Visualization Image Compression Detection Method, System and Equipment for Transmission Lines

Abstract

The present invention discloses a method, system and device for visual image compression detection of transmission lines. This method belongs to the technical field of image data processing and includes the following steps: obtaining the transmission line image data, environmental data and network data sent to the monitoring terminal, and dividing them into dynamic information and static information; obtaining the dynamic information evaluation value and the static information evaluation value; setting the dynamic information evaluation value threshold and the static information evaluation value threshold, and 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; compressing the cut image and sending it to the monitoring terminal for visualization; dynamically adjusting and optimizing according to the transmission effect. The present invention makes the image transmission fast and distortion-free by dynamically adjusting the cutting block size and priority, and solves the problem of image distortion in the prior art when ensuring the transmission speed.

Description

Technical Field

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

[0002] With the rapid development of modern power systems, the monitoring and maintenance of transmission lines have become increasingly important. A transmission line refers to a line used for transmitting electric power in a power supply system, usually including high-voltage transmission lines and distribution lines. Image compression refers to reducing the redundant information of image data to reduce the size of the image file, thereby saving storage space and transmission bandwidth.

[0003] The existing visual image compression detection system for transmission lines is realized by professionals regularly conducting on-site inspections of transmission lines, installing fixed monitoring devices such as on-line monitoring sensors and infrared cameras at key nodes of transmission lines, or using helicopters and drones for inspection.

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

[0005] For example, the image compression method, device, electronic device and storage medium disclosed in the invention patent with the publication number: CN112102320B include: obtaining an initial image to be compressed and the target compression parameters of the initial image; using the target compression method to compress the initial image into 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, degrading the intermediate image according to the target quality to obtain a target image. Through the present application, the initial image is size-compressed according to the target compression method in the obtained target compression parameters 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 it is greater, the intermediate image is degraded according to the target quality in the target compression parameters to obtain a target image.

[0006] However, in the process of implementing the technical solutions of the present invention in the embodiments of the present application, it is found that the above technologies have at least the following technical problems:

[0007] In the prior art, abnormalities occur during the image compression and transmission of power transmission lines. The distortion of the 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] By providing a method, system and device for visual image compression detection of power transmission lines, embodiments of the present application solve the problem of image distortion in the prior art while ensuring the transmission speed, and achieve fast and distortion-free image transmission.

[0009] Embodiments of the present application provide a method for visual image compression detection of power transmission lines, including the following steps: obtaining power transmission line image data, environmental data and network data sent to the monitoring terminal, and dividing the power transmission line image data, environmental data and network data into dynamic information and static information; respectively evaluating the dynamic information and static information, 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; dynamically adjusting and optimizing according to the transmission effect.

[0010] Further, the step of analyzing to obtain the dynamic information evaluation value is: obtaining dynamic information and static information; the dynamic information includes network data and environmental data; the network data is the network data during the process of sending the power transmission line image data to the monitoring terminal; the network data includes bandwidth, maximum value of bandwidth, minimum value of bandwidth, average value of bandwidth, delay, packet loss rate; the environmental data includes the real-time temperature, real-time humidity, and real-time wind speed at the sending end; comprehensively analyzing the network data and environmental data to obtain a dynamic information evaluation value; the dynamic information evaluation value represents the quantization data for evaluating the network environment quality obtained according to the analysis of the dynamic information.

[0011] Further, the specific process of analyzing to obtain the static information evaluation value is: obtaining static information; the static information includes: power transmission line image data sent to the monitoring terminal; the power transmission line image data sent to the monitoring terminal includes: edge density, image block distortion degree, number of key areas, area of key areas, gray level probability; comprehensively evaluating the power transmission line image data sent to the monitoring terminal to obtain a static information evaluation value; the static information evaluation value represents the quantization data for measuring the quality of the sent picture obtained according to the analysis of the static information.

[0012] Further, 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, B max represents the maximum value of the bandwidth, B min represents the minimum value of the bandwidth, L represents the network delay, L0 represents the network delay reference value, P represents the packet loss rate, T represents the real-time temperature, T0 represents the reference temperature value, H represents the real-time humidity, H0 represents the ideal humidity, W represents the real-time wind speed, W0 represents the ideal wind speed, ω1 represents the broadband influence weight, ω2 represents the network delay influence weight, ω3 represents the packet loss rate influence weight, ω4 represents the actual temperature influence weight, ω5 represents the actual humidity influence weight, and ω6 represents the actual wind speed influence weight.

[0015] Further, the specific steps of cutting the image according to the relationship between the dynamic information evaluation value and the static information evaluation value and the thresholds of the dynamic information evaluation value and the static information evaluation value and selecting the corresponding transmission strategy 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 the TCP protocol, and the key area is preferentially transmitted and sent 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 the TCP protocol, and the important area is preferentially transmitted according to the static information and sent 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 in accordance with a preset amplitude based on the first specification image block, and further adjusted according to the instructions 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 the UDP protocol are used, and the key area is preferentially transmitted 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 the UDP protocol is used to transmit in accordance with the priority order to ensure the integrity of the basic image information and sent to the monitoring terminal.

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

[0017] Further, the dynamic adjustment and optimization according to the transmission effect are specifically as follows: reconstruct a complete image through the image data blocks received by the monitoring terminal; evaluate the image reception quality evaluation value according to 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; obtain the image reception quality evaluation value threshold from the database, and compare 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, send an instruction to the image sending end to continue to gradually reduce the image block size level by level according to a preset amplitude until the image reception quality evaluation value is greater than or equal to the image reception quality evaluation value threshold, and then send an instruction to the image sending end to end the gradual reduction of the image block size.

[0018] Further, the dynamic adjustment and optimization according to the transmission effect further include: real-time updating 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, gradually alternately increase the dynamic information evaluation value threshold and the static information evaluation value threshold level by level according to a preset increment until the image reception quality evaluation value is greater than the image reception quality evaluation value threshold.

[0019] Further, a power transmission line visualization image compression detection system is characterized by comprising: 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 classify the power transmission line image data, environmental data, and network data into dynamic information and static information; the information evaluation module is used to respectively evaluate the dynamic information and the static information, and analyze to obtain a dynamic information evaluation value and a 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 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 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 perform dynamic adjustment and optimization according to the transmission effect.

[0020] An embodiment of the present application 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 power transmission line visualization image compression detection.

[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 cutting block size and priority, the transmission strategy is optimized under different network conditions, ensuring improved data integrity and transmission efficiency, thereby achieving fast and distortion-free image transmission and effectively solving the problem of image distortion in the prior art when ensuring the transmission speed.

[0023] 2. By real-time monitoring the network conditions and image content, the transmission order and optimization strategy are dynamically adjusted, enabling the system to quickly adapt to changes in network load and image complexity, and thus achieving precise monitoring and rapid response to the state of the transmission line.

[0024] 3. Through image reconstruction and correction, effective management and optimization of the image data at the receiving end are achieved, reducing distortion and loss in data transmission, and thus 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 It is a flowchart of a method for visual image compression detection of a transmission line provided in an embodiment of the present application;

[0026] Figure 2 It is a graph of image reception quality data provided in an embodiment of the present application;

[0027] Figure 3 It is a schematic structural diagram of a system for visual image compression detection of a transmission line provided in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] In the embodiments of the present application, by providing a method, system, and device for visual image compression detection of a transmission line, the problem of image distortion in the prior art when ensuring the transmission speed is solved, and fast and distortion-free image transmission is achieved by dynamically adjusting the cutting block size and priority.

[0029] The technical solution in the embodiment of the present application aims to solve the problem of image distortion that occurs while ensuring the transmission speed. The general 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; perform dynamic adjustment and optimization according to the transmission effect.

[0030] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the specification drawings and specific implementation manners.

[0031] As Figure 1 shown, it is a flowchart of a method for visual image compression detection of a transmission line provided by an embodiment of the present application. The method includes the following steps: 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; perform dynamic adjustment and optimization according to the transmission effect.

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

[0033] Furthermore, the step of analyzing to obtain the dynamic information evaluation value is as follows: Obtain the dynamic information and static information; the dynamic information includes network data and environmental data; the network data is the network data during the process of sending the transmission line image data to the monitoring terminal; the network data includes bandwidth, the maximum value of the bandwidth, the minimum value of the bandwidth, the average value of the bandwidth, latency, packet loss rate; the environmental data includes the real-time temperature, real-time humidity, and real-time wind speed at the sending end; comprehensively analyze the network data and environmental data to obtain the dynamic information evaluation value; the dynamic information evaluation value represents the quantization data for evaluating the network environment quality obtained according to the dynamic information analysis.

[0034] In this embodiment, the dynamic information includes: bandwidth, latency, 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] Further, the specific process of obtaining the static information evaluation value is as follows: Obtain the 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 degree, number of key areas, area of key areas, and gray-level probability; comprehensively evaluate the static information evaluation value based on the transmission line image data sent to the monitoring terminal; the static information evaluation value represents the quantitative data for measuring the quality of the sent pictures obtained by analyzing the static information.

[0036] In this embodiment,

[0037]

[0038] In the formula, Z2 represents the static information evaluation value, ρ represents the edge density, D represents the image block distortion degree, M represents the number of key areas, S represents the area of key areas, and p i represents the gray-level probability, i represents the gray-level number, i = 1, 2,..., n, and n represents the total number of gray levels. α1 represents the influence weight of the edge density, α2 represents the influence weight of the texture complexity, α3 represents the influence weight of the number of key areas, α4 represents the influence weight of the area of key areas, and α5 represents the influence weight of the 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 the number of edge pixels to the total number of pixels. The image block distortion degree is calculated by comparing the differences between the original image and the processed image (such as using MSE - mean square error). The number of key areas can be identified and counted through image analysis techniques (such as algorithms based on feature recognition). The area of key areas can be obtained by calculating the total number of pixels in these areas after identifying the key areas. The gray-level probability can be obtained by dividing the number of pixels of each gray level in the image by the total number of pixels.

[0040] The edge density influence weight, texture complexity influence weight, key area quantity influence weight, key area area influence weight, and gray level influence weight can be obtained from the static information database. By establishing the relationship between the edge density, texture complexity, key area quantity, key area area, and gray level in the historical data and the static information respectively, mapping sets of the edge density, texture complexity, key area quantity, key area area, and gray level in the historical data and their corresponding weights are established. The real-time edge density, texture complexity, key area quantity, key area area, and gray level are input to obtain the corresponding edge density influence weight, texture complexity influence weight, key area quantity influence weight, key area area influence weight, and gray level influence weight in the mapping set.

[0041] The block distortion of an image is used to evaluate the loss of image quality due to compression or previous transmission errors. A high distortion may require adjusting the compression algorithm or changing the block size to reduce further losses. 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 between the compressed image and the original image are calculated to evaluate the distortion degree introduced by compression. Compression after cutting has the following advantages: 1. Compressing smaller image blocks can manage and schedule network resources more effectively, especially in poor network conditions. Each block can independently optimize the compression rate according to its content and importance. 2. If a certain image block is damaged or lost during transmission, only that block needs to be retransmitted, and there is no need to retransmit the entire image, which can improve the recovery speed and network efficiency. Disadvantages: 1. Multiple image blocks after cutting need to be compressed separately, which may lead to an increase in processing time. The compression settings for each block may need to be adjusted separately, increasing the processing complexity. 2. If the cutting and compression are improper, visual incoherence may be introduced between the image blocks, affecting the overall quality of the image. Considerations in actual application scenarios: Real-time video stream: Usually, the strategy of cutting first and then compressing is adopted to reduce latency and improve the error recovery speed; High-quality image storage: Such as medical imaging, it 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 for the dynamic information evaluation value is:

[0043]

[0044] In the formula, Z1 represents the dynamic information evaluation value, B represents the bandwidth, B max represents the maximum value of the bandwidth, B minLet \(B_{min}\) represent the minimum value of the bandwidth, \(L\) represent the network latency, \(L_0\) represent the reference value of the network latency, \(P\) represent the packet loss rate, \(T\) represent the real-time temperature, \(T_0\) represent the reference temperature value, \(H\) represent the real-time humidity, \(H_0\) represent the ideal humidity, \(W\) represent the real-time wind speed, \(W_0\) represent the ideal wind speed, \(\omega_1\) represent the bandwidth impact weight, \(\omega_2\) represent the network latency impact weight, \(\omega_3\) represent the packet loss rate impact weight, \(\omega_4\) represent the actual temperature impact weight, \(\omega_5\) represent the actual humidity impact weight, and \(\omega_6\) represent the actual wind speed impact weight.

[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 using a bandwidth testing tool such as iPerf. The maximum value of the bandwidth and the minimum value of the bandwidth can be obtained through historical experience. The network latency can be measured by a network diagnostic tool such as Ping (the full English name 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 in real time by a humidity sensor. The real-time wind speed can be measured by an anemometer. The ideal humidity can be obtained through expert experience. The ideal wind speed can be obtained through expert experience.

[0046] The bandwidth impact weight, network latency impact weight, packet loss rate impact weight, actual temperature impact weight, actual humidity impact weight, and actual wind speed impact weight can be obtained from the dynamic information database. Mapping sets of the bandwidth, network latency, packet loss rate, actual temperature, actual humidity, and actual wind speed and their corresponding weights are established respectively based on the relationships between the bandwidth, network latency, packet loss rate, actual temperature, actual humidity, actual wind speed in historical data and the dynamic information. By inputting the real-time bandwidth, network latency, packet loss rate, actual temperature, actual humidity, and actual wind speed, the corresponding bandwidth impact weight, network latency impact weight, packet loss rate impact weight, actual temperature impact weight, actual humidity impact weight, and actual wind speed impact weight in the mapping set can be obtained.

[0047] Further, 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 the TCP protocol, and the key area is preferentially transmitted and sent 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 the TCP protocol, and the important area is preferentially transmitted according to the static information and sent to the monitoring terminal; when the dynamic information evaluation value is less than the dynamic information evaluation value threshold, based on the first specification image block, the image block size is gradually reduced by a preset amplitude and further adjusted according to the instructions 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 the UDP protocol are used, and the key area is preferentially transmitted 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 the UDP protocol is used to transmit according to the priority order to ensure the integrity of the basic image information and sent to the monitoring terminal.

[0048] In this embodiment, the key area refers to the state of the electric circuit, the temperature along the line, and other relevant meteorological data. The important area is the state of the transmission line material, the inclination degree of the transmission tower, etc.

[0049] The method of lossy compression is: WebP compression (the full English name is Web Picture, and the Chinese name is Web picture format compression), which realizes compression through predictive coding, transformation, quantization, and entropy coding; the method of lossless compression is: PNG compression (the full English name is: Portable Network Graphics, and the Chinese name is portable network graphic format compression), which uses a lossless compression algorithm such as Deflate (the full English name is Deflate Compression Algorithm, and the Chinese name is Deflate compression algorithm) to compress the image data. The PNG format reduces data redundancy through prediction and filtering techniques. TCP protocol (the full English name is Transmission Control Protocol, and the Chinese name is transmission control protocol), UDP protocol (the full English name is User Datagram Protocol, and the Chinese name is 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: Adopt the first specification image block, reduce the number of transmissions, improve the transmission efficiency, perform lossless compression, ensure the integrity and high quality of the image data, use the Transmission Control Protocol (TCP), ensure that the data packets arrive in order and without loss, and preferentially transmit the key area to 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: Adopt the first specification image block, reduce the number of transmissions, improve the transmission efficiency, perform lossy compression, reduce the data volume while ensuring a certain image quality, use the Transmission Control Protocol, ensure that the data packets arrive in order and without loss, and preferentially transmit the important area according to the 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: Adopt the second specification image block, reduce the data volume transmitted each time, perform lossless compression, ensure the integrity and high quality of the image data, use the User Datagram Protocol (UDP), reduce the transmission delay, and preferentially transmit the key area to 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: Adopt the second specification image block, reduce the data volume transmitted each time, perform lossy compression, appropriately reduce the image quality to reduce the data volume, use the User Datagram Protocol, reduce the transmission delay, and perform transmission according to the priority order to ensure the integrity of the basic image information.

[0054] Furthermore, the content for visualization includes: Output the changes of network bandwidth, delay, and packet loss rate over time, and the adjustment of the image cutting block size under different network conditions in the form of a line chart; Display the original image, the image blocks after segmentation, and the identified key areas in the form of a heat map; Output the changes of the image reception quality evaluation value over time and the changes of speed-related parameters in the form of a line chart; Output the selection situation of transmission strategies 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 chart, which can clearly show how the network bandwidth, delay, and packet loss rate change over time. In addition, with the change of the network condition, the adjustment of the image cutting block size can also be intuitively shown, thus helping to understand the impact of different network environments on the strategy sent to the monitoring terminal.

[0056] Further, the dynamic adjustment and optimization according to the transmission effect are specifically as follows: reconstruct the complete image by monitoring the image data blocks received by the terminal; evaluate the image reception quality evaluation value according to 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; obtain the image reception quality evaluation value threshold from the database, and compare 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, send an instruction to the image sending end to continue to gradually decrease the image block size by a preset amplitude until the image reception quality evaluation value is greater than or equal to the image reception quality evaluation value threshold, then send an instruction to the image sending end to end the gradual decrease of the image block size.

[0057] In this embodiment, the priority sorting is as follows: the importance of the 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 sorted by priority. The image blocks with high priority will be preferentially processed during transmission to ensure that the transmission of important information is not affected. Real-time monitoring of the network status includes bandwidth, latency, and packet loss rate. Bandwidth is used to monitor the currently available bandwidth in real time, record the maximum, minimum, and average values of the bandwidth, latency is used to detect network latency to ensure the timeliness of packet transmission, and the packet loss rate is used to monitor the packet loss situation to ensure the integrity and stability of data transmission. Dynamically adjust the image cutting block size: The determination method for good network conditions is: if it is detected that the bandwidth is high, the latency is low, and the packet loss rate is low, then 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 the transmission efficiency. The determination method for poor network conditions is: if it is detected that the bandwidth is low, the latency is high, and the packet loss rate is high, then 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. Analyze the image content including analyzing the image complexity and identifying key regions. The image complexity analyzes the texture complexity and edge density of the image through an algorithm to determine the overall complexity of the image. Key region identification: Use image analysis technology to identify the key regions in the image, which usually contain important details and information. Determine the chunking strategy: According to the complexity of the image and the distribution of key regions, formulate an image chunking strategy. Preferentially cut and transmit the key regions in the image to ensure the preferential transmission of important information.

[0058] Adjust the transmission order of image blocks: Combine with the network condition, monitor the network condition in real time, and dynamically adjust the transmission order of image blocks according to the current network bandwidth, latency, and packet loss rate. When the network condition is good, give priority to transmitting large image blocks to reduce the number of transmissions and improve efficiency; when the network condition is poor, give priority to transmitting small image blocks 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, re-compress and transmit these image blocks, and update the transmission strategy in real time through the feedback information from the receiving end to ensure the quality and integrity of the data sent to the monitoring terminal. Optimize the transmission strategy in real time, combine the current network condition and the feedback from the receiving end, and optimize the transmission strategy in real time. Adjust the transmission order of image blocks according to the priority of image blocks and the network condition, set the threshold of the image reception quality evaluation value, and dynamically update this threshold according to the actual transmission situation. Ensure that the image reception quality is within the preset range.

[0059] Furthermore, dynamically adjust and optimize according to the transmission effect: It also includes: updating the dynamic information evaluation value threshold and the static information evaluation value threshold in real time; 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, gradually increase the dynamic information evaluation value threshold and the static information evaluation value threshold alternately in accordance with a preset increment until the image reception quality evaluation value is greater than the image reception quality evaluation value threshold.

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

[0061]

[0062] In the formula, 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 degree, L represents the network latency, P represents the packet loss rate, β1 represents the influence weight of the image block distortion degree, β2 represents the influence weight of the image transmission speed, β3 represents the influence weight of the network latency, and β4 represents the influence weight of the packet loss rate.

[0063] Gradually alternating means that when the dynamic information evaluation value threshold is updated according to a preset increment, the static information evaluation value threshold remains unchanged, and when the static information evaluation value threshold is updated according to a preset increment, the dynamic information evaluation value threshold remains unchanged, so that the image reception quality evaluation value is greater than the value before the gradual alternating 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 with a network testing tool such as iPerf. The image block distortion degree is calculated by comparing the original image and the received image with an image quality evaluation tool such as SSIM (full name: Structural Similarity Index Measure, Chinese name: Structural Similarity Index). The network latency can be measured by a network diagnostic tool such as Ping (full English name: Packet Internet Groper, which can be translated into Chinese as Internet Packet Detector). The packet loss rate can be measured by a network monitoring tool or by conducting a UDP data stream test (User Datagram Protocol, Chinese name: User Datagram Protocol).

[0065] The influence weights of the image block distortion degree, the image transmission speed, the network latency, and the packet loss rate can be obtained from the image reception quality database. By establishing the mapping sets of the image block distortion degree, the image transmission speed, the network latency, and the packet loss rate and their corresponding weights based on the relationships between the image block distortion degree, the image transmission speed, the network latency, the packet loss rate and the image reception quality in historical data, the influence weights of the corresponding image block distortion degree, the image transmission speed, the network latency, and the packet loss rate in the mapping set can be obtained by inputting the real-time image block distortion degree, the image transmission speed, the network latency, and the packet loss rate. As shown in Table 1:

[0066] Table 1 Image Reception Quality Data Table

[0067]

[0068] Among them, β1 = 0.3, β2 = 0.2, β3 = 0.4, β4 = 0.1. From Figure 2 and Table 1, it can be seen that the image reception quality evaluation values of Group 3, Group 4, Group 5, and Group 9 are less than the image reception quality evaluation value threshold, and the image reception quality evaluation value threshold is 0.005, which proves that it is necessary to modify the dynamic information evaluation value threshold and the static information evaluation value threshold of the corresponding group, and increase the dynamic information evaluation value threshold and the static information evaluation value threshold for the first time.

[0069] Such as Figure 3As shown in the figure, it is a schematic structural diagram of a transmission line visualization image compression detection system provided by an embodiment of the present application. The transmission line visualization image compression detection system provided by the 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. Among them, the data acquisition module is used to obtain transmission line image data, environmental data, and network data sent to the monitoring terminal, and classify 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 static information respectively, and analyze to obtain a dynamic information evaluation value and a static information evaluation value. The threshold setting module is used to set a dynamic information evaluation value threshold and a static information evaluation value threshold, and cut the image and select 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 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 perform dynamic adjustment and optimization according to the transmission effect.

[0070] 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 detecting transmission line visualization image compression.

[0071] In summary, this embodiment dynamically adjusts the size and priority of the cut blocks, thereby optimizing the transmission strategy under different network conditions, ensuring the improvement of data integrity and transmission efficiency, and further realizing fast and distortion-free image transmission, effectively solving the problem of image distortion in the prior art when ensuring the transmission speed.

[0072] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can 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.

[0073] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows 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 the processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0074] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0075] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0076] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention.

[0077] 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 equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A visual image compression detection method for power transmission lines, 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; 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.

2. The method for visual image compression detection of power transmission lines according to claim 1, characterized in that: 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, characterized in that: 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 method for visual image compression detection of power transmission lines according to claim 2, characterized in that: The calculation formula of the dynamic information evaluation value is: In the formula, Z1 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, L0 represents the network delay reference value, P represents the packet loss rate, T represents the real-time temperature, T0 represents the reference temperature value, H represents the real-time humidity, H0 represents the ideal humidity, W represents the real-time wind speed, W0 represents the ideal wind speed, ω1 represents the bandwidth influence weight, ω2 represents the network delay influence weight, ω3 represents the packet loss rate influence weight, ω4 represents the actual temperature influence weight, ω5 represents the actual humidity influence weight, and ω6 represents the actual wind speed influence weight.

5. The method for visual image compression detection of power transmission lines according to claim 1, characterized in that: 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.

6. The method for visual image compression detection of power transmission lines according to claim 1, characterized in that: 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.

7. The method for visual image compression detection of power transmission lines according to claim 1, characterized in that: 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.

8. A visual image compression detection system for power transmission lines, used to implement the visual image compression detection method for power transmission lines as claimed in any one of claims 1 to 7, 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.

9. An electronic device, 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 to 7.

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