Control system for infrared image transmission and reproduction

Abstract

The invention belongs to the technical field of infrared images, and particularly relates to a control system for infrared image transmission and reproduction, which comprises an image acquisition module for acquiring infrared image data; the data transmission module is used for carrying out compression transmission on the infrared image data by adopting an image compression algorithm, and in the transmission process, the transmission reliability and safety can be improved through a transmission protocol; and the intelligent resource scheduling module is used for sensing the change of the data flow and dynamically distributing the transmitted and processed data. Through the JPEG2000 compression algorithm, the data volume can be greatly compressed on the premise of ensuring certain image quality, and the transmitted data volume is reduced, so that the transmission time is shortened; the frame rate and the compression ratio can be dynamically adjusted according to the bandwidth through the network adaptive technology, the transmission strategy is actively optimized when the bandwidth is insufficient, delay caused by data accumulation is avoided, and rapid image transmission is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of infrared image technology, in particular to a control system for infrared image transmission and reproduction. Background Art

[0002] With the continuous advancement of science and 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 and prevent malfunctions in advance. In medical diagnosis, they can assist doctors in detecting temperature distribution in the human body and assist in diagnosing diseases. In the military defense field, infrared images play a key role in target identification and night vision navigation. In forest fire prevention, infrared images can be used to promptly identify potential fire sources. However, the effective application of infrared images relies on precise transmission and high-fidelity reproduction, which places stringent demands on the associated control systems.

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

[0004] 1. Regarding the transmission link:

[0005] 1.1 Large data volumes lead to transmission delays: Compared to ordinary images, infrared images typically contain more detailed information and consume extremely large amounts of data. For example, a certain model of high-resolution infrared camera can capture a single-frame image with data volumes of several MB or even larger. In a network environment with limited bandwidth, such as in remote areas or areas covered by outdated network infrastructure, where network bandwidth may be only tens of Mbps, the transmission of large amounts of infrared image data can cause serious latency issues. In forest fire monitoring scenarios, if the transmission delay of infrared image data is too long, it may result in the inability to detect the initial fire source in a timely manner, missing the best opportunity to extinguish the fire, and causing huge losses.

[0006] 1.2 Network instability causes packet loss: In real-world applications, network environments are complex and volatile, with signal interference and congestion often occurring. This can easily lead to packet loss during infrared image data transmission. For example, in industrial plants, the large number of electronic devices can significantly interfere with wireless network signals. When inspection robots transmit infrared images over wireless networks, signal interference can easily cause packet loss, preventing the receiver from obtaining complete image information and hindering accurate judgment of the equipment's operating status.

[0007] 1.3 Inadequate Transmission Security: Infrared images contain sensitive information in some applications, such as those of military facilities and industrial equipment involving core enterprise technologies. However, some existing transmission systems lack robust encryption mechanisms, leaving them vulnerable to hacker attacks and data leaks. For example, in some security monitoring systems, insufficient encryption over the transmission channel could allow hackers to intercept and tamper with infrared image data, disrupting normal monitoring operations and potentially posing serious security risks.

[0008] 2. Regarding the reproduction phase:

[0009] 2.1 Image Distortion: During the reproduction of infrared images after transmission, image distortion often occurs due to factors such as data loss during transmission, differences in color spaces between devices, and limitations of image compression algorithms. For example, in medical diagnosis, distortion during the reproduction of infrared images can lead doctors to misjudge the body's temperature distribution, 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. This results in a loss of detail in the reproduced image, hindering the identification and analysis of the target object. In security surveillance, low-resolution infrared images may not clearly reveal the facial features or behavioral details of suspicious individuals, complicating subsequent investigations.

[0011] 2.3 Display Device Compatibility: Different display devices have different color gamuts, brightness, contrast, and other parameters. This can lead to inconsistent reproduction of infrared images on different devices. For example, compared to a professional indoor surveillance screen, a portable display device used outdoors may not accurately represent subtle temperature differences in infrared images due to its limited brightness and contrast, affecting the user's ability to obtain image information.

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

[0013] To solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions:

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

[0015] An image acquisition module, used for acquiring 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, the transmission reliability and security can be improved through the transmission protocol;

[0017] Intelligent resource scheduling module, used to sense changes in data traffic and dynamically allocate transmission and processing data;

[0018] Data processing module, used for performing denoising, non-uniformity correction and image enhancement processing on infrared image data;

[0019] Dynamic adaptive fusion module, which uses dynamic adaptive algorithms to automatically adjust fusion strategies based on environmental changes and mission requirements;

[0020] An 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] A color mapping conversion module is used to first analyze the infrared image data, then select a color mapping algorithm, and then perform color conversion on the infrared image data using the selected algorithm;

[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 the image for display.

[0025] As a preferred solution of the control system for infrared image transmission and reproduction according to the present invention, the image acquisition module includes:

[0026] Infrared camera module, used to collect 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 solution of the control system for infrared image transmission and reproduction according to the present invention, the data transmission module includes:

[0029] Compression algorithm module, used to compress and transmit infrared image data using JPEG2000 image compression algorithm;

[0030] The transmission protocol module uses the TCP / IP protocol as the transport layer and network layer protocol, and is responsible for the end-to-end transmission and routing addressing of 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 checksum algorithm is used to check the encrypted data packet. The checksum is calculated and added to the data packet before encapsulation. The receiving end verifies the data integrity by recalculating the checksum, promptly detects transmission errors and triggers the retransmission mechanism to ensure the reliability and security of data transmission.

[0031] The network optimization module is used to adopt network adaptive technology to dynamically adjust the transmission strategy according to the network status.

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

[0033] The data monitoring and acquisition module is used to monitor the data of each key node in real time during the transmission and processing of infrared image data. In the data transmission module, the bandwidth usage, delay, and packet loss rate of the network link are monitored. In the data processing module, the utilization rate, memory usage, and task queue length of the CPU and GPU computing devices are collected. At the same time, the flow volume, priority label, and current processing progress data of the infrared image data can also be obtained to provide a comprehensive and accurate basis for subsequent resource scheduling.

[0034] The resource demand analysis module is used to conduct in-depth analysis of the resource requirements for infrared image data transmission and processing tasks based on the collected data. For transmission tasks, the required transmission bandwidth resources are evaluated based on the image data volume, real-time requirements, and network bandwidth conditions. For processing tasks, the CPU, GPU computing resource and memory resource requirements are analyzed based on the complexity of the image algorithm, the data volume, and the urgency of the task. In addition, storage resource requirements are also considered to determine whether temporary storage of large amounts of intermediate data is required.

[0035] The resource assessment and allocation strategy formulation module is used to evaluate the network bandwidth, computing resources, and storage resources within the system based on the current available resource status and the task resource demand analysis results. If resources are sufficient, they are directly allocated according to task priority and demand; 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 reasonably allocates memory resources;

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

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

[0039] Denoising processing module, used to perform denoising on the image using mean filtering and median filtering algorithms;

[0040] A non-uniformity correction module, used to perform non-uniformity correction on the image using a two-point correction method or a multi-point correction method;

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

[0042] As a preferred solution of the control system for infrared image transmission and reproduction described in the present 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 the timestamp, and converting the coordinate system to ensure that the data from different sources are consistent in the temporal and spatial dimensions, 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] Adaptive fusion strategy generation module, which is used to dynamically generate the optimal fusion strategy based on the perceived environment and task parameters, combined with the preset fusion strategy library and machine learning algorithm;

[0046] The data fusion execution module is used to perform fusion operations on the pre-processed 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 any abnormality 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 evaluate the quality of the fused image and then feed the assessment results back to the strategy generation link as the basis for the next strategy adjustment.

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

[0049] The data receiving and analyzing module is used to first receive the 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] A color mapping algorithm selection module is used to select the corresponding color mapping algorithm according to the characteristics of the display device and user needs;

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

[0052] As a preferred solution of the control system for infrared image transmission and reproduction according to the present invention, the resolution adaptation processing module includes:

[0053] The display device parameter acquisition module is used to obtain the resolution and display ratio parameters of the display device to clarify the display capability of the device;

[0054] Image size calculation module, 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 reducing an image, the color values of new pixels are calculated using the interpolation algorithm 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 a clear, undistorted image on the display device.

[0056] As a preferred solution of the control system for infrared image transmission and reproduction according to the present invention, the calibration and output module includes:

[0057] A display device calibration module, used to regularly calibrate the display device using a calibration tool;

[0058] The image output display module is used to transmit the image data after color mapping and resolution adaptation processing to the display device for display.

[0059] Compared with existing technologies:

[0060] 1. Addressing transmission delays caused by large data volumes: The JPEG2000 compression algorithm significantly compresses data while ensuring a certain level of image quality, reducing the volume of transmitted data and, consequently, transmission time. Network adaptive technology dynamically adjusts the frame rate and compression ratio based on bandwidth, proactively optimizing transmission strategies when bandwidth is insufficient to avoid delays caused by data accumulation and ensure rapid image transmission.

[0061] 2. Addressing packet loss caused by network instability: By adding the AES encryption algorithm and verification mechanism to the TCP / IP protocol, not only can data tampering and transmission be prevented, but erroneous data can also be detected and retransmitted in a timely manner. This enables network adaptive technology to increase the number of retransmissions or use redundant transmission when the packet loss rate is high, ensuring complete data delivery and reducing information loss due to network problems.

[0062] 3. Addressing insufficient transmission security: Encrypting transmitted data with the AES encryption algorithm not only defends against hacker attacks and prevents data leakage, but also enables strict user authority management, with different permissions set for different users to prevent internal personnel from illegally accessing sensitive data; and also enables detailed log recording and analysis to promptly identify potential security threats and further ensure data transmission security.

[0063] 4. Addressing image distortion issues: By using 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 the infrared focal plane array can be eliminated, making the image grayscale more accurate. By processing the image with an image enhancement algorithm, the contrast and clarity of the image can be improved, ensuring the authenticity and accuracy of the reproduced image.

[0064] 5. Resolution reduction: Before the image is reproduced, it is first scaled using a bilinear interpolation algorithm and then adjusted according to the resolution of the display device. This can avoid the loss of details caused by the resolution reduction operation while ensuring image quality, ensuring a clear image presentation.

[0065] 6. Regarding display device compatibility: First, select a color mapping algorithm based on the device characteristics, then regularly calibrate the display device to unify parameters such as color gamut and brightness. This will enable infrared images to accurately present key information such as temperature differences on different devices, thereby improving the accuracy of user information acquisition. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

[0069] Figure 4 Schematic diagram of the image reproduction module framework of the present invention. DETAILED DESCRIPTION

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

[0071] The present invention provides a control system for infrared image transmission and reproduction, see Figure 1-Figure 4 ;

[0072] It includes: an image acquisition module for collecting infrared image data; a data transmission module for compressing and transmitting infrared image data using an image compression algorithm, and during the transmission process, it can improve the reliability and security of transmission through a transmission protocol; an intelligent resource scheduling module for sensing changes in data traffic and dynamically allocating transmission and processing 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 according to environmental changes and task requirements using a dynamic adaptive algorithm; 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, which is used to use a high-sensitivity, high-resolution infrared camera to collect infrared image data, while ensuring that the camera has good stability and adaptability and can work normally under different ambient temperature and humidity conditions; an acquisition parameter optimization module, which is used to set the camera acquisition parameters according to specific application scenarios.

[0074] The data transmission module includes: a compression algorithm module, which is used to compress and transmit infrared image data using the JPEG2000 image compression algorithm; a transmission protocol module, which is used to use the TCP / IP protocol as the transport layer and network layer protocol, and is responsible for the end-to-end transmission and routing addressing of 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 check the encrypted data packet, and the check code is calculated and added to the data packet before data encapsulation. The receiving end verifies the data integrity by recalculating the check code, promptly detects transmission errors and triggers the retransmission mechanism, thereby ensuring the reliability and security of data transmission; a network optimization module is used to use network adaptive technology to dynamically adjust the transmission strategy according to the network status.

[0075] The intelligent resource scheduling module includes: a data monitoring and acquisition module, which is used to monitor the data of each key node in real time during the transmission and processing of infrared image data; in the data transmission module, it monitors the bandwidth usage, delay, and packet loss rate indicators of the network link; in the data processing module, it collects the utilization rate, memory occupancy, and task queue length information of the CPU and GPU computing devices; at the same time, it can also obtain the traffic size, priority label and current processing progress data of the infrared image data to provide a comprehensive and accurate basis for subsequent resource scheduling; a resource demand analysis module, which is used to conduct an in-depth analysis of the resource requirements of the transmission and processing tasks of the infrared image data based on the collected data; for transmission tasks, the required transmission bandwidth resources are evaluated based on the image data volume, real-time requirements, and network bandwidth conditions; for processing tasks, the requirements for CPU, GPU computing resources and memory resources are analyzed based on the complexity of the image algorithm, the data volume, and the urgency of the task; in addition, the storage resource requirements will be considered to determine whether a large amount of intermediate data needs to be temporarily stored; a resource evaluation and allocation strategy formulation module, which is used to combine the current available resource status and the task resource demand analysis results to evaluate the system The network bandwidth, computing resources, and storage resources within the system are evaluated; if resources are sufficient, resources are directly allocated according to task priority and demand; if resources are tight, an intelligent allocation strategy is adopted (for example, a priority queue algorithm is used to prioritize the resource requirements of high-priority tasks (such as infrared image transmission and processing related to fire alarms); for low-priority tasks, the resource allocation is appropriately delayed or reduced. At the same time, a dynamic load balancing algorithm is used to reasonably allocate computing tasks to different computing devices to avoid overloading a single device and improve overall processing efficiency); a 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 formulated allocation strategy; in the data transmission module, the network bandwidth allocation is adjusted; in the data processing module, the computing tasks are reallocated to the corresponding CPU core or GPU processing unit, and memory resources are reasonably allocated; a feedback and optimization module is used to collect the actual resource usage data and execution effect of the task execution after the task is completed, and compare and analyze them with the expected resource demand and scheduling goals. Then, based on the feedback results, the resource scheduling strategy and algorithm parameters are optimized to provide a more accurate and efficient solution for subsequent resource scheduling, thereby continuously improving the overall performance of the system.

[0076] By incorporating an intelligent resource scheduling module, the system monitors the data transmission and processing load in real time and intelligently schedules network bandwidth, computing resources, and storage resources based on device performance parameters and task priorities. When large amounts of infrared image data are being transmitted concurrently, bandwidth requirements for critical tasks (such as fire alarm image transmission) are prioritized. During data processing, CPU, GPU, and other computing resources are allocated appropriately based on the complexity and real-time requirements of different algorithms, avoiding resource waste or processing inefficiencies caused by uneven resource allocation, thereby improving overall system efficiency and responsiveness.

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

[0078] The dynamic adaptive fusion module includes: a data access and preprocessing module, which 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 unified data format, timestamp alignment, and coordinate system conversion, to ensure that data from different sources are consistent in time and space dimensions, laying the foundation for subsequent fusion; an environment and task parameter perception module, which is 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, which 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 algorithm; a data fusion execution module, which is used to perform a fusion operation on the preprocessed infrared image data according to the generated fusion strategy, and during the fusion process, monitor the data processing progress and quality in real time. If an abnormality is found, the strategy is adjusted in time or the error handling mechanism is triggered; a fusion result quality assessment and feedback module, which is used to perform quality assessment on the fused image, and then feed the assessment results back to the strategy generation link as the basis for the next strategy adjustment.

[0079] By incorporating a dynamic adaptive fusion module, the system can receive real-time data from multiple sensor types (such as infrared and visible light cameras). Using 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 strengths of infrared and visible light images, achieving complementary fusion and outputting clearer, more informative images. For example, in nighttime security scenarios, the system fuses the thermal information of infrared images with the texture details of visible light images, enabling security personnel to quickly detect targets and clearly identify their features, improving monitoring accuracy and efficiency while also expanding the system's applicability to multiple scenarios.

[0080] The image reproduction module includes: a color mapping conversion module, which is 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, which is used to first determine the display capability of the device, then calculate the image size, and then use a bilinear interpolation or bicubic interpolation algorithm to scale the image; and a calibration and output module, which is 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 for first receiving the infrared image data processed by the dynamic adaptive fusion module, and then analyzing 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 for selecting a corresponding color mapping algorithm based on the characteristics of the display device and user needs; and a color conversion execution module for converting the grayscale value of the infrared image into RGB color value pixel by pixel according to the selected algorithm.

[0082] The resolution adaptation processing module includes: a display device parameter acquisition module, which is used to obtain the resolution and display ratio parameters of the display device to clarify the display capability of the device; an image size calculation module, which is used to calculate the scaling ratio and cropping area based on the display device resolution and the original image resolution; a scaling and interpolation operation module, which is used to use a bilinear interpolation algorithm to scale the image; when reducing the image, the color value of the new pixel point is calculated by the interpolation algorithm to preserve the image details; when enlarging the image, the interpolation algorithm is also used to supplement the missing pixel information to achieve a smooth image transition, ensuring a clear and undistorted image on the display device.

[0083] The calibration and output module includes: a display device calibration module for regularly calibrating the display device using a calibration tool; and an image output display module for transmitting image data processed by color mapping and resolution adaptation to the display device for display.

[0084] In actual 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. During the collection process, the acquisition parameter optimization module also sets the camera's acquisition parameters according to the specific application scenario.

[0086] S2: The JPEG2000 image compression algorithm is used in the compression algorithm module to compress and transmit the infrared image data. During the transmission process, the TCP / IP protocol is used as the transport layer and network layer protocol through the transmission protocol module, which is responsible for the end-to-end transmission and routing addressing of the infrared image data; at the application layer, the AES encryption algorithm is used to encrypt the infrared image data, and the original data is converted into ciphertext to prevent the data from being stolen or tampered with during transmission; at the data link layer, the CRC checksum algorithm is used to check the encrypted data packet, and 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 the retransmission mechanism to ensure the reliability and security of data transmission; at the same time, the network optimization module adopts network adaptive technology to dynamically adjust the transmission strategy according to the network conditions.

[0087] S3: Through the data monitoring and acquisition module, the data of each key node can be monitored in real time during the transmission and processing of infrared image data. At the same time, the flow size, priority label and current processing progress data of the infrared image data can be obtained to provide a comprehensive and accurate basis for subsequent resource scheduling. After monitoring, the resource demand analysis module will conduct an in-depth analysis of the resource requirements of the transmission and processing tasks of the infrared image data based on the collected data; for transmission tasks, the required transmission bandwidth resources are evaluated based on the image data volume, real-time requirements, and network bandwidth conditions; for processing tasks, the demand for CPU, GPU computing resources and memory resources is analyzed based on the complexity of the image algorithm, the amount of data, and the urgency of the task; in addition, it will also consider Storage resource requirements, determine whether a large amount of intermediate data needs to be temporarily stored. After analysis, the resource evaluation and allocation strategy formulation module will be used to combine the current available resource status and the task resource demand analysis results to evaluate the network bandwidth, computing resources, and storage resources in the system. After evaluation, the resource dynamic scheduling execution module will send 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 will collect the actual resource usage data and execution effect of the task execution after the task is completed, and compare and analyze them with the expected resource demand and scheduling goals. Then, based on the feedback results, the resource scheduling strategy and algorithm parameters will be optimized to provide more accurate and efficient solutions for subsequent resource scheduling, and continuously improve the overall performance of the system;

[0088] S4: The denoising module uses the mean filter and median filter algorithms to denoise the image. After denoising, the non-uniformity correction module uses the two-point correction method or the multi-point correction method to correct the non-uniformity of the image. After the non-uniformity correction, the image enhancement module uses the image enhancement algorithm to improve the contrast and clarity of the image.

[0089] S5: The infrared image data processed by the data processing module is first received through the data access and preprocessing module, and then the infrared image data is standardized and preprocessed. After preprocessing, the environment and task parameter perception module will perceive the current environmental conditions and task requirements in real time according to sensor feedback, user input or system preset parameters. After perception, the adaptive fusion strategy generation module will dynamically generate 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 will perform a 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 abnormality is found, the strategy will be adjusted in time or the error handling mechanism will be triggered. After fusion, the fusion result quality assessment and feedback module will be used to assess the quality of the fused image, and then the assessment result will be fed back to the strategy generation link as the basis for the next strategy adjustment.

[0090] S6: The infrared image data processed by the dynamic adaptive fusion module is first received through the data receiving and analysis module, and then the grayscale range and temperature distribution characteristics of the infrared image data are analyzed to provide a basis for color mapping. After the analysis, the color mapping algorithm selection module is used to select the corresponding color mapping algorithm according to the characteristics of the display device and user needs. After the selection, the grayscale value of the infrared image is converted into RGB color value pixel by pixel according to the selected algorithm through the color conversion execution module. After the conversion, the resolution and display ratio parameters of the display device are obtained through the display device parameter acquisition module to clarify the display capability of the device. After the acquisition, the scaling ratio and cropping area are calculated according to the display device resolution and the original image resolution through the image size calculation module. After the calculation, the image is scaled using a bilinear interpolation algorithm through the scaling and interpolation operation module. After scaling, the display device is calibrated regularly using a calibration tool through the display device calibration module. After calibration, the image data processed by color mapping and resolution adaptation is transmitted to the display device for display through the image output display module.

[0091] Although the present invention has been described above with reference to embodiments, various modifications may be made thereto and equivalent components may be substituted without departing from the scope of the present invention. In particular, as long as there are no structural conflicts, the various features of the embodiments disclosed herein may be combined with each other in any manner, and the omission of an exhaustive description of such combinations in this specification is solely for the sake of space 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: An image acquisition module, used for acquiring 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, the transmission reliability and security can be improved through the transmission protocol; Intelligent resource scheduling module, used to sense changes in data traffic and dynamically allocate transmission and processing data; Data processing module, used for performing denoising, non-uniformity correction and image enhancement processing on infrared image data; Dynamic adaptive fusion module, which uses dynamic adaptive algorithms to automatically adjust fusion strategies based on environmental changes and mission requirements; An image reproduction module is used to perform color mapping, resolution adaptation and display device calibration on infrared image data; The image reproduction module includes: A color mapping conversion module is used to first analyze the infrared image data, then select a color mapping algorithm, and then perform color conversion on the infrared image data using the selected algorithm; 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 the image for display.

2. A control system for infrared image transmission and reproduction according to claim 1, characterized in that: The image acquisition module includes: Infrared camera module, used to collect 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. A control system for infrared image transmission and reproduction according to claim 1, characterized in that: The data transmission module includes: Compression algorithm module, used to compress and transmit infrared image data using JPEG2000 image compression algorithm; The transmission protocol module uses the TCP / IP protocol as the transport layer and network layer protocol, and is responsible for the end-to-end transmission and routing addressing of 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 checksum algorithm is used to check the encrypted data packet. The checksum is calculated and added to the data packet before encapsulation. The receiving end verifies the data integrity by recalculating the checksum, promptly detects transmission errors and triggers the retransmission mechanism to ensure the reliability and security of data transmission. The network optimization module is used to adopt network adaptive technology to dynamically adjust the transmission strategy according to the network status.

4. A control system for infrared image transmission and reproduction according to claim 1, characterized in that: The intelligent resource scheduling module includes: The data monitoring and acquisition module is used to monitor the data of each key node in real time during the transmission and processing of infrared image data. In the data transmission module, the bandwidth usage, delay, and packet loss rate of the network link are monitored. In the data processing module, the utilization rate, memory usage, and task queue length of the CPU and GPU computing devices are collected. At the same time, the flow volume, priority label, and current processing progress data of the infrared image data can also be obtained to provide a comprehensive and accurate basis for subsequent resource scheduling. The resource demand analysis module is used to conduct in-depth analysis of the resource requirements for infrared image data transmission and processing tasks based on the collected data. For transmission tasks, the required transmission bandwidth resources are evaluated based on the image data volume, real-time requirements, and network bandwidth conditions. For processing tasks, the CPU, GPU computing resource and memory resource requirements are analyzed based on the complexity of the image algorithm, the data volume, and the urgency of the task. In addition, storage resource requirements are also considered to determine whether temporary storage of large amounts of intermediate data is required. The resource assessment and allocation strategy formulation module is used to evaluate the network bandwidth, computing resources, and storage resources within the system based on the current available resource status and the task resource demand analysis results. If resources are sufficient, resources are directly allocated according to task priority and demand; 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 reasonably allocates memory resources; The feedback and optimization module is used to collect the actual resource usage data and execution results of the task after completion, compare and analyze them with the expected resource requirements and scheduling goals, and then optimize the resource scheduling strategy and algorithm parameters based on the feedback results to provide more accurate and efficient solutions for subsequent resource scheduling, thereby continuously improving the overall performance of the system.

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

6. A control system for infrared image transmission and reproduction according to claim 1, characterized in that: The dynamic adaptive fusion module includes: 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 the timestamp, and converting the coordinate system to ensure that the data from different sources are consistent in the temporal and spatial dimensions, 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; Adaptive fusion strategy generation module, which is used to dynamically generate the optimal fusion strategy based on the perceived environment and task parameters, combined with the preset fusion strategy library and machine learning algorithm; The data fusion execution module is used to perform fusion operations on the pre-processed 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 any abnormality 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 evaluate the quality of the fused image and then feed the assessment results back to the strategy generation link as the basis for the next strategy adjustment.

7. A control system for infrared image transmission and reproduction according to claim 1, characterized in that: The color mapping conversion module includes: The data receiving and analyzing module is used to first receive the 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 is used to select the corresponding color mapping algorithm according to the characteristics of the display device and user needs; The color conversion execution module is used to convert the grayscale value of the infrared image into RGB color value pixel by pixel according to the selected algorithm.

8. The control system for infrared image transmission and reproduction according to claim 1, characterized in that: The resolution adaptation processing module includes: The display device parameter acquisition module is used to obtain the resolution and display ratio parameters of the display device to clarify the display capability of the device; Image size calculation module, 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 reducing an image, the color values of new pixels are calculated using the interpolation algorithm 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 a clear, undistorted image on the display device.

9. The 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 regularly calibrate the display device using a calibration tool; The image output display module is used to transmit the image data after color mapping and resolution adaptation processing to the display device for display.

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