Visual image compression detection method, system and equipment for power transmission line

Abstract

The invention discloses a visual image compression detection method, system and equipment for a power transmission line. The method belongs to the technical field of image data processing, and comprises the following steps: obtaining power transmission line image data, environment data and network data sent to a monitoring terminal, and dividing the data into dynamic information and static information; obtaining a dynamic information evaluation value and a static information evaluation value; setting a dynamic information evaluation value threshold value and a static information evaluation value threshold value, cutting the image according to a relation between the dynamic information evaluation value and the dynamic information evaluation value threshold value and a relation between the static information evaluation value and the static information evaluation value threshold value, and selecting a corresponding transmission strategy; compressing the cut image, and sending the compressed image to a monitoring terminal for visualization; and performing dynamic adjustment and optimization according to the transmission effect. According to the invention, by dynamically adjusting the size and priority of the cutting block, the image is quickly transmitted without distortion, and the problem of image distortion under the condition of ensuring the transmission speed in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of image data processing, and in particular to a method, system and device for visual image compression detection of power transmission lines. Background Art

[0002] With the rapid development of modern power systems, the monitoring and maintenance of transmission lines have become increasingly important. Transmission lines refer to lines used to transmit electricity in power supply systems, usually including high-voltage transmission lines and distribution lines. Image compression refers to reducing the size of image files by reducing redundant information in image data, thereby saving storage space and transmission bandwidth.

[0003] The existing visual image compression detection system for transmission lines is achieved through regular on-site inspections of transmission lines by professionals, installation of online monitoring sensors, infrared cameras and other fixed monitoring equipment at key nodes of the transmission lines, or inspections using helicopters and drones.

[0004] For example, the invention patent with publication number: CN107146222B discloses a medical image compression method based on the similarity of human anatomical structure, which includes: firstly obtaining a candidate region for each organ based on the prior anatomical knowledge of the current data set, and then using a density-based method to accurately extract the organ data in the candidate region. The present invention utilizes the relative position of organs in the body and can be applied to images of different patient sizes. Secondly, this segmentation technique is performed in a progressive manner, first roughly defining the candidate area, and then using a density-based segmentation method to refine the target area. This method makes the segmentation accuracy more ideal.

[0005] For example, the invention patent with publication number: CN112102320B discloses an image compression method, device, electronic device and storage medium, including: obtaining an initial image to be compressed and a target compression parameter of the initial image; using a target compression method to compress the initial image to an intermediate image with a target resolution; if the difference between the actual quality of the intermediate image and the target quality is greater than a first preset threshold, the intermediate image is degraded according to the target quality to obtain a target image. Through this application, the initial image is size compressed according to the target compression method in the acquired target compression parameter to obtain an intermediate image, and it is determined whether the difference between the intermediate image and the target quality is greater than the first preset threshold. If so, the intermediate image is degraded according to the target quality in the target compression parameter to obtain the target image.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, anomalies may occur in the transmission line during image compression and transmission. The distortion of parts with rich details will affect subsequent monitoring and analysis, and there is a problem of image distortion while ensuring the transmission speed. Summary of the invention

[0008] The embodiments of the present application solve the problem of image distortion while ensuring transmission speed in the prior art by providing a method, system and device for transmission line visualization image compression detection, thereby achieving fast and distortion-free image transmission.

[0009] The embodiment of the present application provides a method for compression detection of visual images of power transmission lines, comprising the following steps: acquiring power transmission line image data, environmental data and network data sent to a monitoring terminal, and dividing the power transmission line image data, environmental data and network data into dynamic information and static information; evaluating the dynamic information and the static information respectively, and analyzing to obtain a dynamic information evaluation value and a static information evaluation value; setting a dynamic information evaluation value threshold and a static information evaluation value threshold, and cutting the image and selecting a corresponding transmission strategy according to the relationship between the dynamic information evaluation value and the static information evaluation value and the dynamic information evaluation value threshold and the static information evaluation value threshold; compressing the cut image and sending it to the monitoring terminal for visualization; and dynamically adjusting and optimizing according to the transmission effect.

[0010] Furthermore, the step of analyzing and obtaining a dynamic information evaluation value is as follows: obtaining dynamic information and static information; the dynamic information includes network data and environmental data; the network data is network data in the process of sending transmission line image data to a monitoring terminal; the network data includes bandwidth, maximum bandwidth, minimum bandwidth, average bandwidth, delay, and packet loss rate; the environmental data includes real-time temperature, real-time humidity, and real-time wind speed at the sending end; the dynamic information evaluation value is obtained by comprehensive analysis of the network data and the environmental data; the dynamic information evaluation value represents quantitative data obtained based on the dynamic information analysis for evaluating the quality of the network environment.

[0011] Furthermore, the specific process of analyzing and obtaining the static information evaluation value is as follows: obtaining static information; the static information includes: transmission line image data sent to the monitoring terminal; the transmission line image data sent to the monitoring terminal includes: edge density, image block distortion, number of key areas, area of ​​key areas, grayscale probability; obtaining the static information evaluation value based on a comprehensive evaluation of the transmission line image data sent to the monitoring terminal; the static information evaluation value represents quantitative data obtained based on the static information analysis for measuring the quality of the sent picture.

[0012] Furthermore, the calculation formula of the dynamic information evaluation value is:

[0013]

[0014] In the formula, Z1 represents the dynamic information evaluation value, B represents the bandwidth, and B max Indicates the maximum bandwidth, B min Indicates the minimum bandwidth, L indicates network delay, L0 indicates the reference value of network delay, P indicates the packet loss rate, T indicates the real-time temperature, T0 indicates the reference temperature, H indicates the real-time humidity, H0 indicates the ideal humidity, W indicates the real-time wind speed, and W0 indicates the ideal wind speed. ω1 indicates the influence weight of bandwidth, ω2 indicates the influence weight of network delay, ω3 indicates the influence weight of packet loss rate, ω4 indicates the influence weight of actual temperature, ω5 indicates the influence weight of actual humidity, and ω6 indicates the influence weight of actual wind speed.

[0015] Furthermore, the specific steps of cutting the image and selecting the corresponding transmission strategy according to the relationship between the dynamic information evaluation value and the static information evaluation value and the dynamic information evaluation value threshold and the static information evaluation value threshold are as follows: if the dynamic information evaluation value is greater than the dynamic information evaluation value threshold and the static information evaluation value is greater than the static information evaluation value threshold, the image is cut using the first specification image block, lossless compression and TCP protocol are used, and the key area is preferentially transmitted to the monitoring terminal; if the dynamic information evaluation value is greater than the dynamic information evaluation value threshold and the static information evaluation value is below the static information evaluation value threshold, the image is cut using the first specification image block, lossy compression and TCP protocol are used, and the static information is preferentially transmitted in the important area. Send to the monitoring terminal; when the dynamic information evaluation value is less than the dynamic information evaluation value threshold, the image block size is gradually reduced according to the preset amplitude based on the first specification image block, and further adjusted according to the instruction of the monitoring terminal; if the dynamic information evaluation value is below the dynamic information evaluation value threshold and the static information evaluation value is greater than the static information evaluation value threshold, lossless compression and UDP protocol are adopted to give priority to the transmission of key areas to ensure that important information is not lost and sent to the monitoring terminal; if the dynamic information evaluation value is below the dynamic information evaluation value threshold and the static information evaluation value is below the static information evaluation value threshold, lossy compression is adopted to reduce the image quality and UDP protocol, and the image is transmitted according to priority sorting to ensure the integrity of the basic image information and send it to the monitoring terminal.

[0016] Furthermore, the visualization includes: outputting the changes of network bandwidth, delay, packet loss rate over time, and adjustment of image cutting block size under different network conditions in the form of a line graph; displaying the original image, the divided image blocks, and the identified key areas in the form of a heat map; outputting the changes of image reception quality evaluation value over time and speed-related parameters in the form of a line graph; and outputting the transmission strategy selection under different combinations of dynamic information evaluation values ​​and static information evaluation values ​​in the form of a decision tree.

[0017] Furthermore, the dynamic adjustment and optimization according to the transmission effect is specifically as follows: reconstructing a complete image through the image data blocks received by the monitoring terminal; evaluating an image reception quality assessment value based on the reconstructed complete image; the image reception quality assessment value is used to describe the comprehensive assessment data of the quality of the received image and the transmission efficiency; obtaining an image reception quality assessment value threshold from a database, and comparing the image reception quality assessment value with the image reception quality assessment value threshold; if the image reception quality assessment value is less than the image reception quality assessment value threshold, then issuing an instruction to the image sending end to continue to reduce the image block size step by step according to a preset amplitude until the image reception quality assessment value is greater than or equal to the image reception quality assessment value threshold, then issuing an instruction to the image sending end to end the step-by-step reduction of the image block size.

[0018] Furthermore, the dynamic adjustment and optimization based on the transmission effect also includes: real-time updating of the dynamic information evaluation value threshold and the static information evaluation value threshold; obtaining the image reception quality evaluation value threshold, and comparing the image reception quality evaluation value with the image reception quality evaluation value threshold, if the image reception quality evaluation value is less than the image reception quality evaluation value threshold, then alternately increasing the dynamic information evaluation value threshold and the static information evaluation value threshold step by step according to a preset increment until the image reception quality evaluation value is greater than the image reception quality evaluation value threshold.

[0019] Furthermore, a visual image compression detection system for power transmission lines is characterized in that it includes: a data acquisition module, an information evaluation module, a threshold setting module, a visualization module, and a real-time optimization module; wherein the data acquisition module is used to obtain the power transmission line image data, environmental data, and network data sent to the monitoring terminal, and divide the power transmission line image data, environmental data, and network data into dynamic information and static information; the information evaluation module is used to evaluate the dynamic information and the static information respectively, and analyze to obtain the dynamic information evaluation value and the static information evaluation value; the threshold setting module is used to set the dynamic information evaluation value threshold and the static information evaluation value threshold, and cut the image and select the corresponding transmission strategy according to the relationship between the dynamic information evaluation value and the static information evaluation value and the dynamic information evaluation value threshold and the static information evaluation value threshold; the visualization module is used to compress the cut image and send it to the monitoring terminal for visualization; the real-time optimization module is used to dynamically adjust and optimize according to the transmission effect.

[0020] An embodiment of the present application provides an electronic device, comprising: a processor, and a memory for storing instructions executable by the processor; when the processor is configured to execute the instructions, the electronic device implements a method for visual image compression detection of power transmission lines.

[0021] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0022] 1. By dynamically adjusting the size and priority of the cutting block, the transmission strategy is optimized under different network conditions, ensuring the improvement of data integrity and transmission efficiency, thereby achieving fast and distortion-free image transmission, effectively solving the problem of image distortion in the existing technology while ensuring transmission speed.

[0023] 2. By real-time monitoring of network conditions and image content, the transmission order and optimization strategy can be dynamically adjusted, so that the system can quickly adapt to changes in network load and image complexity, thereby achieving accurate monitoring and rapid response to the status of transmission lines.

[0024] 3. Through image reconstruction and correction, the image data at the receiving end can be effectively managed and optimized, and the distortion and loss in data transmission can be reduced, thereby achieving monitoring reliability and continuous operation, and improving the overall performance and user experience of the transmission line monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A flow chart of a method for visual image compression detection of power transmission lines provided in an embodiment of the present application;

[0026] Figure 2 The image reception quality data diagram provided by the embodiment of the present application;

[0027] Figure 3 A schematic diagram of the structure of a system for visual image compression detection of power transmission lines provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The embodiments of the present application solve the problem of image distortion while ensuring transmission speed in the prior art by providing a method, system and device for transmission line visualization image compression detection, and achieve fast and distortion-free image transmission by dynamically adjusting the cutting block size and priority.

[0029] The technical solution in the embodiment of the present application is to solve the above-mentioned problem of image distortion while ensuring the transmission speed. The overall idea is as follows: obtain the transmission line image data, environmental data and network data sent to the monitoring terminal, and divide the transmission line image data, environmental data and network data into dynamic information and static information; evaluate the dynamic information and static information respectively, and analyze to obtain the dynamic information evaluation value and the static information evaluation value; set the dynamic information evaluation value threshold and the static information evaluation value threshold, and cut the image and select the corresponding transmission strategy according to the relationship between the dynamic information evaluation value and the static information evaluation value and the dynamic information evaluation value threshold and the static information evaluation value threshold; compress the cut image and send it to the monitoring terminal for visualization; dynamically adjust and optimize according to the transmission effect.

[0030] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0031] like Figure 1 As shown, it is a flow chart of a method for visual image compression detection of power transmission lines provided in an embodiment of the present application, and the method includes the following steps: acquiring power transmission line image data, environmental data and network data sent to a monitoring terminal, and dividing the power transmission line image data, environmental data and network data into dynamic information and static information; evaluating the dynamic information and the static information respectively, and analyzing to obtain a dynamic information evaluation value and a static information evaluation value; setting a dynamic information evaluation value threshold and a static information evaluation value threshold, and cutting the image and selecting a corresponding transmission strategy according to the relationship between the dynamic information evaluation value and the static information evaluation value and the dynamic information evaluation value threshold and the static information evaluation value threshold; compressing the cut image and sending it to the monitoring terminal for visualization; and dynamically adjusting and optimizing according to the transmission effect.

[0032] In this embodiment, the acquired images are divided into blocks according to different categories, and image acquisition and processing parameters, such as exposure time, image contrast, etc., are adjusted according to the environmental sensor data.

[0033] Furthermore, the steps of analyzing and obtaining the dynamic information evaluation value are: obtaining dynamic information and static information; the dynamic information includes network data and environmental data; the network data is the network data in the process of sending the transmission line image data to the monitoring terminal; the network data includes bandwidth, maximum bandwidth, minimum bandwidth, average bandwidth, delay, and packet loss rate; the environmental data includes real-time temperature, real-time humidity, and real-time wind speed at the sending end; the dynamic information evaluation value is obtained based on a comprehensive analysis of the network data and the environmental data; the dynamic information evaluation value represents the quantitative data obtained based on the dynamic information analysis for evaluating the network environment quality.

[0034] In this embodiment, the dynamic information includes: bandwidth, delay, packet loss rate, real-time temperature, real-time humidity, and real-time wind speed. The static information includes: edge density, number of key areas, area of ​​key areas, and gray level probability.

[0035] Furthermore, the specific process of analyzing and obtaining the static information evaluation value is as follows: obtaining static information; the static information includes: the transmission line image data sent to the monitoring terminal; the transmission line image data sent to the monitoring terminal includes: edge density, image block distortion, number of key areas, area of ​​key areas, grayscale probability; obtaining the static information evaluation value based on a comprehensive evaluation of the transmission line image data sent to the monitoring terminal; the static information evaluation value represents quantitative data obtained based on the static information analysis for measuring the quality of the sent image.

[0036] In this embodiment,

[0037]

[0038] Where Z2 represents the static information evaluation value, ρ represents the edge density, D represents the image block distortion, M represents the number of key areas, S represents the area of ​​the key area, and p i represents the gray level probability, i represents the gray level number, i=1,2,...,n, n represents the total number of gray levels. α1 represents the influence weight of edge density, α2 represents the influence weight of texture complexity, α3 represents the influence weight of key area number, α4 represents the influence weight of key area area, and α5 represents the influence weight of gray level.

[0039] In the formula, the static information evaluation value can also be obtained through historical experience, the edge density can be obtained by calculating the ratio of edge pixels to total pixels, the image block distortion can be calculated by comparing the difference between the original image and the processed image (such as using MSE-mean square error), the number of key regions can be obtained by identifying and counting through image analysis technology (such as feature recognition-based algorithms), the area of ​​key regions can be obtained by calculating the total number of pixels in these regions after identifying the key regions, and the gray level probability can be obtained by dividing the number of pixels of each gray level in the statistical image by the total number of pixels.

[0040] The edge density influence weight, texture complexity influence weight, key area number influence weight, key area area influence weight, and grayscale influence weight can be obtained through the static information database. Through the relationship between edge density, texture complexity, key area number, key area, grayscale and static information in historical data, a mapping set of edge density, texture complexity, key area number, key area, grayscale and its corresponding weights in historical data is established respectively. The real-time edge density, texture complexity, key area number, key area and grayscale are input to obtain the corresponding edge density influence weight, texture complexity influence weight, key area number influence weight, key area influence weight and grayscale influence weight in the mapping set.

[0041] Image block distortion is used to evaluate image quality loss due to compression or previous transmission errors. High distortion may require adjusting the compression algorithm or changing the image block size to reduce further loss. Before transmission, if the image needs to be compressed, a predetermined compression algorithm can be applied to the original image first, and then the mean square error and peak signal-to-noise ratio of the compressed image and the original image can be calculated to evaluate the degree of distortion introduced by compression. Compression after cutting has the following advantages: 1. Compressing smaller image blocks can more effectively manage and schedule network resources, especially under poor network conditions. Each block can independently optimize the compression rate based on its content and importance. 2. If an image block is damaged or lost during transmission, only the block needs to be retransmitted, and the entire image does not need to be retransmitted, which can improve recovery speed and network efficiency. Disadvantages: 1. Multiple image blocks after cutting need to be compressed separately, which may increase processing time, and the compression settings of each block may need to be adjusted separately, increasing processing complexity. 2. If cutting and compression are not done properly, visual incoherence may be introduced between image blocks, affecting the overall quality of the image. Consider the actual application scenarios: Real-time video streaming: usually adopt the strategy of cutting first and then compressing to reduce delays and improve error recovery speed; high-quality image storage: such as medical imaging, you may choose to compress first and then cut or only compress the entire image to ensure the highest image quality and visual consistency.

[0042] Furthermore, the calculation formula of the dynamic information evaluation value is:

[0043]

[0044] In the formula, Z1 represents the dynamic information evaluation value, B represents the bandwidth, and B max Indicates the maximum bandwidth, B minIndicates the minimum bandwidth, L indicates network delay, L0 indicates the reference value of network delay, P indicates the packet loss rate, T indicates the real-time temperature, T0 indicates the reference temperature, H indicates the real-time humidity, H0 indicates the ideal humidity, W indicates the real-time wind speed, and W0 indicates the ideal wind speed. ω1 indicates the influence weight of bandwidth, ω2 indicates the influence weight of network delay, ω3 indicates the influence weight of packet loss rate, ω4 indicates the influence weight of actual temperature, ω5 indicates the influence weight of actual humidity, and ω6 indicates the influence weight of actual wind speed.

[0045] In this embodiment, the dynamic information evaluation value can also be obtained through historical experience, the bandwidth can be measured by a network monitoring tool or a bandwidth testing tool such as iPerf, the maximum bandwidth and the minimum bandwidth can be obtained through historical experience, the network delay can be measured by a network diagnostic tool such as Ping (the full name in English is Packet Internet Groper, which can be translated into Internet Packet Detector in Chinese), the packet loss rate can be measured by a network monitoring tool or by performing a UDP data stream test (User Datagram Protocol, Chinese name: User Datagram Protocol), the real-time temperature can be measured by an environmental sensor, the reference temperature value can be obtained through historical experience, the real-time humidity can be measured by a humidity sensor, and the real-time wind speed can be measured by an anemometer. The ideal humidity can be obtained through expert experience, and the ideal wind speed can be obtained through expert experience.

[0046] The broadband influence weight, network delay influence weight, packet loss rate influence weight, actual temperature influence weight, actual humidity influence weight, and actual wind speed influence weight can be obtained through the dynamic information database. Through the relationship between the broadband, network delay, packet loss rate, actual temperature, actual humidity, actual wind speed in the historical data and the dynamic information, a mapping set of broadband, network delay, packet loss rate, actual temperature, actual humidity and actual wind speed and their corresponding weights is established respectively. By inputting the real-time broadband, network delay, packet loss rate, actual temperature, actual humidity, and actual wind speed, the corresponding broadband influence weight, network delay influence weight, packet loss rate influence weight, actual temperature influence weight, actual humidity influence weight, and actual wind speed influence weight in the mapping set are obtained.

[0047] Furthermore, the specific steps of cutting the image and selecting the corresponding transmission strategy according to the relationship between the dynamic information evaluation value and the static information evaluation value and the dynamic information evaluation value threshold and the static information evaluation value threshold are as follows: if the dynamic information evaluation value is greater than the dynamic information evaluation value threshold and the static information evaluation value is greater than the static information evaluation value threshold, the image is cut using the first specification image block, lossless compression and TCP protocol are used, and the key area is preferentially transmitted to the monitoring terminal; if the dynamic information evaluation value is greater than the dynamic information evaluation value threshold and the static information evaluation value is below the static information evaluation value threshold, the image is cut using the first specification image block, lossy compression and TCP protocol are used, and the static information is preferentially transmitted in the important area. send it to the monitoring terminal; when the dynamic information evaluation value is less than the dynamic information evaluation value threshold, the image block size is gradually reduced according to the preset amplitude based on the first specification image block, and further adjusted according to the instruction of the monitoring terminal; if the dynamic information evaluation value is below the dynamic information evaluation value threshold and the static information evaluation value is greater than the static information evaluation value threshold, lossless compression and UDP protocol are adopted to give priority to the transmission of key areas to ensure that important information is not lost and sent to the monitoring terminal; if the dynamic information evaluation value is below the dynamic information evaluation value threshold and the static information evaluation value is below the static information evaluation value threshold, lossy compression is adopted to reduce the image quality and UDP protocol, and the image is transmitted according to priority sorting to ensure the integrity of the basic image information and send it to the monitoring terminal.

[0048] In this embodiment, the key areas refer to the state of the power line, the temperature along the line and other relevant meteorological data. The important areas are the state of the transmission line material, the tilt of the transmission tower, etc.

[0049] The lossy compression method is: WebP compression (full name in English: Web Picture, Chinese name: Web Image Format Compression), which realizes compression through predictive coding, transformation, quantization and entropy coding; the lossless compression method is: PNG compression (full name in English: Portable Network Graphics, Chinese name: Portable Network Graphics Format Compression), which uses lossless compression algorithms such as Deflate (full name in English: Deflate Compression Algorithm, Chinese name: Deflate compression algorithm) to compress image data. The PNG format reduces data redundancy through prediction and filtering technology. TCP protocol (full name in English: Transmission Control Protocol, Chinese name: Transmission Control Protocol), UDP protocol (full name in English: User Datagram Protocol, Chinese name: User Datagram Protocol).

[0050] The dynamic information evaluation value is greater than the dynamic information evaluation value threshold and the static information evaluation value is greater than the static information evaluation value threshold: use the first specification image block to reduce the number of transmissions, improve transmission efficiency, lossless compression, ensure the integrity and high quality of image data, use the Transmission Control Protocol (TCP) to ensure that data packets arrive in order and without loss, give priority to transmitting key areas, and ensure the transmission of key image details.

[0051] The dynamic information evaluation value is greater than the dynamic information evaluation value threshold and the static information evaluation value is below the static information evaluation value threshold: use the first specification image block to reduce the number of transmissions, improve transmission efficiency, lossy compression, reduce the amount of data while ensuring a certain image quality, use the transmission control protocol to ensure that data packets arrive in order and without loss, and give priority to transmitting important areas according to static information to ensure the transmission quality of important content.

[0052] The dynamic information evaluation value is below the dynamic information evaluation value threshold and the static information evaluation value is greater than the static information evaluation value threshold: use the second specification image block to reduce the amount of data transmitted each time, lossless compression to ensure the integrity and high quality of the image data, use the User Datagram Protocol (UDP) to reduce transmission delays, give priority to transmission of key areas, and ensure the transmission of important information.

[0053] The dynamic information evaluation value is below the dynamic information evaluation value threshold and the static information evaluation value is below the static information evaluation value threshold: use the second specification image block to reduce the amount of data transmitted each time, lossy compression, appropriately reduce the image quality to reduce the amount of data, use the user datagram protocol, reduce transmission delay, and transmit according to priority sorting to ensure the integrity of basic image information.

[0054] Furthermore, the visualization includes: outputting the changes of network bandwidth, delay, packet loss rate over time, and the adjustment of image cutting block size under different network conditions in the form of a line graph; displaying the original image, the divided image blocks, and the identified key areas in the form of a heat map; outputting the changes of image reception quality evaluation value over time and the changes of speed-related parameters in the form of a line graph; and outputting the transmission strategy selection under different combinations of dynamic information evaluation values ​​and static information evaluation values ​​in the form of a decision tree.

[0055] In this embodiment, this method visualizes the time series data of network parameters through a line graph, which can clearly show how network bandwidth, delay and packet loss rate change over time. In addition, as the network conditions change, the adjustment of the image cut block size can also be intuitively displayed, thereby helping to understand the impact of different network environments on the policies sent to the monitoring terminal.

[0056] Furthermore, dynamic adjustment and optimization according to the transmission effect are specifically as follows: reconstructing a complete image through image data blocks received by the monitoring terminal; evaluating an image reception quality assessment value based on the reconstructed complete image; the image reception quality assessment value is used to describe the comprehensive assessment data of the quality of the received image and the transmission efficiency; obtaining an image reception quality assessment value threshold from a database, and comparing the image reception quality assessment value with the image reception quality assessment value threshold; if the image reception quality assessment value is less than the image reception quality assessment value threshold, an instruction is issued to the image sending end to continue to reduce the image block size step by step according to a preset amplitude, until the image reception quality assessment value is greater than or equal to the image reception quality assessment value threshold, then an instruction is issued to the image sending end to end the step-by-step reduction of the image block size.

[0057] In this embodiment, priority sorting: the importance of image content, the more important the image content, the higher the priority, and the higher the image complexity, the higher the priority. According to the importance and complexity of the image content, each image block is prioritized. Image blocks with high priority will be processed first during transmission to ensure that the transmission of important information is not affected. Real-time monitoring of network conditions includes bandwidth, delay, and packet loss rate. Bandwidth is used to monitor the current available bandwidth in real time, record the maximum, minimum and average values ​​of bandwidth, delay is used to detect network delay, ensure the timeliness of data packet transmission, and packet loss rate is used to monitor packet loss to ensure the integrity and stability of data transmission. Dynamic adjustment of image cutting block size: The judgment method for good network conditions is: if high bandwidth, low delay and low packet loss rate are monitored, the first specification image block is used. In this case, the first specification image block can reduce the number of transmissions, reduce the overhead of the transmission protocol, and improve transmission efficiency. The judgment method for poor network conditions is: if low bandwidth, high delay and high packet loss rate are monitored, the second specification image block is used. The second specification image block can reduce the amount of data transmitted each time, reduce the impact of packet loss and retransmission, and ensure the stability and reliability of transmission. Analyzing image content includes analyzing image complexity and identifying key areas. Image complexity uses algorithms to analyze the texture complexity and edge density of the image to determine the overall complexity of the image. Key area identification: Use image analysis technology to identify key areas in the image, which usually contain important details and information. Determine the block strategy: Develop an image block strategy based on the complexity of the image and the distribution of key areas. Prioritize cutting and transmitting key areas in the image to ensure priority transmission of important information.

[0058] Adjust the transmission order of image blocks: Combined with the network status, monitor the network status in real time, and dynamically adjust the transmission order of image blocks according to the current network bandwidth, delay and packet loss rate. When the network status is good, give priority to transmitting large blocks of images to reduce the number of transmissions and improve efficiency; when the network status is poor, give priority to transmitting small blocks of images to ensure that the amount of data transmitted each time is controllable and reduce the risk of packet loss. Feedback from the receiving end: adjust the transmission strategy of image blocks according to the feedback information from the receiving end. If the receiving end feedbacks that the image blocks are lost or distorted, recompress and transmit these image blocks. Update the transmission strategy in real time through the feedback information from the receiving end to ensure the quality and integrity of the images sent to the monitoring terminal. Real-time optimization of transmission strategy: Combined with the current network status and feedback from the receiving end, optimize the transmission strategy in real time. Adjust the transmission order of image blocks according to the priority and network status of image blocks, set the threshold of image reception quality assessment value, and dynamically update the threshold according to the actual transmission situation. Ensure that the image reception quality is within the preset range.

[0059] Furthermore, dynamic adjustment and optimization are performed according to the transmission effect: it also includes: real-time updating of the dynamic information evaluation value threshold and the static information evaluation value threshold; obtaining the image reception quality evaluation value threshold, and comparing the image reception quality evaluation value with the image reception quality evaluation value threshold; if the image reception quality evaluation value is less than the image reception quality evaluation value threshold, then increasing the dynamic information evaluation value threshold and the static information evaluation value threshold alternately step by step according to the preset increment until the image reception quality evaluation value is greater than the image reception quality evaluation value threshold.

[0060] In this embodiment, the image reception quality evaluation value calculation formula is:

[0061]

[0062] Where Q represents the image reception quality evaluation value, which is used to judge the image reception quality, V represents the image transmission speed, A represents the image block distortion, L represents the network delay, P represents the packet loss rate, β1 represents the influence weight of the image block distortion, β2 represents the influence weight of the image transmission speed, β3 represents the influence weight of the network delay, and β4 represents the influence weight of the packet loss rate.

[0063] Step-by-step alternation means that the static information evaluation value threshold is kept unchanged while the dynamic information evaluation value threshold is updated according to a preset increment, and the dynamic information evaluation value threshold is kept unchanged while the static information evaluation value threshold is updated according to a preset increment, so that the image reception quality evaluation value is greater than the value before the step-by-step alternation update until the maximum value of the image reception quality evaluation value is reached.

[0064] The image reception quality evaluation value can also be obtained through historical experience, the image transmission speed is obtained by measuring the data transmission speed through network testing tools such as iPerf, the image block distortion is calculated by comparing the original image and the received image through image quality evaluation tools such as SSIM (full name: Structural Simi larity Index Measure, the Chinese name is (structural similarity index)), the network delay can be measured by network diagnostic tools such as Ping (the English full name is Packet Internet Groper, which can be translated into Chinese as (Internet packet detector)), and the packet loss rate can be measured by network monitoring tools or UDP data stream testing (User Datagram Protocol, the Chinese name is: User Datagram Protocol).

[0065] The influence weight of image block distortion, image transmission speed, network delay, and packet loss rate can be obtained through the image reception quality database. The relationship between image block distortion, image transmission speed, network delay, packet loss rate and image reception quality in historical data is used to establish a mapping set of image block distortion, image transmission speed, network delay, packet loss rate and their corresponding weights. The corresponding image block distortion influence weight, image transmission speed influence weight, network delay influence weight, and packet loss rate influence weight in the mapping set are obtained by inputting real-time image block distortion, image transmission speed, network delay, and packet loss rate. As shown in Table 1:

[0066] Table 1 Image reception quality data table

[0067]

[0068]

[0069] Among them, β1=0.3, β2=0.2, β3=0.4, β4=0.1. Figure 2 It can be seen from Table 1 that the image reception quality assessment values ​​of group 3, group 4, group 5, and group 9 are less than the image reception quality assessment value threshold, and the image reception quality assessment value threshold is 0.005, which proves that the dynamic information assessment value threshold and the static information assessment value threshold of the corresponding group need to be modified, and the dynamic information assessment value threshold and the static information assessment value threshold need to be increased for the first time.

[0070] like Figure 3As shown, it is a structural schematic diagram of a visual image compression detection system for power transmission lines provided in an embodiment of the present application. The visual image compression detection system for power transmission lines provided in an embodiment of the present application includes a data acquisition module, an information evaluation module, a threshold setting module, a visualization module, and a real-time optimization module; wherein the data acquisition module is used to obtain the power transmission line image data, environmental data, and network data sent to the monitoring terminal, and divide the power transmission line image data, environmental data, and network data into dynamic information and static information; the information evaluation module is used to evaluate the dynamic information and the static information respectively, and analyze to obtain the dynamic information evaluation value and the static information evaluation value; the threshold setting module is used to set the dynamic information evaluation value threshold and the static information evaluation value threshold, and cut the image and select the corresponding transmission strategy according to the relationship between the dynamic information evaluation value and the static information evaluation value and the dynamic information evaluation value threshold and the static information evaluation value threshold; the visualization module is used to compress the cut image and send it to the monitoring terminal for visualization; the real-time optimization module is used to dynamically adjust and optimize according to the transmission effect.

[0071] An embodiment of the present application also provides an electronic device, including: a processor, and a memory for storing instructions executable by the processor; when the processor is configured to execute the instructions, the electronic device implements a method for visual image compression detection of power transmission lines.

[0072] In summary, this embodiment dynamically adjusts the cutting block size and priority, thereby optimizing the transmission strategy under different network conditions, ensuring improved data integrity and transmission efficiency, and further achieving fast and distortion-free image transmission, effectively solving the problem of image distortion in the prior art while ensuring transmission speed.

[0073] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0074] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0075] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0077] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0078] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. Visual image compression detection method for power transmission lines, It is characterized in that The following steps are involved: Acquire the transmission line image data, environmental data and network data sent to the monitoring terminal, and divide the transmission line image data, environmental data and network data into dynamic information and static information; Evaluate the dynamic information and the static information respectively, and obtain the dynamic information evaluation value and the static information evaluation value by analysis; Setting a dynamic information evaluation value threshold and a static information evaluation value threshold, and cutting the image and selecting a corresponding transmission strategy according to the relationship between the dynamic information evaluation value and the static information evaluation value and the dynamic information evaluation value threshold and the static information evaluation value threshold; The cut images are compressed and sent to the monitoring terminal for visualization; Dynamically adjust and optimize according to transmission effect.

2. The visual image compression detection method for power transmission lines according to claim 1, Features: The steps of analyzing and obtaining the dynamic information evaluation value are as follows: Get dynamic and static information; The dynamic information includes network data and environmental data; The network data is the network data in the process of sending the transmission line image data to the monitoring terminal; The network data includes bandwidth, maximum bandwidth, minimum bandwidth, average bandwidth, delay, and packet loss rate; The environmental data includes the real-time temperature, real-time humidity, and real-time wind speed of the sending end; The dynamic information evaluation value is obtained through comprehensive analysis of network data and environmental data; The dynamic information evaluation value represents quantitative data obtained through dynamic information analysis and used to evaluate the quality of the network environment.

3. The visual image compression detection method for power transmission lines according to claim 1, Features: The specific process of analyzing and obtaining the static information evaluation value is as follows: Get static information; The static information includes: transmission line image data sent to the monitoring terminal; The transmission line image data sent to the monitoring terminal includes: edge density, image block distortion, number of key areas, area of ​​key areas, and gray level probability; Obtaining a static information evaluation value based on a comprehensive evaluation of the transmission line image data sent to the monitoring terminal; The static information evaluation value represents quantitative data obtained based on static information analysis and used to measure the quality of the sent picture.

4. The visual image compression detection method for power transmission lines according to claim 2, Features: The calculation formula of the dynamic information evaluation value is: In the formula, Z 1 represents the dynamic information evaluation value, B represents the bandwidth, and B max Indicates the maximum bandwidth, B min represents the minimum bandwidth, L represents the network delay, and L 0 represents the network delay reference value, P represents the packet loss rate, T represents the real-time temperature, and T 0 Indicates the reference temperature value, H indicates the real-time humidity, H 0 represents the ideal humidity, W represents the real-time wind speed, and W 0 Indicates the ideal wind speed. 1 represents the broadband influence weight, ω 2 Indicates the weight of network delay impact, ω 3 Indicates the weight of packet loss rate, ω 4 Indicates the actual temperature influence weight, ω 5 Indicates the actual humidity impact weight, ω 6 Indicates the actual wind speed impact weight.

5. The visual image compression detection method for power transmission lines according to claim 1, Features: The specific steps of cutting the image and selecting the corresponding transmission strategy according to the relationship between the dynamic information evaluation value and the static information evaluation value and the dynamic information evaluation value threshold and the static information evaluation value threshold are: If the dynamic information evaluation value is greater than the dynamic information evaluation value threshold and the static information evaluation value is greater than the static information evaluation value threshold, the image is cut using the first specification image block, lossless compression is used, TCP protocol is used, and the key area is preferentially transmitted to send to the monitoring terminal; If the dynamic information evaluation value is greater than the dynamic information evaluation value threshold and the static information evaluation value is less than the static information evaluation value threshold, the image is cut using the first specification image block, lossy compression is used, and the TCP protocol is used, and the image is sent to the monitoring terminal in a manner that static information is transmitted with priority to important areas; When the dynamic information evaluation value is less than the dynamic information evaluation value threshold, the image block size is gradually reduced according to a preset amplitude based on the first specification image block, and further adjusted according to the instruction of the monitoring terminal; If the dynamic information evaluation value is below the dynamic information evaluation value threshold and the static information evaluation value is greater than the static information evaluation value threshold, lossless compression and UDP protocol are used to transmit the key area first, so as to ensure that important information is not lost and sent to the monitoring terminal; If the dynamic information evaluation value is below the dynamic information evaluation value threshold and the static information evaluation value is below the static information evaluation value threshold, lossy compression is used to reduce the image quality and UDP protocol is used to transmit according to priority sorting to ensure the integrity of basic image information and send it to the monitoring terminal.

6. The visual image compression detection method for power transmission lines according to claim 1, Features: The contents to be visualized include: Output the changes of network bandwidth, delay, packet loss rate over time, and the adjustment of image cutting block size under different network conditions in the form of line graphs; Display the original image, the divided image blocks, and the identified key areas in the form of a heat map; Output the changes of image reception quality evaluation value over time and speed-related parameters in the form of a line graph; The transmission strategy selection situations under different combinations of dynamic information evaluation values ​​and static information evaluation values ​​are output in the form of a decision tree.

7. The visual image compression detection method for power transmission lines according to claim 1, Features: The dynamic adjustment and optimization according to the transmission effect is specifically as follows: Reconstructing a complete image through image data blocks received by the monitoring terminal; evaluating an image reception quality assessment value based on the reconstructed complete image; The image reception quality evaluation value is used to describe the comprehensive evaluation data of the quality and transmission efficiency of the received image; An image reception quality assessment value threshold is obtained from a database, and the image reception quality assessment value is compared with the image reception quality assessment value threshold; if the image reception quality assessment value is less than the image reception quality assessment value threshold, an instruction is issued to the image sending end to continue to reduce the image block size step by step according to a preset amplitude until the image reception quality assessment value is greater than or equal to the image reception quality assessment value threshold, then an instruction is issued to the image sending end to end the step-by-step reduction of the image block size.

8. The visual image compression detection method for power transmission lines according to claim 1, Features: The dynamic adjustment and optimization according to the transmission effect also includes: Update dynamic information evaluation value threshold and static information evaluation value threshold in real time; Obtain an image reception quality assessment value threshold, and compare the image reception quality assessment value with the image reception quality assessment value threshold; if the image reception quality assessment value is less than the image reception quality assessment value threshold, alternately increase the dynamic information assessment value threshold and the static information assessment value threshold step by step according to a preset increment until the image reception quality assessment value is greater than the image reception quality assessment value threshold.

9. A visual image compression detection system for power transmission lines, It is characterized in that include: Data acquisition module, information evaluation module, threshold setting module, visualization module, real-time optimization module; The data acquisition module is used to obtain the transmission line image data, environmental data and network data sent to the monitoring terminal, and divide the transmission line image data, environmental data and network data into dynamic information and static information; The information evaluation module is used to evaluate the dynamic information and the static information respectively, and analyze and obtain the dynamic information evaluation value and the static information evaluation value; The threshold setting module is used to set the dynamic information evaluation value threshold and the static information evaluation value threshold, and cut the image and select the corresponding transmission strategy according to the relationship between the dynamic information evaluation value and the static information evaluation value and the dynamic information evaluation value threshold and the static information evaluation value threshold; The visualization module is used to compress the cut images and send them to the monitoring terminal for visualization; The real-time optimization module is used to perform dynamic adjustment and optimization according to the transmission effect.

10. An electronic device, It is characterized in that include: a processor, a memory for storing instructions executable by the processor; When the processor is configured to execute the instructions, the electronic device implements the method for compression detection of visual images of power transmission lines as described in any one of claims 1-8.

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