Dual-mode communication distribution method and device based on power line carrier and wireless communication

Abstract

The invention relates to the technical field of dual-mode communication, and particularly discloses a dual-mode communication distribution method and device based on power line carrier and wireless communication, and the method comprises the steps: receiving a plurality of data communication requests sent by a user terminal, performing embedded coding of each communication event and each demand description in a plurality of data communication requests by using a data analysis and distribution mode based on artificial intelligence, and then performing semantic fusion on each group of coded communication event embedded features and demand description embedded features; weight of power line carrier communication and weight of wireless communication are automatically allocated according to a significant aggregation representation of attention complementation features between respective plurality of data communication request embedding semantic fusion features. According to the invention, the weight between the power line carrier communication and the wireless communication can be dynamically adjusted according to the change of the real-time communication environment and the user demand, and the communication quality is ensured not to be affected.

Description

Technical Field

[0001] The present application relates to the field of dual-mode communication technology, and more specifically, to a dual-mode communication allocation method and device based on power line carrier and wireless communication. Background Art

[0002] In the digital age, communication technology is vital to people's lives and work. Although power line carrier communication is easy to install and does not require additional wiring, the signal attenuation is serious during long-distance transmission and is susceptible to electrical noise interference. Although wireless communication is flexible and has a wide coverage, it is susceptible to environmental influences due to limited spectrum resources, especially in complex indoor electromagnetic environments. Signal instability. At present, whether it is the daily use of various smart devices by individual users or the control of complex production processes in the industrial field, extremely high requirements are placed on the stability, efficiency and flexibility of communication.

[0003] In this regard, Chinese patent application CN116938812A discloses a communication method that integrates power line carrier and wireless dual-mode. First, carrier communication nodes and base station nodes are respectively established in the power line and wireless systems, and then data exchange and automatic switching are supported by connecting these two nodes. Power line communication is used first during communication, and wireless communication is switched when the signal is poor, and a virtual channel is established to ensure transmission. The priority and weight of the dual modes are set according to communication requirements, so as to combine the two communications to achieve a wider communication coverage.

[0004] In order to optimize resource utilization, ensure the efficient operation of the communication system, and guarantee the transmission quality and bandwidth of key data, it is necessary to allocate weights for power line carrier communication and wireless communication. In this existing patent, dual-mode communication allocation is performed by evaluating communication needs, setting priorities, and determining static fixed weights. However, once the static fixed weights are set, they will not be adjusted according to changes in the real-time communication environment or user needs during actual operation. For example, when the power line carrier communication signal suddenly deteriorates, the system may still use power line carrier communication according to the preset higher weight, resulting in a decrease in communication quality. In addition, when network conditions change rapidly (such as sudden high traffic, temporary interference sources), the static weight allocation method cannot respond in time, which may result in reduced service quality or communication interruption.

[0005] Therefore, an optimized dual-mode communication allocation scheme based on power line carrier and wireless communication is desired. Summary of the invention

[0006] In order to solve the above technical problems, this application is proposed. The embodiments of this application provide a dual-mode communication allocation method and device based on power line carrier and wireless communication, which can dynamically adjust the weight between power line carrier communication and wireless communication according to the changes in real-time communication environment and user needs to ensure that the communication quality is not affected. In addition, when facing rapid changes in network conditions, this application can respond quickly through intelligent analysis and adjust the communication mode, thereby greatly improving the stability and reliability of the system.

[0007] According to one aspect of the present application, a dual-mode communication allocation method based on power line carrier and wireless communication is provided, comprising: S1: deploying at least one carrier communication node in a power line communication network to realize data transmission on the power line; S2: deploying at least one base station node in a wireless communication network to realize the transmission and reception of wireless signals; S3: connecting the power line carrier communication node and the wireless base station node to each other to realize the switching and data interaction of the dual-mode communication; S4: in response to a data communication request issued by a user terminal, communicating through power line carrier communication is preferred, and if the signal quality is poor or the distance is too far, it is automatically switched to wireless communication for communication; S5: when switching between the two modes, a virtual communication channel is created to realize data transmission between the two communication networks; S6: based on the data communication request, determining the allocation weights of the power line carrier communication and the wireless communication to achieve the optimal communication effect; wherein, S6 comprises: receiving a plurality of data communication requests issued by the user terminal, wherein the data communication requests include a communication event and a demand description; Embedding and encoding each communication event and each requirement description in the plurality of data communication requests to obtain a plurality of communication event embedded coding features and a plurality of requirement description embedded coding features; fusing the plurality of communication event embedded coding features and the plurality of requirement description embedded coding features to obtain a plurality of data communication request embedded semantic fusion features; Performing attention-driven complementary feature significant aggregation on the multiple data communication request embedded semantic fusion features to obtain data communication request semantic feature significant aggregation encoding features; Based on the data communication request semantic feature, the coding feature is significantly aggregated to obtain an allocation result for representing the weight of the power line carrier communication and the weight of the wireless communication.

[0008] According to another aspect of the present application, a dual-mode communication distribution device based on power line carrier and wireless communication is provided, including: a power line data transmission module, which is used to deploy at least one carrier communication node in the power line communication network to realize data transmission on the power line; a wireless signal transceiver module, which is used to deploy at least one base station node in the wireless communication network to realize the transceiver of wireless signals; a hybrid network interface module, which is used to connect the power line carrier communication node and the wireless base station node to each other to realize the switching and data interaction of the dual-mode communication; an adaptive communication switching module, which is used to respond to the data communication request issued by the user terminal, and give priority to the communication through the power line carrier communication, if the signal quality is poor or the distance is too far, it will automatically switch to wireless communication for communication; an intelligent virtual tunnel module, which is used to create a virtual communication channel to realize data transmission between the two communication networks when the dual mode is switched; a dynamic communication weight determination module, which is used to determine the allocation weights of the power line carrier communication and the wireless communication based on the data communication request to achieve the optimal communication effect; wherein the allocation weight determination module includes: A data communication request receiving unit, configured to receive a plurality of data communication requests issued by a user terminal, wherein the data communication requests include a communication event and a demand description; A data communication request embedding coding unit, used to embed code each communication event and each requirement description in the multiple data communication requests to obtain multiple communication event embedding coding features and multiple requirement description embedding coding features; A data communication request feature fusion unit, configured to fuse the plurality of communication event embedded coding features and the plurality of requirement description embedded coding features to obtain a plurality of data communication request embedded semantic fusion features; A complementary feature significant aggregation unit, used for performing attention-driven complementary feature significant aggregation on the plurality of data communication request embedded semantic fusion features to obtain a data communication request semantic feature significant aggregation coding feature; The allocation result determination unit is used to obtain an allocation result for representing the weight of the power line carrier communication and the weight of the wireless communication by significantly aggregating the coding features based on the semantic features of the data communication request.

[0009] According to another aspect of the present application, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the dual-mode communication allocation method based on power line carrier and wireless communication.

[0010] Compared with the prior art, the present application provides a dual-mode communication allocation method and device based on power line carrier and wireless communication, which receives multiple data communication requests issued by a user terminal, the data communication requests include communication events and demand descriptions, uses artificial intelligence-based data analysis and allocation methods to embed each communication event and each demand description in the multiple data communication requests, and then semantically fuses each group of encoded communication event embedding features and demand description embedding features, so as to automatically allocate the weight of the power line carrier communication and the weight of the wireless communication according to the significant aggregation representation of the attention complementary features between the semantic fusion features of each multiple data communication request embedding. The present application can dynamically adjust the weight between power line carrier communication and wireless communication according to the changes in real-time communication environment and user needs, ensuring that the communication quality is not affected. In addition, when facing rapid changes in network conditions, the present application can quickly respond through intelligent analysis and adjust the communication mode, thereby greatly improving the stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0012] Figure 1 is a schematic flow chart of a dual-mode communication allocation method based on power line carrier and wireless communication according to an embodiment of the present application; Figure 2 is a schematic flow chart of step S6 in the dual-mode communication allocation method based on power line carrier and wireless communication according to an embodiment of the present application; Figure 3 is a schematic flow chart of step S62 in the dual-mode communication allocation method based on power line carrier and wireless communication according to an embodiment of the present application; Figure 4 It is a flowchart of step S64 in the dual-mode communication allocation method based on power line carrier and wireless communication according to an embodiment of the present application; Figure 5 It is a schematic block diagram of a dual-mode communication distribution device based on power line carrier and wireless communication according to an embodiment of the present application. DETAILED DESCRIPTION

[0013] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0014] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0015] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0016] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0017] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0018] Embodiment 1: Based on Chinese patent application CN116938812A, this application proposes a dual-mode communication allocation method based on power line carrier and wireless communication, such as Figure 1As shown, the dual-mode communication allocation method based on power line carrier and wireless communication includes: S1: deploying at least one carrier communication node in the power line communication network to realize data transmission on the power line; S2: deploying at least one base station node in the wireless communication network to realize the transmission and reception of wireless signals; S3: connecting the power line carrier communication node and the wireless base station node to realize the switching and data interaction of dual-mode communication; S4: responding to the data communication request issued by the user terminal, giving priority to communicating through power line carrier communication, if the signal quality is poor or the distance is too far, automatically switching to wireless communication for communication; S5: when switching between dual modes, creating a virtual communication channel to realize data transmission between the two communication networks; S6: based on the data communication request, determining the allocation weights of the power line carrier communication and the wireless communication to achieve the optimal communication effect. In particular, in step S4, the power line carrier communication is set as the first choice because of its advantages of low cost, stability and existing infrastructure. That is, it defines a priority rule, that is, first trying the power line carrier communication, but retaining the ability to flexibly switch to wireless communication according to actual conditions. Such a design not only fully utilizes the advantages of power line carrier communication, but also ensures the flexibility and reliability of communication.

[0019] Exemplarily, in step S1, at least one carrier communication node is deployed in the power line communication network to realize data transmission on the power line. It should be understood that the power line carrier (PLC) communication technology can modulate the data signal into a carrier signal suitable for transmission on the power line, so that data communication can be carried out through the existing power line network even without additional wiring. This method not only reduces the installation cost, but also provides a relatively stable communication link, especially in the application scenarios of fixed equipment and short-range communication. In addition, when combined with wireless communication, power line carrier communication can be used as a backup communication means when the wireless signal quality is poor or interrupted, thereby enhancing the reliability and resilience of the entire system.

[0020] Exemplarily, in step S2, at least one base station node is deployed in the wireless communication network to realize the transmission and reception of wireless signals. It should be understood that wireless communication has the characteristics of convenience and no need for wiring, and is particularly suitable for mobile devices and long-distance communications. By setting up base station nodes, the effective coverage of wireless signals can be ensured, and the signal strength and service quality can be optimized according to the user distribution. In addition, in the dual-mode fusion communication method, when the power line communication has signal quality problems or interruptions, wireless communication can be used as an alternative to ensure the continuity and stability of communication.

[0021] In one example, at least one base station node is deployed in a wireless communication network to realize the transmission and reception of wireless signals, including: first, selecting a base station device suitable for wireless communication, such as an LTE base station or a Wi-Fi router, which supports the required wireless communication protocol and frequency band. Then, the base station nodes are reasonably arranged based on the communication requirements and network topology, taking into account the coverage range, capacity requirements and user distribution of the wireless signal. At the same time, by adjusting the parameters such as the transmission power, antenna direction and height of the base station node, the signal coverage range is further optimized and interference is reduced.

[0022] Exemplarily, in step S3, the power line carrier communication node and the wireless base station node are interconnected to realize the switching and data interaction of dual-mode communication. It should be understood that the power line carrier communication node and the wireless base station node are interconnected to realize the switching and data interaction of dual-mode communication in order to build a more flexible, reliable and adaptable communication system. Through this connection, it is possible to switch smoothly between different communication modes, ensuring that the continuity and stability of communication can be maintained even when a certain communication mode has signal quality problems or interruptions. In addition, dual-mode fusion can make full use of the respective advantages of power line carrier communication and wireless communication, such as power line carrier communication is suitable for fixed equipment and short-distance communication, while wireless communication is suitable for mobile devices and long-distance communication. Such a design not only improves the overall performance of the network, but also enhances the user experience, and stable and high-quality communication services can be obtained in both indoor and outdoor environments.

[0023] Exemplarily, in step S4, in response to a data communication request issued by a user terminal, communication is performed preferentially through power line carrier communication. If the signal quality is poor or the distance is too far, wireless communication is automatically switched to for communication. It should be understood that power line carrier communication is low-cost, stable, and utilizes existing infrastructure, so it is set as the preferred communication method. It can provide reliable communication services within the coverage of the power line network, and is particularly suitable for fixed equipment and short-range communication scenarios. However, when the power line communication signal quality is poor or the transmission distance exceeds its effective range, in order to ensure that the communication is not interrupted and maintain the quality of service, the current communication conditions will be automatically evaluated and switched to wireless communication mode. Wireless communication, due to its flexibility and wide coverage characteristics, can make up for the shortcomings of power line carrier communication in specific situations and ensure that a stable communication connection can be maintained even in complex environments.

[0024] Exemplarily, in step S5, during dual-mode switching, a virtual communication channel is created to achieve data transmission between the two communication networks. It should be understood that during dual-mode switching, a virtual communication channel is created to achieve data transmission between the two communication networks in order to ensure the continuity of communication and the integrity of data. When switching from one communication mode (such as power line carrier communication) to another (such as wireless communication), the creation of a virtual communication channel can seamlessly bridge two different types of networks to avoid data loss or interruption caused by switching. This method not only improves the reliability and stability of the communication system, but also optimizes the user experience, so that users hardly feel any delays or service interruptions during the transition between different communication environments.

[0025] Exemplarily, in step S6, based on the data communication request, the allocation weights of the power line carrier communication and the wireless communication are determined to achieve the optimal communication effect. It should be understood that the allocation weights of the power line carrier communication and the wireless communication are determined based on the data communication request to achieve the optimal communication effect in order to ensure that resource allocation can be dynamically adjusted under different communication requirements and real-time environmental changes, thereby maximizing communication efficiency and service quality. By evaluating the specific circumstances of each data communication request, including the demand description of the communication event, it is possible to intelligently determine which communication mode is more suitable for the current task, and adjust the usage ratio between the power line carrier communication and the wireless communication accordingly. For example, in data transmission with high real-time requirements, the weight of wireless communication may be increased and the proportion of power line carrier communication may be reduced, because wireless communication generally has lower latency characteristics; while for data with high requirements for stability and bandwidth, it may be more inclined to rely on power line carrier communication.

[0026] Accordingly, in S6, the technical concept of the present application is to receive multiple data communication requests issued by a user terminal, wherein the data communication requests include communication events and demand descriptions, and use artificial intelligence-based data analysis and allocation methods to embed code each communication event and each demand description in the multiple data communication requests, and then semantically fuse each group of encoded communication event embedding features and demand description embedding features, so as to automatically allocate the weight of the power line carrier communication and the weight of the wireless communication according to the significant aggregation representation of the attention complementary features between the semantic fusion features embedded in each of the multiple data communication requests. The present application can dynamically adjust the weight between power line carrier communication and wireless communication according to changes in the real-time communication environment and user needs, ensuring that the communication quality is not affected. Moreover, in the face of rapid changes in network conditions, the present application can respond quickly through intelligent analysis and adjust the communication mode, thereby greatly improving the stability and reliability of the system.

[0027] In one example, if Figure 2As shown, the S6 includes: S61: receiving multiple data communication requests issued by the user terminal, the data communication requests including communication events and demand descriptions; S62: embedding and encoding each communication event and each demand description in the multiple data communication requests respectively to obtain multiple communication event embedded coding features and multiple demand description embedded coding features; S63: fusing the multiple communication event embedded coding features and the multiple demand description embedded coding features to obtain multiple data communication request embedded semantic fusion features; S64: performing attention-driven complementary feature significant aggregation on the multiple data communication request embedded semantic fusion features to obtain data communication request semantic feature significant aggregation coding features; S65: based on the data communication request semantic feature significant aggregation coding features, obtaining the allocation result of the weight for representing the power line carrier communication and the weight of the wireless communication.

[0028] Exemplarily, in step S61, multiple data communication requests issued by the user terminal are received, and the data communication requests include communication events and demand descriptions. It should be understood that by receiving and analyzing multiple data communication requests issued by the user terminal, the actual needs of the user can be accurately understood, and the weight distribution between power line carrier communication and wireless communication can be dynamically adjusted accordingly. This approach helps to optimize resource utilization, ensure the transmission quality and bandwidth of key data, and improve the stability and reliability of the system. By analyzing the detailed information in these communication requests, the communication method that best suits the current situation can be selected more intelligently, thereby achieving the best communication effect.

[0029] Exemplarily, in step S62, each communication event and each requirement description in the multiple data communication requests are embedded and encoded respectively to obtain multiple communication event embedded coding features and multiple requirement description embedded coding features. It should be understood that considering that communication events and requirement descriptions are usually presented in text, classification labels or other non-numerical forms in the original state. For example, a communication event may be a text description such as "video call" or "file download", and a requirement description may be "high bandwidth requirement", "low latency requirement", etc., and these different communication events and requirement descriptions contain potential semantic meanings. Therefore, in order to be able to capture the semantic contextual meaning of the vocabulary, it is very important to understand the user's actual intentions and service requirements. In the technical solution of the present application, each communication event and each requirement description in the multiple data communication requests are embedded and encoded respectively to convert various forms of input into a unified vector form, and capture the implicit semantic information, so as to obtain multiple communication event embedded coding vectors and multiple requirement description embedded coding vectors. In particular, in a specific embodiment of the present application, each communication event in the multiple data communication requests can be embedded and encoded using a communication event embedding matrix, and each requirement description in the multiple data communication requests can be embedded and encoded using a requirement description embedding matrix to accurately reflect their respective key semantic features, thereby obtaining multiple communication event embedding encoding vectors and multiple requirement description embedding encoding vectors. It should be understood that the embedding matrix is ​​essentially a lookup table, in which each row corresponds to a low-dimensional dense vector representation of a word or feature in the vocabulary. For communication events and requirement descriptions, two independent embedding matrices can be constructed respectively, one for processing vocabulary related to communication events and the other for processing vocabulary related to requirement descriptions. That is, the communication event embedding matrix and the requirement description embedding matrix are usually constructed based on a large amount of historical communication data or predefined semantic rules. These matrices contain existing semantic information and feature distribution of different communication events and requirement descriptions, and can analyze the characteristics of each communication request in more detail, thereby laying the foundation for reasonable communication allocation decisions.

[0030] In one example, if Figure 3 As shown, each communication event and each requirement description in the multiple data communication requests are respectively embedded and encoded to obtain multiple communication event embedded coding features and multiple requirement description embedded coding features, including: S621: using the communication event embedding matrix to respectively embed code each communication event in the multiple data communication requests to obtain multiple communication event embedded coding vectors as the multiple communication event embedded coding features; S622: using the requirement description embedding matrix to respectively embed code each requirement description in the multiple data communication requests to obtain multiple requirement description embedded coding vectors as the multiple requirement description embedded coding features.

[0031] Exemplarily, in step S63, the multiple communication event embedded coding features and the multiple demand description embedded coding features are fused to obtain multiple data communication request embedded semantic fusion features. It should be understood that considering that the communication event embedded coding vector focuses on reflecting the characteristics of the specific communication behavior itself in the communication request, such as the communication type such as voice call, file transfer or video conference, as well as the information involved in the communication protocol, application scenario, etc. The demand description embedded coding vector focuses on reflecting the user's expectations and requirements for the communication behavior in terms of bandwidth, delay, stability, etc. These two vectors respectively describe a data communication request from different angles. Therefore, in order to integrate the key information of these two dimensions together, so as to more comprehensively present the complete characteristics of the entire communication request, in the technical solution of the present application, each group of corresponding communication event embedded coding vectors and demand description embedded coding vectors in the multiple communication event embedded coding vectors and the multiple demand description embedded coding vectors are cascaded to integrate information from different sources, and obtain a complete, specific semantic embedded semantic fusion coding vector of multiple data communication requests.

[0032] In one example, the multiple communication event embedded coding features and the multiple requirement description embedded coding features are fused to obtain multiple data communication request embedded semantic fusion features, including: cascading each group of corresponding communication event embedded coding vectors and requirement description embedded coding vectors in the multiple communication event embedded coding vectors and the multiple requirement description embedded coding vectors to obtain multiple data communication request embedded semantic fusion coding vectors as the multiple data communication request embedded semantic fusion features.

[0033] Exemplarily, in step S64, the multiple data communication request embedded semantic fusion features are subjected to attention-driven complementary feature significant aggregation to obtain data communication request semantic feature significant aggregation coding features. It should be understood that, considering that in multiple data communication request embedded semantic fusion coding vectors, different features contain different key information and core features. Moreover, the importance of these features in the entire communication request sequence is not equal. For example, for real-time video calls, the key information of bandwidth and delay features is much more important than some minor protocol-related features. Therefore, in order to highlight the key semantic features in the data communication request and to extract the features that best represent the core semantics of the communication request, the present application adopts an attention-driven complementary feature significant aggregation method to process the multiple data communication request embedded semantic fusion features, so as to dynamically compensate and adjust the attention to each feature according to its nature and importance in the specific communication request, so as to aggregate coding features that better reflect the core semantics of the communication request, thereby obtaining data communication request semantic feature significant aggregation coding features, so that the obtained results can be more in line with the changing demand characteristics in the actual communication scenario.

[0034] In one example, if Figure 4 As shown, the multiple data communication request embedded semantic fusion features are subjected to attention-driven complementary feature significant aggregation to obtain the data communication request semantic feature significant aggregation coding feature, including: S641: sequence hub extraction is performed on the multiple data communication request embedded semantic fusion coding vectors to obtain the data communication request embedded semantic fusion sequence hub feature vector; S642: based on the data communication request embedded semantic fusion sequence hub feature vector, the multiple data communication request embedded semantic fusion coding vectors are subjected to complementary information modulation fusion to obtain the data communication request semantic feature significant aggregation coding feature vector as the data communication request semantic feature significant aggregation coding feature.

[0035] Exemplarily, in step S641, sequence pivot extraction is performed on the plurality of data communication request embedding semantic fusion coding vectors to obtain a data communication request embedding semantic fusion sequence pivot feature vector. Specifically, the process can be expressed by a formula as follows:

[0036] in, is the multiple data communication request embedded semantic fusion coding vector, , , and are respectively the first, second, and third data communication request embedding semantic fusion coding vectors. and Data communication requests are embedded in semantic fusion coding vectors, right Perform sequence hub extraction, yes The corresponding data communication request embedding semantic weight matrix, is matrix multiplication, yes The corresponding data communication request embedding semantic bias vector, is the transformed rating vector, It is the first of the scores of multiple data communication request embedding semantic transformation. Data communication requests embed semantic transformation scores, is the normalization function, Multiple data communication requests are embedded in the semantic weight value Data communication requests are embedded with semantic weight values, is the number of vectors in the semantic fusion encoding vectors embedded with the multiple data communication requests, It is the pivot feature vector of the data communication request embedding semantic fusion sequence.

[0037] It should be understood that the process of performing sequence pivot extraction on multiple data communication request embedded semantic fusion coding vectors to obtain data communication request embedded semantic fusion sequence pivot feature vectors is intended to extract the most representative and stable feature representation from numerous communication requests. In this way, the patterns and dependencies behind these communication requests can be captured, so as to better understand the user's intentions and service requirements. Specifically, when the user terminal sends multiple data communication requests containing different communication events and demand descriptions, each request is converted into a high-dimensional semantic fusion coding vector, which together constitute a complex feature sequence. In the process of sequence pivot extraction, key nodes or "hubs" in the sequence are identified and extracted. These hub feature vectors not only reflect the sequence dependency and pattern of the initial feature distribution, but also contain the invariant features contained in the feature sequence. This means that no matter how the specific content of the communication request changes, those features that appear repeatedly and are critical to the communication behavior will be highlighted. For example, in a series of video call requests, although the specific parameters of each call may be different, all these requests share some common key attributes, such as the requirement for low latency or the requirement for a specific bandwidth. By extracting these hub features, we can more clearly identify which factors are the core elements that determine communication quality.

[0038] In one example, in step S642, based on the data communication request embedded semantic fusion sequence hub feature vector, the multiple data communication request embedded semantic fusion coding vectors are subjected to complementary information modulation and fusion to obtain a data communication request semantic feature significant aggregation coding feature vector, including: calculating the complementary information of each data communication request embedded semantic fusion coding vector in the multiple data communication request embedded semantic fusion coding vectors relative to the data communication request embedded semantic fusion sequence hub feature vector to obtain multiple data communication request node-hub complementary information embedded coding vectors. Specifically, the process can be expressed by the formula:

[0039] in, is the point convolutional coding, is the data communication request weight matrix, is the activation function, yes The corresponding data communication request is embedded in the semantic fusion transformation encoding vector, is the pivot transformation feature vector of the data communication request sequence, It is subtracted by position point. Indicates taking the absolute value, is the first of multiple data communication requests embedded in the semantic node-hub difference vector Data communication requests are embedded in semantic node-hub difference vectors, It is the first one in which multiple data communication request nodes-hub complementary information is embedded in the coding vector The data communication request node-hub complementary information is embedded in the coding vector.

[0040] The multiple data communication request node-hub complementary information embedding coding vectors are subjected to attention information significant modulation to obtain multiple significant modulated data communication request node-hub complementary information embedding coding vectors. Specifically, the process can be expressed by a formula as follows:

[0041]

[0042]

[0043] in, yes The corresponding data communication request node-hub complementary information weight matrix, yes The corresponding data communication request node-hub complementary information bias vector, is the complementary information transformation score vector, It is the first value among multiple data communication request nodes-hub complementary information attention values. Data communication request node-hub complementary information attention value, represents the exponential function value with the natural constant e as the base, is the weight of attention among multiple data communication request nodes and hubs for complementary information. Data communication request node-hub complementary information attention weight, , and are the first, second and third complementary information embedding encoding vectors of the plurality of data communication request nodes and hubs respectively. Data communication request node-hub complementary information embedding coding vector, , and are the first, second and third attention weights of the multiple data communication request nodes-hub complementary information respectively. Data communication request node-hub complementary information attention weight, It is a multiple significant modulation data communication request node-hub complementary information embedded coding vector.

[0044] That is, in one example, the multiple data communication request node-hub complementary information embedded coding vectors are subjected to attention information significant modulation to obtain multiple significant modulated data communication request node-hub complementary information embedded coding vectors, including: performing complementary information significant identification of each motion axis motion timing node-hub complementary information embedded coding vector in the multiple data communication request node-hub complementary information embedded coding vectors by the attention mechanism to obtain multiple data communication request node-hub complementary information attention weights; using the multiple data communication request node-hub complementary information attention weights as a sequence of weights, performing attention degree modulation on the multiple data communication request node-hub complementary information embedded coding vectors to obtain the multiple significant modulated data communication request node-hub complementary information embedded coding vectors.

[0045] The data communication request embedded semantic fusion sequence hub feature vector and the plurality of significantly modulated data communication request node-hub complementary information embedded coding vectors are fused to obtain the data communication request semantic feature significant aggregation coding feature vector. Specifically, the process can be expressed by the formula:

[0046] in, For cascade processing, is a significant aggregation encoding feature vector of the semantic features of the data communication request.

[0047] That is, in one example, the data communication request embedded semantic fusion sequence hub feature vector and the multiple significantly modulated data communication request node-hub complementary information embedded coding vectors are fused to obtain the data communication request semantic feature significantly aggregated coding feature vector, including: cascading the data communication request embedded semantic fusion sequence hub feature vector and the multiple significantly modulated data communication request node-hub complementary information embedded coding vectors to obtain the data communication request semantic feature significantly aggregated coding feature vector.

[0048] Specifically, first, the process of calculating complementary information aims to capture the unique contribution of each communication request beyond the global pattern. Specifically, the sequence hub feature vector represents the commonality or mainstream trend of all communication requests, while the embedded semantic fusion encoding vector of each communication request reflects the specific characteristics of a single request. By comparing the two, the unique properties of each request that are not fully considered in the global pattern can be revealed, such as the special requirements of a specific user for bandwidth, latency, or stability. The extraction of this complementary information helps to identify key factors that may affect communication quality, even if they are not ubiquitous.

[0049] Next, the attention information of these node-hub complementary information embedding encoding vectors is significantly modulated, further enhancing the model's focus on important features. The attention mechanism allows the degree of attention to different parts to be automatically adjusted according to the task requirements, so that important node features receive higher weights, while less relevant node features have lower weights. For example, in a real-time video call scenario, low latency may be the most critical requirement, so relevant features will be given greater weights during the attention modulation process. This process not only increases the sensitivity to important information, but also effectively reduces the impact of noise and other irrelevant factors, ensuring that the final output is more focused on the most relevant information.

[0050] Finally, the significantly modulated node-hub complementary information embedding coding vector is fused with the sequence hub feature vector to construct a data communication request semantic feature significant aggregation coding feature vector that reflects both global trends and local details. This fusion method integrates the global and local information of the original feature distribution and provides a more comprehensive and refined feature representation. It not only retains the overall pattern of the initial communication request, but also highlights the uniqueness of each request, enabling more intelligent and accurate decisions to be made in a complex and changing network environment. For example, when faced with a large number of communication requests with high bandwidth requirements, the weight of wireless communication can be appropriately increased to meet these requests based on the comprehensive information provided by the significantly aggregated coding feature vector; in scenarios with higher stability requirements, more reliance is placed on power line carrier communication.

[0051] Exemplarily, in step S65, based on the data communication request semantic feature significant aggregation coding feature, the distribution result for representing the weight of the power line carrier communication and the weight of the wireless communication is obtained. In one example, based on the data communication request semantic feature significant aggregation coding feature, the distribution result for representing the weight of the power line carrier communication and the weight of the wireless communication is obtained, including: passing the data communication request semantic feature significant aggregation coding feature vector through a classifier-based weight allocator to obtain the distribution result, and the distribution result is used to represent the weight of the power line carrier communication and the weight of the wireless communication. That is, the data communication request semantic feature significant aggregation coding feature obtained by performing a significant interaction of complementary features using the multiple data communication request embedded semantic fusion coding vectors is classified and processed, so as to use the classifier-based weight allocator to convert these abstract semantic features into specific values ​​that can be used to measure the degree of adaptation of the two communication modes, that is, weights. In this way, it can ensure that the advantages of the two communication modes are maximized to avoid waste of resources while meeting the communication needs.

[0052] In one example, the classifier-based weight allocator uses a support vector machine (SVM). SVM is a supervised learning model widely used in binary and multi-classification problems. It can effectively process features in high-dimensional space and performs well in small sample cases. In addition, SVM can solve nonlinear classification problems by introducing kernel functions, which makes it very suitable for processing complex communication request features. In a specific example, if the result of the SVM output is [0.8, 0.2], it means that for the current communication request, the system believes that power line carrier communication (80% of the resources) should be used first, and only a small amount of resources should be reserved for wireless communication (20%). Such an allocation may be based on the fact that the request has higher requirements for stability and bandwidth, and power line carrier communication performs better in this regard. On the contrary, if the output result is [0.2, 0.8], it means that the system determines that wireless communication is more suitable for the current request, which may be because the request has a higher demand for low latency, or is initiated on a mobile device, and wireless communication is more advantageous in this case.

[0053] In a preferred example, inputting the data communication request semantic feature significant aggregation encoding feature vector into a classifier-based weight allocator to obtain an allocation result includes: Determine the mean of the eigenvalues ​​corresponding to the eigenvalue vector of the significant aggregation coding of the semantic features of the data communication request and the standard deviation of the eigenvalues ; The point-wise subtraction vector of the data communication request semantic feature significant aggregation coding feature vector and the feature value mean is multiplied by the feature value standard deviation to obtain a first data communication request semantic feature significant aggregation coding fairness target vector:

[0054] in, A significant aggregation encoding feature vector representing the semantic features of the data communication request, represents dot product, Indicates point reduction, A semantic feature-significant aggregation encoding fairness target vector representing a first data communication request; The point-wise subtraction vector of the data communication request semantic feature significant aggregation encoding feature vector and the feature value standard deviation is multiplied by the feature value mean to obtain a second data communication request semantic feature significant aggregation encoding fairness target vector:

[0055] in, Representing a second data communication request semantic feature significant aggregation encoding fairness target vector; After dot-multiplying the bit-by-bit inverse of the second data communication request semantic feature significant aggregation coding fairness target vector with the first data communication request semantic feature significant aggregation coding fairness target vector, take the bit-by-bit logarithm with base 2 to obtain the data communication request semantic feature significant aggregation coding information correction vector:

[0056] in, represents the bit-by-bit inverse of the second data communication request semantic feature significant aggregation encoding fairness target vector, that is, the inverse of the feature value of each position of the second data communication request semantic feature significant aggregation encoding fairness target vector is calculated, Representing the data communication request semantic features significantly aggregates the encoded information correction vector.

[0057] The eigenvalue mean Divide by the standard deviation of the eigenvalue The square root of the quotient is multiplied by the weight hyperparameter, and then the optimized data communication request semantic feature significant aggregation coding feature vector is obtained by adding the data communication request semantic feature significant aggregation coding information correction vector point:

[0058] in, Indicates point addition, represents the weighted hyperparameter, Representation of optimized data communication request semantic features significantly aggregated encoded feature vector The optimized data communication request semantic feature significant aggregation encoding feature vector is input into a classifier-based weight allocator to obtain an allocation result.

[0059] Here, since each data communication request embedded semantic fusion coding vector in the multiple data communication request embedded semantic fusion coding vectors respectively represents the embedded coding cascade features of communication events and demand descriptions, when performing sequence hub-based feature dynamic compensation aggregation, the source modality difference of each data communication request in the multiple data communication requests will cause the sequence hub anchoring offset and thus lead to feature dynamic compensation aggregation fairness differences, thereby affecting the aggregated feature distribution inclusiveness of the data communication request semantic feature significant aggregation coding feature vector, and reducing the accuracy of the allocation result obtained by inputting the classifier-based weight allocator.

[0060] Therefore, taking into account the attribute level fairness differences of the data population corresponding to the sequence fusion features of the data communication request semantic feature significant aggregation encoding feature vector, in order to improve the aggregation inclusiveness under the feature distribution diversity of the data communication request semantic feature significant aggregation encoding feature vector, the group feature information interactive propagation of the data communication request semantic feature significant aggregation encoding feature vector is corrected by taking the crossover probability value constraint based on the data communication request semantic feature significant aggregation encoding feature vector as the interactive fairness target representation, and taking the unified statistical feature response interaction based on the data communication request semantic feature significant aggregation encoding feature vector as the feature distribution multi-level fairness target bias to achieve the robust distribution fairness unified representation of the data communication request semantic feature significant aggregation encoding feature vector, forming a fair collaboration paradigm under the feature distribution framework of the data communication request semantic feature significant aggregation encoding feature vector, and improving the accuracy of the allocation result obtained by its input classifier-based weight allocator.

[0061] Embodiment 2: The present application also provides a dual-mode communication distribution device based on power line carrier and wireless communication, Figure 5 FIG. 1 is a schematic block diagram of a dual-mode communication distribution device based on power line carrier and wireless communication according to an embodiment of the present application. Figure 5As shown, according to an embodiment of the present application, a dual-mode communication distribution device 100 based on power line carrier and wireless communication includes: a power line data transmission module 110, which is used to deploy at least one carrier communication node in the power line communication network to realize data transmission on the power line; a wireless signal transceiver module 120, which is used to deploy at least one base station node in the wireless communication network to realize the transceiver of wireless signals; a hybrid network interface module 130, which is used to interconnect the power line carrier communication node and the wireless base station node to realize the switching and data interaction of the dual-mode communication; an adaptive communication switching module 140, which is used to respond to the data communication request issued by the user terminal, and give priority to communicating through the power line carrier communication. If the signal quality is poor or the distance is too far, it will automatically switch to wireless communication for communication; an intelligent virtual tunnel module 150, which is used to create a virtual communication channel to realize data transmission between the two communication networks when the dual mode is switched; a dynamic communication weight determination module 160, which is used to determine the allocation weights of the power line carrier communication and the wireless communication based on the data communication request to achieve the optimal communication effect.

[0062] In one example, the allocation weight determination module includes: a data communication request receiving unit, which is used to receive multiple data communication requests issued by a user terminal, and the data communication requests include communication events and demand descriptions; a data communication request embedding coding unit, which is used to embed and code each communication event and each demand description in the multiple data communication requests respectively to obtain multiple communication event embedded coding features and multiple demand description embedded coding features; a data communication request feature fusion unit, which is used to fuse the multiple communication event embedded coding features and the multiple demand description embedded coding features to obtain multiple data communication request embedded semantic fusion features; a complementary feature significant aggregation unit, which is used to perform attention-driven complementary feature significant aggregation on the multiple data communication request embedded semantic fusion features to obtain data communication request semantic feature significant aggregation coding features; an allocation result determination unit, which is used to obtain an allocation result for representing the weight of the power line carrier communication and the weight of the wireless communication based on the data communication request semantic feature significant aggregation coding features.

[0063] Here, those skilled in the art can understand that the specific operations of each module in the above-mentioned dual-mode communication distribution device based on power line carrier and wireless communication have been described in detail in the description of the dual-mode communication distribution method based on power line carrier and wireless communication in the above embodiment one, and therefore, its repeated description will be omitted.

[0064] As described above, the dual-mode communication distribution device 100 based on power line carrier and wireless communication according to the embodiment of the present application can be implemented in various wireless terminals, such as a server with a dual-mode communication distribution algorithm based on power line carrier and wireless communication. In a possible implementation, the dual-mode communication distribution device 100 based on power line carrier and wireless communication according to the embodiment of the present application can be integrated into the wireless terminal as a software module and / or a hardware module. For example, the dual-mode communication distribution device 100 based on power line carrier and wireless communication can be a software module in the operating system of the wireless terminal, or can be an application developed for the wireless terminal; of course, the dual-mode communication distribution device 100 based on power line carrier and wireless communication can also be one of the many hardware modules of the wireless terminal.

[0065] Alternatively, in another example, the dual-mode communication distribution device 100 based on power line carrier and wireless communication and the wireless terminal may also be separate devices, and the dual-mode communication distribution device 100 based on power line carrier and wireless communication may be connected to the wireless terminal via a wired and / or wireless network and transmit interactive information in accordance with an agreed data format.

[0066] Exemplary computer program products and computer readable storage media: In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the dual-mode communication distribution method based on power line carrier and wireless communication according to various embodiments of the present application described in the above "Embodiment 1" section of this specification.

[0067] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0068] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the dual-mode communication allocation method based on power line carrier and wireless communication according to various embodiments of the present application described in the above "Embodiment 1" section of this specification.

[0069] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0070] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, not for limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0071] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0072] It should also be noted that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0073] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0074] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A dual-mode communication allocation method based on power line carrier and wireless communication, comprising: S1: deploying at least one carrier communication node in a power line communication network to realize data transmission on the power line; S2: deploy at least one base station node in the wireless communication network to realize the transmission and reception of wireless signals; S3: connect the power line carrier communication node and the wireless base station node to realize the switching and data interaction of dual-mode communication; S4: respond to the data communication request issued by the user terminal, give priority to the communication through the power line carrier communication, if the signal quality is poor or the distance is too far, automatically switch to wireless communication for communication; S5: when switching between dual modes, create a virtual communication channel to realize data transmission between the two communication networks; S6: based on the data communication request, determine the allocation weight of the power line carrier communication and the wireless communication to achieve the optimal communication effect; characterized in that the S6 includes: receiving a plurality of data communication requests issued by the user terminal, wherein the data communication requests include a communication event and a demand description; Embedding and encoding each communication event and each requirement description in the plurality of data communication requests to obtain a plurality of communication event embedded coding features and a plurality of requirement description embedded coding features; fusing the plurality of communication event embedded coding features and the plurality of requirement description embedded coding features to obtain a plurality of data communication request embedded semantic fusion features; Performing attention-driven complementary feature significant aggregation on the multiple data communication request embedded semantic fusion features to obtain data communication request semantic feature significant aggregation encoding features; Based on the data communication request semantic feature, the coding feature is significantly aggregated to obtain an allocation result for representing the weight of the power line carrier communication and the weight of the wireless communication.

2. The dual-mode communication allocation method based on power line carrier and wireless communication according to claim 1, characterized in that: Each communication event and each requirement description in the multiple data communication requests are respectively embedded and encoded to obtain multiple communication event embedded coding features and multiple requirement description embedded coding features, including: Using a communication event embedding matrix, each communication event in the plurality of data communication requests is respectively embedded and encoded to obtain a plurality of communication event embedding coding vectors as the plurality of communication event embedding coding features; Each requirement description in the multiple data communication requests is embedded and encoded using a requirement description embedding matrix to obtain multiple requirement description embedded coding vectors as the multiple requirement description embedded coding features.

3. The dual-mode communication allocation method based on power line carrier and wireless communication according to claim 2 is characterized in that: The multiple communication event embedded coding features and the multiple requirement description embedded coding features are fused to obtain multiple data communication request embedded semantic fusion features, including: cascading each group of corresponding communication event embedded coding vectors and requirement description embedded coding vectors in the multiple communication event embedded coding vectors and the multiple requirement description embedded coding vectors to obtain multiple data communication request embedded semantic fusion coding vectors as the multiple data communication request embedded semantic fusion features.

4. The dual-mode communication allocation method based on power line carrier and wireless communication according to claim 3 is characterized in that: The multiple data communication request embedded semantic fusion features are subjected to attention-driven complementary feature significant aggregation to obtain data communication request semantic feature significant aggregation encoding features, including: Performing sequence pivot extraction on the plurality of data communication request embedding semantic fusion coding vectors to obtain a data communication request embedding semantic fusion sequence pivot feature vector; Based on the data communication request embedded semantic fusion sequence hub feature vector, the multiple data communication request embedded semantic fusion coding vectors are subjected to complementary information modulation and fusion to obtain a data communication request semantic feature significant aggregation coding feature vector as the data communication request semantic feature significant aggregation coding feature.

5. The dual-mode communication allocation method based on power line carrier and wireless communication according to claim 4 is characterized in that: Based on the data communication request embedded semantic fusion sequence pivot feature vector, the multiple data communication request embedded semantic fusion coding vectors are subjected to complementary information modulation and fusion to obtain a data communication request semantic feature significant aggregation coding feature vector, including: Calculating complementary information of each data communication request embedded semantic fusion coding vector in the plurality of data communication request embedded semantic fusion coding vectors relative to the data communication request embedded semantic fusion sequence hub feature vector to obtain a plurality of data communication request node-hub complementary information embedded coding vectors; Performing attention information significant modulation on the plurality of data communication request node-hub complementary information embedding coding vectors to obtain a plurality of significant modulated data communication request node-hub complementary information embedding coding vectors; The data communication request embedded semantic fusion sequence hub feature vector and the multiple significantly modulated data communication request node-hub complementary information embedded coding vectors are fused to obtain the data communication request semantic feature significantly aggregated coding feature vector.

6. The dual-mode communication allocation method based on power line carrier and wireless communication according to claim 5, characterized in that: The multiple data communication request node-hub complementary information embedding coding vectors are subjected to attention information significant modulation to obtain multiple significant modulated data communication request node-hub complementary information embedding coding vectors, including: Performing a complementary information saliency mark of the attention mechanism on each motion axis motion timing node-hub complementary information embedded coding vector in the multiple data communication request node-hub complementary information embedded coding vectors to obtain multiple data communication request node-hub complementary information attention weights; Taking the multiple data communication request node-hub complementary information attention weights as a sequence of weights, the multiple data communication request node-hub complementary information embedded coding vectors are subjected to attention modulation to obtain the multiple significant modulated data communication request node-hub complementary information embedded coding vectors.

7. The dual-mode communication allocation method based on power line carrier and wireless communication according to claim 6, characterized in that: The data communication request embedded semantic fusion sequence hub feature vector and the multiple significantly modulated data communication request node-hub complementary information embedded coding vectors are integrated to obtain the data communication request semantic feature significantly aggregated coding feature vector, including: cascading the data communication request embedded semantic fusion sequence hub feature vector and the multiple significantly modulated data communication request node-hub complementary information embedded coding vectors to obtain the data communication request semantic feature significantly aggregated coding feature vector.

8. The dual-mode communication allocation method based on power line carrier and wireless communication according to claim 7, characterized in that: Based on the data communication request semantic feature significant aggregation coding feature, an allocation result for representing the weight of the power line carrier communication and the weight of the wireless communication is obtained, including: passing the data communication request semantic feature significant aggregation coding feature vector through a classifier-based weight allocator to obtain the allocation result, and the allocation result is used to represent the weight of the power line carrier communication and the weight of the wireless communication.

9. A dual-mode communication device based on power line carrier and wireless communication, comprising: A power line data transmission module, used to deploy at least one carrier communication node in a power line communication network to realize data transmission on the power line; A wireless signal transceiver module, used to deploy at least one base station node in a wireless communication network to realize the transmission and reception of wireless signals; A hybrid network interface module is used to interconnect the power line carrier communication node and the wireless base station node to realize the switching and data interaction of dual-mode communication; an adaptive communication switching module is used to respond to the data communication request issued by the user terminal, give priority to the power line carrier communication, and automatically switch to wireless communication if the signal quality is poor or the distance is too far; An intelligent virtual tunnel module, used for creating a virtual communication channel to achieve data transmission between two communication networks when dual-mode switching; a dynamic communication weight determination module, used for determining the allocation weights of the power line carrier communication and the wireless communication based on the data communication request to achieve the optimal communication effect; characterized in that the allocation weight determination module includes: A data communication request receiving unit, configured to receive a plurality of data communication requests issued by a user terminal, wherein the data communication requests include a communication event and a demand description; A data communication request embedding coding unit, used to embed code each communication event and each requirement description in the multiple data communication requests to obtain multiple communication event embedding coding features and multiple requirement description embedding coding features; A data communication request feature fusion unit, configured to fuse the plurality of communication event embedded coding features and the plurality of requirement description embedded coding features to obtain a plurality of data communication request embedded semantic fusion features; A complementary feature significant aggregation unit, used for performing attention-driven complementary feature significant aggregation on the plurality of data communication request embedded semantic fusion features to obtain a data communication request semantic feature significant aggregation coding feature; The allocation result determination unit is used to obtain an allocation result for representing the weight of the power line carrier communication and the weight of the wireless communication by significantly aggregating the coding features based on the semantic features of the data communication request.

10. A computer-readable storage medium, characterized in that: Computer program instructions are stored thereon, and when the computer program instructions are executed by a processor, the processor is enabled to execute the dual-mode communication allocation method based on power line carrier and wireless communication as claimed in any one of claims 1 to 8.

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