An emergency communication method for a satellite vehicle station

Abstract

The present invention belongs to the field of emergency communication technology, and particularly relates to an emergency communication method for a satellite vehicle station. The method includes: after the rescue party arrives at the disaster area, it collects and transmits on-site images. The command party obtains the severity of the disaster based on the received on-site images and the standard images of the disaster area, and combines the signal-to-noise ratio during the transmission process to calculate the compression parameters for the next transmission. The compression parameters for the next transmission are fed back to the rescue party, so that the rescue party can compress the on-site images collected next according to the compression parameters, obtaining a lossy compression coding result and a supplementary compression coding result of the on-site images. When the transmission speed is less than the reference value, only the lossy compression coding result is transmitted to the command party; otherwise, a lossless compression coding result composed of the lossy compression coding result and the supplementary compression coding result is transmitted to the command party. The present invention adjusts the compression coding result for transmission according to the transmission speed to ensure that the on-site images can be obtained as soon as possible.

Description

Technical Field

[0001] The present invention relates to the field of emergency communication technology. More specifically, the present invention relates to an emergency communication method for a satellite vehicle station. Background Art

[0002] In natural disaster monitoring scenarios, the scope and degree of the affected area can be quickly evaluated through the collected on-site images; for example, in flood monitoring, the boundaries and depths of the flood inundation areas are shown through on-site images to help the rescue team plan the rescue route; in earthquake monitoring, on-site images are used to identify the damage of buildings and assist the rescue team in locating trapped persons; therefore, timely and accurately obtaining information about the disaster site is crucial for rescue command.

[0003] The data volume of on-site images is usually large. Without compression, transmitting these images will occupy a large amount of network bandwidth, resulting in slow transmission speed and even being unable to be transmitted in time in case of emergency; secondly, in rescue operations, time is a crucial factor, and quickly obtaining on-site images is crucial for the rescue team to plan the rescue route and locate trapped persons; finally, the communication conditions in the affected areas are unstable and the network bandwidth is limited. Compressing and transmitting on-site images can better adapt to this low-bandwidth environment, ensure reliable transmission of image data, and avoid transmission interruption or failure due to excessive data.

[0004] However, existing image transmission methods usually adopt a single compression strategy, and transmit image data at a fixed compression rate regardless of network conditions, resulting in slow image transmission speed or even being unable to complete transmission in time in a low-bandwidth or high-latency network environment, seriously affecting the timeliness of rescue decisions. Summary of the Invention

[0005] To solve the above technical problem that existing image transmission methods usually adopt a single compression strategy, affecting the timeliness of rescue decisions, the present invention provides an emergency communication method for a satellite vehicle station, including: in disaster monitoring, the two parties of emergency communication are the rescue party and the command party; after the rescue party arrives at the affected area, it collects and transmits on-site images ; the command party inputs the received on-site images and the standard images of the affected area into the trained disaster severity assessment model to obtain the disaster severity , and combines the signal-to-noise ratio during the transmission process to calculate the compression parameter for the next transmission , , is the first parameter, represents rounding to the nearest integer; feedback the compression parameter for the next transmission to the rescue party so that the rescue party can compress the on-site images to be collected next time according to the compression parameter Perform compression to obtain on-site images The lossy compression coding result and the supplementary compression coding result of the on-site image; when the transmission speed is less than the reference value, only the lossy compression coding result of the on-site image is transmitted to the command party; otherwise, the lossless compression coding result composed of the lossy compression coding result and the supplementary compression coding result of the on-site image is transmitted to the command party.

[0006] The present invention combines the severity of the disaster in the affected area and the signal-to-noise ratio in the transmission process to determine the compression parameters for the next transmission, so as to, according to the image quality requirements of the command party for the affected areas with different disaster severities and the actual transmission conditions, by adjusting the compression parameters, determine the loss degree of the lossy compression coding result of the on-site image received by the command party. This dynamic adjustment mechanism can minimize the amount of transmitted data to the greatest extent while ensuring the image quality, and at the same time ensure that the most valuable information can be transmitted to the command party in any case, thereby improving the adaptability and accuracy of rescue operations; the present invention adjusts the transmitted compression coding result according to the transmission speed, and can ensure that the command party can obtain the on-site image as soon as possible under limited communication resources, thereby improving the rescue efficiency.

[0007] Preferably, the trained disaster severity assessment model is a convolutional neural network, whose input layer is a dual-channel input, and the input content includes the regional standard image and the on-site image, 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 compression of the on-site image collected next time according to the compression parameters to obtain the lossy compression coding result and the supplementary compression coding result of the on-site image includes: taking the range composed of the minimum gray value and the maximum gray value in the on-site image as the distribution interval of the gray value; according to the compression parameters , dividing the distribution interval of the gray value into multiple sub-intervals with a length equal to ; performing compression coding on the on-site image according to the coding of the representative gray value of the sub-interval where the gray value of the pixel point is located to obtain the lossy compression coding result of the on-site image ; performing compression coding on the on-site image according to the coding of the serial number of the gray value of the pixel point to obtain the supplementary compression coding result of the on-site image .

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

[0021] 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

[0022] Figure 1 is a flow chart schematically illustrating an emergency communication method of a satellite vehicle-mounted station in the present invention;

[0023] Figure 2 It schematically shows the compression parameters for 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;

[0024] 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

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0026] The following will describe in detail the specific implementation manners of the present invention in conjunction with the accompanying drawings.

[0027] An embodiment of the present invention discloses an emergency communication method for a satellite vehicle station. Referring to Figure 1 , it includes steps S1 to S4:

[0028] S1. After the rescue party arrives at the disaster area, it collects and transmits on-site images.

[0029] In disaster monitoring, after the rescue party arrives at the area where the disaster occurs, it collects on-site images of the area , and then transmits the collected on-site images to the command party to reflect the disaster situation in the area where the disaster occurs, facilitating the command party to make quick decisions and take effective actions; at the same time, the rescue party records the sending moment when starting to transmit on-site images . Among them, since drone aerial photography can quickly cover a large area and provide high-resolution images, it is often used in flood and earthquake monitoring.

[0030] The rescue party and the command party perform emergency communication through the carried satellite vehicle station to achieve the transmission of on-site images , that is to say, the two parties of the emergency communication are the rescue party and the command party.

[0031] Among them, the satellite vehicle station has the characteristics of light weight and small volume, does not need to modify the vehicle, can be installed and disassembled according to needs, is applicable to various vehicle types, can provide high-speed data transmission services, and at the same time, the satellite vehicle station can track platforms such as satellites in real time during movement, continuously transmit multimedia information such as voice, data, and images, and can meet the needs of various emergency communications and multimedia communications under mobile conditions.

[0032] S2. The command party inputs the received on-site images and standard images of the disaster area into the trained disaster severity assessment model to obtain the disaster severity, and calculates the compression parameters for the next transmission in combination with the signal-to-noise ratio during the transmission process.

[0033] It should be noted that when the severity of the disaster is relatively high, the command side needs more accurate information to formulate rescue strategies. This is because high-quality on-site images can clearly show the details of the affected area, such as the degree of building damage, the scope and depth of flood inundation, and the situation of blocked roads. These details are crucial for assessing the difficulty of rescue, allocating resources, and planning rescue routes.

[0034] For the command side, the quality of the on-site images finally received by the command side depends not only on the loss degree of the lossy compression coding result of the on-site images, but also on the signal-to-noise ratio during the transmission process. Among them, the loss degree of the lossy compression coding result of the on-site images depends on the size of the set compression parameters: the smaller the set compression parameters, the smaller the loss degree of the lossy compression coding result of the on-site images; the larger the set compression parameters, the larger the loss degree of the lossy compression coding result of the on-site images. And the larger the signal-to-noise ratio during 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 images and the larger the signal-to-noise ratio during the transmission process, the better the quality of the on-site images finally received by the command side.

[0035] Therefore, in order to ensure that the quality of the on-site images finally received by the command side will not affect the final decision-making and actions, by comparing the previously received on-site images with the standard images of the affected area, the severity of the disaster in the affected area is obtained, and then combined with the signal-to-noise ratio during the previous transmission process, the compression parameters for the next compression and transmission of the on-site images are calculated and set. Among them, when the severity of the disaster in the affected area is greater and the signal-to-noise ratio during the previous transmission process is smaller, the higher the quality requirement for the on-site images finally received by the command side, and correspondingly, the smaller the compression parameters for the next compression and transmission of the on-site images.

[0036] Specifically, the command side will input the received on-site images and the standard images of the affected area into the trained disaster severity assessment model to obtain the disaster severity, which can effectively evaluate the disaster situation in the affected area. Among them, the regional standard image refers to the image of the area collected by drone aerial photography when there is no disaster.

[0037] Among them, the disaster severity assessment model is a Convolutional Neural Networks (CNN). Its input layer is a two-channel input, and the input content includes the regional standard image and the on-site image. The content output by its output layer is the disaster severity. The value range of the disaster severity is [0, 1], and the disaster severity assessment model includes 3 convolutional layers, 3 pooling layers, and 2 fully connected layers.

[0038] The training process of the disaster severity assessment model includes: using multiple regional standard images and the corresponding on-site images of the regions as training samples, manually tagging the training samples, and the value range of the tags is [0, 1], which is used to represent the disaster severity of the regions corresponding to the training samples. The larger the value of the tag, the greater the disaster severity of the region; training a convolutional neural network with all the training samples and their tags. 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 by continuously calculating the loss function and updating the network parameters through backpropagation until the network converges, a trained convolutional neural network is obtained, which is used as the trained disaster severity assessment model.

[0039] Further, when the command party receives the on-site images from the rescue party during the period from the start of reception to the completion of reception, the signal-to-noise ratio at each moment is identified and recorded, and the average value of the signal-to-noise ratios at all moments during the period from the start of reception to the completion of reception is used as the signal-to-noise ratio of the transmission process; the moment of completion of reception is recorded as the reception moment, and the reception moment needs to be accurate to the second.

[0040] Among them, the signal-to-noise ratio (SNR) refers to the ratio of the signal power to the 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 is built-in with a signal-to-noise ratio measurement module, and the receiver obtains the signal-to-noise ratio by calculating the ratio of the signal power to the noise power in real time.

[0041] Further, combining the disaster severity and the signal-to-noise ratio of the transmission process, calculate the compression parameter for the next transmission. Then the compression parameter for the next transmission has the following calculation formula:

[0042] ;

[0043] In the formula, is the compression parameter for the next transmission, is the signal-to-noise ratio of the transmission process, is the disaster severity, is the first parameter, represents rounding to the nearest integer.

[0044] Among them, 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, during the process of transmitting the image, the signal-to-noise ratio is usually required to be above 40 dB. Therefore, the value of the first parameter needs to be greater than or equal to 40. In the present invention, the first parameter is set to 40, that is .

[0045] Among them, the more severe the disaster in the disaster-stricken area, the higher the quality requirement of the command party for the finally received on-site image. Correspondingly, when setting the compression parameter for the next transmission , the compression parameter should be smaller; the smaller the signal-to-noise ratio during the transmission process , the worse the signal quality during the transmission process. In order to ensure the quality of the on-site image finally received by the command party and not affect the final decision-making and actions, it is required that the loss degree of the lossy compression coding result of the on-site image is smaller. Correspondingly, when setting the compression parameter for the next transmission , the compression parameter should be smaller.

[0046] Exemplarily, when the signal-to-noise ratio during the transmission process = 45 and the disaster severity = 0.1, the compression parameter for the next transmission = = 16; when the signal-to-noise ratio during the transmission process = 30 and the disaster severity = 0.8, the compression parameter for the next transmission = = 8.

[0047] It should be noted that in order to minimize the amount of transmitted data while ensuring the image quality and ensure that the most valuable information can be transmitted to the command party under any circumstances, when determining the compression parameter for the next transmission, the compression parameter is adjusted according to the image quality requirements of the command party for the disaster-stricken areas with different disaster severities and the actual transmission conditions. Since the disaster severity of the disaster-stricken area determines the image quality requirements of the command party for the disaster-stricken areas with different disaster severities, and the actual transmission conditions can be reflected by the signal-to-noise ratio during the transmission process, therefore, the compression parameter for the next transmission is determined by combining the disaster severity of the disaster-stricken area and the signal-to-noise ratio during the transmission process, so as to improve the adaptability and accuracy of the rescue operation.

[0048] S3. The command party feeds back the compression parameter for the next transmission to the rescue party, so that the rescue party can compress the on-site image collected next time according to the compression parameter, and obtain the lossy compression coding result and the supplementary compression coding result of the on-site image collected next time.

[0049] Specifically, the commander will feed back the compression parameters for the next transmission to the rescue party; based on the compression parameters the rescue party compresses the on-site images to be collected next time to obtain the lossy compression coding result and the supplementary compression coding result of the on-site images . The specific steps are as follows:

[0050] (1) Use the range composed of the minimum gray value and the maximum gray value in the on-site images as the distribution interval of gray values; according to the compression parameters , divide the distribution interval of gray values into multiple sub-intervals with a length equal to . Therefore, the number of gray values in each sub-interval is equal to , and the value range of the serial numbers of the gray values in the sub-interval is .

[0051] Exemplarily, when the minimum gray value in the on-site images is equal to 27 and the maximum gray value is equal to 186, the distribution interval of gray values is [27, 186]; when the compression parameter for the next transmission = 16, divide the distribution interval of gray values [27, 186] into 10 sub-intervals with a length equal to 16, which are [27, 42], [43, 58], [59, 74], [75, 90], [91, 106], [107, 122], [123, 138], [139, 154], [155, 170], [171, 186]; when the compression parameter for the next transmission = 8, divide the distribution interval of gray values [27, 186] into 20 sub-intervals with a length equal to 8, which are [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], [179, 186].

[0052] (2) For each sub-interval, calculate the loss degree of each gray value in the sub-interval as the representative gray value; and use the gray value with the minimum loss degree as the representative gray value of the sub-interval.

[0053] In one embodiment, the formula for calculating the loss degree of each gray value in the sub-interval as the representative gray value is:

[0054] ;

[0055] In the formula, is the th gray value in the sub - interval, is the th gray value in the sub - interval as the loss degree of the representative gray value, is the th gray value in the sub - interval, is the frequency of the gray value , represents taking the absolute value, and are both the serial numbers of the gray values in the sub - interval, and and have the value range of , is the compression parameter for the next transmission.

[0056] Among them, the frequency of the gray value refers to the ratio of the number of pixel points with the gray value equal to in the on - site image to the number of all pixel points.

[0057] It should be noted that when compressing the on - site image subsequently, through the encoding of the representative gray value of the sub - interval, the pixel points with gray values belonging to the sub - interval are encoded. Therefore, in the image obtained after decompressing the subsequent compression encoding result, the gray values of these pixel points are equal to the representative gray value of the sub - interval. Therefore, when the th gray value in the sub - interval is used as the representative gray value, the difference between the th gray value in the sub - interval and the gray value is larger, the more pixel points with the gray value equal to , that is, the larger the frequency of the gray value , the greater the loss degree of the th gray value in the sub - interval as the representative gray value. Therefore, through the difference between the th gray value in the sub - interval and the gray value and the frequency of the gray value , the loss degree of the th gray value in the sub - interval as the representative gray value can be accurately quantified.

[0058] Exemplarily, when the compression parameter When = 8, for the sub - interval [35, 42], there are 8 gray - scale values in the sub - interval, which are 35, 36, 37, 38, 39, 40, 41, 42 respectively. The frequencies of these 8 gray - scale values are 0.00257, 0.00311, 0.00916, 0.00625, 0.00702, 0.00418, 0.00605, 0.00214 respectively. Then, for the = 1st gray - scale value = 35 as the loss degree of the representative gray - scale value =|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, for the = 2nd gray - scale value = 36 as the loss degree of the representative gray - scale value = 0.1051; For the = 3rd gray - scale value = 37 as the loss degree of the representative gray - scale value = 0.07598; For the = 4th gray - scale value = 38 as the loss degree of the representative gray - scale value = 0.06518; For the = 5th gray - scale value = 39 as the loss degree of the representative gray - scale value = 0.06688; For the = 6th gray - scale value = 40 as the loss degree of the representative gray - scale value = 0.08262; For the = 7th gray - scale value = 41 as the loss degree of the representative gray - scale value = 0.10672; For the = 8th gray - scale value = 42 as the loss degree of the representative gray - scale value = 0.14292; Among them, for the = 4th gray - scale value = 38 as the loss degree of the representative gray - scale value = 0.06518 is the smallest. Therefore, take the = 4th gray - scale value = 38 as the representative gray - scale value of the sub - interval [35, 42].

[0059] It should be noted that by selecting the representative gray value of each sub - interval according to the degree of loss, the image quality can be guaranteed to the greatest extent on the premise of reducing the amount of transmitted data.

[0060] (3) Take the sum of the frequencies of all gray values in the sub - interval as the comprehensive frequency of the representative gray value; construct a Huffman tree according to the comprehensive frequencies of all representative gray values ; through the Huffman tree Obtain the encoding of each representative gray value: each representative gray value corresponds to a leaf node in the constructed Huffman tree Therefore, the labels of all paths between the root node and the leaf nodes corresponding to each representative gray value in the Huffman tree form the encoding of each representative gray value.

[0061] In one embodiment, starting from the root node of the Huffman tree mark the left path as 0 and the right path as 1; in another embodiment, starting from the root node of the Huffman tree mark the left path as 0 and the right path as 1.

[0062] (4) Compress - encode the on - site image according to the encoding of the representative gray value of the sub - interval where the gray value of the pixel point is located, and take the obtained compressed - encoding result as the lossy compressed - encoding result of the on - site image

[0063] (5) The serial numbers of the gray values in each sub - interval are integers within a certain range. Take the sum of the frequencies of the gray values with the same serial number in all sub - intervals as the comprehensive frequency of each serial number; construct a Huffman tree according to the comprehensive frequencies of all serial numbers and obtain the encoding of each serial number through the Huffman tree : each serial number corresponds to a leaf node in the constructed Huffman tree Therefore, the labels of all paths between the root node and the leaf nodes corresponding to each serial number in the Huffman tree form the encoding of each serial number.

[0064] In one embodiment, starting from the root node of the Huffman tree mark the left path as 0 and the right path as 1; in another embodiment, starting from the root node of the Huffman tree mark the left path as 0 and the right path as 1.

[0065] (6) Compress - encode the on - site image Perform compression encoding, and use the obtained compression encoding result as the supplementary compression encoding result of the on-site image. The serial number of the grayscale value refers to the serial number of the grayscale value in its corresponding sub-interval.

[0066] In addition, the command side will also feedback the reception time to the rescue side, so that the rescue side can calculate the transmission speed based on the recorded transmission time and the data transmission volume.

[0067] S4. Transmit the compression encoding result of the on-site image to the command side according to the transmission speed.

[0068] It should be noted that in the disaster rescue scenario, adjusting the transmitted compression encoding result according to the transmission speed is an optimization strategy, which can ensure that the command side can obtain the on-site image as soon as possible under limited communication resources, thereby improving the rescue efficiency.

[0069] Specifically, the transmission time recorded by the rescue side needs to be accurate to seconds; the rescue side calculates the difference between the reception time and the transmission time based on the recorded transmission time and the reception time feedback by the command side, and uses it as the transmission duration; the ratio of the data transmission volume to the transmission duration is used as the transmission speed; where the unit of the data volume is MB (Megabyte, megabyte), and the unit of the transmission speed is MB / second.

[0070] Furthermore, when the transmission speed is less than the reference value, only the lossy compression encoding result of the on-site image is transmitted to the command side; otherwise, the lossless compression encoding result composed of the lossy compression encoding result and the supplementary compression encoding result of the on-site image is transmitted to the command side.

[0071] Among them, the specific value of the reference value can be set according to the actual application scenario and requirements. In the present invention, the reference value is set to 4, and the unit is MB / second.

[0072] Exemplarily, when the transmission speed is less than the reference value, the rescue side only transmits the lossy compression encoding result of the on-site image to the command side; when the compression parameter for the next transmission = 8, the command side decompresses the lossy compression encoding result of the on-site image according to the frequency of each grayscale value, and the schematic diagram of the decompressed lossy image obtained is as Figure 2 shown; when the compression parameter for the next transmission = 16, the command side decompresses the lossy compression encoding result of the on-site image according to the frequency of each grayscale value, and the schematic diagram of the decompressed lossy image obtained is as Figure 3 shown.

[0073] It should be noted that the on-site image Although the lossy compression coding result of will sacrifice some image details, it can transmit key information to the command side in a short time. Therefore, only transmitting the lossy compression coding result of the on-site image can significantly reduce the data volume, thus accelerating the transmission speed. So when the transmission speed between the rescue side and the command side is limited, that is, when the transmission speed is less than the reference value, only transmit the lossy compression coding result of the on-site image; the on-site image Although the lossless compression coding result of has a lower compression ratio, it can completely restore the original image to ensure that the command side obtains more accurate on-site information. Therefore, if the transmission speed between the rescue side and the command side meets the requirements, that is, when the transmission speed is greater than or equal to the reference value, then transmit the lossless compression coding result of the on-site image; adjusting the compression coding result transmitted according to the transmission speed can ensure that the command side can obtain the on-site image as soon as possible under limited communication resources, thereby improving the rescue efficiency.

[0074] Furthermore, while transmitting the compression coding result, the rescue side transmits the frequency of each gray value in the distribution interval to the command side as supplementary information, so that the command side can decode the compression coding result according to the frequency of each gray value.

[0075] The present invention transmits the frequency of each gray value in the distribution interval to the command side as supplementary information, 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 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 supplementary compression encoding result of The serial number of the gray value refers to the serial number of the gray value in its sub-interval; 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 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.

4. The emergency communication method of a satellite vehicle station according to claim 3, 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.

5. The emergency communication method of a satellite vehicle station according to claim 1, 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.

6. The emergency communication method of a satellite vehicle station according to claim 1, 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.

7. 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.

8. 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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