5G communication module data transmission system for Internet of Things

Abstract

The invention belongs to the technical field of wireless communication, and particularly relates to an Internet of Things oriented 5G communication module data transmission system, which comprises a communication decision module, a channel state prediction module and a data transmission module which are in communication connection in sequence, and is characterized in that the communication decision module adopts a quantum genetic algorithm, encodes decision variables by quantum bits, and transmits the decision variables to the channel state prediction module; a lightweight scheduling strategy is generated through population iteration, the too high requirement of a complex optimization algorithm on the computing power of an equipment end is avoided, and a low-power-consumption microprocessor is adapted; meanwhile, the collection frequency is dynamically adjusted according to edge node requirements, invalid data collection is reduced, and equipment energy consumption is reduced. The channel state prediction module trains a model by maximizing an average reward value and realizes accurate prediction in combination with an LSTM model of an attention mechanism, and the data transmission module dynamically adjusts a compression encryption strategy according to a channel state, so that the transmission energy consumption is reduced while the safety is guaranteed, and the energy consumption upside down is avoided.

Description

Technical Field

[0001] The present invention belongs to the field of 5G communication transmission technology, and specifically relates to a 5G communication module data transmission system for the Internet of Things. Background Art

[0002] Internet of Things (IoT) terminals are often deployed in unattended or mobile scenarios, relying on battery power and difficult to recharge (e.g., environmental sensors and wearable devices). Although 5G technology provides the IoT with high speed, large capacity, low latency, and high reliability through features such as enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC), the continuous high-speed access of massive IoT devices in 5G networks has led to various network security issues. IoT devices are inherently diverse and numerous. With the rapid development of 5G networks, while the number of connected IoT devices has increased, the widespread harm and spread of IoT device security issues have also increased, resulting in multiple problems and challenges: While existing adaptive communication methods attempt to optimize energy consumption by dynamically adjusting transmission parameters (such as transmit power and modulation order), most devices within the system use low-power microprocessors with limited computing and storage capabilities. Complex optimization algorithms have high hardware requirements and are difficult to run directly on the device side, which restricts the practical application of subsequent adaptive communication methods. Moreover, when low-power MCUs run such algorithms, the computing power consumption can be several times higher than in standby mode, resulting in an energy consumption inversion phenomenon. Therefore, there is a need for a method to adaptively adjust the acquisition frequency of the acquisition end according to the acquisition needs of data transmission, and to simplify and lightweight the adaptive model to reduce the difficulty of model integration, thereby reducing the operating speed of the device end and reducing energy consumption. Summary of the Invention

[0003] The present invention provides a 5G communication module data transmission system for the Internet of Things, which is implemented through the following technical solutions: A 5G communication module data transmission system for the Internet of Things, characterized by comprising a communication decision module, a channel state prediction module, and a data transmission module that are sequentially connected in communication; The communication decision module generates communication scheduling instructions based on the needs of edge nodes using a quantum genetic algorithm. The quantum genetic algorithm uses quantum bits to encode decision variables. The communication scheduling instructions include communication time, collection frequency, data volume, and power. The collection end is controlled according to the communication scheduling instructions. A channel state prediction module is used to train a channel state prediction model based on historical communication data and historical state data to maximize the average reward value, and predict the channel state based on the real-time state of data transmission. The channel state indicators include channel redundancy, SINR, CSI and bit error rate; The data transmission module includes: The data compression and encryption unit compresses the encrypted data frame based on the improved LZ77 algorithm according to the predicted channel state and then encrypts the data collected by the IoT device before transmission; The data optimization unit performs secondary compression on the encrypted data frame using the Huffman algorithm before transmission.

[0004] Preferably, the execution steps of the communication decision module include: S1: Obtaining a demand instruction periodically sent by an edge node, wherein the demand instruction includes data priority, maximum allowable delay, data type identifier, and current load rate of the edge node; S2: Quantum bit encoding: The four decision variables of communication time, acquisition frequency, data volume, and power are encoded using quantum bits. Each variable corresponds to a set of quantum bits, forming a quantum chromosome. S3: Generate the initial population: randomly generate multiple quantum chromosomes and superimpose the quantum bits of each chromosome; S4: Measure each quantum chromosome, obtain a definite binary solution and convert it into an actual decision parameter value; S5: Applying a rotation gate to the qubit adjusts the phase according to the adaptability function and performs a mutation operation on the qubit, where the rotation angle is determined using the corresponding adjustment strategy; S6: When the continuous fitness change rate is lower than the set fitness threshold, the decision parameters corresponding to the optimal quantum chromosome are output, and a communication scheduling instruction is generated based on the decision parameters; S7: The communication scheduling instructions are sent to the IoT device collection end through the 5G control channel to control the device collection end.

[0005] Preferably, the calculation formula of the fitness function is: ; Among them, E is the current decision energy consumption, , P is the transmission power, t is the transmission time, is the maximum energy consumption allowed by the edge node device, is the maximum delay required by the edge device, is the actual transmission delay of the current decision, U is the current channel utilization, is the target channel utilization, and the weight coefficient satisfies: .

[0006] Preferably, the channel state prediction model includes using an average reward function to optimize the output result of the channel state prediction model, and the calculation formula of the average reward function is: ; Among them, K is the total number of prediction rounds, k is the kth round, is the weight coefficient, which is used to adjust the importance of each indicator in the total reward, and .

[0007] Precision reward The calculation formula is: ; in, is the predicted value of the i-th channel parameter at the t-th time step in the k-th round, is the actual value of the i-th channel parameter at the t-th time step in the k-th round, and are the maximum and minimum values ​​of the i-th channel parameter, respectively, which are used to normalize the deviation.

[0008] Preferably, the data compression encryption unit uses an improved chaotic encryption algorithm combined with a permutation operation to generate a key stream, performs multiple chaotic iterations on the key stream and the compressed data frame by byte XOR, and generates a ciphertext data stream, which is then transmitted.

[0009] Preferably, the specific steps of the data compression and encryption unit include: Initialize the chaotic system and select the three-dimensional coupled tent mapping as the basic chaotic model. The initial state Generated by SHA-256 hash operation using the 256-bit initial key K; Preheating the chaotic system and taking the last iteration result of the chaotic system as a valid initial state; generating a key stream based on the chaotic system after preheating, and obtaining a mitigated state value based on the valid initial state; Construct position permutation table P and value permutation table Q to implement row permutation, XOR and position permutation respectively; Replace positions and values; A preamble byte feedback mechanism is introduced, and finally the reverse key stream is XORed with the result of the feedback iteration to obtain the final ciphertext C.

[0010] Preferably, the XOR and position replacement includes: each byte in the compressed data frame D The corresponding byte in the keystream Perform XOR operation, and then place the result in the new position according to the position permutation table P to obtain the intermediate result , then introduce the round key The round key is generated by shifting the initial key K left by 13 bits, and the round key is used to generate Perform a secondary XOR to obtain the permutation result .

[0011] Preferably, the data compression encryption unit further includes a method for compressing the new key according to the set conditions. Update, the update formula is: ; in, is the hash value of the previous ciphertext, t is the current timestamp, The key before the update.

[0012] The beneficial effects of the present invention are: The communication decision module of the present invention uses a quantum genetic algorithm, encoding decision variables with quantum bits, and generates a lightweight scheduling strategy through population iteration, avoiding the excessive computing power requirements of complex optimization algorithms on the device side and adapting to low-power microprocessors. At the same time, it dynamically adjusts the acquisition frequency based on the needs of edge nodes, reducing invalid data collection and lowering device energy consumption. The channel state prediction module uses a model that maximizes the average reward value training and combines it with an LSTM model with an attention mechanism to achieve accurate predictions. The data transmission module dynamically adjusts the compression and encryption strategy based on the channel state, reducing transmission energy consumption while ensuring security and avoiding energy consumption inversion. While ensuring the accuracy of state estimation, the power consumption of channel monitoring is controlled within the power consumption state of the device to solve the energy consumption problem. By binding the channel state and the chaotic system, and using CSI to limit the generation of keys, the corresponding keys are obtained, reducing the need for additional actual energy consumption corresponding to the keys. By having various modules work together, it can improve the security and efficiency of data transmission while simplifying the difficulty of model integration, increasing the speed of device operation, and meeting the low energy consumption and high reliability requirements of IoT devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0014] Figure 1 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0015] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0016] See also Figure 1 , this embodiment provides a 5G communication module data transmission system for the Internet of Things, including a communication decision module, a channel state prediction module and a data transmission module that are sequentially connected in communication; The communication decision module uses a quantum genetic algorithm to generate communication scheduling instructions based on the needs of edge nodes. The quantum genetic algorithm uses quantum bits to encode decision variables. The communication scheduling instructions include communication time, collection frequency, data volume, and power. The collection end is controlled according to the communication scheduling instructions. The communication decision module uses iterative optimization of the quantum genetic algorithm to achieve precise scheduling of IoT device collection terminals. The specific process includes: S1: Obtain the demand instructions periodically sent by the IoT edge node. The demand instructions include data priority (high / medium / low), maximum allowed latency), data type identifier, and the current load rate of the edge node; Instructions adopt a periodic push mechanism, and the push cycle is dynamically adjusted according to the device type; High-priority devices correspond to low acquisition frequency cycles, medium and low-priority devices correspond to high acquisition cycles. Instructions are transmitted through the dedicated signaling format of the 5G control channel, with a CRC checksum to ensure instruction integrity.

[0017] S2: The four decision variables of communication time, acquisition frequency, data volume, and power are encoded using quantum bits. Each variable corresponds to a set of quantum bits, forming a quantum chromosome. Specifically, it includes: Communication time: 16-bit quantum bit encoding is used, the time range covers the future 0-65535ms, quantum state The probability distribution of the corresponding time point; Acquisition frequency: 8-bit quantum bit encoding is used to convert quantum measurement results into continuous values ​​of 0.1Hz-100Hz through linear mapping. The mapping formula here is: ; Where f is the acquisition frequency, is the result of quantum measurement; Data volume: 24-bit quantum bit encoding, supporting discrete values ​​of 0-4GB, using segmented encoding: the first 8 bits represent the number of data blocks (0-255 blocks), and the last 16 bits represent the size of a single block (0-65535KB); Transmission power: 16-bit quantum bit encoding is used, mapped to a power value of 0.1W-5W; The qubits of the four variables are connected in series to form a 64-bit quantum chromosome, and the chromosome structure is [communication time (16 bits) + acquisition frequency (8 bits) + data volume (24 bits) + power (16 bits)].

[0018] S3: Randomly generate multiple quantum chromosomes and superimpose the quantum bits of each chromosome; The population size is set to 50 quantum chromosomes, and the uniform distribution is used for initialization: the number of quantum bits per qubit is and The value is generated by a random function, satisfying , and They are used to represent the ratio of the two probability amplitudes of the quantum state, reflecting the quantum state in different base states. The probability distribution relationship on Quantum superposition state control: For chromosomes of high-priority devices, the initial superposition state variance is less than 0.2 (concentrated near the optimal solution); The chromosome variance of low-priority devices is greater than or equal to 0.5 (expand the search range); After the population is generated, a validity check is performed to remove chromosomes whose power values ​​exceed the upper limit of the device hardware; S4: Measure each quantum chromosome, obtain a definite binary solution and convert it into an actual decision parameter value; Perform collapse measurement on each quantum chromosome: according to the qubit Probabilistically select the classical state of 0 or 1, generate a 64-bit binary string, and convert the binary string into actual parameters piecewise: Communication time: 16-bit binary value is directly converted to decimal milliseconds; Acquisition frequency / power: calculated using the mapping formula in step S2; Data volume: Calculate total capacity = number of blocks If the single block size exceeds 4GB, the maximum value of 4GB is taken. The converted parameters are stored in the decision parameter matrix, and the dimension of the decision parameter matrix is ​​set to 50×4.

[0019] S5: Apply a rotation gate to the qubit according to the fitness function to adjust the phase and perform a mutation operation on the qubit. The rotation angle is determined using the corresponding adjustment strategy. The core goal of the fitness function is to minimize energy consumption while meeting the latency requirement. At the same time, channel utilization and data integrity are introduced as constraints. The specific formula is as follows: ; Where E is the current decision energy consumption , P is the transmission power, t is the transmission time, is the maximum energy consumption allowed by the device, is the maximum delay required by the edge point, is the actual transmission delay of the current decision, U is the channel utilization, that is, the ratio of the actual transmission rate to the maximum channel rate, is the target channel utilization, and the weight coefficient satisfies: ,The ratio of the coefficients is dynamically adjusted according to the data priority; S6: When the continuous fitness change rate is lower than the set fitness threshold, the decision parameters corresponding to the optimal quantum chromosome are output, and a communication scheduling instruction is generated based on the decision parameters; S7: Communication scheduling instructions are sent to the IoT device acquisition terminal via the 5G control channel to control the device acquisition terminal, including determining convergence when the fitness change rate for five consecutive generations is lower than the set threshold; screening of the optimal quantum chromosome: select the three individuals with the highest fitness in the last generation, and determine the final decision parameters through secondary measurement; The generated communication scheduling instructions all contain parameter check codes, which are generated by encrypting the parameter combination using the SHA-256 algorithm; The channel state prediction module is used to train the channel state prediction model based on historical communication data and historical state data to maximize the average reward value, and predict the channel state based on the real-time state of data transmission. The channel state indicators include channel redundancy, SINR, CSI and bit error rate; In order to enhance the feature extraction capability of key time nodes, the channel state prediction model adopts the LSTM model with an attention mechanism as the channel state prediction model; The overall architecture of the channel prediction model consists of a five-layer structure: input layer, embedding layer, LSTM encoding layer, attention layer, and fully connected output layer. The input layer receives preprocessed time series data (including channel redundancy, SINR, CSI, and bit error rate). The output layer uses a softmax activation function to generate predicted values ​​of channel state parameters for a certain time in the future. Preferably, one sample point per second can be set, for a total of 20 predicted values. The output format is a 4×5 matrix (four parameters, five time steps). Setting up the attention mechanism: Step 1: LSTM hidden state extraction: The LSTM encoding layer processes the input sequence (length 100) and generates the hidden state for each time step t. , , forming the hidden state matrix H, ; Step 2: Calculate attention weights: Introducing a learnable weight matrix and the bias vector , used to perform a linear transformation on the hidden state: the linear transformation result is calculated as: ; Through the context vector Calculating attention score , , The attention score used to represent the time step t reflects the importance of the hidden state at that time step; Then use the softmax function for normalization to get the attention weight of time step t , ; is the attention weight of time step t, ranging from 0 to 1, and the sum of the attention weights of all time steps is 1. By calculating the attention weights of the hidden states at different time steps , which enables the model to automatically focus on information at time nodes that are more important to the current prediction task. In channel state prediction, the corresponding attention weights for key time nodes where the channel state changes dramatically will be significantly increased, thereby enhancing the model's ability to extract key features and improving prediction accuracy; Step 3: Perform weighted summation of the attention weight and the hidden state to generate the context vector c: The calculation formula is: ; Because the hidden states at different time steps have varying importance for prediction, the context vector c can integrate important information from different time steps, avoiding the information loss that can occur when traditional LSTMs rely solely on the hidden state at the last time step. By adjusting the attention weights, the context vector can highlight information at key time points, providing more valuable input for subsequent predictions and further improving the model's predictive performance. Step 4: For scenarios where the channel state suddenly changes (reference setting value: SINR drops by more than 10dB), a gating mechanism is introduced to modify the weights. Specifically: ; in, is the attention weight of the dynamically adjusted time step t, is the gate control value, which is determined by the hidden state And the context vector c is calculated, and the calculation formula is: , For the learnable weight matrix here, For the bias vector here, when the gate value is detected When it approaches 1, the attention weight at the mutation moment is enhanced. By setting a gating mechanism, the channel state prediction model can dynamically adjust the attention weight according to the change of the channel state. When the channel state mutates, the attention weight at the mutation moment can be promptly enhanced, allowing the model to pay more attention to these key mutation information, thereby improving the model's adaptability and prediction accuracy in complex and changing channel environments; Step 5: Model output: So the output layer combines the context vector c with the LSTM hidden state of the last time step The prediction value is output through the fully connected layer after concatenation. The attention weight changes dynamically with the input sequence, and key features are extracted at the time points when the channel state fluctuates violently.

[0020] Finally, the prediction results of the channel prediction model are sent to the data transmission module in JSON format as the basis for data compression and transmission optimization.

[0021] In the construction of the channel prediction model, the average reward function is used to evaluate the model's long-term performance in multiple rounds of prediction tasks. First, the reward and penalty values ​​for each round are calculated separately, and then they are weighted and summed using the weight coefficient to obtain the total reward value for each round. Finally, the total reward values ​​of multiple rounds are averaged to obtain the average reward function, which is expressed as: ; Among them, K is the total number of prediction rounds, k is the kth round, is the weight coefficient, which is used to adjust the importance of each indicator in the total reward. , the default values ​​are 0.4, 0.3, 0.2, 0.1; specifically, they include: is the precision reward item, which is used to measure the deviation between the predicted value and the actual value in the kth round; ; is the predicted value of the i-th channel parameter at the t-th time step in the k-th round, is the actual value of the i-th channel parameter at the t-th time step in the k-th round, and are the maximum and minimum values ​​of the i-th channel parameter, respectively, which are used to normalize the deviation; when the prediction is completely correct, , the larger the error, the closer the reward value is to 0; is the trend reward item, which is used to measure the consistency between the predicted trend and the actual trend. The trend sign function is defined for each parameter i and time step t; ; ; ; in, is an indicator function, which takes the value 1 when the condition in the brackets is met, otherwise it is 0. By maximizing the trend reward, the model can better predict the direction of change of the channel state; It is a robustness reward item used to measure the model's resistance to noise; ; The predicted value after adding Gaussian noise (mean 0, variance 0.01) to the input. The closer the value is to 1, the more robust the model is. It is a penalty term used to constrain the occurrence of fatal errors. For key parameters such as bit error rate, when the deviation between the predicted value and the actual value exceeds the set threshold, the penalty term is triggered. The threshold is specified based on the actual difference between the four output predicted values ​​and the true value; Average reward value The value range of is limited to 0-1. When the model has high prediction accuracy, accurate trend judgment, and strong robustness, the average reward value approaches 1. By maximizing the average reward value function, the channel state prediction model can balance various performance indicators in complex channel environments, especially in critical scenarios such as sudden SINR changes and CSI fluctuations.

[0022] The data transmission module includes: The data compression and encryption unit compresses the encrypted data frame based on the improved LZ77 algorithm according to the predicted channel state and then encrypts the data collected by the IoT device before transmission; During the compression operation phase, the data compression and encryption unit uses an improved LZ77 algorithm to process the raw data collected by IoT devices. Compared with the traditional LZ77 algorithm, the improved version has made targeted optimizations in the sliding window design and matching mechanism. The size of the sliding window is no longer fixed, but is dynamically adjusted based on the predicted channel redundancy: that is, the window size is set to multiple levels. When channel redundancy is sufficient, to reduce compression time and prioritize real-time performance, the minimum window length is used. When channel redundancy is medium, the window length is adjusted to medium to strike a balance between compression efficiency and time consumption. When channel redundancy is tight, the window length is expanded to the maximum, improving the compression rate by matching historical data over a wider range.

[0023] During the matching process, the algorithm introduces a two-layer hash table mechanism: the first-layer hash table records the starting position of all 3-byte strings within the window, while the second-layer hash table establishes separate indexes for frequently occurring long strings. This design can quickly locate repeated data blocks and avoid the inefficiency of byte-by-byte comparison in traditional algorithms. For example, in periodic temperature data collected by industrial sensors, the algorithm can directly match the continuously repeated "25.3℃, 25.4℃" sequence through the hash table and replace it with a pointer marker of "(offset: 120, length: 16)", thereby improving the compression rate; After compression is complete, the data is encrypted.

[0024] The data compression encryption unit also includes using an improved chaotic encryption algorithm combined with a permutation operation to generate a key stream, performing multiple chaotic iterations on the key stream and the compressed data frame by byte XOR to generate a ciphertext data stream, and transmitting the ciphertext data stream. The specific steps include: In the initialization stage of the chaotic system, the three-dimensional coupled tent mapping is selected as the basic chaotic model; The three-dimensional coupled tent forms complex nonlinear dynamic behavior through three mutually coupled state variables x, y, and z. The update of each state variable not only depends on its own value at the previous moment, but is also affected by other state variables. This coupling relationship greatly enhances the chaotic characteristics and anti-cracking ability of the system. The control parameters of the system They are all set in the range of 1-2, with the initial default value of 1.8 and the coupling coefficient λ of 0.05 to ensure that the system is in a completely chaotic state; Initial state The 256-bit initial key K is generated by the SHA-256 hash operation. The hash operation converts the key into a fixed-length hash value, which is then split into three 85-bit binary numbers. , and then divided by , and obtain three initial state values ​​in the range of 0-1.

[0025] To avoid possible periodic problems in the initial state, the system is preheated for 1000 iterations, the results of the first 999 iterations are discarded, and only the result of the last iteration is retained as the valid initial state for the subsequent encryption process.

[0026] Generate a key stream, based on the chaotic state after preheating, to generate a key stream N with the same length as the compressed data frame. For each byte in the key stream , it is necessary to iterate the chaotic system three times to obtain the state variables at this time Add these three state variables and multiply by 256, take the integer part and modulo 256 to get the mixed state value .

[0027] In order to further enhance the randomness of the key stream, Perform nonlinear transformation, that is and The result of the right shift by 4 bits is XORed and then modulo 256 to obtain the bytes in the key stream. .

[0028] At the same time, a dynamic permutation table is constructed, including a position permutation table P and a value permutation table Q. The position permutation table P is based on the chaotic state Generate a random permutation from 0 to L-1, It is the state value output by the chaotic system, which is the sequence element generated by chaotic iteration. It is used to construct the random source of the permutation table and reflects the chaotic characteristics. 0 to L-1 is the index range of the permutation table, j is the index variable, traversing from 0 to L-1, And satisfy: ; Ensure that the data is not randomly scattered in position, is the chaotic state transformation formula, The 1000+j state value in the chaotic sequence, that is, skipping the first 1000 initial values, avoiding the influence of chaotic transients and ensuring randomness ,right After the sequence is sorted, take the "index position" of the original sequence element; if , after sorting becomes , the corresponding index number is ; The value substitution table Q is based on Generate byte mapping relationship, It is another set of state values ​​of the chaotic system, which needs to be distinguished from position replacement and is used to achieve random replacement of data values.

[0029] Multi-round iterative encryption is the core step to ensure encryption strength. The first round is XOR and position replacement, which converts each byte in the compressed data frame D into The corresponding byte in the keystream Perform XOR operation, and then place the result in the new position according to the position permutation table P to obtain the intermediate result Then, the round key is introduced , which is generated by shifting the initial key K left by 13 bits, and using the round key Perform a secondary XOR to obtain the permutation result .

[0030] The second round is value replacement and chaos perturbation. Each byte in is replaced by the value replacement table Q, and we get , combined with the chaotic state Perturb, generate .

[0031] The third round is feedback iteration and final encryption, which introduces a previous byte feedback mechanism, that is, the encryption result of the current byte depends on the encryption result of the previous byte. Finally, the reverse key stream is XORed with the result of the feedback iteration to obtain the final ciphertext C.

[0032] In terms of ciphertext verification and key update, the CRC32 check value is calculated for the ciphertext data stream C and embedded into the ciphertext header to form the final transmitted data to ensure that the data is not tampered with during transmission.

[0033] At the same time, to improve the security of long-term encryption, the data compression encryption unit also includes updating the initial key. The new key is generated by the SHA-256 hash operation using the old key, the hash value of the previous ciphertext frame, the current timestamp, and the CSI update frequency, and the key is dynamically changed: The update formula is: ; in, is the hash value of the previous frame ciphertext, t is the current timestamp; the new key The update setting conditions are: synchronized with the update frequency of CSI, or automatically updating the initial key after every 1024 frames of encrypted data.

[0034] The data compression and encryption unit also uses an improved chaotic encryption algorithm combined with multiple permutation operations, significantly increasing the encryption strength of the data. The complex dynamic characteristics of the three-dimensional coupled tent mapping make the state of the chaotic system difficult to predict. The key stream generation and data encryption process are closely dependent on subtle changes in the chaotic state. Even small differences in the initial key can lead to significant differences in the encryption results, effectively resisting common attack methods such as brute force cracking and differential attacks. The introduction of a dynamic permutation table fully scrambles the data in both position and value dimensions, further enhancing the randomness of the ciphertext and making it difficult for attackers to extract patterns from the original data. The dynamic key update mechanism avoids the security risks brought about by the long-term use of the same key. Even if the key is leaked at a certain moment, it will not affect the security of subsequent data, providing continuous security for the system.

[0035] In terms of adaptability, the data compression and encryption unit integrates seamlessly into the overall data transmission system. It works in conjunction with the data compression unit to encrypt data after compression, ensuring both efficient and secure data transmission. During encryption, the length of the key stream is equal to the length of the compressed data frame, eliminating the need for excessive data volume and meeting the data transmission rate and bandwidth requirements of 5G communication environments. Furthermore, the encryption unit's moderate computational complexity allows for efficient operation within the hardware resource constraints of IoT devices, without significantly impacting the real-time performance of the overall system.

[0036] From a reliability perspective, the ciphertext verification mechanism ensures data integrity during transmission. The CRC32 checksum promptly detects errors caused by channel interference and other factors during data transmission, facilitating data retransmission or error correction at the receiving end, thereby improving the reliability of the entire data transmission system. Furthermore, the stability of the chaotic system and the maturity of the encryption algorithm ensure the consistency and repeatability of the encryption process, enabling accurate decryption at the receiving end and ensuring proper data reception and processing.

[0037] The data optimization unit uses the Huffman algorithm to perform secondary compression on the encrypted ciphertext data, further reducing the data volume and improving channel utilization. This process must maximize the compression ratio without compromising the security of the ciphertext. Specifically: The core of the Huffman algorithm is to assign short codes to frequently occurring bytes and long codes to less frequently occurring bytes. However, the encrypted ciphertext exhibits a near-random distribution, making the traditional Huffman algorithm ineffective. To address this, the data optimization unit employs a "block statistics + dynamic coding table" strategy: encrypted data frames are divided into 1KB blocks, and byte frequency statistics are independently calculated for each block.

[0038] Bytes whose frequency exceeds the set threshold are directly included in the coding table; for low-frequency bytes, statistical efficiency is improved by merging similar items (such as treating three consecutive low-frequency bytes as a composite symbol).

[0039] The coding table construction process is linked to the channel state: when channel redundancy is high, a fixed coding table (pre-generated based on historical ciphertext statistics) is used to reduce computational time. When channel redundancy is low, a real-time coding table is used. Although computational effort increases, it more accurately matches the current data distribution and improves the compression ratio. The coding table itself is stored in a serialized Huffman tree format to reduce byte usage.

[0040] The secondary compression process consists of three steps: first, an integrity check is performed on the encrypted data to ensure that the ciphertext has not been tampered with; then, frequency statistics are generated based on the block strategy to generate a coding table; finally, each block of data is Huffman encoded and the index of the coding table for that block is appended to the end of the block. Before decryption, the receiving end must first use the index to call the corresponding coding table to complete the decoding process and then decrypt the data.

[0041] It's worth noting that the secondary compression process strictly controls compression time. Using hardware acceleration modules (such as a dedicated Huffman coding IP core), the single-frame processing time is kept below 1ms, ensuring real-time data transmission. Actual tests have shown that in typical IoT scenarios, secondary compression can reduce data volume by an additional 15% to 20%. Combined with the initial LZ77 compression, this improves the overall compression ratio and significantly reduces channel bandwidth requirements.

[0042] The technical solution of the present invention uses a lightweight quantum genetic algorithm and a dynamic acquisition strategy to enable low-power devices to achieve adaptive communication without running complex algorithms, reduce computing power consumption, and solve the problem of energy consumption inversion. Through the collaborative work of various modules, while improving the security and efficiency of data transmission, it simplifies the difficulty of model integration and increases the operating speed of the device. While ensuring the accuracy of state estimation, the channel monitoring power consumption is controlled within the power consumption state of the device to solve the energy consumption problem. By binding the channel state and the chaotic system, and using CSI to limit the generation of keys, the corresponding keys are obtained, further reducing the need for additional corresponding energy consumption for the keys.

[0043] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A 5G communication module data transmission system for the Internet of Things, characterized by: It includes a communication decision module, a channel state prediction module and a data transmission module which are sequentially connected in communication; The communication decision module generates communication scheduling instructions based on the needs of edge nodes using a quantum genetic algorithm. The quantum genetic algorithm uses quantum bits to encode decision variables. The communication scheduling instructions include communication time, collection frequency, data volume, and power. The collection end is controlled according to the communication scheduling instructions. A channel state prediction module is used to train a channel state prediction model based on historical communication data and historical state data to maximize the average reward value, and predict the channel state based on the real-time state of data transmission. The channel state indicators include channel redundancy, SINR, CSI and bit error rate; The data transmission module includes: The data compression and encryption unit compresses the encrypted data frame based on the improved LZ77 algorithm according to the predicted channel state and then encrypts the data collected by the IoT device before transmission; The data optimization unit performs secondary compression on the encrypted data frame using the Huffman algorithm before transmission.

2. A 5G communication module data transmission system for the Internet of Things according to claim 1, characterized in that: The execution steps of the communication decision module include: S1: Obtaining a demand instruction periodically sent by an edge node, wherein the demand instruction includes data priority, maximum allowable delay, data type identifier, and current load rate of the edge node; S2: Quantum bit encoding: The four decision variables of communication time, acquisition frequency, data volume, and power are encoded using quantum bits. Each variable corresponds to a set of quantum bits, forming a quantum chromosome. S3: Generate the initial population: randomly generate multiple quantum chromosomes and superimpose the quantum bits of each chromosome; S4: Measure each quantum chromosome, obtain a definite binary solution and convert it into an actual decision parameter value; S5: Applying a rotation gate to the qubit to adjust the phase according to the adaptability function and performing a mutation operation on the qubit, where the rotation angle is determined using the corresponding adjustment strategy; S6: When the continuous fitness change rate is lower than the set fitness threshold, the decision parameters corresponding to the optimal quantum chromosome are output, and a communication scheduling instruction is generated based on the decision parameters; S7: The communication scheduling instructions are sent to the IoT device collection end through the 5G control channel to control the device collection end.

3. A 5G communication module data transmission system for the Internet of Things according to claim 2, characterized in that: The calculation formula of the fitness function is: ； Among them, E is the current decision energy consumption, , P is the transmission power, t is the transmission time, is the maximum energy consumption allowed by the edge node device, is the maximum delay required by the edge device, is the actual transmission delay of the current decision, U is the current channel utilization, is the target channel utilization, and the weight coefficient satisfies: .

4. The 5G communication module data transmission system for the Internet of Things according to claim 2, characterized in that: The channel state prediction model includes optimizing the output result of the channel state prediction model using an average reward function, and the calculation formula of the average reward function is: ； Among them, K is the total number of prediction rounds, k is the kth round, is the weight coefficient, which is used to adjust the importance of each indicator in the total reward, and .

5. The 5G communication module data transmission system for the Internet of Things according to claim 1, characterized in that: Accuracy Reward The calculation formula is: ； in, is the predicted value of the i-th channel parameter at the t-th time step in the k-th round, is the actual value of the i-th channel parameter at the t-th time step in the k-th round, and are the maximum and minimum values ​​of the i-th channel parameter, respectively, which are used to normalize the deviation.

6. The 5G communication module data transmission system for the Internet of Things according to claim 1, characterized in that: The data compression encryption unit also includes using an improved chaotic encryption algorithm combined with a permutation operation to generate a key stream, performing multiple chaotic iterations on the key stream and the compressed data frame by byte XOR to generate a ciphertext data stream, and transmitting the ciphertext data stream.

7. The 5G communication module data transmission system for the Internet of Things according to claim 6, characterized in that: The specific steps of encryption by the data compression encryption unit include: Initialize the chaotic system and select the three-dimensional coupled tent mapping as the basic chaotic model. The initial state Generated by SHA-256 hash operation using the 256-bit initial key K; Preheating the chaotic system and taking the last iteration result of the chaotic system as a valid initial state; generating a key stream based on the chaotic system after preheating, and obtaining a mitigated state value based on the valid initial state; Construct position permutation table P and value permutation table Q to implement row permutation, XOR and position permutation respectively; Replace positions and values; A preamble byte feedback mechanism is introduced, and finally the reverse key stream is XORed with the result of the feedback iteration to obtain the final ciphertext C.

8. The 5G communication module data transmission system for the Internet of Things according to claim 7, characterized in that: The XOR and position replacement includes: each byte in the compressed data frame D The corresponding byte in the keystream Perform XOR operation, and then place the result in the new position according to the position permutation table P to obtain the intermediate result , then introduce the round key The round key is generated by shifting the initial key K left by 13 bits, and the round key is used to generate Perform a secondary XOR to obtain the permutation result .

9. The 5G communication module data transmission system for the Internet of Things according to claim 8, characterized in that: The data compression encryption unit also includes a new key according to the set conditions Update, the update formula is: ； in, is the hash value of the previous ciphertext, t is the current timestamp, The key before the update.

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