Semantic communication optimization method, device and system based on priority

Abstract

The invention discloses a priority-based semantic communication optimization method, device and system, and the method comprises the steps: obtaining key semantic information through analyzing to-be-transmitted data, carrying out the calculation according to the information, obtaining an information priority, and merging a coding data packet; a first network resource allocation strategy is obtained through calculation according to the network state index and the collaborative optimization algorithm based on a network resource allocation algorithm, the allocation strategy is optimized according to the network state index and the collaborative optimization algorithm, and a data packet is obtained and transmitted according to a first collaborative optimization result. And meanwhile, collecting and adjusting a first collaborative optimization result based on a distribution algorithm according to a transmission performance index, and transmitting data according to an adjusted strategy. The integrity and transmission reliability of important semantic information in to-be-transmitted data during data transmission are ensured through information priority confirmation, and the data volume of data transmission is reduced by combining coded data packets; the resource allocation algorithm and the collaborative optimization algorithm improve the practicability of the resource allocation strategy, so that the strategy can effectively cope with the change of the network environment.

Description

Technical Field

[0001] The present invention relates to the field of semantic communication technology, and in particular to a priority-based semantic communication optimization method, device and system. Background Art

[0002] Semantic communication is a technology that first extracts, compresses and transmits the original signal selectively, and then uses the semantic level information for communication. Its advantage is that it understands the task first and then transmits it, which greatly improves the transmission efficiency and reliability of the communication system. Semantic communication technology is a complete change in the design ideas and concepts of communication systems, bringing new ideas and means to future communications, and will fundamentally change the traditional information and communication technology system.

[0003] With the development of applications such as the Internet of Things, autonomous driving, and telemedicine, the requirements for low latency and high reliability of communication technology are becoming increasingly higher. Traditional communication methods are mainly based on transmitting as much data as possible to ensure the integrity of information, but this method often leads to network congestion, increased latency, and reduced transmission reliability. How semantic communication determines the priority of key information and how to coordinate with network resource allocation to achieve low-latency, high-reliability, high-speed data transmission has become a difficult problem in this field. Summary of the invention

[0004] The present invention provides a priority-based semantic communication optimization method, device and system to achieve the technical effect of improving the reliability and practicality of data transmission while reducing the network delay of data transmission.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a priority-based semantic communication optimization method, comprising the following steps:

[0006] In response to a data transmission signal, receiving and parsing data to be transmitted, obtaining key semantic information, and performing priority calculation and semantic data merge encoding on the data to be transmitted according to the key semantic information to obtain information priority and merge encoded data packets;

[0007] Based on a preset network resource allocation algorithm, a first network resource allocation strategy is obtained by calculating the information priority and the merged coded data packet, and then the first network resource allocation strategy is processed based on a network status indicator and a collaborative optimization algorithm to obtain a first collaborative optimization result, and the merged coded data packet is transmitted according to the first collaborative optimization result;

[0008] During the data transmission process, the transmission performance index is collected in real time, and the first collaborative optimization result is adjusted according to the transmission performance index and the network resource allocation algorithm, so that the combined coded data packet is transmitted according to the adjusted strategy.

[0009] After parsing the data to be transmitted, the semantic communication method provided by the present invention can calculate the information priority and merge the data according to the key semantic information obtained by the analysis, and obtain the information priority of the key semantic information and the data packet after the merge coding. The information priority obtained by calculation can be used as reference data when the subsequent system adjusts the network resource allocation strategy, that is, the network resource allocation weight of the data with high information priority is adjusted accordingly, so as to ensure the integrity and transmission reliability of the important semantic information in the data to be transmitted during data transmission. Merging and coding the data to obtain a merged coded data packet reduces the amount of data transmitted while ensuring the integrity of the data, further improving the reliability and practicality of data transmission.

[0010] Based on the network resource allocation algorithm, the first network resource allocation strategy is calculated according to the information priority and the merged coded data packets obtained by calculation, so that the calculated first network resource allocation strategy not only takes into account the protection priority of key semantic information and the network resource allocation priority, but also takes into account the number and data volume of the merged coded data packets, thereby improving the practicality and reliability of the resource allocation strategy. At the same time, the first network resource allocation strategy is collaboratively optimized based on the network status indicator through the collaborative optimization algorithm, so that the first collaborative optimization result after the algorithm collaborative optimization processing also takes into account the real-time network status, further improving the reliability and efficiency of data transmission, and also enabling the optimized allocation strategy to effectively respond to changes in the network environment.

[0011] After the data packet starts to be transmitted, the system will also collect transmission performance indicators in real time, and update the parameters of the network resource allocation algorithm based on the collected performance indicator data, and solve the updated algorithm to obtain the corresponding solution result, and adjust and update the first collaborative optimization result based on the solution result, so that the data packet is transmitted according to the updated strategy, thereby realizing adaptive feedback adjustment of the data packet transmission strategy, and adjusting the transmission strategy in real time according to the transmission performance indicator, so that the adjusted transmission strategy is more adaptable to the network status, and further improving the communication efficiency and communication data transmission reliability.

[0012] As a preferred example, the receiving and parsing of the data to be transmitted, obtaining key semantic information, and performing priority calculation and semantic data merge encoding on the data to be transmitted according to the key semantic information to obtain information priority and merge encoded data packets are specifically as follows:

[0013] Receive and perform noise removal and standardization processing on the data to be transmitted in sequence, output corresponding processed data, call an automatic encoder or a convolutional neural network according to the processed data to perform feature extraction on the processed data, and obtain the key semantic information;

[0014] The key semantic information is evenly divided into a training data set and a test data set, and the training data set is input into a neural network model for training, so that the trained model performs feature analysis and semantic importance evaluation on the test data set to obtain semantic importance, and then calculates the information priority of each key semantic information through a weighted algorithm based on the semantic importance;

[0015] The merged coded data packet is obtained by merging and encoding the data corresponding to the information whose similarity reaches a similarity threshold in the key semantic information.

[0016] After receiving the data to be transmitted, the system will first perform denoising and standardization on the data, that is, preprocess the data to be transmitted, so as to improve the data quality and improve the adaptability of the processed data to the subsequent neural network model. After preprocessing, the system will select a method to extract features from the data, that is, obtain key semantic information, based on the processed data. The system will select an autoencoder or a convolutional neural network for feature extraction based on the processed data, which not only improves the flexibility of the selection of key semantic information extraction methods, but also makes the selected extraction method more adaptable to the processed data.

[0017] After extracting the key semantic information, the system divides it equally into a training data set and a test data set, and trains the neural network model with the training data set to improve the model's sensitivity to the processed data, thereby improving the accuracy of the model's feature analysis and semantic importance assessment of the test data set, providing reference data for the subsequent calculation of the data's information priority.

[0018] As a preferred example, the combined encoding data packet is obtained by combining and encoding the data corresponding to the information whose similarity reaches the similarity threshold in the key semantic information, specifically:

[0019] Selecting cosine similarity or Jaccard similarity coefficient according to the key semantic information to measure the semantic similarity of each piece of information in the key semantic information, and obtaining corresponding multiple similarities;

[0020] Comparing the plurality of similarities with the preset similarity threshold, clustering the data items corresponding to the key semantic information whose comparison result is that the similarity is greater than or equal to the similarity threshold, and then obtaining a plurality of data groups, and sorting the data items in the plurality of data groups according to the information priority of the corresponding key semantic information;

[0021] The sorted data items in each data group are combined and coded by a preset Huffman coding method to obtain a plurality of combined coded data packets.

[0022] In order to improve data transmission efficiency and achieve efficient communication, the semantic communication optimization method provided by the present invention will also merge and encode the data whose key semantic information similarity reaches the threshold when calculating the information priority of the data. First, according to the key semantic information, a similarity calculation method such as cosine similarity or Jaccard similarity coefficient is selected to calculate and quantify the similarity between each key information, and the calculated similarity is compared with the preset similarity threshold to determine whether the two data items corresponding to the two key semantic information corresponding to the similarity can be classified as the same group of data, and then all key semantic information is traversed and all data items are clustered according to the determined similarity to obtain several groups of data, thereby reducing the amount of transmitted data and improving data transmission efficiency.

[0023] Before merging and encoding the data items in each data group, the data items in each data group will be sorted according to the information priority of the key semantic information corresponding to each data item, so as to ensure that the key semantic information with higher priority can be sent and processed faster, thereby improving the responsiveness and response efficiency of the system, as well as the timeliness and accuracy of the system data transmission.

[0024] As a preferred example, the preset network resource allocation algorithm is based on the information priority and the combined coded data packet to calculate the first network resource allocation strategy, specifically:

[0025] The network load is calculated based on the number and data volume of the combined encoded data packets, and the average network delay and packet loss rate are collected and monitored in real time, and then the network congestion index is calculated based on the average network delay and the packet loss rate;

[0026] Based on the network resource allocation algorithm, calculation and solution are performed according to the network load, the network congestion index and the information priority to obtain the first network resource allocation strategy.

[0027] After obtaining several combined coded data packets, the system will determine the first network resource allocation strategy based on the information priority and combined coded data packets. First, the system will calculate the network load based on the number and data volume of the generated combined coded packets, and will also collect the current average network delay and packet loss rate, and then calculate the current network congestion index based on the average network delay and packet loss rate, that is, the degree of network congestion.

[0028] After determination, the network load, network congestion index and information priority are calculated and solved based on the network resource allocation algorithm to obtain the first network resource allocation strategy, which improves the adaptability of the network allocation strategy to the current network status, and also ensures that data with higher priority will be transmitted and processed more quickly.

[0029] As a preferred example, the first network resource allocation strategy is processed based on the network status indicator and the collaborative optimization algorithm to obtain the first collaborative optimization result, specifically:

[0030] Collecting network delay and network utilization rate in the network status indicator in real time, and obtaining an adjustment factor through a preset activation function calculation based on the network delay and the network utilization rate;

[0031] The weight factor in the collaborative optimization algorithm is obtained by optimizing the adjustment factor, and then the collaborative optimization algorithm is updated according to the calculated weight factor, and the first network resource allocation strategy is collaboratively optimized through the updated collaborative optimization algorithm to output the first collaborative optimization result.

[0032] After obtaining the first network resource allocation strategy, the system will further optimize and adjust the allocation strategy based on the network status, obtain the adjustment factor through the real-time collection of network delay and network utilization rate and activation function calculation, and then calculate the weight factor in the collaborative optimization algorithm according to the adjustment factor, so as to adjust the weight factor through the adjustment factor, and then update the collaborative optimization algorithm according to the weight factor, so as to improve the adaptability of the collaborative optimization algorithm to the current network status, and optimize the first network resource allocation strategy through the updated collaborative optimization algorithm, and output the optimized first collaborative optimization result, so as to improve the adaptability of the optimized network resource allocation strategy to the current network status, that is, further improve the reliability, practicality and accuracy of data transmission.

[0033] As a preferred example, the real-time collection of transmission performance indicators and the adjustment of the first collaborative optimization result according to the transmission performance indicators and the network resource allocation algorithm are specifically:

[0034] During the data packet transmission process, the bandwidth utilization and transmission power efficiency in the transmission performance indicators are collected in real time, and the adjustment coefficient in the network resource allocation algorithm is obtained by calculating the bandwidth utilization and the transmission power efficiency through a preset adaptive control algorithm;

[0035] The network resource allocation algorithm is parameter updated according to the calculated adjustment coefficient, and the updated network resource allocation algorithm is solved, and then the first collaborative optimization result is adjusted according to the solution result.

[0036] When the system transmits data packets according to the first collaborative optimization result, it will also collect the bandwidth utilization and transmission power efficiency of the network's transmission performance indicators in real time, and input the above two types of data as input data into a preset adaptive control algorithm for calculation and solution to obtain corresponding calculation results. The calculation results will be added to the network resource allocation algorithm as an adjustment coefficient in the network resource allocation algorithm to update the algorithm parameters accordingly, so that the updated network resource allocation algorithm is more adapted to the current network transmission performance, thereby improving the data transmission reliability and practicality of the system.

[0037] The system's data transmission strategy is adjusted through the network transmission performance status data fed back by data transmission, which not only improves the data transmission efficiency, transmission security and reliability, but also realizes dynamic and flexible adjustment of the data transmission strategy and the system's adaptability, while also enhancing the stability and response speed of the entire communication system.

[0038] Accordingly, an embodiment of the present invention further provides a priority-based semantic communication optimization device, the semantic communication optimization device comprising a priority calculation module, an allocation strategy transmission module and an indicator feedback optimization module;

[0039] The priority calculation module is used to respond to the data transmission signal, receive and parse the data to be transmitted, obtain key semantic information, and perform priority calculation and semantic data merge encoding on the data to be transmitted according to the key semantic information to obtain information priority and merge encoded data packets;

[0040] The allocation strategy transmission module is used to calculate a first network resource allocation strategy based on a preset network resource allocation algorithm through the information priority and the merged coded data packet, and then retrieve and process the first network resource allocation strategy through a collaborative optimization algorithm to obtain a first collaborative optimization result, and transmit the merged coded data packet according to the first collaborative optimization result;

[0041] The indicator feedback optimization module is used to collect network transmission performance indicators in real time during data transmission, and adjust the first collaborative optimization result through the network resource allocation algorithm according to the network transmission performance indicators, so that the combined encoded data packet is transmitted according to the adjusted strategy.

[0042] As a preferred example, the priority calculation module receives and parses the data to be transmitted, obtains key semantic information, and performs priority calculation and semantic data merge encoding on the data to be transmitted according to the key semantic information to obtain information priority and merged encoding data packets, specifically:

[0043] Receive and perform noise removal and standardization processing on the data to be transmitted in sequence, output corresponding processed data, call an automatic encoder or a convolutional neural network according to the processed data to perform feature extraction on the processed data, and obtain the key semantic information;

[0044] The key semantic information is evenly divided into a training data set and a test data set, and the training data set is input into a neural network model for training, so that the trained model performs feature analysis and semantic importance evaluation on the test data set to obtain semantic importance, and then calculates the information priority of each key semantic information through a weighted algorithm based on the semantic importance;

[0045] The merged coded data packet is obtained by merging and encoding the data corresponding to the information whose similarity reaches a similarity threshold in the key semantic information.

[0046] Correspondingly, an embodiment of the present invention also provides a priority-based semantic communication optimization system, which includes a memory, a processor, and a computer program stored in the memory and running on the processor. When processing the computer program, the processor implements any of the above-described priority-based semantic communication optimization systems.

[0047] Correspondingly, an embodiment of the present invention further provides a storage medium, on which a computer program is stored. The computer program is called and executed by a processor to implement a priority-based semantic communication optimization system as described in any one of the above items. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 : A flow chart of an embodiment of a priority-based semantic communication optimization method provided by the present invention;

[0049] Figure 2 : A structural diagram of an embodiment of the priority-based semantic communication optimization device provided by the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Embodiment 1

[0052] Please refer to Figure 1, which is a flow chart of an embodiment of the priority-based semantic communication optimization method provided by the present invention, including steps 101 to 103, each of which is specifically as follows:

[0053] Step 101: In response to a data transmission signal, receive and parse data to be transmitted, obtain key semantic information, and perform priority calculation and semantic data merge encoding on the data to be transmitted according to the key semantic information to obtain information priority and merged encoding data packet.

[0054] After parsing the data to be transmitted, the semantic communication method provided by the embodiment of the present invention can calculate the information priority and merge the data according to the key semantic information obtained from the analysis, and obtain the information priority of the key semantic information and the merged and encoded data packet. The information priority obtained by calculation can be used as reference data when the subsequent system adjusts the network resource allocation strategy, that is, the network resource allocation weight of the data with high information priority is adjusted accordingly, so as to ensure the integrity and transmission reliability of the important semantic information in the data to be transmitted during data transmission. Merging and encoding the data to obtain a merged coded data packet reduces the amount of data transmitted while ensuring the integrity of the data, further improving the reliability and practicality of data transmission.

[0055] Specifically, the present embodiment receives and parses the data to be transmitted, obtains key semantic information, and performs priority calculation and semantic data merging encoding on the data to be transmitted according to the key semantic information to obtain information priority and merged encoding data packets, specifically as follows:

[0056] Receive and perform noise removal and standardization processing on the data to be transmitted in sequence, output corresponding processed data, call an automatic encoder or a convolutional neural network according to the processed data to perform feature extraction on the processed data, and obtain the key semantic information;

[0057] The key semantic information is evenly divided into a training data set and a test data set, and the training data set is input into a neural network model for training, so that the trained model performs feature analysis and semantic importance evaluation on the test data set to obtain semantic importance, and then calculates the information priority of each key semantic information through a weighted algorithm based on the semantic importance;

[0058] The merged coded data packet is obtained by merging and encoding the data corresponding to the information whose similarity reaches a similarity threshold in the key semantic information.

[0059] After receiving the data to be transmitted, the system will first perform denoising and standardization on the data, that is, preprocess the data to be transmitted, so as to improve the data quality and improve the adaptability of the processed data to the subsequent neural network model. After preprocessing, the system will select a method to extract features from the data, that is, obtain key semantic information, based on the processed data. The system will select an autoencoder or a convolutional neural network for feature extraction based on the processed data, which not only improves the flexibility of the selection of key semantic information extraction methods, but also makes the selected extraction method more adaptable to the processed data.

[0060] After extracting the key semantic information, the system divides it equally into a training data set and a test data set, and trains the neural network model with the training data set to improve the model's sensitivity to the processed data, thereby improving the accuracy of the model's feature analysis and semantic importance assessment of the test data set, providing reference data for the subsequent calculation of the data's information priority.

[0061] In this embodiment, the specific steps of training the neural network model through the training data set are: using a long short-term memory network (LSTM) to process time series data to identify and predict key events and information points in the data stream, wherein the LSTM network structure can be expressed as:

[0062] f t =σ(W f ·[h t-1 , x t ]+b f )

[0063] i t =σ(W i ·[h t-1 , x t ]+b i )

[0064]

[0065] o t =σ(W o ·[h t-1 , x t ]+b o )

[0066] h t =o t *tanh(C t )

[0067] Among them, ft, it, ot are the forget gate, input gate and output gate respectively, Ct and ht are the unit state and hidden state respectively, W and b are the weight parameter and bias parameter, σ and tanh are the sigmoid function and hyperbolic tangent activation function respectively. The model is trained through the above process, and the test data set is input into the trained model for key semantic information analysis to evaluate the semantic importance of each piece of information, and then the evaluated semantic importance is output, and the information priority pi of each piece of information is calculated by a weighted algorithm in combination with the urgency and impact range of the information. The specific calculation formula is as follows:

[0068] p i =w1*urgency i +w2*impact i

[0069] Furthermore, in this embodiment, the data corresponding to the information whose similarity reaches the similarity threshold in the key semantic information is merged and encoded to obtain the merged encoded data packet, specifically:

[0070] Selecting cosine similarity or Jaccard similarity coefficient according to the key semantic information to measure the semantic similarity of each piece of information in the key semantic information, and obtaining corresponding multiple similarities;

[0071] Comparing the plurality of similarities with the preset similarity threshold, clustering the data items corresponding to the key semantic information whose comparison result is that the similarity is greater than or equal to the similarity threshold, and then obtaining a plurality of data groups, and sorting the data items in the plurality of data groups according to the information priority of the corresponding key semantic information;

[0072] The sorted data items in each data group are combined and coded by a preset Huffman coding method to obtain a plurality of combined coded data packets.

[0073] In order to improve data transmission efficiency and achieve efficient communication, the semantic communication optimization method provided by the embodiment of the present invention will also merge and encode the data whose key semantic information similarity reaches the threshold when calculating the information priority of the data. First, according to the key semantic information, a similarity calculation method such as cosine similarity or Jaccard similarity coefficient is selected to calculate and quantify the similarity between each key information, and the calculated similarity is compared with the preset similarity threshold to determine whether the two data items corresponding to the two key semantic information corresponding to the similarity can be classified as the same group of data, and then traverse all key semantic information and cluster all data items according to the determined similarity to obtain several groups of data groups, thereby reducing the amount of transmitted data and improving data transmission efficiency.

[0074] Before merging and encoding the data items in each data group, the data items in each data group will be sorted according to the information priority of the key semantic information corresponding to each data item, so as to ensure that the key semantic information with higher priority can be sent and processed faster, thereby improving the responsiveness and response efficiency of the system, as well as the timeliness and accuracy of the system data transmission.

[0075] In this embodiment, after the system extracts features from the data to be transmitted to obtain corresponding key semantic information, it will also cluster the data to be transmitted into multiple groups C1, C2, ..., Cn according to the similarities corresponding to their corresponding key semantic information based on predefined semantic similarity metrics, such as cosine similarity, Jaccard similarity and other similarity calculation methods, where each group contains semantically similar data items, that is, the similarity between the data items in each group is greater than or equal to a preset similarity threshold.

[0076] After completing the clustering grouping, the system merges the data items in each group Ck into a single data packet through Huffman coding, that is, the data in each group is merged and encoded, specifically: the frequency of occurrence f(x) of each unique data item in Ck is calculated and sorted, and the two nodes with the lowest frequency are selected each time to merge, and a new parent node is created, whose frequency is the sum of the frequencies of the two child nodes, and this process is repeated until a complete Huffman tree is constructed. Then starting from the root node of the Huffman tree, "0" is assigned to the left and "1" is assigned to the right, and a unique code is generated for each leaf node (original data item). The generated Huffman code is used to encode the data in Ck, and then all the codes are merged into a data packet. The specific formula is:

[0077]

[0078] Where code(x) is the Huffman code of the data item x.

[0079] In addition to the above-mentioned merge encoding process, this embodiment also provides another merge encoding method. In its specific implementation steps, after each data item in the data to be transmitted is clustered into multiple groups such as C1, C2,..., Cn according to the similarity corresponding to its corresponding key semantic information, the data items in each group will be sorted according to the information priority, and because the data items in each group are semantically similar, each group will also be prioritized.

[0080] When merging and encoding the data in the group after sorting, the system will calculate the weighted frequency f(x)×px for each data item x in each group Ck, where px is the priority of the data item x, and sort the data items in Ck according to the weighted frequency f(x)×px. Each time, two nodes with the lowest weighted frequency are selected and merged to create a new parent node whose frequency is the sum of the frequencies of the two child nodes. This process is repeated until a complete Huffman tree is constructed. Then, starting from the root node of the Huffman tree, "0" is assigned to the left and "1" to the right, a unique code is generated for each leaf node, the generated Huffman code is used to encode the data in Ck, and then all the codes are merged into one data packet. The specific formula is:

[0081]

[0082] By adding information priority to the frequency calculation of Huffman coding, it can ensure that key information is sent and processed faster, thereby improving the responsiveness and efficiency of the system. This method is more suitable for application scenarios with high requirements on the timeliness and accuracy of data transmission.

[0083] Step 102: Based on a preset network resource allocation algorithm, a first network resource allocation strategy is obtained through the information priority and the merged coded data packet calculation, and then the first network resource allocation strategy is processed based on the network status indicator and the collaborative optimization algorithm to obtain a first collaborative optimization result, and the merged coded data packet is transmitted according to the first collaborative optimization result.

[0084] Based on the network resource allocation algorithm, the first network resource allocation strategy is calculated according to the information priority and the merged coded data packets obtained by calculation, so that the calculated first network resource allocation strategy not only takes into account the protection priority of key semantic information and the network resource allocation priority, but also takes into account the number and data volume of the merged coded data packets, thereby improving the practicality and reliability of the resource allocation strategy. At the same time, the first network resource allocation strategy is collaboratively optimized based on the network status indicator through the collaborative optimization algorithm, so that the first collaborative optimization result after the algorithm collaborative optimization processing also takes into account the real-time network status, further improving the reliability and efficiency of data transmission, and also enabling the optimized allocation strategy to effectively respond to changes in the network environment.

[0085] Specifically, the preset network resource allocation algorithm described in this embodiment is based on the information priority and the combined coded data packet to calculate the first network resource allocation strategy, which is specifically:

[0086] The network load is calculated based on the number and data volume of the combined encoded data packets, and the average network delay and packet loss rate are collected and monitored in real time, and then the network congestion index is calculated based on the average network delay and the packet loss rate;

[0087] Based on the network resource allocation algorithm, calculation and solution are performed according to the network load, the network congestion index and the information priority to obtain the first network resource allocation strategy.

[0088] After obtaining several combined coded data packets, the system will determine the first network resource allocation strategy based on the information priority and combined coded data packets. First, the system will calculate the network load based on the number and data volume of the generated combined coded packets, and will also collect the current average network delay and packet loss rate, and then calculate the current network congestion index based on the average network delay and packet loss rate, that is, the degree of network congestion.

[0089] After determination, the network load, network congestion index and information priority are calculated and solved based on the network resource allocation algorithm to obtain the first network resource allocation strategy, which improves the adaptability of the network allocation strategy to the current network status, and also ensures that data with higher priority will be transmitted and processed more quickly.

[0090] In this embodiment, the network resource allocation algorithm formula is as follows:

[0091] R t =γ×BW(t)+δ×TP(t)

[0092] Among them, R(t) represents the resource allocation at time t, BW(t) represents the available bandwidth, TP(t) represents the transmission power, and γ and δ are adjustment coefficients. The specific calculation formula for calculating the network load based on the data volume and number of the combined encoded data packets is as follows:

[0093]

[0094] Where Si(t) is the size of the i-th packet transmitted at time t, and N is the total number of packets.

[0095] The network congestion index C(t) is calculated based on network delay and packet loss rate to indicate the degree of network congestion. The specific calculation formula is as follows:

[0096] C t =k1×D(t)+k2×P(t)

[0097] Where D(t) is the average network delay, P(t) is the packet loss rate, K1 and K2 are weight factors used to adjust the impact of delay and packet loss rate on the congestion index. Users can adjust the weight of network delay and packet loss rate on the network congestion index by adjusting K1 and K2.

[0098] The first network resource allocation strategy can be calculated based on the network congestion index, network load and information priority, that is, according to the congestion index C(t), the allocation of bandwidth and transmission power is dynamically adjusted. If C(t) exceeds the preset threshold Cmax, the resource allocation is reduced to alleviate congestion; otherwise, if C(t) is less than the preset threshold Cmax, the resource allocation is increased to improve network efficiency. The resource adjustment strategy can be expressed as:

[0099]

[0100] Wherein, ρ and σ are response coefficients for adjusting bandwidth and transmission power. The first network resource allocation strategy can be calculated by the above algorithm. At the same time, the network resources can also be adjusted in real time according to the above algorithm to cope with changes in network status.

[0101] Furthermore, in this embodiment, the first network resource allocation strategy is processed based on the network status indicator and the collaborative optimization algorithm to obtain a first collaborative optimization result, which is specifically:

[0102] Collecting network delay and network utilization rate in the network status indicator in real time, and obtaining an adjustment factor through a preset activation function calculation based on the network delay and the network utilization rate;

[0103] The weight factor in the collaborative optimization algorithm is obtained by optimizing the adjustment factor, and then the collaborative optimization algorithm is updated according to the calculated weight factor, and the first network resource allocation strategy is collaboratively optimized through the updated collaborative optimization algorithm to output the first collaborative optimization result.

[0104] After obtaining the first network resource allocation strategy, the system will further optimize and adjust the allocation strategy based on the network status, obtain the adjustment factor through the real-time collection of network delay and network utilization rate and activation function calculation, and then calculate the weight factor in the collaborative optimization algorithm according to the adjustment factor, so as to adjust the weight factor through the adjustment factor, and then update the collaborative optimization algorithm according to the weight factor, so as to improve the adaptability of the collaborative optimization algorithm to the current network status, and optimize the first network resource allocation strategy through the updated collaborative optimization algorithm, and output the optimized first collaborative optimization result, so as to improve the adaptability of the optimized network resource allocation strategy to the current network status, that is, further improve the reliability, practicality and accuracy of data transmission.

[0105] In this embodiment, the objective function of the collaborative optimization algorithm provided by the system includes minimizing delay and maximizing reliability, so its expression is:

[0106] f(D, R) = α × D + β × (1-R)

[0107] Wherein, D is delay, R is reliability, and α and β are weight factors. This embodiment optimizes the weights α and β in the collaborative optimization algorithm by introducing a dynamic adjustment factor, and its specific implementation method is as follows: first, the influencing factors of the network status indicator are determined as network delay L and network utilization rate U, and the weight factors α and β are adjusted according to the above two parameters.

[0108] First, assuming that there are initial weights α0 and β0, which are set as the optimal weights under ideal network conditions, two dynamic adjustment factors αadj and βadj are introduced, namely the adjustment factors described in this embodiment, and the two introduced dynamic adjustment factors will be adjusted based on the network delay L and the utilization rate U, that is, the dynamic adjustment factors are calculated based on the network delay L and the utilization rate U, and the weight factors α and β are calculated based on the dynamic adjustment factors. The definition expression of the dynamic adjustment factor is as follows:

[0109]

[0110]

[0111] Among them, k is a constant that adjusts the steepness of the curve, while Lthreshold and Uthreshold are the thresholds of network delay and network utilization, respectively. The above function is a Sigmoid function, which is used to smoothly adjust the weight factor to ensure that the change of weight will not cause system instability due to sudden changes. After the dynamic adjustment factor is calculated and determined, the weight factor is calculated according to the dynamic adjustment factor. The calculation formula is as follows:

[0112] α=α0*αadj

[0113] β=β0*βadj,

[0114] It can be seen from the above formula that when the network delay L is higher than Lthreshold, αadj increases, which increases α, thereby paying more attention to reducing the delay. On the contrary, when the network utilization rate U is higher than Uthreshold, βadj increases, which increases β, thereby paying more attention to enhancing the reliability of the network. Therefore, the adjusted weight factor is used in the optimization algorithm, and the real-time status of the network is continuously monitored. According to the monitoring results, the values ​​of k, Lthreshold and Uthreshold are continuously fine-tuned to adapt to different network environments and requirements, ensuring that the algorithm can always perform optimally under different conditions. At the same time, the system will also generate a user interface to display a visual network status chart, and an accompanying data stream is convenient for users to watch more intuitively, and users can manually adjust α and β according to the network status on this interface.

[0115] Step 103: During the data transmission process, the transmission performance index is collected in real time, and the first collaborative optimization result is adjusted according to the transmission performance index and the network resource allocation algorithm, so that the combined coded data packet is transmitted according to the adjusted strategy.

[0116] After the data packet starts to be transmitted, the system will also collect transmission performance indicators in real time, and update the parameters of the network resource allocation algorithm based on the collected performance indicator data, and solve the updated algorithm to obtain the corresponding solution result, and adjust and update the first collaborative optimization result based on the solution result, so that the data packet is transmitted according to the updated strategy, thereby realizing adaptive feedback adjustment of the data packet transmission strategy, and adjusting the transmission strategy in real time according to the transmission performance indicator, so that the adjusted transmission strategy is more adaptable to the network status, and further improving the communication efficiency and communication data transmission reliability.

[0117] Specifically, the real-time collection of transmission performance indicators in this embodiment, and the adjustment of the first collaborative optimization result according to the transmission performance indicators and the network resource allocation algorithm, are specifically:

[0118] During the data packet transmission process, the bandwidth utilization and transmission power efficiency in the transmission performance indicators are collected in real time, and the adjustment coefficient in the network resource allocation algorithm is obtained by calculating the bandwidth utilization and the transmission power efficiency through a preset adaptive control algorithm;

[0119] The network resource allocation algorithm is parameter updated according to the calculated adjustment coefficient, and the updated network resource allocation algorithm is solved, and then the first collaborative optimization result is adjusted according to the solution result.

[0120] When the system transmits data packets according to the first collaborative optimization result, it will also collect the bandwidth utilization and transmission power efficiency of the network's transmission performance indicators in real time, and input the above two types of data as input data into a preset adaptive control algorithm for calculation and solution to obtain corresponding calculation results. The calculation results will be added to the network resource allocation algorithm as an adjustment coefficient in the network resource allocation algorithm to update the algorithm parameters accordingly, so that the updated network resource allocation algorithm is more adapted to the current network transmission performance, thereby improving the data transmission reliability and practicality of the system.

[0121] The system's data transmission strategy is adjusted through the network transmission performance status data fed back by data transmission, which not only improves the data transmission efficiency, transmission security and reliability, but also realizes dynamic and flexible adjustment of the data transmission strategy and the system's adaptability, while also enhancing the stability and response speed of the entire communication system.

[0122] In this embodiment, the formula of the network allocation algorithm is:

[0123] R t =γ×BW(t)+δ×TP(t)

[0124] Therefore, the system will update the allocation strategy by adjusting the adjustment coefficient in the formula. The adaptive adjustment method of the adjustment coefficient provided in this embodiment first requires real-time collection of network transmission performance indicator data. This embodiment defines the network transmission performance indicator data as bandwidth utilization U and transmission power efficiency E, and sets target values ​​for them, which are Utarget and Etarget respectively.

[0125] After the determination, the collected transmission performance index data can be calculated and solved through an adaptive control algorithm based on performance feedback. The specific formula is as follows:

[0126] γ(t+1)=γ(t)+k1·(U target -U(t))

[0127] δ(t+1)=δ(t)+k2·(E target -E(t))

[0128] Among them, k1 and k2 are adjustment coefficients, which are set according to the sensitivity and speed of system performance adjustment. The system continuously monitors real-time network performance indicators such as bandwidth utilization and transmission power efficiency, and automatically adjusts γ and δ according to the monitoring data to optimize resource allocation and meet performance goals. The above two adjustment coefficients can also be displayed on the user interface, with data streams attached for users to view more intuitively. Users can also manually adjust the parameters of γ and δ according to the network status on the interface.

[0129] In order to better illustrate the working principle and step flow of a priority-based semantic communication optimization method, device and system of the present invention, it is possible but not limited to refer to the relevant records above.

[0130] Accordingly, see Figure 2 , Figure 2 The schematic diagram of the structure of an embodiment of the priority-based semantic communication optimization device provided by the present invention. Figure 2 As shown, the semantic communication optimization device includes a priority calculation module 201, an allocation strategy transmission module 202 and an indicator feedback optimization module 203.

[0131] Among them, the priority calculation module 201 is used to respond to the data transmission signal, receive and parse the data to be transmitted, obtain key semantic information, and perform priority calculation and semantic data merge encoding on the data to be transmitted according to the key semantic information to obtain information priority and merged encoding data packet.

[0132] Further, the priority calculation module 201 receives and parses the data to be transmitted, obtains key semantic information, and performs priority calculation and semantic data merge encoding on the data to be transmitted according to the key semantic information, and obtains information priority and merged encoding data packets, specifically:

[0133] Receive and perform noise removal and standardization processing on the data to be transmitted in sequence, output corresponding processed data, call an autoencoder or a convolutional neural network according to the processed data to perform feature extraction on the processed data to obtain the key semantic information; divide the key semantic information into a training data set and a test data set, and input the training data set into a neural network model for training, so that the trained model performs feature analysis and semantic importance evaluation on the test data set to obtain semantic importance, and then calculate the information priority of each key semantic information through a weighted algorithm based on the semantic importance;

[0134] The merged coded data packet is obtained by merging and encoding the data corresponding to the information whose similarity reaches a similarity threshold in the key semantic information.

[0135] Furthermore, the priority calculation module 201 obtains the merged coded data packet by merging and encoding the data corresponding to the information whose similarity reaches the similarity threshold in the key semantic information, specifically:

[0136] Selecting cosine similarity or Jaccard similarity coefficient according to the key semantic information to measure the semantic similarity of each piece of information in the key semantic information, and obtaining corresponding multiple similarities;

[0137] The multiple similarities are compared with the preset similarity threshold, and the data items corresponding to the key semantic information whose comparison result is that the similarity is greater than or equal to the similarity threshold are clustered, and then a plurality of data groups are obtained, and the data items in the plurality of data groups are sorted according to the information priority of the corresponding key semantic information; the sorted data items in each data group are merged and encoded by a preset Huffman coding method to obtain a plurality of merged coded data packets.

[0138] The allocation strategy transmission module 202 is used to obtain a first network resource allocation strategy based on a preset network resource allocation algorithm through the information priority and the merged coded data packet calculation, and then call and process the first network resource allocation strategy through a collaborative optimization algorithm to obtain a first collaborative optimization result, and transmit the merged coded data packet according to the first collaborative optimization result.

[0139] Further, the allocation strategy transmission module 202 obtains a first network resource allocation strategy based on a preset network resource allocation algorithm by calculating the information priority and the combined coded data packet, specifically:

[0140] The network load is calculated based on the number and data volume of the merged encoded data packets, and the average network delay and packet loss rate are monitored and collected in real time. Then, the network congestion index is calculated based on the average network delay and the packet loss rate. Based on the network resource allocation algorithm, the network load, the network congestion index and the information priority are calculated and solved to obtain the first network resource allocation strategy.

[0141] Furthermore, the allocation strategy transmission module 202 processes the first network resource allocation strategy based on the network status indicator and the collaborative optimization algorithm to obtain a first collaborative optimization result, which is specifically:

[0142] The network delay and network utilization rate in the network status indicator are collected in real time, and an adjustment factor is calculated based on the network delay and the network utilization rate through a preset activation function; a weight factor in the collaborative optimization algorithm is obtained by optimizing the calculation according to the adjustment factor, and then the collaborative optimization algorithm is updated according to the calculated weight factor, and the first network resource allocation strategy is collaboratively optimized through the updated collaborative optimization algorithm, and the first collaborative optimization result is output.

[0143] The indicator feedback optimization module 203 is used to collect network transmission performance indicators in real time during data transmission, and adjust the first collaborative optimization result through the network resource allocation algorithm according to the network transmission performance indicators, so that the combined encoded data packet is transmitted according to the adjusted strategy.

[0144] Further, the standard feedback optimization module 203 collects transmission performance indicators in real time, and adjusts the first collaborative optimization result according to the transmission performance indicators and the network resource allocation algorithm, specifically:

[0145] During the data packet transmission process, the bandwidth utilization and transmission power efficiency of the transmission performance indicators are collected in real time, and the adjustment coefficient in the network resource allocation algorithm is obtained based on the bandwidth utilization and the transmission power efficiency through a preset adaptive control algorithm; the parameters of the network resource allocation algorithm are updated according to the calculated adjustment coefficient, and the updated network resource allocation algorithm is solved, and then the first collaborative optimization result is adjusted according to the solution result.

[0146] Correspondingly, an embodiment of the present invention also provides a priority-based semantic communication optimization system, which includes a memory, a processor, and a computer program stored in the memory and running on the processor. When processing the computer program, the processor implements any of the above-described priority-based semantic communication optimization systems.

[0147] Correspondingly, an embodiment of the present invention further provides a storage medium, on which a computer program is stored. The computer program is called and executed by a processor to implement a priority-based semantic communication optimization system as described in any one of the above items.

[0148] In summary, the embodiments of the present invention also provide a priority-based semantic communication optimization method, device and system, which obtains key semantic information by parsing the data to be transmitted, and obtains information priority and merges coded data packets based on information calculation; obtains a first network resource allocation strategy based on the above two calculations based on a network resource allocation algorithm, and optimizes the allocation strategy based on network status indicators and collaborative optimization algorithms, obtains and transmits data packets based on the first collaborative optimization result, and collects and adjusts the first collaborative optimization result based on the allocation algorithm according to the transmission performance indicator, and transmits data according to the adjusted strategy. The integrity and transmission reliability of important semantic information in the data to be transmitted during data transmission are ensured by confirming the information priority, and the merged coded data packets reduce the amount of data transmitted; the resource allocation algorithm and the collaborative optimization algorithm improve the practicality of the resource allocation strategy, so that the strategy can effectively respond to changes in the network environment.

[0149] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A priority-based semantic communication optimization method, characterized in that: The following steps are involved: In response to a data transmission signal, receiving and parsing data to be transmitted, obtaining key semantic information, and performing priority calculation and semantic data merge encoding on the data to be transmitted according to the key semantic information to obtain information priority and merge encoded data packets; Based on a preset network resource allocation algorithm, a first network resource allocation strategy is obtained by calculating the information priority and the merged coded data packet, and then the first network resource allocation strategy is processed based on a network status indicator and a collaborative optimization algorithm to obtain a first collaborative optimization result, and the merged coded data packet is transmitted according to the first collaborative optimization result; During the data transmission process, the transmission performance index is collected in real time, and the first collaborative optimization result is adjusted according to the transmission performance index and the network resource allocation algorithm, so that the combined coded data packet is transmitted according to the adjusted strategy.

2. A priority-based semantic communication optimization method as claimed in claim 1, characterized in that: The receiving and parsing of the data to be transmitted, obtaining key semantic information, and performing priority calculation and semantic data merge encoding on the data to be transmitted according to the key semantic information to obtain information priority and merge encoded data packets are specifically as follows: Receive and perform noise removal and standardization processing on the data to be transmitted in sequence, output corresponding processed data, call an automatic encoder or a convolutional neural network according to the processed data to perform feature extraction on the processed data, and obtain the key semantic information; The key semantic information is evenly divided into a training data set and a test data set, and the training data set is input into a neural network model for training, so that the trained model performs feature analysis and semantic importance evaluation on the test data set to obtain semantic importance, and then calculates the information priority of each key semantic information through a weighted algorithm based on the semantic importance; The merged coded data packet is obtained by merging and encoding the data corresponding to the information whose similarity reaches a similarity threshold in the key semantic information.

3. A priority-based semantic communication optimization method as described in claim 2, characterized in that: The step of merging and encoding the data corresponding to the information whose similarity reaches a similarity threshold in the key semantic information to obtain the merged coded data packet is specifically as follows: Selecting cosine similarity or Jaccard similarity coefficient according to the key semantic information to measure the semantic similarity of each piece of information in the key semantic information, and obtaining corresponding multiple similarities; Comparing the plurality of similarities with the preset similarity threshold, clustering the data items corresponding to the key semantic information whose comparison result is that the similarity is greater than or equal to the similarity threshold, and then obtaining a plurality of data groups, and sorting the data items in the plurality of data groups according to the information priority of the corresponding key semantic information; The sorted data items in each data group are combined and coded by a preset Huffman coding method to obtain a plurality of combined coded data packets.

4. A priority-based semantic communication optimization method as claimed in claim 1, characterized in that: The first network resource allocation strategy is obtained by calculating the information priority and the combined coded data packet based on the preset network resource allocation algorithm, specifically: The network load is calculated based on the number and data volume of the combined encoded data packets, and the average network delay and packet loss rate are collected and monitored in real time, and then the network congestion index is calculated based on the average network delay and the packet loss rate; Based on the network resource allocation algorithm, calculation and solution are performed according to the network load, the network congestion index and the information priority to obtain the first network resource allocation strategy.

5. A priority-based semantic communication optimization method as claimed in claim 1, characterized in that: The first network resource allocation strategy is processed based on the network status indicator and the collaborative optimization algorithm to obtain a first collaborative optimization result, specifically: Collecting network delay and network utilization rate in the network status indicator in real time, and obtaining an adjustment factor through a preset activation function calculation based on the network delay and the network utilization rate; The weight factor in the collaborative optimization algorithm is obtained by optimizing the adjustment factor, and then the collaborative optimization algorithm is updated according to the calculated weight factor, and the first network resource allocation strategy is collaboratively optimized through the updated collaborative optimization algorithm to output the first collaborative optimization result.

6. A priority-based semantic communication optimization method as claimed in claim 1, characterized in that: The real-time collection of transmission performance indicators and the adjustment of the first collaborative optimization result according to the transmission performance indicators and the network resource allocation algorithm are specifically: During the data packet transmission process, the bandwidth utilization and transmission power efficiency in the transmission performance indicators are collected in real time, and the adjustment coefficient in the network resource allocation algorithm is obtained by calculating the bandwidth utilization and the transmission power efficiency through a preset adaptive control algorithm; The network resource allocation algorithm is parameter updated according to the calculated adjustment coefficient, and the updated network resource allocation algorithm is solved, and then the first collaborative optimization result is adjusted according to the solution result.

7. A priority-based semantic communication optimization device, characterized in that: The semantic communication optimization device includes a priority calculation module, an allocation strategy transmission module and an indicator feedback optimization module; The priority calculation module is used to respond to the data transmission signal, receive and parse the data to be transmitted, obtain key semantic information, and perform priority calculation and semantic data merge encoding on the data to be transmitted according to the key semantic information to obtain information priority and merge encoded data packets; The allocation strategy transmission module is used to calculate a first network resource allocation strategy based on a preset network resource allocation algorithm through the information priority and the merged coded data packet, and then process the first network resource allocation strategy with a collaborative optimization algorithm to obtain a first collaborative optimization result, and transmit the merged coded data packet according to the first collaborative optimization result; The indicator feedback optimization module is used to collect network transmission performance indicators in real time during data transmission, and adjust the first collaborative optimization result according to the network transmission performance indicators and the network resource allocation algorithm, so that the combined encoded data packet is transmitted according to the adjusted strategy.

8. A priority-based semantic communication optimization device as claimed in claim 7, characterized in that: The priority calculation module receives and parses the data to be transmitted, obtains key semantic information, and performs priority calculation and semantic data merge encoding on the data to be transmitted according to the key semantic information, and obtains information priority and merged encoding data packets, specifically: Receive and perform noise removal and standardization processing on the data to be transmitted in sequence, output corresponding processed data, call an automatic encoder or a convolutional neural network according to the processed data to perform feature extraction on the processed data, and obtain the key semantic information; The key semantic information is evenly divided into a training data set and a test data set, and the training data set is input into a neural network model for training, so that the trained model performs feature analysis and semantic importance evaluation on the test data set to obtain semantic importance, and then calculates the information priority of each key semantic information through a weighted algorithm based on the semantic importance; The merged coded data packet is obtained by merging and encoding the data corresponding to the information whose similarity reaches a similarity threshold in the key semantic information.

9. A priority-based semantic communication optimization system, characterized in that: The semantic communication optimization system includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor processes the computer program, it implements a priority-based semantic communication optimization system as described in any one of claims 1-6.

10. A storage medium, characterized in that: The storage medium stores a computer program, which is called and executed by a processor to implement a priority-based semantic communication optimization system as described in any one of claims 1 to 6.

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