Adaptive transmission method, device, equipment and storage medium based on dynamic scrambling

Abstract

The embodiments of the present disclosure provide an adaptive transmission method, device, equipment and storage medium based on dynamic scrambling. Applied to the field of wireless communication technology, the method includes obtaining the key features of the current channel, generating a dynamic scrambling sequence based on a machine learning algorithm; obtaining a random key book, and using the random key book and the dynamic scrambling sequence to encrypt and scramble the information to be transmitted; dynamically adjusting the redundancy and retransmission strategy of the information to be transmitted according to the importance of the information to be transmitted and the key features of the channel, and transmitting the encrypted and scrambled information to be transmitted. In this way, bandwidth can be effectively saved while ensuring the reliability of data transmission, thereby improving the overall performance of the communication system.

Description

Technical Field

[0001] The present disclosure relates to the field of wireless communication technology, and further to the field of encrypted transmission technology, and in particular to an adaptive transmission method, device, equipment and storage medium based on dynamic scrambling. Background Art

[0002] Existing wireless communication systems face many challenges in ensuring the security and reliability of data transmission. Especially in complex and changeable channel environments, traditional encryption technologies and error correction mechanisms often cannot take into account both high security and low transmission overhead. Therefore, there is an urgent need for technical solutions that can dynamically adapt to channel conditions and improve the overall performance of data transmission. Summary of the invention

[0003] In view of this, the present disclosure provides an adaptive transmission method, apparatus, device and storage medium based on dynamic scrambling.

[0004] According to a first aspect of the present disclosure, there is provided an adaptive transmission method based on dynamic scrambling. Applied to a communication server, the method comprises:

[0005] Obtain the key features of the current channel and generate a dynamic scrambling sequence based on a machine learning algorithm;

[0006] Obtain a random key book, and use the random key book and a dynamic scrambling sequence to encrypt and scramble the information to be transmitted;

[0007] According to the importance of the information to be transmitted and the key characteristics of the channel, the redundancy of the information to be transmitted and the retransmission strategy are dynamically adjusted to transmit the information to be transmitted after encryption and scrambling.

[0008] In some implementations of the first aspect, obtaining a key feature of a current channel and generating a dynamic scrambling sequence based on a machine learning algorithm includes:

[0009] Receive channel state information in real time, and identify key features of the current channel based on the channel state information;

[0010] According to the key features of the current channel and based on the machine learning algorithm, a scrambling sequence parameter prediction model is pre-trained to predict the scrambling sequence generation parameters, and a dynamic scrambling sequence for the current channel state is generated according to the generation parameters.

[0011] In some implementations of the first aspect, according to key features of the current channel, based on a machine learning algorithm, a scrambling sequence parameter prediction model is pre-trained, including:

[0012] The key features of the current channel at each moment are used as the input data of the model, and the effective scrambling sequence generation parameters corresponding to the key features of the current channel at each moment are used as labels to generate a training set, perform model training and update parameters until a scrambling sequence parameter prediction model is generated.

[0013] In some implementations of the first aspect, obtaining a random key book, and using the random key book and a dynamic scrambling sequence to perform encryption and scrambling processing on information to be transmitted include:

[0014] A random secret book is selected from a preset secret book pool, and a unique secret book identifier of the selected random secret book is sent to a receiving end. After receiving a confirmation feedback from the receiving end, the selected random secret book is activated, and the information to be transmitted is encrypted using an encryption algorithm to obtain an encrypted ciphertext.

[0015] The encrypted ciphertext is scrambled using a dynamic scrambling sequence.

[0016] In some implementations of the first aspect, according to the importance of the information to be transmitted and the key characteristics of the channel, the redundancy of the information to be transmitted and the retransmission strategy are dynamically adjusted, and the information to be transmitted after the encryption and scrambling processing is transmitted, including:

[0017] Based on the importance of the information to be transmitted and the key characteristics of the channel, the information to be transmitted is divided into key data and ordinary data;

[0018] For critical data, increase FEC redundancy to reduce the need for retransmission;

[0019] For common data, a flexible retransmission strategy is adopted to save bandwidth.

[0020] In some implementations of the first aspect, for ordinary data, a flexible retransmission strategy is adopted to save bandwidth, including:

[0021] For ordinary data, determine its highest data loss ratio; according to the highest data loss ratio, match the first retransmission threshold for data that is sensitive to loss in ordinary data to increase the chance of retransmission; match the second retransmission threshold for data that is insensitive to loss in ordinary data to reduce unnecessary retransmission; wherein the first retransmission threshold is less than the second retransmission threshold;

[0022] Set a priority queue to manage retransmission requests for common data. When bandwidth resources are limited, higher priority retransmission requests in the queue are processed first.

[0023] Continuously monitor the importance of the information to be transmitted and the key characteristics of the channel, and adaptively adjust the first retransmission threshold and the second retransmission threshold according to real-time data to save bandwidth.

[0024] According to a second aspect of the present disclosure, an adaptive transmission device based on dynamic scrambling is provided. The device comprises:

[0025] The first processing module is used to obtain key features of the current channel and generate a dynamic scrambling sequence based on a machine learning algorithm;

[0026] The second processing module is used to obtain a random key book, and use the random key book and the dynamic scrambling sequence to perform encryption and scrambling processing on the information to be transmitted;

[0027] The third processing module is used to dynamically adjust the redundancy and retransmission strategy of the information to be transmitted according to the importance of the information to be transmitted and the key characteristics of the channel, and transmit the information to be transmitted after encryption and scrambling.

[0028] According to a third aspect of the present disclosure, an electronic device is provided. The electronic device includes: at least one processor; and a memory connected to the at least one processor in communication; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described above.

[0029] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, where the computer instructions are used to cause a computer to execute the method described above.

[0030] According to a fifth aspect of the present disclosure, a data transmission radio station is provided, which uses the method as described above to perform data transmission.

[0031] In the present disclosure, by encrypting with random key book, analyzing key channel features in real time and generating dynamic scrambling sequences using machine learning algorithms, combined with redundancy settings and adaptive retransmission strategies, bandwidth is effectively saved while ensuring data transmission reliability, thereby improving the overall performance of the communication system.

[0032] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0034] Figure 1 A flowchart of an adaptive transmission method based on dynamic scrambling provided by an embodiment of the present disclosure is shown;

[0035] Figure 2 An interactive schematic diagram of an adaptive transmission method based on dynamic scrambling provided by an embodiment of the present disclosure is shown;

[0036] Figure 3 A block diagram of an adaptive transmission device based on dynamic scrambling according to an embodiment of the present disclosure is shown;

[0037] Figure 4 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0039] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0040] In response to the problems mentioned in the background technology, the present disclosure provides an adaptive transmission method, apparatus, device and storage medium based on dynamic scrambling.

[0041] Specifically, the key features of the current channel are obtained, and a dynamic scrambling sequence is generated based on a machine learning algorithm; a random key book is obtained, and the information to be transmitted is encrypted and scrambled using the random key book and the dynamic scrambling sequence; according to the importance of the information to be transmitted and the key features of the channel, the redundancy and retransmission strategy of the information to be transmitted are dynamically adjusted, and the encrypted and scrambled information to be transmitted is transmitted.

[0042] In this way, the bandwidth can be effectively saved while ensuring the reliability of data transmission, thereby improving the overall performance of the communication system.

[0043] The following describes in detail an adaptive transmission method, device, equipment and storage medium based on dynamic scrambling provided by the present disclosure in conjunction with the accompanying drawings and specific embodiments.

[0044] Figure 1 A flowchart of an adaptive transmission method based on dynamic scrambling provided by an embodiment of the present disclosure is shown; Figure 1As shown, the adaptive transmission method 100 based on dynamic scrambling may include the following steps:

[0045] S110, obtaining key features of the current channel, and generating a dynamic scrambling sequence based on a machine learning algorithm.

[0046] Specifically, channel state information is received in real time, and key features of the current channel are identified based on the channel state information; based on the key features of the current channel and based on a machine learning algorithm, a scrambling sequence parameter prediction model is pre-trained to predict scrambling sequence generation parameters, and a dynamic scrambling sequence for the current channel state is generated based on the generation parameters.

[0047] It is understandable that the channel state information usually includes key parameters such as the amplitude, phase, and delay of the signal. These parameters reflect the characteristics of the signal during transmission, such as fading, scattering, and multipath effects. Therefore, the original signal of the channel state can be received and preprocessed. The parameters reflecting the channel characteristics (for example, signal strength, phase information, delay spread, etc.) can be extracted from the preprocessed signal, and the parameters reflecting the channel characteristics can be identified to obtain the key characteristics of the previous channel; wherein:

[0048] 1) Preprocessing may include: filtering, synchronization and channel estimation.

[0049] 2) Parameters reflecting channel characteristics may include:

[0050] Signal strength; such as received signal strength (RSS) or signal-to-noise ratio (SNR), can reflect the degree of signal attenuation during transmission.

[0051] Phase information: The signal propagation path and multipath effect can be inferred from the phase change.

[0052] Delay spread: It can reflect the delay difference caused by multipath effect when the signal propagates in the channel.

[0053] Doppler shift; can reflect the frequency deviation caused by the movement of the user or base station.

[0054] 3) Identification of the key features obtained may include:

[0055] Channel type (line-of-sight or non-line-of-sight), whether the channel has multipath effects, channel quality, and whether the channel quality meets current transmission requirements.

[0056] Furthermore, according to the key features of the current channel, based on the machine learning algorithm, a scrambling sequence parameter prediction model is pre-trained, including:

[0057] The key features of the current channel at each moment are used as the input data of the model, and the effective scrambling sequence generation parameters corresponding to the key features of the current channel at each moment are used as labels to generate a training set, perform model training and update parameters until a scrambling sequence parameter prediction model is generated.

[0058] Specifically, a recurrent neural network (RNN) or its variant long short-term memory network (LSTM) that can better capture the temporal dependency in the data can be selected to train the model. The input data in the model training set selects the key feature data of the current channel at each moment, and the data label selects the effective scrambling sequence generation parameters (such as sequence length, sequence type, modulation method, etc.) corresponding to the feature. During the training process, the model parameters can be adjusted through iterative optimization algorithms (such as gradient descent method) to minimize the prediction error. At the same time, evaluation methods such as cross-validation are used to evaluate the model performance, and the model structure and hyperparameters are adjusted as needed until the model achieves a prediction accuracy that meets the requirements.

[0059] Model building, training, and evaluation and tuning can be implemented in many ways. Here is a simple code example:

[0060] # Data loading and preprocessing

[0061] def generate_data(num_samples, timesteps, features):

[0062] # Generate feature data

[0063] X = np.random.rand(num_samples, timesteps, features)

[0064] # Generate sequence length (regression task)

[0065] y_length = np.random.randint(10, 50, size=num_samples)

[0066] # Generate sequence types (classification task, assuming there are 3 types)

[0067] y_type = np.random.randint(0, 3, size=num_samples)

[0068] return X, y_length, y_type

[0069] # Parameter settings

[0070] num_samples = 1000

[0071] timesteps = 10

[0072] features = 5

[0073] # Generate data

[0074] X, y_length, y_type = generate_data(num_samples, timesteps, features)

[0075] # Data normalization (only for feature data)

[0076] scaler = MinMaxScaler(feature_range=(0, 1))

[0077] X_scaled = scaler.fit_transform(X.reshape(-1, features)).reshape(num_samples, timesteps, features)

[0078] # Encode sequence types

[0079] encoder = LabelEncoder()

[0080] y_type_encoded = encoder.fit_transform(y_type)

[0081] # Divide into training set and test set

[0082] X_train, X_test, y_length_train, y_length_test, y_type_train, y_type_test = train_test_split(

[0083] X_scaled, y_length, y_type_encoded, test_size=0.2, random_state=42 )

[0085] #Build LSTM model - regression (sequence length)

[0086] model_length = Sequential([

[0087] LSTM(50, return_sequences=False, input_shape=(timesteps,features)),

[0088] Dense(1) ])

[0090] model_length.compile(optimizer='adam', loss='mse')

[0091] model_length.fit(X_train, y_length_train, epochs=50, batch_size=32,verbose=1, validation_data=(X_test, y_length_test))

[0092] # Evaluate the regression model

[0093] y_length_pred = model_length.predict(X_test)

[0094] mse_length = mean_squared_error(y_length_test, y_length_pred.flatten())

[0095] print(f'Test MSE for Sequence Length: {mse_length}')

[0096] #Build LSTM model - classification (sequence type)

[0097] model_type = Sequential([

[0098] LSTM(50, return_sequences=False, input_shape=(timesteps,features)),

[0099] Dense(3, activation='softmax') # 3 output categories, using softmax activation function ])

[0101] model_type.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

[0102] model_type.fit(X_train, y_type_train, epochs=50, batch_size=32,verbose=1, validation_data=(X_test, y_type_test))

[0103] # Evaluate the classification model

[0104] y_type_pred = model_type.predict(X_test)

[0105] y_type_pred_classes = np.argmax(y_type_pred, axis=1)

[0106] accuracy_type = accuracy_score(y_type_test, y_type_pred_classes)

[0107] print(f'Test Accuracy for Sequence Type: {accuracy_type}') .

[0108] In the above examples, two LSTM models are built to cope with different types of prediction tasks, one for predicting sequence length (regression task) and the other for predicting sequence type (classification task). Loss functions and evaluation indicators are used to train and evaluate the models respectively. These two models are implemented based on the TensorFlow Keras framework and make full use of the Scikit-learn library for data preprocessing and partitioning.

[0109] For the sake of convenience, the above is a simple example. In actual applications, it is necessary to consider more complex models based on multiple factors.

[0110] According to an embodiment of the present disclosure, by receiving channel state information in real time and predicting scrambling sequence generation parameters based on a machine learning algorithm, the method can dynamically adapt to channel changes and generate a scrambling sequence that is highly matched with the current channel state, which not only enhances the encryption effect, but also improves the anti-interference ability and security of data transmission.

[0111] S120, obtaining a random key book, and using the random key book and a dynamic scrambling sequence to encrypt and scramble the information to be transmitted.

[0112] Specifically, a random secret book is selected from a preset secret book pool, and the unique secret book identifier of the selected random secret book is sent to the receiving end. After receiving the confirmation feedback from the receiving end, the selected random secret book is enabled, and the information to be transmitted is encrypted using an encryption algorithm to obtain the encrypted ciphertext; the encrypted ciphertext is scrambled using a dynamic scrambling sequence. Among them:

[0113] The random key book can be selected by using a random number generator (such as a hardware-based random number generator HWRNG) to generate a random number as an index, randomly selecting a key book from a key book pool, and securely transmitting the selected key book identifier to the receiving end using a secure communication protocol (such as TLS / SSL or DTLS) according to the network environment.

[0114] After receiving the secret book identifier, the receiving end confirms that the local secret book pool contains the secret book and returns a confirmation packet, indicating that the secret book and the corresponding key exist locally and can be enabled.

[0115] After receiving the confirmation feedback, the sender officially activates the selected cipher book and uses the key provided in the cipher book and a predefined encryption algorithm (such as AES-128, AES-256, etc.) to encrypt the information to be transmitted.

[0116] The encrypted information (i.e. ciphertext) will be considered as the secure output of the encryption layer and is ready for the next step of scrambling.

[0117] In order to improve the anti-analysis and concealment of data transmission, a dynamic scrambling sequence is further used to scramble the encrypted ciphertext. The dynamic scrambling sequence can be dynamically adjusted according to the key characteristics of the real-time channel to ensure the unpredictability and uniqueness of the scrambling sequence.

[0118] During the scrambling process, the encrypted ciphertext is subjected to a bit-level or byte-level exclusive OR (XOR) operation with the dynamically generated scrambling sequence to generate the final scrambled ciphertext, which not only inherits the security provided by the encryption process, but also increases the ability to resist interception and analysis during data transmission.

[0119] According to the embodiments of the present disclosure, a secret book is randomly selected from a preset secret book pool, and the applicability of the secret book is ensured through confirmation feedback at the receiving end. This method effectively improves the security of the data transmission process. At the same time, the encrypted ciphertext is further scrambled in combination with a dynamic scrambling sequence, which effectively enhances the anti-interception and anti-analysis capabilities during the data transmission process.

[0120] S130, dynamically adjusting the redundancy of the information to be transmitted and the retransmission strategy according to the importance of the information to be transmitted and the key characteristics of the channel, and transmitting the information to be transmitted after the encryption and scrambling processing.

[0121] Specifically, based on the importance of the information to be transmitted and the key characteristics of the channel, the information to be transmitted is divided into key data and ordinary data; for key data, FEC (forward error correction) redundancy is increased to reduce the need for retransmission; for ordinary data, a flexible retransmission strategy is adopted to save bandwidth.

[0122] It should be noted that FEC adds additional checksum information to the data, allowing the receiver to correct a certain number of errors on its own without relying on retransmission from the sender. The amount of redundancy increase depends on the channel quality, the sensitivity of key data, and business continuity requirements. For example, in the case of poor channel quality, a higher FEC coding rate (such as R=1 / 2 or lower) may be selected to provide stronger error correction capabilities. For ordinary data, too much FEC redundancy will not be added by default to save bandwidth resources. However, this does not mean that the transmission reliability of ordinary data is ignored, but it is guaranteed through flexible retransmission strategies.

[0123] According to the embodiments of the present disclosure, key data and ordinary data are intelligently divided according to the importance of the information to be transmitted and the key characteristics of the channel, and different redundancy and retransmission strategies are adopted respectively, which not only ensures the high-reliability transmission of key data, but also saves bandwidth resources through flexible retransmission strategies and improves the overall transmission efficiency.

[0124] Furthermore, for common data, a flexible retransmission strategy is adopted to save bandwidth, including:

[0125] For ordinary data, the maximum data loss ratio (Maximum Tolerable Loss Ratio, MTLR) is determined, that is, the allowable data loss ratio without affecting the normal operation of the business; according to the maximum data loss ratio, the first retransmission threshold is matched for the important data that is sensitive to loss in the ordinary data to increase the retransmission opportunity; the second retransmission threshold is matched for the unimportant data that is not sensitive to loss in the ordinary data to reduce unnecessary retransmissions to save bandwidth resources; wherein, the first retransmission threshold is less than the second retransmission threshold; it can be understood that the first retransmission threshold is a relatively low retransmission threshold, and the second retransmission threshold is a relatively high retransmission threshold.

[0126] Set up a priority queue to manage retransmission requests for common data. When bandwidth resources are limited, higher priority retransmission requests in the queue are processed first. Priority can be set based on multiple factors such as data sensitivity, business requirements, and user permissions.

[0127] On the above basis, the importance of the information to be transmitted and the key characteristics of the channel are continuously monitored, and the first retransmission threshold and the second retransmission threshold are adaptively adjusted according to real-time data to better balance the reliability of data transmission and the utilization efficiency of bandwidth resources. For example, when the channel quality suddenly deteriorates, the system may temporarily lower the retransmission threshold of all data to increase the reliability of transmission; and after the channel quality recovers, it will gradually adjust back to the initial setting.

[0128] According to the embodiments of the present disclosure, by setting different retransmission thresholds and priority queue management for ordinary data, the retransmission strategy can be dynamically adjusted according to the actual data needs and channel conditions, effectively reducing unnecessary retransmission operations, reducing bandwidth consumption, and ensuring that sensitive data can be restored in time if lost.

[0129] A specific embodiment is provided below to provide a more detailed and intuitive description of the above content.

[0130] 1. Random password selection and encryption and scrambling

[0131] Step 1.1: Random password selection

[0132] Preset secret book pool: Preset a secret book pool containing multiple random secret books, each secret book has a unique secret book identifier (ID); suppose there are N secret books in the secret book pool, and the secret book ID is ID 1 , ID 2 , ..., ID N .

[0133] Random selection: Use a pseudo-random number generator (such as Mersenne Twister) to generate a random number R between 1 and N as the selected secret book index. The selected secret book identifier is ID R .

[0134] Step 1.2: Encryption Identifier Transmission and Confirmation

[0135] Send the secret book identifier: Send the selected secret book identifier ID R Sent to the recipient via a secure channel (such as TLS / SSL).

[0136] Receiving confirmation: The receiving end receives the ID R After that, confirm that the local secret book pool contains the secret book ID R , and returns a string containing the ID R The confirmation package indicates that the secret book and the corresponding key exist locally and can be used.

[0137] Step 1.3: Encryption

[0138] Activate the secret book: After receiving the confirmation feedback, the sender activates the IDR The corresponding cipher book is used to encrypt the information to be transmitted (plaintext M) using the key and encryption algorithm (such as AES-128, AES-256, etc.) to generate the encrypted ciphertext C.

[0139] The encryption process can be expressed as: C = Enc(K, M)

[0140] Among them, Enc represents the encryption function, K is the key in the cipher book, and M is the plaintext to be transmitted.

[0141] For example:

[0142] Secret book pool: contains 1024 random secret books (N=1024).

[0143] Secret book selection: randomly select secret book ID as ID 567 .

[0144] Encryption: Use AES-256 encryption algorithm and ID 567 The corresponding key encrypts the key data M key and normal data M com , generate encrypted ciphertext C key and C com .

[0145] Step 1.4: Dynamic scrambling

[0146] Dynamic scrambling sequence generation: Generate a dynamic scrambling sequence S based on the current channel key characteristics (such as signal-to-noise ratio SNR, bit error rate BER) and machine learning algorithms (such as LSTM network).

[0147] Scrambling: Perform an XOR operation on the encrypted ciphertext C and the dynamic scrambling sequence S to generate the scrambled ciphertext C'.

[0148] The scrambling process can be expressed as: C' = C ⊕ S, where ⊕ represents the XOR operation.

[0149] 2. Redundancy and retransmission strategy adjustment

[0150] Step 2.1: Data Partitioning

[0151] Importance Assessment: A preset importance scoring system divides the information to be transmitted into key data according to its importance. key and normal data D com .

[0152] Step 2.2: Redundancy Adjustment

[0153] Key data: Add forward error correction (FEC) redundancy to the key data Dkey to reduce the need for retransmission. Assume that the FEC coding rate is R key, (for example, R key = 1 / 2), the encoded key data is D key_encoded , its length is twice the original data.

[0154] Step 2.3: Adaptive retransmission strategy

[0155] Maximum data loss ratio of ordinary data: Set the maximum tolerable data loss ratio of ordinary data to P loss_max , (e.g., P loss_max = 0.05).

[0156] Retransmission threshold setting:

[0157] For sensitive general data D com_sens , set a relatively low first retransmission threshold T low , (for example, T low = 0.01), that is, when the loss rate is detected to be close to or exceeds T low Retransmission is triggered.

[0158] For insensitive general data D com_insens , set a relatively high second retransmission threshold T high , (for example, T high = 0.04), reducing unnecessary retransmissions.

[0159] Priority queue setting: Assign priorities and generate queues based on the importance and real-time requirements of data. When bandwidth is limited, high-priority retransmission requests are processed first.

[0160] Dynamic adjustment: preset adjustment range, dynamically adjust the retransmission threshold T according to the key characteristics of the real-time channel (such as SNR, BER) and the importance of the information to be transmitted low and T high , to optimize bandwidth usage.

[0161] Step 2.4: Based on the redundancy and retransmission strategy, the encrypted and scrambled data is transmitted.

[0162] Figure 2 An interactive schematic diagram of an adaptive transmission method based on dynamic scrambling provided by an embodiment of the present disclosure is shown; Figure 2 An example of a secret book pool is given, which contains 200 secret books. In actual applications, the number of secret books can be adjusted according to needs and is not limited to this.

[0163] like Figure 2As shown, the sending end selects a random secret book from a preset secret book pool, and sends the unique secret book identifier of the selected random secret book to the receiving end. After the receiving end confirms that the secret book and the corresponding key exist in the local secret book pool, it sends a confirmation feedback to the sending end, indicating that it has the corresponding decryption capability; after receiving, the sending end activates the selected random secret book, uses the encryption algorithm to encrypt the information to be transmitted, and obtains the encrypted ciphertext; uses the dynamic scrambling sequence to scramble the encrypted ciphertext, and finally sends the encrypted and scrambled ciphertext to the receiving end; and the receiving end decrypts the ciphertext according to the corresponding secret book, and descrambles the decrypted information to obtain the original information before encryption and scrambling; further, the receiving end can also send ciphertext to the sending end based on this method, and the two can achieve secure and efficient information exchange in this way.

[0164] like Figure 2 As shown, the adaptive transmission method based on dynamic scrambling provided by the present invention constructs a multi-level data security protection method by introducing a key book pool, a key book identifier, encryption scrambling and other mechanisms, thereby providing a strong guarantee for data transmission.

[0165] According to the embodiments of the present disclosure, the following technical effects are achieved:

[0166] Through random key encryption, real-time analysis of key channel features, and generation of dynamic scrambling sequences using machine learning algorithms, combined with redundancy settings and adaptive retransmission strategies, bandwidth can be effectively saved while ensuring data transmission reliability, thereby improving the overall performance of the communication system.

[0167] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0168] The above is an introduction to the method embodiment. The following is a further explanation of the scheme disclosed in the present invention through an apparatus embodiment.

[0169] Figure 3 FIG. 4 shows a block diagram of an adaptive transmission device based on dynamic scrambling according to an embodiment of the present disclosure; Figure 3 As shown, the adaptive transmission device 300 based on dynamic scrambling may include:

[0170] The first processing module 310 is used to obtain key features of the current channel and generate a dynamic scrambling sequence based on a machine learning algorithm;

[0171] The second processing module 320 is used to obtain a random key book, and use the random key book and the dynamic scrambling sequence to perform encryption and scrambling processing on the information to be transmitted;

[0172] The third processing module 330 is used to dynamically adjust the redundancy of the information to be transmitted and the retransmission strategy according to the importance of the information to be transmitted and the key characteristics of the channel, and transmit the information to be transmitted after the encryption and scrambling processing.

[0173] Understandably, Figure 3 The various modules / units in the adaptive transmission device 300 based on dynamic scrambling shown have the function of implementing the various steps in the adaptive transmission method 100 based on dynamic scrambling provided in the embodiment of the present disclosure, and can achieve the corresponding technical effects. The specific working process of the described modules can refer to the corresponding process in the aforementioned method embodiment. For the convenience and conciseness of the description, it will not be repeated here.

[0174] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0175] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0176] Figure 4 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown.

[0177] like Figure 4 As shown, electronic device 400 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0178] The electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in the ROM 402 or a computer program loaded from the storage unit 408 into the RAM 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An I / O interface 405 is also connected to the bus 404.

[0179] Multiple components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0180] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the method 100 in any other appropriate manner (e.g., by means of firmware).

[0181] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0182] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0183] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0184] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0185] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0186] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0187] According to an embodiment of the present disclosure, the present disclosure further provides a data transmission radio station, which uses the method 100 as described above to perform data transmission.

[0188] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0189] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. An adaptive transmission method based on dynamic scrambling, applied to a communication server, characterized in that: include: receiving channel state information in real time, and identifying key features of the current channel based on the channel state information; According to the key features of the current channel, based on the machine learning algorithm, a scrambling sequence parameter prediction model is pre-trained to predict the scrambling sequence generation parameters, and a dynamic scrambling sequence for the current channel state is generated according to the generation parameters; the key channel features include: signal strength, phase information, delay spread, and Doppler frequency shift; Obtaining a random key book, and using the random key book and the dynamic scrambling sequence to perform encryption and scrambling processing on the information to be transmitted; According to the importance of the information to be transmitted and the key characteristics of the channel, the redundancy of the information to be transmitted and the retransmission strategy are dynamically adjusted to transmit the information to be transmitted after encryption and scrambling; including: A preset importance scoring system is used to divide the information to be transmitted into key data and ordinary data based on the importance of the information to be transmitted and the key characteristics of the channel. For key data, FEC redundancy is increased to reduce the need for retransmission. For ordinary data, a flexible retransmission strategy is adopted to save bandwidth. For ordinary data, a flexible retransmission strategy is adopted to save bandwidth, including: For ordinary data, determine its maximum data loss ratio, and according to the maximum data loss ratio, match the first retransmission threshold for the data that is sensitive to loss in the ordinary data to increase the retransmission opportunity; match the second retransmission threshold for the data that is not sensitive to loss in the ordinary data to reduce unnecessary retransmissions; continuously monitor the importance of the information to be transmitted and the key characteristics of the channel, and adaptively adjust the first retransmission threshold and the second retransmission threshold according to the real-time data to save bandwidth; wherein, the first retransmission threshold is less than the second retransmission threshold.

2. The method according to claim 1, characterized in that The method of pre-training the scrambling sequence parameter prediction model based on the key features of the current channel and the machine learning algorithm includes: The key features of the current channel at each moment are used as the input data of the model, and the valid scrambling sequence generation parameters corresponding to the key features of the current channel at each moment are used as labels to generate a training set, perform model training and update parameters until the scrambling sequence parameter prediction model is generated.

3. The method according to claim 1, characterized in that The obtaining of a random key book and using the random key book and the dynamic scrambling sequence to perform encryption and scrambling processing on the information to be transmitted includes: A random secret book is selected from a preset secret book pool, and a unique secret book identifier of the selected random secret book is sent to a receiving end. After receiving a confirmation feedback from the receiving end, the selected random secret book is activated, and the information to be transmitted is encrypted using an encryption algorithm to obtain an encrypted ciphertext. The encrypted ciphertext is scrambled using the dynamic scrambling sequence.

4. The method according to claim 1, characterized in that For common data, a flexible retransmission strategy is adopted to save bandwidth, and further includes: Set a priority queue to manage retransmission requests for common data. When bandwidth resources are limited, higher priority retransmission requests in the queue are processed first.

5. An adaptive transmission device based on dynamic scrambling, characterized in that: include: A first processing module, configured to receive channel state information in real time and identify key features of a current channel based on the channel state information; According to the key features of the current channel, based on the machine learning algorithm, a scrambling sequence parameter prediction model is pre-trained to predict the scrambling sequence generation parameters, and a dynamic scrambling sequence for the current channel state is generated according to the generation parameters; the key channel features include: signal strength, phase information, delay spread, and Doppler frequency shift; The second processing module is used to obtain a random key book, and use the random key book and the dynamic scrambling sequence to perform encryption and scrambling processing on the information to be transmitted; The third processing module is used to dynamically adjust the redundancy and retransmission strategy of the information to be transmitted according to the importance of the information to be transmitted and the key characteristics of the channel, and transmit the information to be transmitted after encryption and scrambling; including: A preset importance scoring system is used to divide the information to be transmitted into key data and ordinary data based on the importance of the information to be transmitted and the key characteristics of the channel. For key data, FEC redundancy is increased to reduce the need for retransmission. For ordinary data, a flexible retransmission strategy is adopted to save bandwidth. For ordinary data, a flexible retransmission strategy is adopted to save bandwidth, including: For ordinary data, determine its maximum data loss ratio, and according to the maximum data loss ratio, match the first retransmission threshold for the data that is sensitive to loss in the ordinary data to increase the retransmission opportunity; match the second retransmission threshold for the data that is not sensitive to loss in the ordinary data to reduce unnecessary retransmissions; continuously monitor the importance of the information to be transmitted and the key characteristics of the channel, and adaptively adjust the first retransmission threshold and the second retransmission threshold according to the real-time data to save bandwidth; wherein, the first retransmission threshold is less than the second retransmission threshold.

6. An electronic device, characterized in that: The device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-4.

8. A digital radio station, characterized in that: The data transmission station uses the method according to any one of claims 1 to 4 to perform data transmission.

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