Method for optimizing power wireless local area network communication and related device

Abstract

This invention belongs to the field of power system communication technology and discloses a method and related apparatus for optimizing power wireless local area network communication. The terminal acquires attribute reporting data and reports it to the wireless access point in the control time slot of the current superframe using a superframe-based dynamic time division multiple access mechanism. The wireless access point generates data time slot allocation information and physical layer parameter configuration information based on the attribute reporting data and sends it in the beacon frame of the next superframe. The terminal encodes data according to the configuration information and transmits it in the designated data time slot. The wireless access point receives the data. By jointly reporting service type, data volume, service quality level, and channel state information to the wireless access point, a complete decision-making basis is provided for cross-layer collaborative scheduling. This enables dynamic time slot scheduling of the media access control layer and adaptive configuration of carrier parameters and preamble sequences of the physical layer, effectively overcoming the problems of fixed resource allocation being unable to adapt to the dynamic needs of differentiated services and low resource utilization efficiency caused by independent optimization of two layers.

Description

Technical Field

[0001] This invention belongs to the field of power system communication technology, and relates to a power wireless local area network communication optimization method and related devices. Background Technology

[0002] With the deepening of smart grid construction, various types of power service terminals are deployed in substations, including remote control equipment, condition monitoring sensors, and embodied intelligent robots. These services place stringent performance requirements on communication networks. For example, remote control commands require millisecond-level ultra-low latency guarantees, while protection services require extremely low packet loss rates to ensure the absolute safety of power grid operation.

[0003] However, most existing power WLANs employ fixed resource allocation strategies, failing to adjust time slot resources in real time according to dynamic service quality requirements. This leads to difficulties in guaranteeing latency for high-priority services, while resources remain idle and wasted during low-load periods. Furthermore, in traditional communication protocol stacks, the physical layer and media access control layer (MAC layer) are typically optimized independently, lacking effective information exchange and coordination mechanisms. Specifically, the physical layer usually only focuses on channel conditions and independently adjusts modulation and coding schemes, while the MAC layer makes scheduling decisions solely based on queue length. The physical and MAC layers cannot share critical information such as service type and service quality requirements, resulting in inefficient resource scheduling and transmission parameter matching during channel quality changes or service bursts, thus limiting the overall efficiency of network resource utilization. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and related apparatus for optimizing power wireless local area network communication.

[0005] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, the present invention provides a method for optimizing communication in a power wireless local area network, comprising: acquiring attribute reporting data of data to be transmitted, and employing a superframe-based dynamic time division multiple access mechanism to send the attribute reporting data to a wireless access point in the control time slot of the current superframe; wherein the attribute reporting data is used to trigger the wireless access point to generate data time slot allocation information and physical layer parameter configuration information for the terminal based on the attribute reporting data, and send it to the terminal in the beacon frame of the next superframe; receiving the data time slot allocation information and physical layer parameter configuration information sent by the wireless access point, encoding the data to be transmitted according to the physical layer parameter configuration information, and sending the encoded data to be transmitted to the wireless access point in the data time slot of the next superframe according to the data time slot allocation information; wherein the attribute reporting data includes service type, data volume, service quality level, and channel state information.

[0006] Optionally, when obtaining the attribute reporting data of the data to be transmitted, the service type is obtained in the following way: obtaining the service feature data of the data to be transmitted, and generating a service feature vector based on the service feature data; embedding the service feature vector as a graph node into a preset service knowledge graph to obtain the graph to be identified; wherein, the service knowledge graph uses the service feature vectors of the transmission data of several service types as graph nodes, and constructs it with the similarity between the service feature vectors as edge weights; based on the graph to be identified, calling a pre-trained service type identification model to obtain the service type of the data to be transmitted; wherein, the service type identification model includes a graph neural network layer and a fully connected classification layer.

[0007] Optionally, during training, the pre-trained service type identification model obtains training data in the following manner: acquiring service feature data of transmission data for different service types, performing data cleaning and normalization to obtain preprocessed service feature data, generating service feature vectors of transmission data for different service types based on the preprocessed service feature data, and forming labeled original training data by combining the service types; performing feature enhancement and sample expansion on the original training data to obtain training data; wherein, the service feature data includes one or more of the following: transmission rate, transmission delay, transmission reliability, transmission jitter, transmission synchronization accuracy, and transmission encryption level.

[0008] Optionally, the superframe includes a beacon frame, a control time slot, and several data time slots arranged in chronological order; wherein the duration of the data time slots can be dynamically set; and a guard interval time slot is set between the control time slots and the data time slots, as well as between adjacent data time slots.

[0009] Optionally, the step of generating terminal data time slot allocation information and physical layer parameter configuration information based on attribute-reported data includes: determining the preamble sequence, delay level, and reliability level based on the service type; determining the number of subcarriers based on the data volume; determining the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level based on the channel state information and reliability level; using the preamble sequence, number of subcarriers, and the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level as the terminal's physical layer parameter configuration information; determining the scheduling priority based on the service quality level and delay level; determining the transmission time slot length based on the data volume and target modulation and coding scheme; and allocating data time slots for the terminal within the data time slots of the superframe according to the scheduling priority and transmission time slot length, thereby generating terminal data time slot allocation information.

[0010] In a second aspect, the present invention provides a method for optimizing communication in a power wireless local area network, comprising: employing a superframe-based dynamic time division multiple access mechanism; receiving attribute reporting data sent by a terminal in the control time slot of the current superframe; generating data time slot allocation information and physical layer parameter configuration information for the terminal based on the attribute reporting data; and sending these information to the terminal in the beacon frame of the next superframe; wherein the data time slot allocation information and physical layer parameter configuration information are used to trigger the terminal to encode data to be transmitted according to the physical layer parameter configuration information, and to send the encoded data to be transmitted to the wireless access point in the data time slot of the next superframe according to the data time slot allocation information; and receiving the encoded data to be transmitted sent by the terminal in the data time slot of the next superframe; wherein the attribute reporting data includes service type, data volume, service quality level, and channel state information.

[0011] Optionally, the superframe includes a beacon frame, a control time slot, and several data time slots arranged in chronological order; wherein the duration of the data time slots can be dynamically set; and a guard interval time slot is set between the control time slots and the data time slots, as well as between adjacent data time slots.

[0012] Optionally, the step of generating terminal data time slot allocation information and physical layer parameter configuration information based on attribute-reported data includes: determining the preamble sequence, delay level, and reliability level based on the service type; determining the number of subcarriers based on the data volume; determining the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level based on the channel state information and reliability level; using the preamble sequence, number of subcarriers, and the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level as the terminal's physical layer parameter configuration information; determining the scheduling priority based on the service quality level and delay level; determining the transmission time slot length based on the data volume and target modulation and coding scheme; and allocating data time slots for the terminal within the data time slots of the superframe according to the scheduling priority and transmission time slot length, thereby generating terminal data time slot allocation information.

[0013] In a third aspect, the present invention provides a method for optimizing communication in a power wireless local area network, comprising: a terminal acquiring attribute reporting data of data to be transmitted, and using a superframe-based dynamic time division multiple access (TDMA) mechanism to send the attribute reporting data to a wireless access point in the control time slot of the current superframe; the wireless access point using the superframe-based TDMA mechanism to receive the attribute reporting data sent by the terminal in the control time slot of the current superframe, and generating data time slot allocation information and physical layer parameter configuration information of the terminal based on the attribute reporting data, and sending them to the terminal in the beacon frame of the next superframe; the terminal receiving the data time slot allocation information and physical layer parameter configuration information sent by the wireless access point, encoding the data to be transmitted according to the physical layer parameter configuration information, and sending the encoded data to be transmitted to the wireless access point in the data time slot of the next superframe according to the data time slot allocation information; and the wireless access point receiving the encoded data to be transmitted sent by the terminal in the data time slot of the next superframe; wherein the attribute reporting data includes service type, data volume, service quality level, and channel state information.

[0014] Optionally, when obtaining the attribute reporting data of the data to be transmitted, the service type is obtained in the following way: obtaining the service feature data of the data to be transmitted, and generating a service feature vector based on the service feature data; embedding the service feature vector as a graph node into a preset service knowledge graph to obtain the graph to be identified; wherein, the service knowledge graph uses the service feature vectors of the transmission data of several service types as graph nodes, and constructs it with the similarity between the service feature vectors as edge weights; based on the graph to be identified, calling a pre-trained service type identification model to obtain the service type of the data to be transmitted; wherein, the service type identification model includes a graph neural network layer and a fully connected classification layer.

[0015] Optionally, the step of generating terminal data time slot allocation information and physical layer parameter configuration information based on attribute-reported data includes: determining the preamble sequence, delay level, and reliability level based on the service type; determining the number of subcarriers based on the data volume; determining the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level based on the channel state information and reliability level; using the preamble sequence, number of subcarriers, and the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level as the terminal's physical layer parameter configuration information; determining the scheduling priority based on the service quality level and delay level; determining the transmission time slot length based on the data volume and target modulation and coding scheme; and allocating data time slots for the terminal within the data time slots of the superframe according to the scheduling priority and transmission time slot length, thereby generating terminal data time slot allocation information.

[0016] In a fourth aspect, the present invention provides a power wireless local area network communication optimization device, comprising: an attribute reporting module, configured to acquire attribute reporting data of data to be transmitted, and employ a superframe-based dynamic time division multiple access mechanism to send the attribute reporting data to a wireless access point in the control time slot of the current superframe; wherein the attribute reporting data is used to trigger the wireless access point to generate data time slot allocation information and physical layer parameter configuration information for the terminal based on the attribute reporting data, and send it to the terminal in the beacon frame of the next superframe; and an encoding and transmission module, configured to receive the data time slot allocation information and physical layer parameter configuration information sent by the wireless access point, encode the data to be transmitted according to the physical layer parameter configuration information, and send the encoded data to be transmitted to the wireless access point in the data time slot of the next superframe according to the data time slot allocation information; wherein the attribute reporting data includes service type, data volume, service quality level, and channel state information.

[0017] Optionally, when obtaining the attribute reporting data of the data to be transmitted, the service type is obtained in the following way: obtaining the service feature data of the data to be transmitted, and generating a service feature vector based on the service feature data; embedding the service feature vector as a graph node into a preset service knowledge graph to obtain the graph to be identified; wherein, the service knowledge graph uses the service feature vectors of the transmission data of several service types as graph nodes, and constructs it with the similarity between the service feature vectors as edge weights; based on the graph to be identified, calling a pre-trained service type identification model to obtain the service type of the data to be transmitted; wherein, the service type identification model includes a graph neural network layer and a fully connected classification layer.

[0018] Optionally, during training, the pre-trained service type identification model obtains training data in the following manner: acquiring service feature data of transmission data for different service types, performing data cleaning and normalization to obtain preprocessed service feature data, generating service feature vectors of transmission data for different service types based on the preprocessed service feature data, and forming labeled original training data by combining the service types; performing feature enhancement and sample expansion on the original training data to obtain training data; wherein, the service feature data includes one or more of the following: transmission rate, transmission delay, transmission reliability, transmission jitter, transmission synchronization accuracy, and transmission encryption level.

[0019] Optionally, the superframe includes a beacon frame, a control time slot, and several data time slots arranged in chronological order; wherein the duration of the data time slots can be dynamically set; and a guard interval time slot is set between the control time slots and the data time slots, as well as between adjacent data time slots.

[0020] Optionally, the step of generating terminal data time slot allocation information and physical layer parameter configuration information based on attribute-reported data includes: determining the preamble sequence, delay level, and reliability level based on the service type; determining the number of subcarriers based on the data volume; determining the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level based on the channel state information and reliability level; using the preamble sequence, number of subcarriers, and the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level as the terminal's physical layer parameter configuration information; determining the scheduling priority based on the service quality level and delay level; determining the transmission time slot length based on the data volume and target modulation and coding scheme; and allocating data time slots for the terminal within the data time slots of the superframe according to the scheduling priority and transmission time slot length, thereby generating terminal data time slot allocation information.

[0021] In a fifth aspect, the present invention provides a power wireless local area network communication optimization device, comprising: a resource allocation module, configured to use a superframe-based dynamic time division multiple access mechanism to receive attribute reporting data sent by a terminal in the control time slot of the current superframe, and to generate data time slot allocation information and physical layer parameter configuration information of the terminal based on the attribute reporting data, and to send them to the terminal in the beacon frame of the next superframe; wherein the data time slot allocation information and physical layer parameter configuration information are used to trigger the terminal to encode data to be transmitted according to the physical layer parameter configuration information, and to send the encoded data to be transmitted to the wireless access point in the data time slot of the next superframe according to the data time slot allocation information; and a receiving module, configured to receive the encoded data to be transmitted sent by the terminal in the data time slot of the next superframe; wherein the attribute reporting data includes service type, data volume, service quality level, and channel state information.

[0022] Optionally, the superframe includes a beacon frame, a control time slot, and several data time slots arranged in chronological order; wherein the duration of the data time slots can be dynamically set; and a guard interval time slot is set between the control time slots and the data time slots, as well as between adjacent data time slots.

[0023] Optionally, the step of generating terminal data time slot allocation information and physical layer parameter configuration information based on attribute-reported data includes: determining the preamble sequence, delay level, and reliability level based on the service type; determining the number of subcarriers based on the data volume; determining the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level based on the channel state information and reliability level; using the preamble sequence, number of subcarriers, and the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level as the terminal's physical layer parameter configuration information; determining the scheduling priority based on the service quality level and delay level; determining the transmission time slot length based on the data volume and target modulation and coding scheme; and allocating data time slots for the terminal within the data time slots of the superframe according to the scheduling priority and transmission time slot length, thereby generating terminal data time slot allocation information.

[0024] In a sixth aspect, the present invention provides a power wireless local area network (WLAN) communication optimization system, comprising a terminal and a wireless access point; the terminal is equipped with the power WLAN communication optimization device described in the fourth aspect of the present invention; and the wireless access point is equipped with the power WLAN communication optimization device described in the fifth aspect of the present invention.

[0025] In a seventh aspect, the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described power wireless local area network communication optimization method.

[0026] In an eighth aspect, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described power wireless local area network communication optimization method.

[0027] Compared with the prior art, the present invention has the following beneficial effects: This invention presents a power wireless local area network (WLAN) communication optimization method. By using service type, data volume, service quality level, and channel status information as attribute reporting data, it can simultaneously obtain information on differentiated service requirements and real-time channel conditions. This provides a complete decision-making basis for cross-layer collaborative scheduling, enabling precise on-demand resource allocation. Furthermore, it employs a superframe-based dynamic time-division multiple access (TDMA) mechanism, dividing time into periodic superframes and reporting them to the wireless access point (WAIP) in the control time slot of the current superframe. This allows the WAIP to dynamically schedule time slots based on attribute reporting data at the media access control layer, and simultaneously adaptively configure carrier parameters and preamble sequences at the physical layer based on the attribute reporting data. Through two-layer collaborative decision-making, the time slot allocation results and physical layer configuration parameters are uniformly distributed to the terminal in the beacon frame of the next superframe. This achieves unified intelligent scheduling of time and frequency resources, effectively overcoming the technical shortcomings of fixed resource allocation, which struggles to adapt to the dynamic needs of differentiated services, and the low resource utilization efficiency caused by independent optimization at the physical and media access control layers. This significantly improves the latency guarantee capability, transmission reliability, and spectrum resource utilization of power WLANs in diverse concurrent service scenarios. Attached Figure Description

[0028] Figure 1 This is a flowchart of a power wireless local area network communication optimization method applied to a terminal according to an embodiment of the present invention.

[0029] Figure 2 This is a schematic diagram of the superframe structure according to an embodiment of the present invention.

[0030] Figure 3 This is a flowchart of a power wireless local area network communication optimization method applied to wireless access points according to an embodiment of the present invention.

[0031] Figure 4 This is a flowchart of a power wireless local area network communication optimization method applied to a power wireless local area network system according to an embodiment of the present invention.

[0032] Figure 5 This is a structural block diagram of a power wireless local area network communication optimization device applied to a terminal according to an embodiment of the present invention.

[0033] Figure 6 This is a block diagram of a power wireless local area network communication optimization device applied to a wireless access point according to an embodiment of the present invention. Detailed Implementation

[0034] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0036] The present invention will now be described in further detail with reference to the accompanying drawings: In response to the problems mentioned in the background art, this invention provides a power wireless local area network communication optimization method and related apparatus to meet the service quality requirements of differentiated services in substations. This method enables a dynamic resource allocation method that coordinates physical layer and MAC layer optimization to improve the performance of power wireless local area network communication.

[0037] Specifically, the typical application scenario for this power wireless local area network (WLAN) communication optimization method and related devices is a power WLAN communication system. This system typically includes several wireless access points and several terminals, with the terminals interacting with the access points wirelessly.

[0038] See Figure 1 In one embodiment of the present invention, a power wireless local area network communication optimization method is provided. This power wireless local area network communication optimization method is applied to the terminal of a power wireless local area network communication system.

[0039] Specifically, the power wireless local area network communication optimization method in this embodiment includes the following steps: S11: Obtain the attribute reporting data of the data to be transmitted, and use a superframe-based dynamic time division multiple access mechanism to send the attribute reporting data to the wireless access point in the control time slot of the current superframe.

[0040] Among them, the attribute reporting data is used to trigger the wireless access point to generate the terminal's data time slot allocation information and physical layer parameter configuration information based on the attribute reporting data, and send them to the terminal in the beacon frame of the next superframe.

[0041] S12: Receive data time slot allocation information and physical layer parameter configuration information sent by the wireless access point, encode the data to be transmitted according to the physical layer parameter configuration information, and send the encoded data to be transmitted to the wireless access point in the data time slot of the next superframe according to the data time slot allocation information.

[0042] The attribute reporting data includes service type, data volume, service quality level, and channel status information.

[0043] Explained, a superframe refers to dividing time resources into periodic structural units of fixed length. Dynamic time division multiple access (TDMA) refers to dynamically determining the number of time slots within the data time slot area of ​​a superframe, the length of each time slot, and the correspondence between time slots and terminals. The allocation results are then sent in the beacon frame of the next superframe, thereby achieving on-demand allocation of time slot resources and avoiding waste caused by fixed allocation.

[0044] Interpretive attribute-reported data includes four parts: service type, data volume, service quality level, and channel state information. Service type refers to the specific service category to which the terminal's currently transmitted data belongs, such as emergency control commands, video streams, or status monitoring data. Data volume refers to the total number of bytes of data to be transmitted, obtained by the terminal counting the data size in the transmission queue. Service quality level refers to the quantified level of the service's performance requirements for latency and reliability, usually read from a preset mapping table based on the service type; for example, emergency control commands are mapped to the highest level 7, while environmental monitoring data is mapped to a lower level 2. Channel state information refers to the quality parameters of the current wireless channel between the terminal and the wireless access point, including signal-to-noise ratio and multipath delay spread, calculated by the terminal by measuring the pilot or probe signals transmitted by the wireless access point.

[0045] This invention presents a power wireless local area network (WLAN) communication optimization method. By using service type, data volume, service quality level, and channel status information as attribute reporting data, it can simultaneously obtain information on differentiated service requirements and real-time channel conditions. This provides a complete decision-making basis for cross-layer collaborative scheduling, enabling precise on-demand resource allocation. Furthermore, it employs a superframe-based dynamic time-division multiple access (TDMA) mechanism, dividing time into periodic superframes and reporting them to the wireless access point (WAIP) in the control time slot of the current superframe. This allows the WAIP to dynamically schedule time slots based on attribute reporting data at the media access control layer, and simultaneously adaptively configure carrier parameters and preamble sequences at the physical layer based on the attribute reporting data. Through two-layer collaborative decision-making, the time slot allocation results and physical layer configuration parameters are uniformly distributed to the terminal in the beacon frame of the next superframe. This achieves unified intelligent scheduling of time and frequency resources, effectively overcoming the technical shortcomings of fixed resource allocation, which struggles to adapt to the dynamic needs of differentiated services, and the low resource utilization efficiency caused by independent optimization at the physical and media access control layers. This significantly improves the latency guarantee capability, transmission reliability, and spectrum resource utilization of power WLANs in diverse concurrent service scenarios.

[0046] In one possible implementation, when acquiring the attribute reporting data of the data to be transmitted, the service type is obtained in the following way: acquiring the service feature data of the data to be transmitted, and generating a service feature vector based on the service feature data; embedding the service feature vector as a graph node into a preset service knowledge graph to obtain a graph to be identified; wherein, the service knowledge graph uses the service feature vectors of the transmission data of several service types as graph nodes, and is constructed using the similarity between the service feature vectors as edge weights; based on the graph to be identified, calling a pre-trained service type identification model to obtain the service type of the data to be transmitted; wherein, the service type identification model includes a graph neural network layer and a fully connected classification layer.

[0047] Explanatoryly, due to the limited types of services offered by substations, the automated identification of service types in the data to be transmitted faces significant challenges, including insufficient sample size, scarce labeled data, and imbalanced dataset categories. This makes it difficult for traditional machine learning methods to achieve high-precision service type identification. Therefore, this embodiment employs a graph neural network-based service type identification model to achieve service type identification in small-sample scenarios.

[0048] Specifically, graph neural networks no longer treat each sample as an isolated data point. Instead, they construct a business knowledge graph, organizing historical samples and samples to be identified into a graph structure. Each sample acts as a graph node, and edges between nodes are established based on feature similarity. This allows samples to be identified to proactively aggregate feature and label information from their most similar historical samples through a message passing mechanism. This design enables the model to still rely on the "indirect supervision" of neighboring samples in the graph structure to help determine the category of the current sample when faced with insufficient labeled samples. Essentially, the graph structure amplifies the supervision signal of limited samples, thus achieving accurate business type identification even with limited labeled data. This effectively overcomes the problems of low recognition accuracy and poor generalization ability caused by sample scarcity and class imbalance in traditional machine learning methods.

[0049] In one possible implementation, the pre-trained service type identification model obtains training data in the following manner during training: acquiring service feature data of transmission data of different service types, performing data cleaning and normalization to obtain preprocessed service feature data, generating service feature vectors of transmission data of different service types based on the preprocessed service feature data, and forming labeled original training data by combining the service types; and performing feature enhancement and sample expansion on the original training data to obtain training data.

[0050] The service characteristic data includes one or more of the following: transmission rate, transmission delay, transmission reliability, transmission jitter, transmission synchronization accuracy, and transmission encryption level.

[0051] To address the issues of insufficient sample size and class imbalance in substation operations, the training data is constructed through a specialized data preprocessing and enhancement process.

[0052] Specifically, the process begins by collecting transmission data from different service types, such as emergency control commands, video streams, and status monitoring, during actual operation. Service feature data, including transmission rate, transmission latency, transmission reliability, transmission jitter, transmission synchronization accuracy, and transmission encryption level, is extracted. After data cleaning to remove outliers and data normalization to eliminate the influence of dimensions, labeled service feature vectors are formed as the original training data. Due to the imbalance in the original data, where some service samples are too few, feature enhancement techniques such as statistical feature expansion and frequency domain feature extraction are used to increase feature diversity. Combined with sample augmentation techniques such as synthetic minority class oversampling and adding small noise to the original samples to generate new samples, the number of samples in each category tends to be balanced. This results in a class-balanced and sufficiently large training dataset, providing high-quality input for subsequent graph neural network model training and ensuring that the model can still learn accurate service recognition capabilities even with small sample sizes.

[0053] For example, during data cleaning, missing values ​​in the original data are imputed using the mean of similar samples. Samples deviating from the mean by more than three standard deviations are identified as outliers and removed or replaced with feature boundary values. During data normalization, a min-max normalization method can be used to map all business feature data to the 0-1 range, eliminating the impact of different feature units on model training.

[0054] For example, when performing feature enhancement, to address the imbalance problem of a small number of minority class samples in the original training data, statistical feature expansion can be used. This involves adding second-order statistics such as variance, skewness, and kurtosis within a sliding window to the original statistical features. Frequency domain feature extraction can also be employed, which involves performing a Fast Fourier Transform on the time series of the business flow and extracting the spectral energy of the top five frequency points as new features, thereby improving the diversity and discriminative power of the feature vectors. When expanding the sample size, for business categories with a small number of samples, synthetic minority class oversampling techniques can be used to generate new samples. Specifically, for each sample in the minority class, random linear interpolation is performed between its nearest neighbors in the feature space of the same class samples to generate new synthetic samples. Simultaneously, for samples in all categories, a small amount of Gaussian noise is added to the original business feature vector, with the noise amplitude controlled within 5% of the feature standard deviation. Random pruning and dithering of the time series data are then used to generate more diverse samples. After the above processing, the number of samples in each category tends to be balanced, and finally, training data with balanced categories and sufficient samples is formed, which provides high-quality input for subsequent graph neural network model training and ensures that the model can still learn accurate business recognition capabilities under small sample conditions.

[0055] In one possible implementation, see Figure 2 The superframe includes a beacon frame, a control time slot, and several data time slots arranged in chronological order; the duration of the data time slots can be dynamically set; and guard interval time slots are set between the control time slots and the data time slots, as well as between adjacent data time slots.

[0056] Explained in this embodiment, the superframe structure adopts a fixed period length and sequentially includes a beacon frame, a control time slot, and several data time slots in chronological order. The beacon frame is fixed at the beginning of each superframe and is used to send time synchronization information and resource scheduling results. The control time slot follows immediately after the beacon frame and is used by each terminal to report attribute data. The data time slots follow the control time slots, and their number is not fixed but dynamically determined by the wireless access point based on the amount and priority of data reported by each terminal within the current superframe. The duration of each data time slot is also dynamically adjusted according to the actual transmission needs of the corresponding terminal. For example, higher priority or larger data volume services are allocated longer data time slots, while lower priority or smaller data volume services are allocated shorter data time slots, thereby achieving on-demand allocation of time slot resources.

[0057] In addition, guard interval time slots are set between the control time slot and the first data time slot, as well as between adjacent data time slots. No data is transmitted within the guard interval time slots. Their function is to absorb the time slot boundary offset that may be caused by clock synchronization error or signal propagation delay difference, prevent time slot overlap and collision of data transmission from different terminals, and ensure the reliable execution of dynamic time slot allocation.

[0058] Through the above superframe structure design, this embodiment achieves flexible adjustment of the number and length of data time slots while ensuring isolation between time slots, providing structural support for dynamic resource scheduling of differentiated services.

[0059] In one possible implementation, the step of generating terminal data time slot allocation information and physical layer parameter configuration information based on attribute-reported data includes: determining the preamble sequence, delay level, and reliability level based on the service type; determining the number of subcarriers based on the data volume; determining the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level based on the channel state information and reliability level; using the preamble sequence, number of subcarriers, and the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level as the terminal's physical layer parameter configuration information; determining the scheduling priority based on the service quality level and delay level; determining the transmission time slot length based on the data volume and target modulation and coding scheme; and allocating data time slots for the terminal within the data time slots of the superframe according to the scheduling priority and transmission time slot length, thereby generating the terminal's data time slot allocation information.

[0060] For physical layer parameter configuration, the wireless access point determines the corresponding preamble sequence identifier from a pre-configured mapping table based on the service type. This identifier is used by the receiver to quickly identify the service type and complete synchronization. Simultaneously, it determines the latency and reliability levels based on the service type. The latency level reflects the service's sensitivity to transmission delay, while the reliability level reflects the service's requirements for bit error rate and packet loss rate. The wireless access point determines the number of subcarriers allocated to the terminal based on the amount of data reported. Services with large data volumes are allocated more subcarriers to achieve parallel transmission, while services with small data volumes are allocated fewer subcarriers to avoid resource waste.

[0061] Specifically, the wireless access point (WAIT) integrates channel state information and reliability level to determine the modulation and coding schemes (MCS) and cyclic prefix lengths that both support. First, the WAIT determines the set of candidate MCS and cyclic prefix lengths that the channel can support based on the channel state information. For example, when the signal-to-noise ratio (SNR) is higher than 22dB, it supports candidate modulation schemes such as 64QAM (64th-order quadrature amplitude modulation), 16QAM (16th-order quadrature amplitude modulation), and QPSK (Quadrature Phase Shift Keying). When the multipath delay spread is small, it supports short cyclic prefixes. Simultaneously, it determines the service's transmission robustness requirements based on the reliability level. For example, a high reliability level requires low-order modulation and a long cyclic prefix. The WAIT takes the intersection of the candidate set and the reliability level requirements, selects a MCS and cyclic prefix length from the intersection as configuration parameters, and sends them to the terminal. If multiple options exist in the intersection, the WAIT can select the one with the highest efficiency or lowest power consumption based on the current network load. Alternatively, it can send all multiple MCS and cyclic prefix lengths that meet the reliability level requirements from the candidate set to the terminal, allowing the terminal to choose autonomously based on local conditions during actual transmission.

[0062] Specifically, regarding data time slot allocation, the wireless access point calculates a comprehensive scheduling priority based on service quality level and latency level. Terminals with higher service quality and latency levels receive higher scheduling priority. The wireless access point calculates the transmission time slot length required for the terminal to complete data transmission based on the terminal's data volume and the determined target modulation and coding scheme. Larger data volumes or lower-efficiency modulation and coding schemes require longer time slots. The wireless access point allocates data time slots of corresponding length to each terminal sequentially within the data time slot area of ​​the current superframe, according to the scheduling priority from high to low, and records the start position and length of each terminal's time slot, generating data time slot allocation information. Finally, the wireless access point encodes the data time slot allocation information and physical layer parameter configuration information and uniformly distributes it to each terminal in the beacon frame of the next superframe.

[0063] See Figure 3 In another embodiment of the present invention, a power wireless local area network (WLAN) communication optimization method is provided. This WLAN communication optimization method is applied to the wireless access point of a power WLAN communication system. Specifically, the WLAN communication optimization method includes the following steps: S21: Employ a superframe-based dynamic time division multiple access mechanism to receive attribute reporting data sent by the terminal in the control time slot of the current superframe, generate data time slot allocation information and physical layer parameter configuration information for the terminal based on the attribute reporting data, and send them to the terminal in the beacon frame of the next superframe.

[0064] Among them, the data time slot allocation information and physical layer parameter configuration information are used to trigger the terminal to encode the data to be transmitted according to the physical layer parameter configuration information, and to send the encoded data to be transmitted to the wireless access point in the data time slot of the next superframe according to the data time slot allocation information.

[0065] S22: The received terminal sends encoded data to be transmitted in the data time slot of the next superframe.

[0066] The attribute reporting data includes service type, data volume, service quality level, and channel status information.

[0067] Explained, the wireless access point (WAP), as the core decision-making unit for resource scheduling, collects attribute reporting data from all terminals in the control time slot of each superframe. Upon receiving this information, the WAP generates data time slot allocation information and physical layer parameter configuration information for the terminals based on the attribute reporting data. This information is then encoded and broadcast in the beacon frame of the next superframe, enabling each terminal to transmit data using the appropriate physical layer parameters in the designated time slot. Through this method, the WAP achieves closed-loop resource scheduling with a superframe cycle, performing cross-layer collaborative optimization of time slot allocation in the media access control layer and parameter configuration in the physical layer, effectively ensuring the low-latency and high-reliability communication requirements of differentiated services in substations.

[0068] For example, when a wireless access point generates data time slot allocation information and physical layer parameter configuration information for a terminal based on attribute-reported data, it can do so using a preset optimization algorithm.

[0069] For example, an online optimization algorithm based on reinforcement learning can be used. The wireless access point acts as an agent, taking the service type, data volume, service quality level, and channel state information reported by each terminal as input states, and time slot allocation and physical layer parameter configuration as output actions. The optimization objectives are service latency satisfaction rate and system throughput. This algorithm learns the scheduling strategy through offline simulation training and continuously optimizes it based on transmission results during actual operation, gradually bringing the scheduling decision closer to the optimal state.

[0070] In one possible implementation, the superframe includes a beacon frame, a control time slot, and several data time slots arranged in chronological order; wherein the duration of the data time slots can be dynamically set; and guard interval time slots are provided between the control time slots and the data time slots, as well as between adjacent data time slots.

[0071] In one possible implementation, the step of generating terminal data time slot allocation information and physical layer parameter configuration information based on attribute-reported data includes: determining the preamble sequence, delay level, and reliability level based on the service type; determining the number of subcarriers based on the data volume; determining the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level based on the channel state information and reliability level; using the preamble sequence, number of subcarriers, and the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level as the terminal's physical layer parameter configuration information; determining the scheduling priority based on the service quality level and delay level; determining the transmission time slot length based on the data volume and target modulation and coding scheme; and allocating data time slots for the terminal within the data time slots of the superframe according to the scheduling priority and transmission time slot length, thereby generating the terminal's data time slot allocation information.

[0072] See Figure 4 In another embodiment of the present invention, a power wireless local area network communication optimization method is provided, which is applied to a power wireless local area network communication system.

[0073] Specifically, the power wireless local area network communication optimization method in this embodiment includes the following steps: S31: The terminal obtains the attribute reporting data of the data to be transmitted, and uses a superframe-based dynamic time division multiple access mechanism to send the attribute reporting data to the wireless access point in the control time slot of the current superframe.

[0074] S32: The wireless access point adopts a superframe-based dynamic time division multiple access mechanism to receive attribute reporting data sent by the terminal in the control time slot of the current superframe, and to generate data time slot allocation information and physical layer parameter configuration information of the terminal based on the attribute reporting data, and send them to the terminal in the beacon frame of the next superframe.

[0075] S33: The terminal receives data time slot allocation information and physical layer parameter configuration information sent by the wireless access point, encodes the data to be transmitted according to the physical layer parameter configuration information, and sends the encoded data to be transmitted to the wireless access point in the data time slot of the next superframe according to the data time slot allocation information.

[0076] S34: Encoded data to be transmitted by the wireless access point receiving terminal in the data time slot of the next superframe.

[0077] The attribute reporting data includes service type, data volume, service quality level, and channel status information.

[0078] This interpretive approach, based on the collaborative interaction between terminals and wireless access points, uses service type, data volume, service quality level, and channel state information as decision-making criteria for cross-layer optimization. Terminals report attributes and data in control time slots, while wireless access points uniformly distribute time slot allocations and physical layer parameter configurations in beacon frames. Terminals then transmit data according to the configured parameters in designated data time slots, forming a closed-loop scheduling mechanism with superframes as the cycle. Dynamic time slot scheduling based on service priority is implemented at the media access control layer, while adaptive configuration of carrier parameters and preamble sequences is achieved at the physical layer. This two-layer collaboration enables unified intelligent scheduling of time and frequency resources, effectively solving the problems of fixed resource allocation in substation scenarios being unable to adapt to the dynamic needs of differentiated services, and the low resource utilization efficiency caused by independent optimization of the physical and media access control layers. This improves the latency guarantee capability, transmission reliability, and spectrum resource utilization of power WLANs in diverse concurrent service scenarios.

[0079] In one possible implementation, when acquiring the attribute reporting data of the data to be transmitted, the service type is obtained in the following way: acquiring the service feature data of the data to be transmitted, and generating a service feature vector based on the service feature data; embedding the service feature vector as a graph node into a preset service knowledge graph to obtain a graph to be identified; wherein, the service knowledge graph uses the service feature vectors of the transmission data of several service types as graph nodes, and is constructed using the similarity between the service feature vectors as edge weights; based on the graph to be identified, calling a pre-trained service type identification model to obtain the service type of the data to be transmitted; wherein, the service type identification model includes a graph neural network layer and a fully connected classification layer.

[0080] In one possible implementation, the step of generating terminal data time slot allocation information and physical layer parameter configuration information based on attribute-reported data includes: determining the preamble sequence, delay level, and reliability level based on the service type; determining the number of subcarriers based on the data volume; determining the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level based on the channel state information and reliability level; using the preamble sequence, number of subcarriers, and the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level as the terminal's physical layer parameter configuration information; determining the scheduling priority based on the service quality level and delay level; determining the transmission time slot length based on the data volume and target modulation and coding scheme; and allocating data time slots for the terminal within the data time slots of the superframe according to the scheduling priority and transmission time slot length, thereby generating the terminal's data time slot allocation information.

[0081] In another embodiment of the present invention, the power wireless local area network communication optimization method of the present invention is illustrated by taking a power wireless local area network communication system deployed in a ±800kV intelligent ultra-high voltage converter station as an example.

[0082] In this power wireless local area network (WLAN) communication system, various service terminals, including inspection robots, status monitoring terminals, and switch opening and closing devices, access the network through the WLAN. Data reported by the terminals is identified by a pre-trained service type recognition model deployed locally on the terminal, such as identifying it as "emergency control instructions" or "routine monitoring data." The service type, along with data volume, service quality level, and channel state information, is input to the resource scheduler at the wireless access point. The resource scheduler then allocates high-priority, short-cycle data slots for "emergency control instructions" and configures them with more robust physical layer parameters (including longer cyclic prefixes) to ensure microsecond-level latency and extremely high reliability. For "routine monitoring data," low-priority data slots are allocated, and a high-efficiency modulation and coding scheme is configured, thereby ensuring overall network performance while efficiently utilizing wireless spectrum resources.

[0083] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the apparatus embodiments, please refer to the embodiments of the method of the present invention.

[0084] See Figure 5 In another embodiment of the present invention, a power wireless local area network communication optimization device is provided, which can be used to achieve the above-mentioned... Figure 1 The power wireless local area network communication optimization method provided in the embodiment specifically includes an attribute reporting module and an encoding transmission module.

[0085] The attribute reporting module acquires attribute reporting data for the data to be transmitted and uses a superframe-based dynamic time division multiple access mechanism to send the attribute reporting data to the wireless access point in the control time slot of the current superframe. The attribute reporting data triggers the wireless access point to generate data time slot allocation information and physical layer parameter configuration information for the terminal, which is then sent to the terminal in the beacon frame of the next superframe. The encoding and transmission module receives the data time slot allocation information and physical layer parameter configuration information sent by the wireless access point, encodes the data to be transmitted according to the physical layer parameter configuration information, and sends the encoded data to be transmitted to the wireless access point in the data time slot of the next superframe according to the data time slot allocation information. The attribute reporting data includes service type, data volume, service quality level, and channel state information.

[0086] In one possible implementation, when acquiring the attribute reporting data of the data to be transmitted, the service type is obtained in the following way: acquiring the service feature data of the data to be transmitted, and generating a service feature vector based on the service feature data; embedding the service feature vector as a graph node into a preset service knowledge graph to obtain a graph to be identified; wherein, the service knowledge graph uses the service feature vectors of the transmission data of several service types as graph nodes, and is constructed using the similarity between the service feature vectors as edge weights; based on the graph to be identified, calling a pre-trained service type identification model to obtain the service type of the data to be transmitted; wherein, the service type identification model includes a graph neural network layer and a fully connected classification layer.

[0087] In one possible implementation, the pre-trained service type identification model is trained by obtaining training data in the following manner: acquiring service feature data of transmission data of different service types, performing data cleaning and normalization to obtain preprocessed service feature data, generating service feature vectors of transmission data of different service types based on the preprocessed service feature data, and forming labeled original training data by combining the service types; performing feature enhancement and sample expansion on the original training data to obtain training data; wherein, the service feature data includes one or more of the following: transmission rate, transmission delay, transmission reliability, transmission jitter, transmission synchronization accuracy, and transmission encryption level.

[0088] In one possible implementation, the superframe includes a beacon frame, a control time slot, and several data time slots arranged in chronological order; wherein the duration of the data time slots can be dynamically set; and guard interval time slots are provided between the control time slots and the data time slots, as well as between adjacent data time slots.

[0089] In one possible implementation, the step of generating terminal data time slot allocation information and physical layer parameter configuration information based on attribute-reported data includes: determining the preamble sequence, delay level, and reliability level based on the service type; determining the number of subcarriers based on the data volume; determining the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level based on the channel state information and reliability level; using the preamble sequence, number of subcarriers, and the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level as the terminal's physical layer parameter configuration information; determining the scheduling priority based on the service quality level and delay level; determining the transmission time slot length based on the data volume and target modulation and coding scheme; and allocating data time slots for the terminal within the data time slots of the superframe according to the scheduling priority and transmission time slot length, thereby generating the terminal's data time slot allocation information.

[0090] See Figure 6 In another embodiment of the present invention, a power wireless local area network communication optimization device is provided, which can be used to achieve the above-mentioned... Figure 3 The power wireless local area network communication optimization method provided in the embodiment specifically includes a resource allocation module and a receiving module.

[0091] The resource allocation module employs a superframe-based dynamic time division multiple access mechanism to receive attribute reporting data sent by the terminal in the control time slot of the current superframe, and generates data time slot allocation information and physical layer parameter configuration information for the terminal based on the attribute reporting data, which are then sent to the terminal in the beacon frame of the next superframe. The data time slot allocation information and physical layer parameter configuration information trigger the terminal to encode the data to be transmitted according to the physical layer parameter configuration information, and to send the encoded data to be transmitted to the radio access point in the data time slot of the next superframe according to the data time slot allocation information. The receiving module receives the encoded data to be transmitted sent by the terminal in the data time slot of the next superframe. The attribute reporting data includes service type, data volume, service quality level, and channel state information.

[0092] In one possible implementation, the superframe includes a beacon frame, a control time slot, and several data time slots arranged in chronological order; wherein the duration of the data time slots can be dynamically set; and guard interval time slots are provided between the control time slots and the data time slots, as well as between adjacent data time slots.

[0093] In one possible implementation, the step of generating terminal data time slot allocation information and physical layer parameter configuration information based on attribute-reported data includes: determining the preamble sequence, delay level, and reliability level based on the service type; determining the number of subcarriers based on the data volume; determining the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level based on the channel state information and reliability level; using the preamble sequence, number of subcarriers, and the modulation and coding scheme and cyclic prefix length jointly supported by the channel state information and reliability level as the terminal's physical layer parameter configuration information; determining the scheduling priority based on the service quality level and delay level; determining the transmission time slot length based on the data volume and target modulation and coding scheme; and allocating data time slots for the terminal within the data time slots of the superframe according to the scheduling priority and transmission time slot length, thereby generating the terminal's data time slot allocation information.

[0094] In another embodiment of the present invention, a power wireless local area network communication optimization system is provided, which can be used to achieve the above-mentioned... Figure 4 The power wireless local area network (WLAN) communication optimization method provided in the embodiment specifically includes a WLAN communication optimization system comprising a terminal and a wireless access point; the terminal is equipped with the aforementioned... Figure 5 The power wireless local area network communication optimization device provided in the embodiment; the wireless access point is equipped with the above-mentioned... Figure 6 The embodiment provides a power wireless local area network communication optimization device.

[0095] All relevant content of each step involved in the aforementioned embodiments of the power wireless local area network communication optimization method can be referenced to the functional description of the corresponding functional module of the power wireless local area network communication optimization device / system in the embodiments of the present invention, and will not be repeated here.

[0096] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0097] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of a power wireless local area network communication optimization method.

[0098] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the power wireless local area network communication optimization method in the above embodiments.

[0099] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0100] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0101] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0102] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing power wireless local area network communication, characterized in that, include: The attribute reporting data of the data to be transmitted is obtained, and the attribute reporting data is sent to the wireless access point in the control time slot of the current superframe using a superframe-based dynamic time division multiple access mechanism. The attribute reporting data is used to trigger the wireless access point to generate the terminal's data time slot allocation information and physical layer parameter configuration information based on the attribute reporting data, and send them to the terminal in the beacon frame of the next superframe. It receives data time slot allocation information and physical layer parameter configuration information sent by the wireless access point, encodes the data to be transmitted according to the physical layer parameter configuration information, and sends the encoded data to be transmitted to the wireless access point in the data time slot of the next superframe according to the data time slot allocation information. The attribute reporting data includes service type, data volume, service quality level, and channel status information.

2. The power wireless local area network communication optimization method according to claim 1, characterized in that, When obtaining the attribute reporting data of the data to be transmitted, the service type is obtained in the following way: Obtain the business characteristic data of the data to be transmitted, and generate a business characteristic vector based on the business characteristic data; The business feature vectors are embedded as graph nodes into a pre-set business knowledge graph to obtain the graph to be identified. The business knowledge graph uses the business feature vectors of the transmission data of several business types as graph nodes and is constructed using the similarity between the business feature vectors as edge weights. Based on the target graph, a pre-trained service type identification model is invoked to obtain the service type of the data to be transmitted; the service type identification model includes a graph neural network layer and a fully connected classification layer.

3. The power wireless local area network communication optimization method according to claim 2, characterized in that, The pre-trained business type identification model obtains its training data in the following manner during training: The process involves acquiring service feature data of transmission data for different service types, performing data cleaning and normalization to obtain preprocessed service feature data, generating service feature vectors of transmission data for different service types based on the preprocessed service feature data, and combining the service types to form labeled original training data. The original training data is augmented with features and samples to obtain the training data. The service characteristic data includes one or more of the following: transmission rate, transmission delay, transmission reliability, transmission jitter, transmission synchronization accuracy, and transmission encryption level.

4. The power wireless local area network communication optimization method according to claim 1, characterized in that, The superframe includes beacon frames, control time slots, and several data time slots arranged in chronological order; The duration of the data time slot can be dynamically set; a protection interval time slot is set between the control time slot and the data time slot, as well as between adjacent data time slots.

5. The power wireless local area network communication optimization method according to claim 1, characterized in that, The process of generating terminal data time slot allocation information and physical layer parameter configuration information based on attribute-reported data includes: Determine the preamble sequence, latency level, and reliability level based on the type of service. The number of subcarriers is determined based on the amount of data; the modulation and coding scheme and cyclic prefix length supported by the channel state information and reliability level are determined based on the channel state information and reliability level; and the preamble sequence, the number of subcarriers, and the modulation and coding scheme and cyclic prefix length supported by the channel state information and reliability level are used as the physical layer parameter configuration information of the terminal. The scheduling priority is determined based on the service quality level and latency level, and the transmission time slot length is determined based on the data volume and target modulation and coding scheme. Data time slots are allocated to the terminal within the data time slots of the superframe according to the scheduling priority and transmission time slot length, generating the terminal's data time slot allocation information.

6. A method for optimizing power line wireless local area network communication, characterized in that, include: A superframe-based dynamic time division multiple access mechanism is adopted. The system receives attribute reporting data sent by the terminal in the control time slot of the current superframe, and generates data time slot allocation information and physical layer parameter configuration information for the terminal based on the attribute reporting data. These information are then sent to the terminal in the beacon frame of the next superframe. The data time slot allocation information and physical layer parameter configuration information are used to trigger the terminal to encode the data to be transmitted according to the physical layer parameter configuration information, and to send the encoded data to be transmitted to the wireless access point in the data time slot of the next superframe according to the data time slot allocation information. The receiving terminal sends encoded data to be transmitted in the data time slot of the next superframe; The attribute reporting data includes service type, data volume, service quality level, and channel status information.

7. The power wireless local area network communication optimization method according to claim 6, characterized in that, The superframe includes beacon frames, control time slots, and several data time slots arranged in chronological order; The duration of the data time slot can be dynamically set; a protection interval time slot is set between the control time slot and the data time slot, as well as between adjacent data time slots.

8. The power wireless local area network communication optimization method according to claim 6, characterized in that, The process of generating terminal data time slot allocation information and physical layer parameter configuration information based on attribute-reported data includes: Determine the preamble sequence, latency level, and reliability level based on the type of service. The number of subcarriers is determined based on the amount of data; the modulation and coding scheme and cyclic prefix length supported by the channel state information and reliability level are determined based on the channel state information and reliability level; and the preamble sequence, the number of subcarriers, and the modulation and coding scheme and cyclic prefix length supported by the channel state information and reliability level are used as the physical layer parameter configuration information of the terminal. The scheduling priority is determined based on the service quality level and latency level, and the transmission time slot length is determined based on the data volume and target modulation and coding scheme. Data time slots are allocated to the terminal within the data time slots of the superframe according to the scheduling priority and transmission time slot length, generating the terminal's data time slot allocation information.

9. A method for optimizing power wireless local area network communication, characterized in that, include: The terminal acquires the attribute reporting data of the data to be transmitted and uses a superframe-based dynamic time division multiple access mechanism to send the attribute reporting data to the wireless access point in the control time slot of the current superframe. The wireless access point adopts a superframe-based dynamic time division multiple access mechanism to receive attribute reporting data sent by the terminal in the control time slot of the current superframe, and to generate data time slot allocation information and physical layer parameter configuration information of the terminal based on the attribute reporting data, and send them to the terminal in the beacon frame of the next superframe. The terminal receives data time slot allocation information and physical layer parameter configuration information sent by the wireless access point, encodes the data to be transmitted according to the physical layer parameter configuration information, and sends the encoded data to be transmitted to the wireless access point in the data time slot of the next superframe according to the data time slot allocation information. The wireless access point receives the encoded data to be transmitted sent by the terminal in the data time slot of the next superframe; The attribute reporting data includes service type, data volume, service quality level, and channel status information.

10. The power wireless local area network communication optimization method according to claim 9, characterized in that, When obtaining the attribute reporting data of the data to be transmitted, the service type is obtained in the following way: Obtain the business characteristic data of the data to be transmitted, and generate a business characteristic vector based on the business characteristic data; The business feature vectors are embedded as graph nodes into a pre-set business knowledge graph to obtain the graph to be identified. The business knowledge graph uses the business feature vectors of the transmission data of several business types as graph nodes and is constructed using the similarity between the business feature vectors as edge weights. Based on the target graph, a pre-trained service type identification model is invoked to obtain the service type of the data to be transmitted; the service type identification model includes a graph neural network layer and a fully connected classification layer.

11. The power wireless local area network communication optimization method according to claim 9, characterized in that, The process of generating terminal data time slot allocation information and physical layer parameter configuration information based on attribute-reported data includes: Determine the preamble sequence, latency level, and reliability level based on the type of service. The number of subcarriers is determined based on the amount of data; the modulation and coding scheme and cyclic prefix length supported by the channel state information and reliability level are determined based on the channel state information and reliability level; and the preamble sequence, the number of subcarriers, and the modulation and coding scheme and cyclic prefix length supported by the channel state information and reliability level are used as the physical layer parameter configuration information of the terminal. The scheduling priority is determined based on the service quality level and latency level, and the transmission time slot length is determined based on the data volume and target modulation and coding scheme. Data time slots are allocated to the terminal within the data time slots of the superframe according to the scheduling priority and transmission time slot length, generating the terminal's data time slot allocation information.

12. A power wireless local area network communication optimization device, characterized in that, include: The attribute reporting module is used to acquire attribute reporting data of the data to be transmitted, and adopts a superframe-based dynamic time division multiple access mechanism to send the attribute reporting data to the wireless access point in the control time slot of the current superframe. The attribute reporting data is used to trigger the wireless access point to generate data time slot allocation information and physical layer parameter configuration information of the terminal based on the attribute reporting data, and send them to the terminal in the beacon frame of the next superframe. The encoding and transmission module is used to receive data time slot allocation information and physical layer parameter configuration information sent by the wireless access point, encode the data to be transmitted according to the physical layer parameter configuration information, and send the encoded data to be transmitted to the wireless access point in the data time slot of the next superframe according to the data time slot allocation information. The attribute reporting data includes service type, data volume, service quality level, and channel status information.

13. The power wireless local area network communication optimization device according to claim 12, characterized in that, When obtaining the attribute reporting data of the data to be transmitted, the service type is obtained in the following way: Obtain the business characteristic data of the data to be transmitted, and generate a business characteristic vector based on the business characteristic data; The business feature vectors are embedded as graph nodes into a pre-set business knowledge graph to obtain the graph to be identified. The business knowledge graph uses the business feature vectors of the transmission data of several business types as graph nodes and is constructed using the similarity between the business feature vectors as edge weights. Based on the target graph, a pre-trained service type identification model is invoked to obtain the service type of the data to be transmitted; the service type identification model includes a graph neural network layer and a fully connected classification layer.

14. The power wireless local area network communication optimization device according to claim 13, characterized in that, The pre-trained business type identification model obtains its training data in the following manner during training: The process involves acquiring service feature data of transmission data for different service types, performing data cleaning and normalization to obtain preprocessed service feature data, generating service feature vectors of transmission data for different service types based on the preprocessed service feature data, and combining the service types to form labeled original training data. The original training data is augmented with features and samples to obtain the training data. The service characteristic data includes one or more of the following: transmission rate, transmission delay, transmission reliability, transmission jitter, transmission synchronization accuracy, and transmission encryption level.

15. The power wireless local area network communication optimization device according to claim 12, characterized in that, The superframe includes beacon frames, control time slots, and several data time slots arranged in chronological order; The duration of the data time slot can be dynamically set; a protection interval time slot is set between the control time slot and the data time slot, as well as between adjacent data time slots.

16. The power wireless local area network communication optimization device according to claim 12, characterized in that, The process of generating terminal data time slot allocation information and physical layer parameter configuration information based on attribute-reported data includes: Determine the preamble sequence, latency level, and reliability level based on the type of service. The number of subcarriers is determined based on the amount of data; the modulation and coding scheme and cyclic prefix length supported by the channel state information and reliability level are determined based on the channel state information and reliability level; and the preamble sequence, the number of subcarriers, and the modulation and coding scheme and cyclic prefix length supported by the channel state information and reliability level are used as the physical layer parameter configuration information of the terminal. The scheduling priority is determined based on the service quality level and latency level, and the transmission time slot length is determined based on the data volume and target modulation and coding scheme. Data time slots are allocated to the terminal within the data time slots of the superframe according to the scheduling priority and transmission time slot length, generating the terminal's data time slot allocation information.

17. A power wireless local area network communication optimization device, characterized in that, include: The resource allocation module is used to receive attribute reporting data sent by the terminal in the control time slot of the current superframe using a superframe-based dynamic time division multiple access mechanism, and to generate data time slot allocation information and physical layer parameter configuration information for the terminal based on the attribute reporting data, and send them to the terminal in the beacon frame of the next superframe; wherein, the data time slot allocation information and physical layer parameter configuration information are used to trigger the terminal to encode the data to be transmitted according to the physical layer parameter configuration information, and to send the encoded data to be transmitted to the wireless access point in the data time slot of the next superframe according to the data time slot allocation information; The receiving module is used to receive encoded data to be transmitted sent by the terminal in the data time slot of the next superframe; The attribute reporting data includes service type, data volume, service quality level, and channel status information.

18. The power wireless local area network communication optimization device according to claim 17, characterized in that, The superframe includes beacon frames, control time slots, and several data time slots arranged in chronological order; The duration of the data time slot can be dynamically set; a protection interval time slot is set between the control time slot and the data time slot, as well as between adjacent data time slots.

19. The power wireless local area network communication optimization device according to claim 17, characterized in that, The process of generating terminal data time slot allocation information and physical layer parameter configuration information based on attribute-reported data includes: Determine the preamble sequence, latency level, and reliability level based on the type of service. The number of subcarriers is determined based on the amount of data; the modulation and coding scheme and cyclic prefix length supported by the channel state information and reliability level are determined based on the channel state information and reliability level; and the preamble sequence, the number of subcarriers, and the modulation and coding scheme and cyclic prefix length supported by the channel state information and reliability level are used as the physical layer parameter configuration information of the terminal. The scheduling priority is determined based on the service quality level and latency level, and the transmission time slot length is determined based on the data volume and target modulation and coding scheme. Data time slots are allocated to the terminal within the data time slots of the superframe according to the scheduling priority and transmission time slot length, generating the terminal's data time slot allocation information.

20. A power wireless local area network communication optimization system, characterized in that, It includes a terminal and a wireless access point; the terminal is equipped with the power wireless local area network communication optimization device according to any one of claims 12 to 16; the wireless access point is equipped with the power wireless local area network communication optimization device according to any one of claims 17 to 19.

21. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the power wireless local area network communication optimization method as described in any one of claims 1 to 11.

22. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the power wireless local area network communication optimization method as described in any one of claims 1 to 11.

Images