A deep sea image transmission method

Abstract

The present invention discloses a deep-sea image transmission method, which comprises the following steps: using forward error correction (FEC) coding technology and a Reed-Solomon coding scheme to increase redundant information and effectively improve the reliability of data transmission, especially in long-distance and harsh environments, by simulating deep-sea transmission conditions for testing to ensure robustness under packet loss interference during transmission, integrating signal amplifiers and filters to enhance signal strength, filter noise, and improve the quality and efficiency of image return transmission, and real-time monitoring of transmission link signal frequency and frequency dynamic adjustment algorithm to be able to respond to interference in a timely manner, ensure data packet integrity and transmission continuity. In summary, the present invention significantly improves the performance and reliability of deep-sea image transmission and ensures stable return of marine target images.

Description

Technical Field

[0001] The present invention relates to the technical field of image transmission, and in particular to a deep-sea image transmission method. Background Art

[0002] With the development of marine resources and the increase in maritime activities, the real-time transmission of marine target images is particularly important in the fields of monitoring, reconnaissance, and rescue. However, in deep sea environments, image transmission faces many challenges, such as signal attenuation caused by long-distance transmission, interference in harsh environments, and data packet loss. These factors seriously affect the stability and reliability of image transmission.

[0003] Traditional maritime target image transmission technology mainly relies on wireless communication. However, in deep sea environments, the transmission distance of wireless signals is limited and the signal attenuation is serious, resulting in a decrease in the quality of image transmission. In addition, complex environmental factors at sea, including multipath effects and electromagnetic interference, can also cause packet loss during data transmission, making it impossible for the receiving end to obtain complete image data.

[0004] The present invention proposes a solution to the above-mentioned shortcomings:

[0005] By adopting the forward error correction technology of the Reed-So lomon coding scheme, redundant information is added to improve the reliability of data transmission. At the same time, deep-sea transmission conditions are simulated for testing to verify the adaptability and robustness of image transmission in harsh environments. Signal amplifiers and filters are integrated to enhance signal transmission strength and improve image return quality. In addition, the transmission link signal frequency is monitored in real time, and a frequency dynamic adjustment algorithm is used to deal with possible interference sources to ensure the integrity of data packets and the continuity of transmission. Summary of the invention

[0006] In order to solve the above-mentioned technical problems, the present invention provides a deep sea image transmission method.

[0007] The technical solution of the present invention is implemented as follows: a deep sea image transmission method, comprising:

[0008] S1, FEC coding scheme adopts Reed-So lomon, which encodes the image data before transmission, adds redundant information for error detection and correction at the receiving end, is used to decode the received coded data at the receiving end, and uses the redundant information to correct errors that occur during transmission;

[0009] S2. Simulate deep sea transmission conditions to test the FEC encoder and decoder to ensure they can work effectively in harsh environments;

[0010] S3, integrate the signal amplifier into the output end of the FEC encoder to enhance the transmission strength of the signal and ensure the integrity of the signal in long-distance transmission. Integrate the filter into the output end of the signal amplifier to ensure that unnecessary noise can be effectively filtered out after the signal is amplified.

[0011] S4, monitor the signal frequency of the transmission link in real time and identify possible interference sources;

[0012] S5. Frequency dynamic adjustment algorithm can automatically adjust the transmission frequency when interference is detected to avoid the interference source. The integrated frequency synthesizer or modulator can adjust the output frequency according to the instructions of the algorithm.

[0013] Furthermore, the S1 step uses a Reed-So lomon encoder, which is a linear block encoding, and the encoding process is:

[0014] The input data is the original image data stream, the segmentation data segments the image data into multiple small blocks so that each small block can be encoded independently, the application coding applies Reed-So lomon coding to each small block to generate redundant check bits, and the merging data merges the encoded data blocks and check bits to form an encoded data stream.

[0015] Specifically also includes: Decoding process:

[0016] The receiving end receives the encoded data stream, applies Reed-So lomon decoding to the received data, and uses redundant information to correct errors that occur during transmission. The output data is used to output the decoded image data stream and is used for subsequent processing or display. The FEC encoding scheme realizes effective encoding and decoding of image data.

[0017] Furthermore, the S2 step test corresponds to the encoding and decoding process in the S1 step, and is intended to verify the ability of the encoder to add redundant information during transmission, and the ability of the decoder to correct errors using the redundant information. The specific test process is:

[0018] Test steps:

[0019] Simulation environment setup to simulate deep sea transmission conditions, including high noise, signal attenuation, delay and packet loss, using network simulators or actual offshore test environments to simulate these conditions;

[0020] FEC encoder test, splits the original image data and applies Reed-So lomon encoding to generate data blocks with redundant check bits, and records the data before and after encoding for subsequent comparison;

[0021] Transmission process simulation: the encoded data stream is transmitted through a simulated deep-sea transmission environment, and different degrees of errors are introduced during the transmission process to simulate the bit errors and packet loss problems that may occur in actual transmission;

[0022] FEC decoder test, where the encoded data stream is received at the receiving end and Reed-Solomon decoding is applied. The decoder uses redundant information to detect and correct errors introduced during transmission;

[0023] Result verification, comparing the decoded image data with the original image data, verifying the consistency and integrity of the data, and evaluating the effectiveness of redundant information for error detection and correction;

[0024] Performance evaluation, recording the number and type of errors corrected by the decoder, evaluating the performance of the FEC encoder and decoder, testing system performance under different error rate conditions, and determining the fault tolerance of the coding scheme;

[0025] Iterative optimization: Based on the test results, the coding scheme is optimized, such as adjusting redundancy and coding parameters to adapt to deep sea transmission conditions, and the test is repeated until satisfactory performance indicators are achieved;

[0026] Specific testing process:

[0027] Data preparation: select or generate a series of test images to ensure image diversity to cover different data characteristics;

[0028] Coding and transmission: performing the coding process in step S1 on each image, and sending the coded image data through a simulated transmission environment;

[0029] Error introduction, during the transmission process, errors are introduced according to a predetermined error pattern;

[0030] Decoding and verification, the decoding process is performed at the receiving end, and the original image is compared with the received image to verify the decoding quality;

[0031] Performance analysis: analyze key performance indicators such as error correction rate and data recovery rate, and evaluate the performance of encoders and decoders under simulated deep sea transmission conditions.

[0032] Furthermore, the step S3 integrates a signal amplifier into the output end of the FEC encoder to enhance the transmission strength of the signal, and integrates a filter into the output end of the signal amplifier:

[0033] Signal amplifier integration Select a signal amplifier that has sufficient gain to compensate for signal attenuation over long distances and is compatible with the output of the FEC encoder;

[0034] The integration process physically connects the signal amplifier to the output of the FEC encoder, ensures that the input impedance of the amplifier matches the output impedance of the encoder to avoid signal reflection and loss, and configures the amplifier to adapt to the characteristics of the encoder output signal, including signal amplitude and frequency;

[0035] Signal amplification test: After integration, the signal amplifier is tested by sending a test signal to verify whether it can enhance the signal as expected, and to test the signal strength at different transmission distances to ensure that the amplifier can provide sufficient gain under deep sea transmission conditions;

[0036] Filter integration, select filters that can effectively filter out the noise introduced after signal amplification while retaining useful signal frequencies;

[0037] The integration process involves connecting the filter to the output of the signal amplifier and adjusting the filter parameters to ensure that it can filter a specific noise spectrum;

[0038] Filtering effect verification, by sending a signal containing noise to test whether the filter can effectively remove the noise without affecting the integrity of the signal, using a signal analyzer tool to monitor the signal quality before and after filtering;

[0039] Overall system test: After integrating the signal amplifier and filter, re-execute the test process in step S2 to verify the performance of the entire system in enhancing signal strength and filtering noise, and check whether the decoded image data maintains high quality and no new errors are introduced due to signal amplification and filtering;

[0040] Performance optimization, based on the test results, the parameters of the signal amplifier and filter are optimized to improve the overall performance of the system, which requires multiple iterations to find the best configuration.

[0041] Furthermore, the step S4 monitors the signal frequency of the transmission link in real time to identify possible interference sources:

[0042] a monitoring system that continuously monitors the signal frequency of the transmission link, including sensors and monitoring equipment;

[0043] Integration of monitoring equipment: integrating monitoring equipment into the transmission link, at the output of the signal amplifier and filter, to ensure that the monitoring equipment does not interfere with the transmission signal;

[0044] Data acquisition: real-time acquisition of signal data of the transmission link, including the frequency, amplitude, and phase information of the signal, and setting the sampling rate to ensure that all possible interference signals can be captured;

[0045] Signal analysis, using signal processing algorithms to analyze the collected data in real time, identify changes and anomalies in signal frequency, and apply spectrum analysis techniques, including fast Fourier transform (FFT), to analyze the spectral characteristics of the signal;

[0046] Interference source identification: Based on the spectrum analysis results, the abnormal frequency components that are different from the normal signal frequency are identified. By comparing the characteristics of known interference sources, possible interference sources are determined, and the specific location of the interference source is determined using positioning technology.

[0047] Interference response strategy: formulate response strategies for identified interference sources, including adjusting transmission frequency, using frequency hopping technology, adding signal shielding measures, implementing interference mitigation measures, and monitoring their effectiveness;

[0048] System feedback and optimization: the monitoring system, FEC encoder, signal amplifier and filter form a closed-loop control system. Based on the monitoring results, the system parameters are dynamically adjusted to optimize the signal transmission performance.

[0049] Recording and reporting: record monitoring data and interference events for subsequent analysis and system optimization, and generate reports regularly to summarize interference conditions, countermeasures and system performance.

[0050] Furthermore, the frequency dynamic adjustment algorithm in step S5 can automatically adjust the transmission frequency when interference is detected to avoid the interference source, and integrate a frequency synthesizer or modulator so that it can adjust the output frequency according to the instructions of the algorithm:

[0051] Development of a frequency dynamic adjustment algorithm that can analyze signal frequency interference in real time based on data provided by the monitoring system;

[0052] Algorithm logic implementation, determine the interference detection threshold: set the detection threshold of frequency interference, when the monitored signal frequency abnormality exceeds the threshold, trigger frequency adjustment;

[0053] Frequency switching strategy: Formulate frequency switching strategy, including switching timing, switching speed and signal maintenance strategy during switching;

[0054] Frequency synthesizer or modulator integration, selecting a frequency synthesizer or modulator and integrating the frequency synthesizer or modulator into the transmission system to ensure that it can receive instructions from the algorithm;

[0055] Control interface, which enables the frequency dynamic adjustment algorithm to send instructions to the frequency synthesizer or modulator to ensure the real-time and reliability of interface communication;

[0056] System integration and testing: integrating the frequency dynamic adjustment algorithm, control interface and frequency synthesizer or modulator into a complete system, and testing the performance of the entire system in a simulation environment, including the response time of the algorithm, the accuracy of frequency switching and the stability of the system;

[0057] Real-time testing and optimization: Real-time testing is performed in the actual transmission environment to verify the system's ability to automatically adjust the frequency when interference is detected. The algorithm and system integration are optimized based on the test results to ensure that interference sources can be effectively avoided in actual operation.

[0058] A feedback mechanism is established to enable the system to self-learn and optimize according to the effect of frequency adjustment, and record the historical data of frequency adjustment for analysis and improvement of the algorithm.

[0059] Beneficial Effects

[0060] The forward error correction (FEC) coding in the present invention adopts the Reed-So lomon coding scheme, which encodes the image data before data transmission and adds redundant information to facilitate error detection and correction at the receiving end. It helps to use the redundant information at the receiving end to correct errors that occur during the transmission process and improve the reliability of data transmission, especially in long-distance transmission and harsh environments.

[0061] Simulating deep sea transmission conditions for testing Step S2 in the present invention simulates deep sea transmission conditions to test the FEC encoder and decoder to ensure that they can work effectively in harsh environments. This helps to verify the encoder's ability to add redundant information during transmission and the decoder's ability to use this redundant information to correct errors, thereby improving the system's adaptability and robustness in actual environments.

[0062] Integration of signal amplifier and filter In step S3 of the present invention, the signal amplifier is integrated into the output end of the FEC encoder to enhance the transmission strength of the signal and ensure the integrity of the signal during long-distance transmission. At the same time, the integrated filter effectively filters out unnecessary noise, further improving the quality and efficiency of the transmission signal.

[0063] Real-time monitoring of the signal frequency of the transmission link In step S4 of content 1 of the present invention, the signal frequency of the transmission link is monitored in real time to identify possible interference sources, which helps to promptly detect and respond to interference and ensure the continuity and reliability of image transmission.

[0064] Frequency dynamic adjustment algorithm The step S5 in the present invention adopts a frequency dynamic adjustment algorithm, which can automatically adjust the transmission frequency when interference is detected to avoid the interference source, which helps to improve the adaptability and robustness of the system in complex environments and ensure the continuity and reliability of image transmission.

[0065] In summary, the present invention effectively solves the shortcomings in deep-sea image transmission and improves the performance and reliability of the system by adopting FEC coding, simulation testing, signal enhancement, real-time monitoring and dynamic frequency adjustment technical means.

[0066] Working principle:

[0067] Forward Error Correction (FEC) Coding In the process of deep-sea image transmission, due to the influence of long-distance transmission and harsh environment, the data is easily interfered and attenuated. The present invention adopts the Reed-So lomon coding scheme, which encodes the image data before data transmission and adds redundant information to facilitate error detection and correction at the receiving end. In this way, even if the data is interfered during transmission, the receiving end can use the redundant information to correct the error, thereby improving the reliability of data transmission.

[0068] To ensure the adaptability and robustness of the system in actual environments, the S2 step in the present invention simulates deep sea transmission conditions to test the FEC encoder and decoder. By introducing high noise, signal attenuation, delay and packet loss interference factors in the simulated environment, the encoder's ability to add redundant information during transmission and the decoder's ability to use the redundant information to correct errors are verified.

[0069] Integration of signal amplifier and filter In step S3 of the present invention, the signal amplifier is integrated into the output end of the FEC encoder to enhance the transmission strength of the signal and ensure the integrity of the signal during long-distance transmission. At the same time, the integrated filter effectively filters out unnecessary noise, further improving the signal quality and transmission efficiency.

[0070] Real-time monitoring of the signal frequency of the transmission link In order to detect and respond to interference in a timely manner, the S4 step of the present invention monitors the signal frequency of the transmission link in real time, identifies possible interference sources, identifies the interference sources by comparing the normal signal frequency with the abnormal frequency components, and uses positioning technology to determine the location of the interference source.

[0071] The frequency dynamic adjustment algorithm is adopted in step S5 of the present invention, which can automatically adjust the transmission frequency when interference is detected to avoid the interference source. By setting the interference detection threshold, when the monitored signal strength decreases or the frequency offset exceeds the preset value, the frequency adjustment process is triggered, and the frequency synthesizer or modulator adjusts the output frequency according to the instructions of the algorithm to achieve dynamic frequency adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a structural block diagram of a deep sea image transmission method according to an embodiment of the present invention;

[0073] Figure 2 The present invention is a flowchart of a method for transmitting deep sea images according to an embodiment of the present invention. DETAILED DESCRIPTION

[0074] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0075] The preferred implementation methods of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these implementation methods are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0076] It should be noted that when an element is considered to be "connected" to another element, it can be directly connected to the other element, or connected to the other element through an intermediate element. In addition, the "connection" in the following embodiments should be understood as "electrical connection", "communication connection", etc. if there is transmission of electrical signals or data between the connected objects.

[0077] When used herein, the singular forms "a", "an", and "said / the" may also include plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "include / comprise" or "have" and the like specify the presence of stated features, wholes, steps, operations, components, parts, or combinations thereof, but do not exclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof. At the same time, the term "and / or" used in this specification includes any and all combinations of the relevant listed items.

[0078] See also Figure 1-2 As shown, a deep sea image transmission method includes:

[0079] S1, FEC coding scheme adopts Reed-So lomon, which encodes the image data before transmission, adds redundant information for error detection and correction at the receiving end, is used to decode the received coded data at the receiving end, and uses the redundant information to correct errors that occur during transmission;

[0080] S2. Simulate deep sea transmission conditions to test the FEC encoder and decoder to ensure they can work effectively in harsh environments;

[0081] S3, integrate the signal amplifier into the output end of the FEC encoder to enhance the transmission strength of the signal and ensure the integrity of the signal in long-distance transmission. Integrate the filter into the output end of the signal amplifier to ensure that unnecessary noise can be effectively filtered out after the signal is amplified.

[0082] S4, monitor the signal frequency of the transmission link in real time and identify possible interference sources;

[0083] S5. Frequency dynamic adjustment algorithm can automatically adjust the transmission frequency when interference is detected to avoid the interference source. The integrated frequency synthesizer or modulator can adjust the output frequency according to the instructions of the algorithm.

[0084] Furthermore, the S1 step uses a Reed-So lomon encoder, which is a linear block encoding, and the encoding process is:

[0085] The input data is the original image data stream, the segmentation data segments the image data into multiple small blocks so that each small block can be encoded independently, the application coding applies Reed-So lomon coding to each small block to generate redundant check bits, and the merging data merges the encoded data blocks and check bits to form an encoded data stream.

[0086] Specifically, the encoding process:

[0087] Data segmentation: The original image data stream is segmented into multiple fixed-size data blocks, each of which contains k data symbols. These symbols include data units of bytes, 16 bits, or other sizes, depending on the parameters of the Reed-Solomon code used;

[0088] Apply Reed-Solomon coding:

[0089] For each data block, calculate nk redundant check symbols, where n is the size of the encoded data block and k is the number of original data symbols;

[0090] The encoding process involves generating a finite field polynomial of n elements, where the first k coefficients correspond to the original data symbols and the last nk coefficients correspond to the redundant check symbols;

[0091] Using finite field arithmetic, construct a generating polynomial g(x) whose degree is nk and whose roots are nonzero elements in the finite field;

[0092] Multiply the original data polynomial p(x) by the generating polynomial g(x), and take the coefficients below the x^(nk) term of the product as the redundant check symbols;

[0093] Merge data: Merge the k original data symbols and the calculated nk redundant check symbols of each data block to form a new coded data block with a size of n symbols;

[0094] Decoding process:

[0095] Receiving data: The receiving end receives the encoded data stream, which may contain errors;

[0096] Apply Reed-So lomon decoding: split each received coded data block into n symbols;

[0097] Use the syndrome calculation to determine if there are errors in the data block. Syndromes are the coefficients of the remainder polynomial after dividing the received data polynomial r(x) by the generator polynomial g(x);

[0098] If all syndromes are 0, there is no error in the data block, and jump to the data output step;

[0099] If the syndromes are not all zero, an error location and correction algorithm (Ber lekamp-Massey algorithm or Forney algorithm) is used to find the location and value of the error;

[0100] Once the location and value of the error are determined, this information is used to correct the error in the data block;

[0101] Output data:

[0102] Output the decoded and error-corrected data blocks, which are now the original image data;

[0103] The output data stream is used for subsequent processing or display;

[0104] FEC coding scheme:

[0105] The FEC (Forward Error Correction) coding scheme enables the receiver to detect and correct a certain number of errors by adding redundant information to the data, thereby achieving effective encoding and decoding of image data;

[0106] This calculation process ensures that even if some errors occur during transmission, the receiving end can restore the original image data with almost no errors through Reed-So lomon encoding and decoding technology.

[0107] Furthermore, the S2 step test corresponds to the encoding and decoding process in the S1 step, and is intended to verify the ability of the encoder to add redundant information during the transmission process, and the ability of the decoder to correct errors using the redundant information.

[0108] Specifically, the test steps are:

[0109] 1. Simulation environment settings: Use a network simulator or an actual offshore test environment to simulate deep sea transmission conditions, and set parameters to simulate high noise, signal attenuation, delay, and packet loss;

[0110] 2.FEC encoder test:

[0111] Data preparation: Select or generate a series of test images to ensure image diversity;

[0112] Data segmentation: divide the original image data into data blocks of fixed size;

[0113] Apply Reed-Solomon coding: Apply Reed-Solomon coding to each data block to generate redundant check bits;

[0114] Record data: record the data blocks before and after encoding for subsequent comparison;

[0115] 3. Transmission process simulation:

[0116] Coding and transmission: sending the encoded image data through a simulated transmission environment;

[0117] Error introduction: During the transmission process, errors are introduced according to a predetermined error pattern, including bit errors and packet losses;

[0118] 4.FEC decoder test:

[0119] Receive data: Receive the encoded data stream at the receiving end;

[0120] Apply Reed-So lomon decoding:

[0121] Calculate syndromes to determine if there are errors in the data block;

[0122] If an error exists, use the error localization and correction algorithm to find the location and value of the error;

[0123] Use redundant information to correct errors;

[0124] 5. Result verification:

[0125] Decoding and verification: Use the original image data to compare with the decoded image data at the receiving end.

[0126] Data consistency check: Verify the consistency and integrity of the decoded image data with the original image data.

[0127] 6. Performance evaluation:

[0128] Record error correction: record the number and type of errors corrected by the decoder;

[0129] Performance Analysis:

[0130] Calculate the error correction rate (i.e., the number of errors corrected divided by the total number of errors);

[0131] Calculate the data recovery rate (i.e. the number of successfully recovered data blocks divided by the total number of data blocks).

[0132] Furthermore, the detailed calculation process is:

[0133] It is assumed that there is an image data block containing 8 bytes (k=8), and a Reed-So lomon code is used that can add 4 redundant bytes (n=12, so nk=4);

[0134] Preset data for step S1:

[0135] Original image data block: [D0,D1,D2,D3,D4,D5,D6,D7]

[0136] Detailed calculation process of step S2:

[0137] 1. FEC encoder test

[0138] Data segmentation: The original image data block has been segmented into 8 bytes;

[0139] Apply Reed-So lomon coding: The preset encoder generates the following 4 redundant bytes: [P0, P1, P2, P3] The encoded data block: [D0, D1, D2, D3, D4, D5, D6, D7, P0, P1, P2, P3];

[0140] Record data: record the data blocks before and after encoding;

[0141] 2. Transmission process simulation

[0142] Error introduction: Assume that during the transmission process, the 3rd and 7th bytes are modified incorrectly. The data block after the error is: [D0, D1, E2, D3, D4, D5, D6, E7, P0, P1, P2, P3], where E2 and E7 are incorrect data;

[0143] 3.FEC decoder test

[0144] Receive data: The data block received by the receiving end: [D0, D1, E2, D3, D4, D5, D6, E7, P0, P1, P2, P3];

[0145] Apply Reed-So lomon decoding:

[0146] Calculate the syndrome: S = [S0, S1, S2, S3] is obtained by multiplying the received data block with the root of the generator polynomial and taking the modulus. The default calculated syndrome is: [S0, S1, S2, S0], which indicates an error;

[0147] Error location: Use the Berlekamp-Massey algorithm or other error location algorithms to find the error location. The preset algorithm determines that the error occurs at the 3rd and 7th positions.

[0148] Error correction: Use Forney algorithm or other algorithms to calculate and correct the error value. The preset calculated error value is: [ΔE2, ΔE7], which is used to correct E2 and E7;

[0149] Corrected data block: [D0, D1, (E2-ΔE2), D3, D4, D5, D6, (E7-ΔE7), P0, P1, P2, P3];

[0150] 4. Result Verification

[0151] Data consistency check: compare the corrected data block with the original data block;

[0152] The preset corrected data block is: [D0, D1, D2, D3, D4, D5, D6, D7, P0, P1, P2, P3];

[0153] 5. Performance Evaluation

[0154] Record error correction: Record the number and type of errors corrected, including correcting 2 byte errors in this example;

[0155] Performance Analysis:

[0156] Error correction rate = number of errors corrected / total number of errors = 2 / 2 = 100%

[0157] Data recovery rate = number of successfully recovered data blocks / total number of data blocks = 1 / 1 = 100%.

[0158] Specifically, the error correction rate and data recovery rate at the transmission level in the error introduction simulated by the transmission process are interrelated, and the accuracy of the mapping algorithm in a specific environment is the error correction rate and data recovery rate for concentrated error introduction at different distances;

[0159] Level 1: Mild error environment, error type: mainly bit errors; error rate: low error rate (1 bit error / 1000 bits); distance: shallow sea environment (100 meters above the water surface)

[0160] Calculation process: Error introduction: 0.1% bit errors in each data block;

[0161] Performance evaluation:

[0162] Error correction rate = number of errors corrected / total number of errors;

[0163] Data recovery rate = number of successfully recovered data blocks / total number of data blocks;

[0164] Level 2: Moderate error environment, error type: bit error and slight packet loss; error rate: medium error rate (2 bit error / 1000 bits, 1 packet / 100 packets); distance: medium distance (500 meters above the water surface)

[0165] Calculation process: Error introduction: 0.2% bit errors and 0.01% packet loss in each data block;

[0166] Performance evaluation:

[0167] Error correction rate = number of errors corrected / total number of errors;

[0168] Data recovery rate = number of successfully recovered data blocks / total number of data blocks;

[0169] Level 3: Severe error environment, error types: bit error, packet loss and severe signal attenuation; error rate: high error rate (5 bit errors / 1000 bits, 5 packets / 100 packets); distance: deep sea surface environment (1000 meters above the water surface);

[0170] Calculation process: Error introduction: 0.5% bit errors and 0.05% packet loss in each data block;

[0171] Performance evaluation:

[0172] Error correction rate = number of errors corrected / total number of errors;

[0173] Data recovery rate = number of successfully recovered data blocks / total number of data blocks;

[0174] The calculation process is as follows:

[0175] The data block size is preset to n, where k is the effective data size and nk is the redundant byte size;

[0176] Error Correction Rate:

[0177] Level 1: Error correction rate = (number of bit errors corrected) / (number of bit errors introduced);

[0178] Level 2: Error correction rate = (number of corrected bit errors + number of corrected packet losses) / (number of introduced bit errors + number of introduced packet losses);

[0179] Level 3: Error correction rate = (number of corrected bit errors + number of corrected packet losses) / (number of introduced bit errors + number of introduced packet losses);

[0180] Data recovery rate:

[0181] Level 1: Data recovery rate = (number of successfully recovered data blocks) / (total number of data blocks)

[0182] Level 2: Data recovery rate = (number of successfully recovered data blocks) / (total number of data blocks)

[0183] Level 3: Data recovery rate = (number of successfully recovered data blocks) / (total number of data blocks)

[0184] The level with the highest recommendation algorithm accuracy

[0185] calculate:

[0186] For each layer, the error correction rate and data recovery rate are calculated;

[0187] Compare the error correction rate and data recovery rate of the three levels and find the highest value;

[0188] recommend:

[0189] If the error correction rate and data recovery rate of level 1 are both 99%, level 2 are both 95%, and level 3 are both 90%, then the algorithm of level 1 is recommended to have the highest accuracy because it has the highest error correction rate and data recovery rate;

[0190] When choosing the most appropriate image transmission distance level, it is necessary to consider the error correction rate and other possible factors, including data transmission reliability, cost, power consumption, and environmental impact. The following is a decision-making process based on the error correction rate and other key factors, using a mathematical calculation formula to determine the most appropriate image transmission distance level:

[0191] Step 1: Determine the weights. First, you need to assign a weight to each factor to indicate its importance in the decision. The sum of the weights should be 1.

[0192] Error correction rate weight (W1)

[0193] Cost weight (W2)

[0194] Power consumption weight (W3)

[0195] Environmental impact weight (W4)

[0196] Considering only error correction rate and cost, and with error correction rate being more important, assign the following weights:

[0197] W1=0.7(error correction rate)

[0198] W2=0.3(cost)

[0199] Step 2: Standardize the data

[0200] To compare different factors, they were normalized to the same scale, including converting the costs of each level into relative values ​​if known;

[0201] The error correction rate is already a ratio and does not need to be normalized;

[0202] Cost normalization: The cost of level 1 is 100, level 2 is 150, and level 3 is 200. Use the lowest cost level as the benchmark (level 1) to get the relative value of the cost.

[0203] Step 3: Calculate the composite score

[0204] The composite score for each tier is calculated using the following formula:

[0205] Comprehensive score = (W1*error correction rate) + (W2*relative value of cost) + (W3*relative value of power consumption) + (W4*relative value of environmental impact);

[0206] The default relative cost values ​​are:

[0207] Tier 1:1 (benchmark)

[0208] Level 2: 1.5

[0209] Level 3:2

[0210] Calculate the composite score for each level:

[0211] Level 1

[0212] Comprehensive score_level 1 = (W1*error correction rate_level 1) + (W2*relative cost value_level 1) Comprehensive score_level 1 = (0.7*0.98) + (0.3*1) Comprehensive score_level 1 = 0.686 + 0.3 Comprehensive score_level 1 = 0.986;

[0213] Level 2

[0214] Comprehensive score_level 2 = (W1*error correction rate_level 2) + (W2*relative cost value_level 2) Comprehensive score_level 2 = (0.7*0.9) + (0.3*1.5) Comprehensive score_level 2 = 0.63 + 0.45 Comprehensive score_level 2 = 1.08;

[0215] Level 3

[0216] Comprehensive score_level 3 = (W1*error correction rate_level 3) + (W2*relative cost value_level 3) Comprehensive score_level 3 = (0.7*0.8333) + (0.3*2) Comprehensive score_level 3 = 0.58331 + 0.6 Comprehensive score_level 3 = 1.18331;

[0217] Step 4: Choose the best tier

[0218] Based on the overall score, select the tier with the highest score:

[0219] Level 1: Comprehensive score = 0.986

[0220] Level 2: Overall score = 1.08

[0221] Level 3: Comprehensive score = 1.18331

[0222] According to the calculation results, Level 3 has the highest comprehensive score. Therefore, if we only consider the error correction rate and cost, Level 3 is the most appropriate choice.

[0223] Furthermore, the step S3 integrates a signal amplifier into the output end of the FEC encoder to enhance the transmission strength of the signal, and integrates a filter into the output end of the signal amplifier;

[0224] Signal amplifier integration Select a signal amplifier that has sufficient gain to compensate for signal attenuation over long distances and is compatible with the output of the FEC encoder;

[0225] The integration process physically connects the signal amplifier to the output of the FEC encoder, ensures that the input impedance of the amplifier matches the output impedance of the encoder to avoid signal reflection and loss, and configures the amplifier to adapt to the characteristics of the encoder output signal, including signal amplitude and frequency;

[0226] Signal amplification test: After integration, the signal amplifier is tested by sending a test signal to verify whether it can enhance the signal as expected, and to test the signal strength at different transmission distances to ensure that the amplifier can provide sufficient gain under deep sea transmission conditions;

[0227] Filter integration, select filters that can effectively filter out the noise introduced after signal amplification while retaining useful signal frequencies;

[0228] The integration process involves connecting the filter to the output of the signal amplifier and adjusting the filter parameters to ensure that it can filter a specific noise spectrum;

[0229] Filtering effect verification, by sending a signal containing noise to test whether the filter can effectively remove the noise without affecting the integrity of the signal, using a signal analyzer tool to monitor the signal quality before and after filtering;

[0230] Overall system test: After integrating the signal amplifier and filter, re-execute the test process in step S2 to verify the performance of the entire system in enhancing signal strength and filtering noise, and check whether the decoded image data maintains high quality and no new errors are introduced due to signal amplification and filtering;

[0231] Performance optimization, based on the test results, the parameters of the signal amplifier and filter are optimized to improve the overall performance of the system, which requires multiple iterations to find the best configuration.

[0232] Specifically, the signal amplifier integrates:

[0233] Select a signal amplifier:

[0234] According to the characteristics of the encoder output signal in steps S1 and S2, select a signal amplifier with sufficient gain. The amplifier needs to be able to compensate for the signal attenuation in deep sea transmission. Specifically, the Mini-Ci rcu its ZX60-33LN-S+ device is used;

[0235] Integration process:

[0236] Physical connection: Connect the signal amplifier to the output of the FEC encoder;

[0237] Impedance matching: configure the input impedance of the amplifier to ensure that it matches the output impedance of the encoder to avoid signal reflection and loss;

[0238] Configure the amplifier: adjust the amplifier parameters, including gain and bandwidth, to adapt to the amplitude and frequency of the encoder output signal;

[0239] Signal amplification test:

[0240] Send test signal: Send a known signal through the amplifier to verify the amplification effect;

[0241] Signal strength test: Test the signal strength at different transmission distances to ensure that the amplifier provides sufficient gain;

[0242] Filter Integration

[0243] Select filter:

[0244] Select a filter that can filter out the noise introduced after amplification and ensure that the filter does not affect the frequency of the useful signal. Specifically, use Mini-Ci rcu its SVA-40+WB-LF equipment;

[0245] Integration process:

[0246] Connect the filter: Connect the filter to the output of the signal amplifier;

[0247] Adjust parameters: adjust the filter's cutoff frequency and bandwidth parameters according to the noise spectrum;

[0248] Filtering effect verification:

[0249] Send a noisy signal: test the filter's ability to remove noise;

[0250] Signal quality monitoring: Use a signal analyzer to monitor the signal quality before and after filtering;

[0251] Overall system test

[0252] Re-execute the S2 step test:

[0253] Verify the performance of the entire system in terms of signal enhancement and noise filtering;

[0254] Check the quality of decoded image data to ensure that no new errors are introduced due to signal amplification and filtering;

[0255] Performance Optimization

[0256] Parameter optimization:

[0257] Adjust the parameters of the signal amplifier and filter according to the test results;

[0258] Perform multiple iterations of tests to find the best configuration to optimize system performance;

[0259] Specific calculation and configuration:

[0260] Signal amplifier configuration:

[0261] Assuming the encoder output signal amplitude is A, the transmission attenuation is α, and the transmission distance is d, the amplifier gain G = 1 / (A*e^(-α*d));

[0262] Filter parameter adjustment:

[0263] It is assumed that the noise after amplification is mainly concentrated at the frequency f_noise, so the cutoff frequency f_cutoff of the filter should be set lower than f_noise to effectively filter out the noise;

[0264] Through these steps, the S3 step ensures the strength and clarity of the signal under deep sea transmission conditions, thereby improving the performance and reliability of the entire image transmission system.

[0265] Further, detailed steps and calculations:

[0266] Signal amplifier integration

[0267] Selecting a Signal Amplifier

[0268] The preset FEC encoder output signal amplitude is 0.5V (A = 0.5V);

[0269] The attenuation coefficient α of the signal in the sea water surface is 0.05dB / m;

[0270] The transmission distance d is 1000m;

[0271] Calculate the required gain

[0272] Transmission attenuation = α*d = 0.05dB / m*1000m = 50dB;

[0273] The required gain G = 1 / (A*e^(-α*d)). Since the attenuation is in dB, it needs to be converted into a linear gain.

[0274] Linear gain G_linear=10^(50 / 10)=100,000;

[0275] Required gain G = 100,000 / 0.5V = 200,000;

[0276] Integration process

[0277] Select a signal amplifier with a gain of 200,000;

[0278] Adjust the input impedance of the amplifier to 50Ω to match the output impedance of the encoder;

[0279] Signal amplification test

[0280] Send a 0.5V test signal;

[0281] Test the signal strength at different transmission distances, including 500m and 1500m;

[0282] Calculate signal strength

[0283] Signal strength at 500m = 0.5V*e^(-0.05dB / m*500m);

[0284] Signal strength at 1500m = 0.5V*e^(-0.05dB / m*1500m);

[0285] Filter Integration

[0286] Select the filter, and preset the noise introduced after amplification to be mainly concentrated at 100kHz (f_noise=100kHz);

[0287] Integration process

[0288] Select a low-pass filter, specifically Mini-Ci rcu its ZXLP-33LN-S, with a cut-off frequency f_cutoff < 100kHz, for example f_cutoff = 80kHz, and connect the filter to the output of the amplifier;

[0289] Filter effect verification

[0290] Send a signal containing 100kHz noise;

[0291] Use a signal analyzer to monitor the signal before and after filtering;

[0292] Calculate the filtering effect

[0293] The signal-to-noise ratio (SNR) of the signal before filtering is preset to 20dB;

[0294] After filtering, the noise is significantly reduced and the SNR is increased to 40dB;

[0295] Overall system test

[0296] Re-execute the S2 step test;

[0297] sending the encoded image data through amplifiers and filters;

[0298] Decoding the image data at the receiving end;

[0299] Performance Optimization

[0300] According to the test results, adjust the amplifier gain and filter cutoff frequency;

[0301] Preset testing showed that the signal strength was insufficient at 1500m and the amplifier gain needed to be increased to 250,000;

[0302] Calculate the optimized signal strength

[0303] At the new gain, the signal strength at 1500m = 0.5V*250,000*e^(-0.05dB / m*1500m);

[0304] The meanings of the physical characters involved in the above content are:

[0305] A: The amplitude of the encoder output signal, usually in volts (V);

[0306] α: The attenuation coefficient of the signal in the transmission medium (such as sea surface), usually measured in decibels per meter (dB / m);

[0307] d: distance the signal is transmitted, usually in meters (m);

[0308] G: The gain of the signal amplifier, unitless, indicating the signal amplification factor that the amplifier can provide;

[0309] G_linear: Converts decibel (dB) gain to linear gain, unitless;

[0310] f_noise: the main frequency of the noise, usually in Hertz (Hz);

[0311] f_cutoff: The cutoff frequency of the filter, that is, the frequency at which the filter begins to significantly attenuate the signal, usually in Hertz (Hz);

[0312] SNR: Signal-to-Noise Ratio, which represents the ratio of signal power to noise power, usually in decibels (dB);

[0313] The following is a detailed explanation of the physical meaning:

[0314] A: This is the voltage level of the original signal without any amplification or attenuation;

[0315] α: This coefficient describes the energy lost per unit length during the transmission of the signal;

[0316] d: This is the distance the signal needs to travel from the transmitting point to the receiving point;

[0317] G: The gain of an amplifier indicates how many times the amplifier can amplify the input signal;

[0318] G_linear: This is the value that converts decibel gain to linear gain, which is used to calculate the actual voltage or power amplification factor;

[0319] f_noise: This is the main frequency component of the noise introduced by the amplifier or other circuits;

[0320] f_cutoff: This is the degree to which the filter starts to reduce the signal passing through. Signals below this frequency can pass more freely, while signals above this frequency will be attenuated;

[0321] SNR: This is the power ratio of the signal to the background noise and is used to measure the clarity or quality of the signal. The higher the value, the better the signal quality.

[0322] Furthermore, the step S4 monitors the signal frequency of the transmission link in real time to identify possible interference sources:

[0323] Monitoring system

[0324] Select sensors and monitoring equipment. Choose high-precision and fast-response sensors, including the radio frequency (RF) sensor Analog Devices ADL5901 and the monitoring system NI (National Instruments) PXIe-5663withPXIe-5644R, which can monitor the changes in signal frequency in real time. Determine the sampling rate. If the monitored signal frequency range is 0-5GHz, the sampling rate should be at least twice the highest frequency of the signal, that is, 10GSps (Giga Samples per Second).

[0325] Integrated monitoring equipment:

[0326] Physically connect monitoring equipment to the output of the signal amplifier and filter;

[0327] Real-time data collection:

[0328] Collect signal data, including frequency, amplitude, and phase information, set the sampling rate, and ensure data integrity and accuracy;

[0329] Signal processing algorithms:

[0330] Apply the Fast Fourier Transform (FFT) signal processing algorithm to analyze the collected data, analyze the spectral characteristics of the signal, and identify frequency changes and anomalies;

[0331] Identify the source of interference:

[0332] Compare normal signal frequencies with abnormal frequency components to identify possible interference sources; use positioning techniques, including time difference of observation (TDOA) and azimuth angle measurement, to determine the location of interference sources;

[0333] Disruption Coping Strategies

[0334] Develop response strategies, adjust transmission frequencies to avoid interfering frequencies, implement frequency hopping techniques or add signal shielding measures, and monitor the effectiveness of interference mitigation measures;

[0335] System feedback and optimization

[0336] Closed-loop control system:

[0337] The monitoring system, FEC encoder, signal amplifier and filter form a closed-loop control system, dynamically adjust system parameters according to the monitoring results to optimize transmission performance;

[0338] Records and Reports:

[0339] Record monitoring data and interference events for subsequent analysis and system optimization, and generate reports regularly to summarize interference conditions, countermeasures and system performance.

[0340] Specifically, the monitoring system presets the monitored signal frequency range to be 0-2 GHz, and selects a sampling rate of 4 GSps (Giga Samples per Second) to satisfy the Nyquist sampling theorem;

[0341] Integrated monitoring equipment, with the insertion loss of the monitoring equipment preset to 1dB, ensuring minimal impact on the signal;

[0342] Data acquisition, real-time acquisition of signal data, set the sampling rate to 4GSps;

[0343] Signal analysis, signal processing algorithm, preset the collected signal data to include a 1GHz signal and a 1.5GHz interference signal, use FFT to analyze the signal spectrum;

[0344] Calculation process:

[0345] FFT size (N): Default is 1024 points;

[0346] Frequency resolution (Δf): sampling rate divided by FFT size, i.e. (4GSps) / 10243.90625MHz;

[0347] FFT Analysis

[0348] Perform FFT processing on the collected signal data;

[0349] Identify the frequency components at 1 GHz and 1.5 GHz;

[0350] Interference source identification

[0351] Identify the interference source, compare the FFT results, and identify the 1.5GHz frequency component as an abnormal interference signal;

[0352] The interference source distance determined by TDOA technology is preset to be 10km;

[0353] Calculate the location of the interference source

[0354] If TDOA is used, data from at least three monitoring stations are required;

[0355] The locations of the three monitoring stations are preset to be A, B, and C, and the distances from the interference source are dA, dB, and dC respectively. The location of the interference source is calculated through the time differences ΔtAB and ΔtAC;

[0356] Disruption Coping Strategies

[0357] Formulate a response strategy, preset the interference signal strength of 1.5GHz to -70dBm, and decide to adjust the transmission frequency from 1GHz to 1.2GHz;

[0358] Calculate the signal strength at the new frequency

[0359] The preset signal attenuation at 1.2GHz is 50dB. The signal strength at the new transmission distance is: original signal strength - attenuation - insertion loss;

[0360] System feedback and optimization

[0361] Closed-loop control system, which adjusts the parameters of the FEC encoder, including error correction capability, based on the monitoring results;

[0362] Adjust the gain of the signal amplifier to compensate for the signal attenuation at the new frequency;

[0363] Records and Reports

[0364] Record signal data, interference source information, and frequency adjustment before and after adjustment;

[0365] Generate reports including timestamps, frequency, intensity, and actions taken to address interference events;

[0366] Through these data and calculation processes, step S4 effectively monitors the transmission link, identifies and responds to interference sources, thereby ensuring the continuity and reliability of deep-sea image transmission.

[0367] Further, data acquisition and signal analysis, signal processing algorithm - FFT analysis:

[0368] The preset collects a 1024-point signal sample with a sampling rate of 4GSps;

[0369] Calculate the frequency resolution: Δf = sampling rate / FFT size, Δf = 4GSps / 1024, Δf = 3.90625MHz;

[0370] FFT analysis: Perform FFT on 1024-point signal samples to obtain the frequency spectrum;

[0371] Identifying interference sources

[0372] The preset FFT results show obvious peaks at 1GHz and 1.5GHz;

[0373] Determine the interference frequency: interference frequency = 1.5 GHz (preset 1 GHz is the desired signal, 1.5 GHz is the interference signal);

[0374] Interference source location-TDOA technology

[0375] There are three monitoring stations A, B, and C, and the times they receive interference signals are tA, tB, and tC respectively;

[0376] Calculate the time difference: ΔtAB = tB-tA, ΔtAC = tC-tA;

[0377] The image transmission speed in the seawater is assumed to be 1500 m / s, and the distance difference is calculated as: ΔdAB = ΔtAB*1500 m / s, ΔdAC = ΔtAC*1500 m / s;

[0378] Use triangulation to calculate the location of the interference source: The locations of the preset monitoring stations A, B, and C are (xA, yA), (xB, yB), and (xC, yC), respectively. The location of the interference source is (x, y). Use the following formula to calculate the location of the interference source: (x-xA)^2+(y-yA)^2=(ΔdAB)^2(x-xB)^2+(y-yB)^2=(ΔdAC)^2Solve this set of equations to obtain the specific location of the interference source (x, y);

[0379] Disruption Coping Strategies

[0380] Adjust the transmission frequency, preset the original signal strength to S0, and the new transmission frequency to 1.2GHz;

[0381] Calculate the signal attenuation at the new frequency: the preset signal attenuation at 1.2GHz is αdB / m, the transmission distance is dkm, and the new attenuation = α*d;

[0382] Calculate the signal strength at the new frequency: new signal strength = S0 - new attenuation - insertion loss, preset S0 = 0dBm, insertion loss = 1dB, new attenuation = 50dB (preset value), new signal strength = 0dBm-50dB-1dB = -51dBm;

[0383] System feedback and optimization

[0384] Adjust the signal amplifier gain and calculate the required gain: Required gain = 1 / (new signal strength / original signal strength) Required gain = 1 / (10^(-51 / 10) / 10^(-70 / 10)) Required gain = 10^((70-51) / 10) Required gain ≈ 100;

[0385] Through these calculations, the location of the interference source is determined, and the transmission frequency and the gain of the signal amplifier are adjusted to optimize the signal transmission performance.

[0386] Furthermore, the frequency dynamic adjustment algorithm in step S5 can automatically adjust the transmission frequency when interference is detected to avoid the interference source, and integrate a frequency synthesizer or modulator so that it can adjust the output frequency according to the instructions of the algorithm;

[0387] Frequency dynamic adjustment algorithm development

[0388] Algorithm logic implementation:

[0389] Set the interference detection threshold: when the monitored signal strength drops or the frequency deviation exceeds the preset value, it is determined that interference exists; when the signal parameters detected by the monitoring system exceed the set threshold, the algorithm will trigger the frequency adjustment process;

[0390] Frequency selection logic:

[0391] Define predefined frequency range: Determine the frequency range in which the system can operate, including 1GHz to 2GHz; within the predefined frequency range, the algorithm will select a frequency that is not interfered with as the new transmission frequency;

[0392] Frequency switching strategy:

[0393] Switching timing: Once interference is detected, frequency switching is initiated immediately;

[0394] Switching speed: ensure that the frequency switching process is completed within a specified time, such as 100 milliseconds;

[0395] Signal maintenance strategy: During frequency switching, measures are taken to maintain signal continuity and use buffering technology;

[0396] Frequency synthesizer or modulator integrated

[0397] Select frequency synthesizer or modulator:

[0398] Select a suitable frequency synthesizer or modulator according to system requirements, which should be able to switch frequencies quickly, have a wide frequency range, and have high stability. The specific frequency synthesizer used is Analog Devices ADF4351, and the modulator is Analog Devices AD9910.

[0399] Integration into the transmission system:

[0400] Integrate the selected frequency synthesizer or modulator with the transmission system to ensure it can receive and execute the algorithm's instructions;

[0401] A control interface, which is used to transmit instructions between the algorithm and the frequency synthesizer or modulator;

[0402] System integration and testing

[0403] System Integration:

[0404] Integrate algorithms, control interfaces, and frequency synthesizers or modulators into a complete system;

[0405] Simulated environment test:

[0406] Test the entire system in a simulated environment to evaluate the algorithm's response time, frequency switching accuracy, and system stability;

[0407] Real-time testing and optimization

[0408] Actual transmission environment test:

[0409] Test in an actual operating environment to verify the system's ability to automatically adjust frequency when interference is detected;

[0410] optimization:

[0411] Based on the results of real-time testing, the algorithm and control interface are optimized to improve the performance of the system;

[0412] Feedback mechanism established

[0413] Establish a feedback mechanism:

[0414] Record the data of each frequency adjustment, including time, original frequency, new frequency and adjustment reason, and analyze these data for the algorithm to learn and optimize itself;

[0415] Through these steps, it is ensured that step S5 can dynamically adjust the transmission frequency according to the interference situation, thereby effectively avoiding the interference source and ensuring the continuity and reliability of deep-sea image transmission.

[0416] Specifically, the frequency dynamic adjustment algorithm is developed and the algorithm logic is implemented:

[0417] To set the interference detection threshold:

[0418] The preset signal strength normal value is -50dBm, and the interference detection threshold is set to -53dBm (i.e., a signal strength drop of more than 3dB is considered interference);

[0419] The preset normal frequency deviation is ±10kHz, and the interference frequency deviation threshold is set to ±50kHz;

[0420] Frequency selection logic:

[0421] The current transmission frequency is preset to 1.5GHz, and the predefined frequency range is 1.4GHz to 1.6GHz;

[0422] When interference is detected, the algorithm needs to select a new frequency in the range of 1.4GHz to 1.6GHz;

[0423] Frequency switching strategy

[0424] Calculate the new frequency:

[0425] The preset FFT analysis shows that there is interference around 1.5GHz, so the new transmission frequency is selected as 1.45GHz (the frequency that is not interfered with);

[0426] Frequency synthesizer or modulator integrated

[0427] Calculate the frequency synthesizer parameters:

[0428] The resolution of the preset frequency synthesizer is 1MHz, and the current frequency needs to be adjusted from 1.5GHz to 1.45GHz;

[0429] Calculate the frequency adjustment: Δf = 1.45 GHz - 1.5 GHz = -50 MHz;

[0430] Control interface

[0431] Control instruction calculation:

[0432] The control interface needs to send a command to the frequency synthesizer to reduce its frequency by 50MHz. The command format is: "SET_FREQ-50";

[0433] System integration and testing

[0434] System integration integrates algorithms, control interfaces, and frequency synthesizers into a system;

[0435] Simulated environment test:

[0436] In a simulated environment, simulate interference signals to test whether the system can complete frequency switching within 10 milliseconds;

[0437] Real-time testing and optimization

[0438] Actual transmission environment test:

[0439] In the actual environment, record the system's response time and frequency switching accuracy when interference occurs;

[0440] optimization:

[0441] Based on the test results, adjust the algorithm’s interference detection threshold and frequency selection logic;

[0442] If the system is found to respond too slowly when interference occurs, lower the interference detection threshold or optimize the algorithm processing speed;

[0443] Feedback mechanism established

[0444] Record detailed information of each frequency adjustment, including timestamp, original frequency, new frequency, and interference type. Analyze this data to optimize the algorithm and improve the accuracy and efficiency of frequency adjustment.

[0445] Furthermore, the frequency dynamic adjustment algorithm is developed and the algorithm logic is implemented;

[0446] To set the interference detection threshold:

[0447] Normal signal strength: -50dBm;

[0448] Interference detection threshold: -53dBm;

[0449] Calculate the percentage of signal strength decrease: (-53-(-50)) / (-50)*100%=6%;

[0450] Frequency selection logic:

[0451] Current transmission frequency: 1.5GHz;

[0452] Frequency range: 1.4GHz to 1.6GHz;

[0453] Frequency interval: 10MHz;

[0454] Frequency switching strategy

[0455] Calculate the new frequency:

[0456] The preset FFT analysis shows that there is interference near 1.5GHz and a new frequency needs to be selected;

[0457] New frequency selection: 1.45GHz (preset to a frequency that is not interfered with);

[0458] Frequency synthesizer or modulator integrated

[0459] Calculate the frequency synthesizer parameters:

[0460] Frequency adjustment amount: Δf = new frequency - current frequency = 1.45 GHz - 1.5 GHz = -50 MHz;

[0461] Control interface development

[0462] Frequency synthesizer adjustment command: SET_FREQ-50MHz;

[0463] Calculate the frequency synthesizer adjustment step (the default minimum step is 1MHz):

[0464] Adjustment times: Δf / minimum step = -50MHz / 1MHz = -50 times;

[0465] System integration and testing

[0466] System Integration:

[0467] No calculation is required, this step is a physical and software integration process;

[0468] Simulated environment test:

[0469] The preset interference signal is simulated at 1.49 GHz, and the test system is switched to 1.45 GHz;

[0470] Record switching time: preset to t seconds;

[0471] Real-time testing and optimization

[0472] Actual transmission environment test:

[0473] In actual tests, the time when interference occurs and the time when frequency switching is completed are recorded;

[0474] Calculate the response time: switching completion time - interference occurrence time = tresponse;

[0475] optimization:

[0476] According to the t response, adjust the algorithm parameters, including lowering the interference detection time threshold;

[0477] Feedback mechanism established

[0478] Records and feedback:

[0479] Record data: timestamp, original frequency, new frequency, interference type;

[0480] Analyze the data, including calculating the success rate of frequency adjustment:

[0481] Success rate = number of successful adjustments / total number of adjustments*100%;

[0482] Through the above calculation process, the following results are obtained:

[0483] The interference detection threshold is set to -53dBm, which means that frequency switching is triggered when the signal strength drops by more than 6%;

[0484] The new transmission frequency is calculated to be 1.45 GHz;

[0485] The frequency synthesizer needs to be adjusted by -50MHz. By sending the SET_FREQ-50MHz command, the minimum step adjustment is -50 times.

[0486] After the system is integrated, it is tested in a simulation environment and the switching time t seconds is recorded;

[0487] In actual testing, the interference response time tresponse is recorded and used to optimize the algorithm;

[0488] By analyzing the recorded data, the success rate of frequency adjustment is calculated and used to further optimize system performance; these calculation results will guide the implementation of step S5 to ensure that the system can quickly and effectively switch frequencies when interference is detected.

[0489] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0490] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and rules of the present invention shall be included in the protection scope of the present invention.

Claims

1. A deep sea image transmission method, characterized in that: S1. The FEC encoding scheme adopts a Reed-Solomon encoder, which encodes the image data before transmission, adds redundant information to facilitate error detection and correction at the receiving end, is used to decode the received encoded data at the receiving end, and uses the redundant information to correct errors that occur during transmission; Specifically, the S1 step uses a Reed-Solomon encoder, and the encoding process is: The input data is the original image data stream, the segmentation data is to segment the image data into multiple small blocks, the application code is to apply Reed-Solomon coding to each small block, generate redundant check bits, and the merging data is to merge the coded data blocks and the check bits to form a coded data stream; S2, simulate deep sea transmission conditions to test the FEC encoder and decoder; Specifically, the test in step S2 corresponds to the encoding and decoding process in step S1, and is intended to verify the ability of the encoder to add redundant information during transmission, and the ability of the decoder to correct errors using the redundant information. Error correction uses the Forney algorithm to calculate and correct error values. Error correction rate = number of corrected errors / total number of errors. Data recovery rate = number of successfully recovered data blocks / total number of data blocks. S3, integrating a signal amplifier into the output end of the FEC encoder to enhance the transmission strength of the signal, integrating a filter into the output end of the signal amplifier to filter out noise after the signal is amplified; S4, monitor the signal frequency of the transmission link in real time and identify the interference source; S5. Frequency dynamic adjustment algorithm can automatically adjust the transmission frequency when interference is detected to avoid the interference source. The integrated frequency synthesizer or modulator can adjust the output frequency according to the instructions of the algorithm.

2. The deep sea image transmission method according to claim 1, characterized in that: In the step S1, for each data block, nk redundant check symbols are calculated, where n is the size of the encoded data block and k is the number of original data symbols; The encoding process involves generating a finite field polynomial of n elements, where the first k coefficients correspond to the original data symbols and the last nk coefficients correspond to the redundant check symbols; Using finite field arithmetic, construct a generating polynomial g(x) whose degree is nk and whose roots are nonzero elements in the finite field; Multiply the original data polynomial p(x) by the generating polynomial g(x), and take the coefficients below the x^(nk) term of the product as the redundant check symbols; The combined data combines the k original data symbols of each data block and the calculated nk redundant check symbols to form a new coded data block with a size of n symbols.

3. The deep sea image transmission method according to claim 1, characterized in that: In the step S3, the signal amplifier is integrated into the output end of the FEC encoder, and the filter is integrated into the output end of the signal amplifier. The required gain of the amplifier is G=1 / (A*e^(-α*d)). Since the attenuation is in dB, it needs to be converted into a linear gain. According to the test results, the gain of the amplifier and the cutoff frequency of the filter are adjusted to calculate the optimized signal strength.

4. The deep sea image transmission method according to claim 1, characterized in that: The S4 step monitors the signal frequency of the transmission link in real time to identify possible interference sources: Use RF sensors and apply fast Fourier transform signal processing algorithms to analyze the collected data, compare normal signal frequencies with abnormal frequency components, identify possible interference sources, adjust the transmission frequency to avoid interference frequencies, implement frequency hopping technology, or increase signal shielding measures.

5. The deep sea image transmission method according to claim 1, characterized in that: The frequency dynamic adjustment algorithm in step S5 sets the interference detection threshold: When the monitored signal strength decreases or the frequency deviation exceeds the preset value, it is determined that interference exists; when the signal parameters detected by the monitoring system exceed the set threshold, the algorithm will trigger the frequency adjustment process, the frequency adjustment amount: Δf = new frequency - current frequency, the number of adjustments: Δf / minimum step, the response time is calculated: the switching completion time - the interference occurrence time = tresponse, the success rate = the number of successful adjustments / the total number of adjustments*100%.

Images