggered. After fusion, the fusion result quality evaluation and feedback module evaluates the quality of the fused image and then feeds the evaluation results back to the strategy generation stage as the basis for the next strategy adjustment.

[0090] S6: The data receiving and analysis module first receives the infrared image data processed by the dynamic adaptive fusion module. Then, it analyzes the grayscale range and temperature distribution characteristics of the infrared image data to provide a basis for color mapping. After analysis, the color mapping algorithm selection module selects the corresponding color mapping algorithm according to the display device characteristics and user needs. After selection, the color conversion execution module converts the grayscale values ​​of the infrared image pixel by pixel into RGB color values ​​according to the selected algorithm. After conversion, the display device parameter acquisition module obtains the resolution and display ratio parameters of the display device to determine the display capability of the device. After acquisition, the image size calculation module calculates the scaling ratio and cropping area according to the display device resolution and the original image resolution. After calculation, the scaling and interpolation operation module uses a bilinear interpolation algorithm to scale the image. After scaling, the display device calibration module periodically uses a calibration tool to calibrate the display device. After calibration, the image output display module transmits the image data after color mapping and resolution adaptation processing to the display device for display.

[0091] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A control system for infrared image transmission and reproduction, characterized in that, include: The image acquisition module is used to acquire infrared image data; The data transmission module is used to compress and transmit infrared image data using an image compression algorithm, and during the transmission process, it can improve the reliability and security of the transmission through a transmission protocol. The intelligent resource scheduling module is used to sense changes in data traffic and dynamically allocate data for transmission and processing. The data processing module is used to perform noise reduction, non-uniformity correction, and image enhancement processing on infrared image data; The dynamic adaptive fusion module is used to automatically adjust the fusion strategy according to environmental changes and task requirements using a dynamic adaptive algorithm; The image reproduction module is used for color mapping, resolution adaptation, and display device calibration of infrared image data; The image reproduction module includes: The color mapping conversion module is used to first analyze the infrared image data, then select a color mapping algorithm, and finally use the selected algorithm to convert the infrared image data into colors. The resolution adaptation processing module is used to first determine the display capabilities of the device, then calculate the image size, and then use bilinear interpolation or bicubic interpolation algorithms to scale the image. The calibration and output module is used to first calibrate the display device and then output and display the image. The dynamic adaptive fusion module includes a data access and preprocessing module, which first receives the infrared image data processed by the data processing module, and then performs standardized preprocessing on the infrared image data, including unifying the data format, aligning timestamps, and transforming coordinate systems, to ensure that data from different sources are consistent in the spatiotemporal dimension, laying the foundation for subsequent fusion. The environment and task parameter perception module is used to perceive the current environmental conditions and task requirements in real time through sensor feedback, user input or system preset parameters. The adaptive fusion strategy generation module is used to dynamically generate the optimal fusion strategy based on the perceived environment and task parameters, combined with a preset fusion strategy library and machine learning algorithms. The data fusion execution module is used to perform fusion operations on the preprocessed infrared image data according to the generated fusion strategy. During the fusion process, the data processing progress and quality are monitored in real time. If an anomaly is found, the strategy is adjusted in time or the error handling mechanism is triggered. The fusion result quality assessment and feedback module is used to assess the quality of the fused image and then feed the assessment results back to the strategy generation stage as the basis for the next strategy adjustment.

2. The control system for infrared image transmission and reproduction according to claim 1, characterized in that, The image acquisition module includes an infrared camera module, used to acquire infrared image data using a high-sensitivity, high-resolution infrared camera; The acquisition parameter optimization module is used to manually set the camera's acquisition parameters according to specific application scenarios.

3. The control system for infrared image transmission and reproduction according to claim 1, characterized in that, The data transmission module includes: a compression algorithm module, used to compress and transmit infrared image data using the JPEG2000 image compression algorithm; The transmission protocol module uses TCP / IP as the transport and network layer protocol to handle end-to-end transmission and routing of infrared image data. At the application layer, it uses AES encryption to encrypt the infrared image data, converting the original data into ciphertext to prevent the data from being stolen or tampered with during transmission. At the data link layer, it uses CRC checksum to verify the encrypted data packets. Before data encapsulation, a checksum is calculated and added to the data packet. The receiving end verifies the data integrity by recalculating the checksum, promptly detects transmission errors, and triggers a retransmission mechanism to ensure the reliability and security of data transmission. The network optimization module is used to dynamically adjust the transmission strategy based on network conditions using network adaptive technology.

4. A control system for infrared image transmission and reproduction according to claim 1, characterized in that, The intelligent resource scheduling module includes: a data monitoring and acquisition module, used to monitor data from key nodes in real time during infrared image data transmission and processing; in the data transmission module, monitoring network link bandwidth usage, latency, and packet loss rate; in the data processing module, acquiring information on CPU and GPU computing device utilization, memory usage, and task queue length; and also acquiring infrared image data traffic volume, priority tags, and current processing progress data, providing a comprehensive and accurate basis for subsequent resource scheduling. The resource requirements analysis module is used to perform in-depth analysis of the resource requirements for the transmission and processing of infrared image data based on the collected data. For transmission tasks, it assesses the required transmission bandwidth resources based on the size of the image data, real-time requirements, and network bandwidth conditions. For processing tasks, it analyzes the requirements for CPU, GPU computing resources, and memory resources based on the complexity of the image algorithm, the amount of data, and the urgency of the task. In addition, it also considers storage resource requirements to determine whether it is necessary to temporarily store a large amount of intermediate data. The resource assessment and allocation strategy formulation module is used to assess the network bandwidth, computing resources, and storage resources within the system based on the current available resource status and the results of task resource requirement analysis. If resources are sufficient, resources are allocated directly according to task priority and requirements; if resources are scarce, an intelligent allocation strategy is adopted. The resource dynamic scheduling execution module is used to send resource scheduling instructions to the data transmission module and the data processing module according to the established allocation strategy; in the data transmission module, it adjusts the network bandwidth allocation; in the data processing module, it reallocates computing tasks to the corresponding CPU cores or GPU processing units and allocates memory resources reasonably. The feedback and optimization module is used to collect actual resource usage data and execution results after the task is completed, compare and analyze them with expected resource requirements and scheduling goals, and then optimize resource scheduling strategies and algorithm parameters based on the feedback results, providing more accurate and efficient solutions for subsequent resource scheduling and continuously improving the overall performance of the system.

5. A control system for infrared image transmission and reproduction according to claim 1, characterized in that, The data processing module includes: a denoising module, used to denoise the image using mean filtering and median filtering algorithms; The non-uniformity correction module is used to perform non-uniformity correction on images using two-point correction or multi-point correction methods. The image enhancement module is used to improve the contrast and clarity of images using image enhancement algorithms.

6. A control system for infrared image transmission and reproduction according to claim 1, characterized in that, The color mapping conversion module includes a data receiving and analysis module, which first receives infrared image data processed by the dynamic adaptive fusion module, and then analyzes the grayscale range and temperature distribution characteristics of the infrared image data to provide a basis for color mapping. The color mapping algorithm selection module is used to select the corresponding color mapping algorithm based on the characteristics of the display device and user needs; The color conversion execution module is used to convert the grayscale values ​​of an infrared image pixel by pixel into RGB color values ​​according to the selected algorithm.

7. A control system for infrared image transmission and reproduction according to claim 1, characterized in that, The resolution adaptation processing module includes: a display device parameter acquisition module, used to acquire the resolution and display ratio parameters of the display device to determine the display capability of the device; The image size calculation module is used to calculate the scaling ratio and cropping area based on the display device resolution and the original image resolution; The scaling and interpolation module is used to scale images using a bilinear interpolation algorithm. When shrinking an image, the interpolation algorithm is used to calculate the color value of new pixels to preserve image details. When enlarging an image, the interpolation algorithm is also used to supplement missing pixel information, making the image transition smooth and ensuring that a clear and undistorted image is presented on the display device.

8. A control system for infrared image transmission and reproduction according to claim 1, characterized in that, The calibration and output module includes: a display device calibration module, used to periodically calibrate the display device using calibration tools; The image output display module is used to transmit image data, which has undergone color mapping and resolution adaptation processing, to a display device for display.

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