Emergency communication method of satellite vehicle-mounted station

Abstract

The invention belongs to the technical field of emergency communication, and particularly relates to an emergency communication method of a satellite vehicle-mounted station, which comprises the following steps: a rescue party acquires and transmits a field image after arriving at a disaster-affected area, and a command party obtains the severity of a disaster through the received field image and a standard image of the disaster-affected area, and determines the severity of the disaster according to the signal-to-noise ratio in the transmission process. Calculating a compression parameter of the next transmission, feeding the compression parameter of the next transmission back to the rescue party, so that the rescue party compresses the field image collected next time according to the compression parameter and obtains a lossy compression coding result and a supplementary compression coding result of the field image, and when the transmission speed is smaller than a reference value, sending the lossy compression coding result and the supplementary compression coding result to the rescue party. And if not, only transmitting the lossy compression coding result to the command side, otherwise, transmitting a lossless compression coding result jointly formed by the lossy compression coding result and the supplementary compression coding result to the command side. The method adjusts the transmitted compression coding result according to the transmission speed, and ensures that the field image is obtained as soon as possible.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency communication, and more specifically, to an emergency communication method of a satellite vehicle-mounted station. Background Art

[0002] In natural disaster monitoring scenarios, the scope and extent of the disaster-stricken area can be quickly assessed through the collected on-site images. For example, in flood monitoring, on-site images can be used to display the boundaries and depths of flooded areas, helping rescue teams plan rescue routes. In earthquake monitoring, on-site images are used to identify damage to buildings and assist rescue teams in locating trapped people. Therefore, timely and accurate information on the disaster site is crucial for rescue command.

[0003] The data volume of on-site images is usually large. If they are not compressed, the transmission of these images will take up a lot of network bandwidth, resulting in slow transmission speed, or even failure to transmit in time in emergency situations. Secondly, time is a crucial factor in rescue operations. Rapid acquisition of on-site images is essential for rescue teams to plan rescue routes and locate trapped people. Finally, communication conditions in disaster-stricken areas are unstable and network bandwidth is limited. Compressing on-site images before transmission can better adapt to this low-bandwidth environment, ensure that image data can be transmitted reliably, and avoid transmission interruptions or failures due to excessive data.

[0004] However, existing image transmission methods usually adopt a single compression strategy, transmitting image data at a fixed compression rate regardless of network conditions. This results in slow image transmission speed or even failure to complete transmission in a low-bandwidth or high-latency network environment, seriously affecting the timeliness of rescue decisions. Summary of the invention

[0005] In order to solve the technical problem that the existing image transmission methods usually adopt a single compression strategy, which affects the timeliness of rescue decisions, the present invention provides an emergency communication method of a satellite vehicle station, including: in disaster monitoring, the two parties of emergency communication are the rescue party and the command party; after arriving at the disaster-stricken area, the rescue party collects and transmits on-site images ; The commander will receive the on-site images and standard images of disaster-affected areas, and input them into the trained disaster severity assessment model to obtain the disaster severity , and combined with the signal-to-noise ratio of the transmission process , calculate the compression parameters for the next transmission , , is the first parameter, Indicates rounding to the nearest integer; the compression parameters for the next transmission are fed back to the rescuer so that the rescuer can use the compression parameters to collect the scene image next time. Compress and obtain live images The lossy compression encoding result and the supplementary compression encoding result of the transmission speed are less than the reference value. The lossy compression encoding result is transmitted to the commander; otherwise, the live image The lossless compression coding result composed of the lossy compression coding result and the supplementary compression coding result is transmitted to the commander.

[0006] The present invention combines the severity of the disaster in the disaster-stricken area and the signal-to-noise ratio of the transmission process to determine the compression parameters for the next transmission, so as to adjust the compression parameters according to the image quality requirements of the commander for the disaster-stricken areas with different disaster severity and the actual transmission conditions, and determine the quality of the on-site images received by the commander. The loss degree of lossy compression coding results of the present invention can minimize the amount of transmitted data while ensuring image quality, while ensuring that the most valuable information can be delivered to the commander under any circumstances, thereby improving the adaptability and accuracy of rescue operations; the present invention adjusts the transmission compression coding results according to the transmission speed, and can ensure that the commander can obtain on-site images as soon as possible under limited communication resources, thereby improving rescue efficiency.

[0007] Preferably, the trained disaster severity assessment model is a convolutional neural network, whose input layer is a dual-channel input, the input content includes regional standard images and on-site images, and the output content of its output layer is the disaster severity, and the value range of the disaster severity is [0,1].

[0008] Preferably, the scene image collected next time is compressed according to the compression parameter. Compress and obtain live images The lossy compression coding results and supplementary compression coding results include: converting the live image The range of the smallest grayscale value and the largest grayscale value in is used as the distribution interval of the grayscale value; according to the compression parameter , divide the gray value distribution interval into two parts with length equal to According to the encoding of the representative gray value of the sub-interval where the gray value of the pixel point is located, the scene image Perform compression encoding to obtain live images The lossy compression coding result; according to the coding of the gray value of the pixel point, the scene image Perform compression encoding to obtain live images The result of the complementary compression encoding.

[0009] The present invention combines the high efficiency of lossy compression and the flexibility of supplementary compression, and can achieve a balance between transmission speed and image quality.

[0010] Preferably, the method for obtaining the representative grayscale value of the subinterval is: calculating the loss degree of each grayscale value in the subinterval as the representative grayscale value; and taking the grayscale value with the smallest loss degree as the representative grayscale value of the subinterval.

[0011] The present invention selects the representative grayscale value of each sub-interval according to the loss degree, and can guarantee the image quality to the maximum extent under the premise of reducing the amount of transmission data.

[0012] Preferably, the calculation of each grayscale value in the subinterval as a representative grayscale value loss degree includes: ; In the formula, For the subinterval Gray value, For the subinterval The gray value is used as the loss degree of gray value. For the subinterval Gray value, Gray value The frequency, Indicates taking the absolute value, and are the grayscale values ​​in the subintervals, and and The value range of , The compression parameter for the next transmission.

[0013] The present invention uses the sub-interval Grayscale value Gray value The Difference And gray value The frequency of the subinterval is accurately quantified. The gray value is used as the loss degree of the gray value.

[0014] Preferably, the method for obtaining the encoding of the representative grayscale value of the sub-interval is: taking the sum of the frequencies of all grayscale values ​​in the sub-interval as the comprehensive frequency of the representative grayscale value; constructing a Huffman tree according to the comprehensive frequency of all representative grayscale values ​​and obtaining the encoding of each representative grayscale value.

[0015] Preferably, the method for obtaining the encoding of the gray value serial number is: taking the sum of the frequencies of the gray values ​​of the same serial number in all sub-intervals as the comprehensive frequency of each serial number; constructing a Huffman tree according to the comprehensive frequency of all serial numbers and obtaining the encoding of each serial number.

[0016] Preferably, the serial number of the grayscale value refers to the serial number of the grayscale value in the sub-interval to which it belongs.

[0017] Preferably, the method for obtaining the transmission speed is: the rescue party calculates the difference between the receiving time and the sending time according to the recorded sending time and the receiving time fed back by the commander as the transmission duration; and takes the ratio of the data transmission amount to the transmission duration as the transmission speed.

[0018] Preferably, the method further comprises: the rescuer transmits the frequency of each grayscale value in the distribution interval as supplementary information to the commander while transmitting the compression coding result, so that the commander can decode the compression coding result according to the frequency of each grayscale value.

[0019] The decodability and decoding accuracy of the compression encoding results of the on-site images are guaranteed.

[0020] The beneficial effects of the present invention are: The present invention combines the severity of the disaster in the disaster-stricken area and the signal-to-noise ratio of the transmission process to determine the compression parameters for the next transmission, so as to adjust the compression parameters according to the image quality requirements of the commander for the disaster-stricken areas with different disaster severity and the actual transmission conditions, and determine the quality of the on-site images received by the commander. The loss degree of lossy compression coding results of the present invention can minimize the amount of transmitted data while ensuring image quality, while ensuring that the most valuable information can be delivered to the commander under any circumstances, thereby improving the adaptability and accuracy of rescue operations; the present invention adjusts the transmission compression coding results according to the transmission speed, and can ensure that the commander can obtain on-site images as soon as possible under limited communication resources, thereby improving rescue efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flow chart schematically illustrating an emergency communication method of a satellite vehicle-mounted station in the present invention; Figure 2 It is a schematic diagram showing the compression parameters of the next transmission. =8, the commander will analyze the scene image according to the frequency of each gray value. A schematic diagram of a decompressed lossy image obtained by decompressing a lossy compression encoding result; Figure 3 It schematically shows the compression parameters for the next transmission. =16, the commander will analyze the scene image according to the frequency of each gray value. A schematic diagram of decompressing a lossy compression encoding result to obtain a decompressed lossy image. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0024] The embodiment of the present invention discloses an emergency communication method of a satellite vehicle station, referring to Figure 1 , including steps S1 to S4: S1. After arriving at the disaster-stricken area, the rescuers collect and transmit on-site images.

[0025] In disaster monitoring, after the rescue team arrives at the disaster area, they collect on-site images of the area. Then, the collected scene images Transmitted to the commander to reflect the disaster situation in the area where the disaster occurred, so that the commander can make quick decisions and take effective actions; at the same time, the rescue party records and starts transmitting on-site images Among them, drone aerial photography is often used in flood and earthquake monitoring because it can quickly cover large areas and provide high-resolution images.

[0026] The rescuers and commanders communicate with each other through the satellite vehicle station to realize on-site image monitoring. That is to say, the two parties of emergency communication are the rescue party and the commander.

[0027] Among them, the satellite vehicle-mounted station has the characteristics of light weight and small size. It does not require modification of the vehicle and can be installed and disassembled according to needs. It is suitable for various types of vehicles and can provide high-speed data transmission services. At the same time, the satellite vehicle-mounted station can track satellites and other platforms in real time during movement, and continuously transmit multimedia information such as voice, data, and images, which can meet the needs of various emergency communications and multimedia communications under mobile conditions.

[0028] S2. The commander inputs the received on-site images and standard images of the disaster-stricken area into the trained disaster severity assessment model to obtain the severity of the disaster, and calculates the compression parameters for the next transmission based on the signal-to-noise ratio of the transmission process.

[0029] It should be noted that when the severity of the disaster is high, the commander needs more accurate information to formulate rescue strategies. This is because high-quality on-site images can clearly show the details of the disaster-stricken areas, such as the extent of damage to buildings, the scope and depth of flooding, road blockages, etc. These details are crucial for assessing the difficulty of rescue, allocating resources and planning rescue routes.

[0030] For the commander, the quality of the on-site image finally received by the commander not only depends on the loss degree of the lossy compression coding result of the on-site image, but also depends on the signal-to-noise ratio of the transmission process; wherein, the loss degree of the lossy compression coding result of the on-site image depends on the size of the set compression parameter: the smaller the set compression parameter, the smaller the loss degree of the lossy compression coding result of the on-site image, and the larger the set compression parameter, the greater the loss degree of the lossy compression coding result of the on-site image; and the greater the signal-to-noise ratio of the transmission process, the better the signal quality during the transmission process; therefore, the smaller the loss degree of the lossy compression coding result of the on-site image and the greater the signal-to-noise ratio of the transmission process, the better the quality of the on-site image finally received by the commander.

[0031] Therefore, in order to ensure that the quality of the on-site images finally received by the commander will not affect the final decision and action, the severity of the disaster in the disaster-stricken area is obtained by comparing the on-site images received last time with the standard images of the disaster-stricken area, and then combined with the signal-to-noise ratio of the previous transmission process, the compression parameters for compressing and transmitting the on-site images next time are calculated and set; among them, the greater the severity of the disaster in the disaster-stricken area and the smaller the signal-to-noise ratio of the previous transmission process, the higher the quality requirements for the on-site images finally received by the commander, and accordingly, the smaller the compression parameters for compressing and transmitting the on-site images next time.

[0032] Specifically, the commander will receive the on-site images and standard images of the disaster-stricken areas are input into the trained disaster severity assessment model to obtain the severity of the disaster, which can effectively assess the disaster situation in the disaster-stricken areas; wherein the standard image of the area refers to the image of the area collected by drone aerial photography when no disaster occurs.

[0033] Among them, the disaster severity assessment model is a convolutional neural network (CNN), whose input layer is a dual-channel input, and the input content includes regional standard images and on-site images. The output layer outputs the disaster severity, and the value range of the disaster severity is [0,1]. The disaster severity assessment model includes 3 convolutional layers, 3 pooling layers and 2 fully connected layers.

[0034] The training process of the disaster severity assessment model includes: taking multiple regional standard images and on-site images corresponding to the regions as training samples, manually labeling the training samples, and the value range of the labels is [0,1], which is used to indicate the disaster severity of the region corresponding to the training samples. The larger the value of the label, the greater the disaster severity of the region; training a convolutional neural network through all training samples and their labels. During training, the loss function used is the mean squared error (MSE), the optimizer used is the Adam optimizer, the learning rate is set to 0.001, and the loss function is repeatedly calculated, and the network parameters are updated through back propagation until the network converges, so as to obtain a trained convolutional neural network as a trained disaster severity assessment model.

[0035] Furthermore, the commanding party receives on-site images from the rescue party. During the period from the start of reception to the completion of reception, identify and record the signal-to-noise ratio at each moment, and take the average of the signal-to-noise ratios of all moments during the period from the start of reception to the completion of reception as the signal-to-noise ratio of the transmission process; the moment of completion of reception is recorded as the receiving moment, and the receiving moment needs to be accurate to seconds.

[0036] Among them, the signal-to-noise ratio (SNR) refers to the ratio of signal power to noise power, usually expressed in decibels (dB); the signal-to-noise ratio can reflect the noise level and signal quality of the link, and is an important indicator for measuring the quality of the communication link. A higher signal-to-noise ratio means better signal quality; the receiver in the communication system of the satellite vehicle station has a built-in signal-to-noise ratio measurement module, and the receiver obtains the signal-to-noise ratio by calculating the ratio of signal power to noise power in real time.

[0037] Furthermore, the compression parameter of the next transmission is calculated based on the severity of the disaster and the signal-to-noise ratio of the transmission process. The calculation formula is: ; In the formula, is the compression parameter for the next transmission, is the signal-to-noise ratio of the transmission process, The severity of the disaster, is the first parameter, Indicates rounding to the nearest integer.

[0038] The specific value of the first parameter can be set according to the actual application scenario and requirements. In order to ensure the quality of the image, the signal-to-noise ratio is usually required to be above 40 dB during image transmission. Therefore, the value of the first parameter needs to be greater than or equal to 40. The present invention sets the first parameter to 40, that is, .

[0039] The greater the severity of the disaster in the affected area, the higher the quality requirements of the final received on-site image. Accordingly, the compression parameters for the next transmission are set. When the compression parameter Should be smaller; the signal-to-noise ratio of the transmission process The smaller the value, the worse the signal quality during transmission. In order to ensure the quality of the on-site image finally received by the commander, and not to affect the final decision and action, the loss of the lossy compression encoding result of the on-site image is required to be as small as possible. Accordingly, when setting the compression parameters for the next transmission, When the compression parameter It should be smaller.

[0040] For example, when the signal-to-noise ratio of the transmission process is =45, severity of disaster =0.1, the compression parameter for the next transmission = =16; when the signal-to-noise ratio of the transmission process =30, severity of disaster =0.8, the compression parameter for the next transmission = =8.

[0041] It should be noted that in order to minimize the amount of transmitted data while ensuring image quality, and to ensure that the most valuable information can be delivered to the commander under any circumstances, when determining the compression parameters for the next transmission, the compression parameters are adjusted based on the commander's image quality requirements for disaster-stricken areas with different disaster severity levels and actual transmission conditions. Since the severity of the disaster in the disaster-stricken area determines the commander's image quality requirements for disaster-stricken areas with different disaster severity levels, and the actual transmission conditions can be reflected through the signal-to-noise ratio of the transmission process, therefore, the compression parameters for the next transmission are determined in combination with the severity of the disaster in the disaster-stricken area and the signal-to-noise ratio of the transmission process, thereby improving the adaptability and accuracy of the rescue operation.

[0042] S3. The commander feeds back the compression parameters of the next transmission to the rescuer, so that the rescuer can compress the scene images collected next time according to the compression parameters to obtain the lossy compression coding results and supplementary compression coding results of the scene images collected next time.

[0043] Specifically, the commander will transmit the compression parameters of the next Feedback to the rescuer; the rescuer will respond according to the compression parameters For the next collected live image Compress and obtain live images The lossy compression encoding result and the complementary compression encoding result of are as follows: (1) Live images The range of the smallest grayscale value and the largest grayscale value in is used as the distribution interval of the grayscale value; according to the compression parameter , divide the gray value distribution interval into two parts with length equal to Therefore, the number of gray values ​​in each subinterval is equal to , the range of the gray value sequence in the sub-interval is .

[0044] For example, when the scene image The minimum grayscale value is 27 and the maximum grayscale value is 186, and the grayscale value distribution range is [27,186]; when the compression parameter of the next transmission =16, the gray value distribution interval [27,186] is divided into 10 sub-intervals of length 16, namely [27,42], [43,58], [59,74], [75,90], [91,106], [107,122], [123,138], [139,154], [155,170], [171,186]; when the compression parameter of the next transmission is =8, the gray value distribution interval [27,186] is divided into 20 subintervals with a length of 8, namely [27,34], [35,42], [43,50], [51,58], [59,66], [67,74], [75,82], [83,90], [91,98], [99,106], [107,114], [115,122], [123,130], [131,138], [139,146], [147,154], [155,162], [163,170], [171,178], and [179,186].

[0045] (2) For each sub-interval, calculate the loss degree of each grayscale value in the sub-interval as the representative grayscale value; and use the grayscale value with the smallest loss degree as the representative grayscale value of the sub-interval.

[0046] In one embodiment, the formula for calculating the loss degree of each gray value in the sub-interval as a representative gray value is: ; In the formula, For the subinterval Gray value, For the subinterval The gray value is used as the loss degree of gray value. For the subinterval Gray value, Gray value The frequency, Indicates taking the absolute value, and are the grayscale values ​​in the subintervals, and and The value range of , The compression parameter for the next transmission.

[0047] Among them, the gray value The frequency refers to the scene image The mid-gray value is equal to The ratio of the number of pixels to the number of all pixels.

[0048] It should be noted that in the subsequent on-site image When compressing, the pixels whose grayscale values ​​belong to the subinterval are encoded by encoding the representative grayscale value of the subinterval. Therefore, in the image obtained after the compression coding result is decompressed, the grayscale values ​​of these pixels are equal to the representative grayscale value of the subinterval. Grayscale value As the representative gray value, the Grayscale value Gray value The Difference The larger the gray value is, the The more pixels there are, the higher the gray value The larger the frequency of The greater the loss of the gray value as the representative gray value, the greater the loss of the gray value as the representative gray value. Grayscale value Gray value The Difference And gray value The frequency of The gray value is used as the loss degree of the gray value.

[0049] For example, when the compression parameter of the next transmission =8, for the subinterval [35,42], there are 8 grayscale values ​​in the subinterval, namely 35, 36, 37, 38, 39, 40, 41, and 42. The frequencies of these 8 grayscale values ​​are 0.00257, 0.00311, 0.00916, 0.00625, 0.00702, 0.00418, 0.00605, and 0.00214 respectively; then the subinterval = 1 gray value =35 represents the degree of gray value loss =|35-35|×0.00257+|36-35|×0.00311+|37-35|×0.00916+|38-35|×0.00625+|39-35|×0.00702+|40-35|×0.00418+|41-35|×0.00605+|42-35|×0.00214=0.14044; Similarly, in the subinterval = 2 grayscale values =36 represents the degree of gray value loss =0.1051; in the subinterval = 3 grayscale values =37 represents the degree of gray value loss =0.07598; in the subinterval =4 grayscale values =38 represents the degree of gray value loss =0.06518; in the subinterval =5 grayscale values =39 represents the degree of gray value loss =0.06688; in the subinterval =6 grayscale values =40 represents the degree of gray value loss =0.08262; in the subinterval =7 grayscale values =41 represents the degree of gray value loss =0.10672; in the subinterval =8 grayscale values =42 represents the degree of gray value loss =0.14292; among them, the =4 grayscale values =38 represents the degree of gray value loss =0.06518 is the smallest, so the first =4 grayscale values =38 is used as the representative grayscale value of the subinterval [35,42].

[0050] It should be noted that by selecting the representative grayscale value of each sub-interval according to the loss degree, it is possible to maximize the image quality while reducing the amount of transmitted data.

[0051] (3) The sum of the frequencies of all gray values ​​in the subinterval is taken as the comprehensive frequency representing the gray value; based on the comprehensive frequencies of all gray values ​​representing the gray value, a Huffman tree is constructed. ; Through the Huffman tree Get the encoding of each representative gray value: each representative gray value is constructed in the Huffman tree Corresponding to a leaf node, therefore, the Huffman tree The labels of all paths between the root node and the leaf nodes corresponding to each representative grayscale value constitute the encoding of each representative grayscale value.

[0052] In one embodiment, from the Huffman tree Starting from the root node of the Huffman tree, the path on the left is marked as 0 and the path on the right is marked as 1; in another embodiment, Starting from the root node, mark the path on the left as 0 and the path on the right as 1.

[0053] (4) According to the encoding of the representative gray value of the sub-interval where the gray value of the pixel point is located, the scene image Perform compression coding and use the obtained compression coding result as the scene image The lossy compression encoding result.

[0054] (5) The serial number of the gray value in each sub-interval is The sum of the frequencies of the gray values ​​of the same sequence number in all subintervals is taken as the comprehensive frequency of each sequence number; based on the comprehensive frequency of all sequence numbers, a Huffman tree is constructed. , through the Huffman tree Get the encoding of each serial number: each serial number is constructed in the Huffman tree Corresponding to a leaf node, therefore, the Huffman tree The labels of all paths from the root node to the leaf nodes corresponding to each sequence number constitute the code of each sequence number.

[0055] In one embodiment, from the Huffman tree Starting from the root node, the path on the left is marked as 0, and the path on the right is marked as 1; in another embodiment, from the Huffman tree Starting from the root node, mark the path on the left as 0 and the path on the right as 1.

[0056] (6) According to the serial number encoding of the gray value of the pixel point, the scene image Perform compression coding and use the obtained compression coding result as the scene image The supplementary compression encoding result; wherein the serial number of the grayscale value refers to the serial number of the grayscale value in its sub-interval.

[0057] In addition, the commander will also feed back the receiving time to the rescuer so that the rescuer can calculate the transmission speed based on the recorded sending time and data transmission volume.

[0058] S4. According to the transmission speed, the compression encoding result of the on-site image is transmitted to the commander.

[0059] It should be noted that in disaster relief scenarios, adjusting the compression encoding results of transmission according to the transmission speed is an optimization strategy that can ensure that the commander can obtain on-site images as quickly as possible under limited communication resources, thereby improving rescue efficiency.

[0060] Specifically, the sending time recorded by the rescuer needs to be accurate to the second; the rescuer calculates the difference between the receiving time and the sending time based on the recorded sending time and the receiving time fed back by the commander as the transmission duration; the ratio of the data transmission volume to the transmission duration is used as the transmission speed; the unit of data volume is MB (Megabyte), and the unit of transmission speed is MB / second.

[0061] Furthermore, when the transmission speed is lower than the reference value, only the live image The lossy compression encoding result is transmitted to the commander; otherwise, the live image The lossless compression coding result composed of the lossy compression coding result and the supplementary compression coding result is transmitted to the commander.

[0062] The specific numerical value of the benchmark value can be set according to the actual application scenario and requirements. The present invention sets the benchmark value to 4, and the unit is MB / second.

[0063] For example, when the transmission speed is less than the reference value, the rescuer only transmits the scene image to the The lossy compression encoding result is transmitted to the commander; when the compression parameter of the next transmission =8, the commander will analyze the scene image according to the frequency of each gray value. The lossy compression encoding result is decompressed, and the schematic diagram of the decompressed lossy image is as follows Figure 2 As shown; the compression parameters of the next transmission =16, the commander will analyze the scene image according to the frequency of each gray value. The lossy compression encoding result is decompressed, and the schematic diagram of the decompressed lossy image is as follows Figure 3 shown.

[0064] It should be noted that the on-site images Although the lossy compression encoding result will sacrifice some image details, it can deliver key information to the commander in a short time. Therefore, only the on-site image is transmitted. The lossy compression coding result can significantly reduce the amount of data, thereby speeding up the transmission speed. Therefore, when the transmission speed between the rescuer and the commander is limited, that is, when the transmission speed is less than the reference value, only the lossy compression coding result of the on-site image is transmitted; the on-site image Although the compression rate of the lossless compression coding result is low, it can completely restore the original image and ensure that the commander obtains more accurate on-site information. Therefore, if the transmission speed between the rescuer and the commander meets the requirements, that is, the transmission speed is greater than or equal to the benchmark value, the lossless compression coding result of the on-site image is transmitted; adjusting the transmission compression coding result according to the transmission speed can ensure that the commander can obtain the on-site image as soon as possible under limited communication resources, thereby improving the rescue efficiency.

[0065] Furthermore, when transmitting the compression coding result, the rescue party transmits the frequency of each gray value in the distribution interval as supplementary information to the commander, so that the commander can decode the compression coding result according to the frequency of each gray value.

[0066] The present invention transmits the frequency of each gray value in the distribution interval as supplementary information to the commander, thereby ensuring the decodability and decoding accuracy of the compression coding result of the on-site image.

Claims

1. An emergency communication method for a satellite vehicle station, characterized in that: include: In disaster monitoring, the two parties involved in emergency communications are the rescue party and the commander; After arriving at the disaster area, rescuers collect and transmit on-site images ; The commander will receive the on-site images and standard images of disaster-affected areas, and input them into the trained disaster severity assessment model to obtain the disaster severity , and combined with the signal-to-noise ratio of the transmission process , calculate the compression parameters for the next transmission , , is the first parameter, Indicates rounding to the nearest integer; Feedback the compression parameters of the next transmission to the rescuer so that the rescuer can adjust the scene image collected next time according to the compression parameters. Compress and obtain live images The lossy compression encoding result and the complementary compression encoding result; When the transmission speed is lower than the reference value, only the live image The lossy compression encoding result is transmitted to the commander; Otherwise, the live image The lossless compression coding result composed of the lossy compression coding result and the supplementary compression coding result is transmitted to the commander.

2. The emergency communication method of a satellite vehicle station according to claim 1, characterized in that: The trained disaster severity assessment model is a convolutional neural network, whose input layer is a dual-channel input, the input content includes regional standard images and on-site images, and the output content of its output layer is the disaster severity, and the value range of the disaster severity is [0,1].

3. The emergency communication method of a satellite vehicle station according to claim 1, characterized in that: The scene image collected next time is compressed according to the compression parameters Compress and obtain live images The lossy compression encoding results and complementary compression encoding results include: The scene image The range of the smallest grayscale value and the largest grayscale value in is used as the distribution interval of the grayscale value; according to the compression parameter , divide the gray value distribution interval into two parts with length equal to Multiple subintervals of ; According to the encoding of the representative gray value of the sub-interval where the gray value of the pixel point is located, the scene image is Perform compression encoding to obtain live images The lossy compression encoding result of According to the gray value of the pixel, the scene image is encoded Perform compression encoding to obtain live images The result of the complementary compression encoding.

4. The emergency communication method of a satellite vehicle station according to claim 3, characterized in that: The method for obtaining the representative grayscale value of the sub-interval is: Calculate each gray value in the subinterval as the loss degree of the representative gray value; The gray value with the smallest loss degree is taken as the representative gray value of the sub-interval.

5. The emergency communication method of a satellite vehicle station according to claim 4, characterized in that: The calculation of each gray value in the subinterval as a loss degree of a representative gray value includes: ； In the formula, For the subinterval Gray value, For the subinterval The gray value is used as the loss degree of gray value. For the subinterval Gray value, Gray value The frequency, Indicates taking the absolute value, and are the grayscale values ​​in the subintervals, and and The value range of , The compression parameter for the next transmission.

6. The emergency communication method of a satellite vehicle station according to claim 3, characterized in that: The method for obtaining the encoding of the representative grayscale value of the sub-interval is: The sum of the frequencies of all gray values ​​in the subinterval is taken as the comprehensive frequency representing the gray value; According to the comprehensive frequency of all representative gray values, a Huffman tree is constructed and the encoding of each representative gray value is obtained.

7. The emergency communication method of a satellite vehicle station according to claim 3, characterized in that: The method for obtaining the coding of the gray value sequence number is: The sum of the frequencies of the gray values ​​with the same serial number in all subintervals is taken as the comprehensive frequency of each serial number; According to the comprehensive frequency of all serial numbers, a Huffman tree is constructed and the encoding of each serial number is obtained.

8. The emergency communication method of a satellite vehicle station according to claim 3, characterized in that: The serial number of the grayscale value refers to the serial number of the grayscale value in the sub-interval to which it belongs.

9. The emergency communication method of a satellite vehicle station according to claim 1, characterized in that: The method for obtaining the transmission speed is: The rescue party calculates the difference between the receiving time and the sending time based on the recorded sending time and the receiving time fed back by the commander as the transmission duration; and takes the ratio of the data transmission volume to the transmission duration as the transmission speed.

10. The emergency communication method of a satellite vehicle station according to claim 1, characterized in that: The method further includes: the rescuer transmits the frequency of each gray value in the distribution interval as supplementary information to the commander while transmitting the compression coding result, so that the commander can decode the compression coding result according to the frequency of each gray value.

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