Multi-service fusion method and system for low-voltage power line carrier communication

Abstract

The invention discloses a multi-service fusion method and system for low-voltage power line carrier communication, and relates to the technical field of communication. The method comprises the following steps: constructing a spectrum sensing mechanism, and selecting a frequency band with an optimal channel state as an initial communication channel; using historical channel state data and real-time data of spectrum sensing to train a deep learning model, and generating a prediction result; an intelligent service priority mechanism is established, transmitted data packets are analyzed through an intelligent service identification module, different types of services are identified, and data flow priorities of different services are dynamically adjusted according to a service classification result and real-time channel state prediction. According to the invention, through intelligent spectrum sensing, deep learning channel prediction and intelligent service identification, multi-service fusion of low-voltage power line carrier communication is realized. In a complex channel environment, communication parameters and service priorities can be dynamically adjusted, the communication quality of key services is ensured, and meanwhile resource utilization is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of communications, and in particular to a multi-service integration method and system for low-voltage power line carrier communications. Background Art

[0002] With the rapid development of the Internet of Things and smart grid technologies, low-voltage power line carrier communication, as a technology that does not require the laying of additional communication lines, has been widely used, especially in smart homes, industrial automation and smart grids. Low-voltage power line networks use existing power lines for data transmission, with a wide coverage and low cost, and have good market prospects. However, low-voltage power lines themselves are not designed for data communication, and the communication environment is complex and changeable. They are affected by multiple factors such as multipath effects, narrowband interference, and electromagnetic noise, resulting in unstable communication quality.

[0003] In modern application scenarios, different types of services (such as voice, video, data transmission, remote control, etc.) have different requirements for communication latency, bandwidth, and reliability. In traditional low-voltage power line communications, various types of services are usually transmitted and processed in a fixed manner, lacking dynamic adaptability and flexibility to business needs. This simple business transmission strategy makes it difficult to fully utilize spectrum resources, and is prone to cause communication bottlenecks and reduced service quality. Especially in the face of complex network environments with multiple services running in parallel, how to effectively integrate multiple business types under limited channel and bandwidth resources, ensure that key businesses are prioritized, and achieve efficient transmission of services under various network conditions has become a difficult problem that needs to be solved by current technology.

[0004] Multi-service convergence refers to the technology of flexible scheduling and optimization processing for different types of business needs in the same communication network. Existing low-voltage power line communication solutions are often unable to effectively integrate multiple services, resulting in high-priority critical services being occupied by low-priority services, thus affecting the overall communication quality. This limitation is particularly evident when the business volume increases suddenly or the communication environment is poor, which directly affects the stability and reliability of the system. Therefore, there is an urgent need for an innovative communication method that can achieve multi-service convergence, through real-time perception of channel status, intelligent business classification, and dynamic adjustment of transmission strategies, to cope with the complex challenges in low-voltage power line networks. Summary of the invention

[0005] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] In view of the problems existing in the above-mentioned existing multi-service integration method and system for low-voltage power line carrier communication, the present invention is proposed.

[0007] Therefore, the purpose of the present invention is to provide a multi-service fusion method and system for low-voltage power line carrier communication, which aims to achieve efficient fusion and transmission of multiple services in a low-voltage power line network through spectrum sensing, channel prediction and intelligent service classification.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] A multi-service fusion method for low-voltage power line carrier communication, the method comprising the following steps:

[0010] Step 1: Build a spectrum sensing mechanism and use it to scan all available frequency bands in the power line in real time, detect the channel status of each frequency band, and evaluate the communication quality of each frequency band based on the spectrum sensing results, and select the frequency band with the best channel status as the initial communication channel;

[0011] Step 2: Use historical channel status data and real-time spectrum sensing data to train a deep learning model, which is used to predict the changing trend of channel status in the future and generate prediction results.

[0012] Step 3: Establish an intelligent service priority mechanism, analyze the transmitted data packets through the intelligent service identification module, identify different types of services, and dynamically adjust the data flow priority of different services based on the service classification results and real-time channel status prediction.

[0013] As a preferred solution of the multi-service integration method for low-voltage power line carrier communication described in the present invention, the spectrum sensing mechanism includes the following steps:

[0014] S101: For all available frequency bands f in the power line i Perform real-time scanning to obtain the signal strength S(f i ,t) and the noise level N(f i ,t);

[0015] S102: Calculate the channel interference metric, that is, for each frequency band f i Calculate its interference measure D(f i ), which reflects the degree of external interference to the frequency band;

[0016] S103: According to the signal strength, noise level and interference measurement data, each frequency band f i Evaluate the channel quality Q(f i ), and in all evaluated channel quality values ​​Q(fi ), select the frequency band with the largest value as the initial communication channel.

[0017] As a preferred solution of the multi-service fusion method for low-voltage power line carrier communication described in the present invention, the calculation formula of the signal strength is:

[0018] S(f i ,t)=P t ·H(f i ,t);

[0019] Among them, P t is the transmitting power, H(f i ,t) is the channel gain, which means in the frequency band f i and signal attenuation and gain at time t;

[0020] The noise level is calculated as follows:

[0021] N(f i ,t)=N0·(1+γ(f i ,t));

[0022] Where N0 is the reference noise power, γ(f i ,t) is the noise gain of the external dry silver enterprise;

[0023] The calculation formula of the channel interference metric is:

[0024]

[0025] Among them, I(f i ,t) is the frequency band f at time t i The interference signal strength on i ,t) is the spectrum weight function of the interference signal, which indicates the influence of the interference signal.

[0026] As a preferred solution of the multi-service fusion method for low-voltage power line carrier communication described in the present invention, wherein: the channel quality Q(f i ) is calculated as:

[0027]

[0028] Among them, λ represents the noise impact factor, which represents the attenuation effect of noise on channel quality. The attenuation exponential function representing the noise level is used to reduce the impact of noise on the signal. i ) The value range represents the relative quality of the channel. The larger the value, the better the channel status.

[0029] As a preferred solution of the multi-service fusion method for low-voltage power line carrier communication described in the present invention, in the step 2, the training process of the deep learning model is:

[0030] S201: Construct a feature vector X(t), including but not limited to the following features:

[0031] X(t)=[Q(f i ),S(f i ,t),N(f i ,t),D(f i )];

[0032] S202: Select a convolutional neural network as the deep learning model structure, use the feature vector X(t) and the corresponding target value Y(t+Δt) as training data, and the target value is expressed as: Y(t+Δt)=Q(f i ,t+Δt);

[0033] The loss function L is defined to measure the prediction error of the model, and its expression is:

[0034]

[0035] Among them, θ is the parameter of the model; Y k is the actual channel quality; is the channel quality predicted by the model; N is the number of samples. The model parameter θ is continuously adjusted through the SGD optimizer to minimize the loss function L(θ), and finally a trained deep learning model is obtained.

[0036] As a preferred solution of the multi-service integration method for low-voltage power line carrier communication described in the present invention, the intelligent service classification mechanism in step three includes the following steps:

[0037] S301: Construct a priority function P(T i ,t), determine the priority of each service based on the service quality requirements and channel status prediction;

[0038] S302: Calculate the normalized priorities of all services so as to allocate bandwidth later;

[0039] S303: Dynamically adjust the data flow bandwidth allocation for each service based on the calculated priority.

[0040] As a preferred solution of the multi-service fusion method for low-voltage power line carrier communication described in the present invention, wherein: the priority function P(T i ,t) is:

[0041]

[0042] Where Z is a normalization factor used to ensure that the sum of the priorities is 1. This item represents the service quality requirement S(T i ) and the current channel bandwidth W(T i ), and the weights of the two are adjusted by an exponential function, where α is the adjustment parameter;

[0043] The expression of the normalization factor Z for all services is:

[0044]

[0045] The present invention also discloses a multi-service fusion system for low-voltage power line carrier communication, which includes the following modules:

[0046] The spectrum sensing module scans all available frequency bands in the power line in real time, detects the channel status of each frequency band, and evaluates the communication quality;

[0047] The channel state prediction module uses a deep learning model to predict future channel state change trends based on historical channel state data and real-time spectrum sensing data;

[0048] Intelligent service classification module, which analyzes transmitted data packets, identifies different types of services, and classifies services based on service requirements and channel status prediction results;

[0049] The priority dynamic adjustment module dynamically adjusts the data flow priority of different services based on the results of intelligent service classification and channel status prediction;

[0050] The bandwidth allocation module dynamically allocates bandwidth resources and optimizes transmission efficiency based on service priorities and current channel conditions.

[0051] The present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the above-mentioned multi-service fusion method for low-voltage power line carrier communication are implemented.

[0052] The present invention also discloses a computer-readable storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of the multi-service fusion method for low-voltage power line carrier communication are implemented.

[0053] Beneficial effects of the present invention:

[0054] The method of the present invention realizes the multi-service integration of low-voltage power line carrier communication through intelligent spectrum perception, deep learning channel prediction and intelligent service identification. In a complex channel environment, it can dynamically adjust communication parameters and service priorities to ensure the communication quality of key services while optimizing resource utilization.

[0055] Specifically:

[0056] 1. Improve spectrum resource utilization: Through the real-time spectrum sensing mechanism, the optimal communication channel is dynamically selected, effectively improving the utilization efficiency of spectrum resources;

[0057] 2. Enhanced communication stability: The deep learning model is used to predict the trend of channel state changes and respond to possible fluctuations in communication quality in advance, thus enhancing the stability and reliability of the system.

[0058] 3. Optimize service transmission: Through intelligent service classification and dynamic priority adjustment mechanism, it ensures the priority transmission of key services in complex environments, thereby improving the transmission efficiency and service quality of the overall network. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0060] Figure 1 The present invention is a flowchart of a multi-service integration method for low-voltage power line carrier communication. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0062] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0063] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0064] Reference Figure 1 , is an embodiment of the present invention, and provides a multi-service fusion method and system for low-voltage power line carrier communication, the method comprising the steps of:

[0065] Step 1: Build a spectrum sensing mechanism and use it to scan all available frequency bands in the power line in real time, detect the channel status of each frequency band, and evaluate the communication quality of each frequency band based on the spectrum sensing results, and select the frequency band with the best channel status as the initial communication channel;

[0066] Furthermore, the spectrum sensing mechanism includes the following steps:

[0067] S101: For all available frequency bands f in the power line i Perform real-time scanning to obtain the signal strength S(f i ,t) and the noise level N(f i ,t);

[0068] S(f i ,t)=P t ·H(f i ,t);

[0069] Among them, P t is the transmitting power, H(f i ,t) is the channel gain, which means in the frequency band f i and signal attenuation and gain at time t;

[0070] The noise level is calculated as follows:

[0071] N(f i ,t)=N0·(1+γ(f i ,t));

[0072] Where N0 is the reference noise power, γ(f i ,t) is the noise gain of the external dry silver

[0073] S102: Calculate the channel interference metric, that is, for each frequency band f i Calculate its interference measure D(f i ), which reflects the degree of external interference to the frequency band;

[0074] Furthermore, the calculation formula of the channel interference metric is:

[0075]

[0076] Among them, I(f i ,t) is the frequency band f at time t i The interference signal strength oni ,t) is the spectrum weight function of the interference signal, which indicates the influence of the interference signal.

[0077] S103: According to the signal strength, noise level and interference measurement data, each frequency band f i Evaluate the channel quality Q(f i ), and in all evaluated channel quality values ​​Q(f i ), select the frequency band with the largest value as the initial communication channel.

[0078] In summary, the channel quality Q(f i ) is calculated as:

[0079]

[0080] Among them, λ represents the noise impact factor, which indicates the attenuation effect of noise on channel quality. It is usually a positive value and is used to adjust the impact of noise on quality assessment. The attenuation exponential function representing the noise level is used to reduce the impact of noise on the signal. i ) represents the relative quality of the channel. The larger the value, the better the channel status. This value is determined by the signal strength, noise level, and interference measurement.

[0081] Step 2: Use historical channel status data and real-time spectrum sensing data to train a deep learning model, which is used to predict the changing trend of channel status in the future and generate prediction results.

[0082] Specifically, in step 2, the training process of the deep learning model is:

[0083] S201: Construct a feature vector X(t), including but not limited to the following features:

[0084] X(t)=[Q(f i ),S(f i ,t),N(f i ,t),D(f i )];

[0085] S202: Select a convolutional neural network as the deep learning model structure, use the feature vector X(t) and the corresponding target value Y(t+Δt) [the channel quality at the future time t+Δt] as training data, and the target value is expressed as: Y(t+Δt)=Q(f i ,t+Δt);

[0086] The loss function L is defined to measure the prediction error of the model, and its expression is:

[0087]

[0088] Among them, θ is the parameter of the model; Y k is the actual channel quality; is the channel quality predicted by the model; N is the number of samples. The model parameter θ is continuously adjusted through the SGD optimizer to minimize the loss function L(θ), and finally a trained deep learning model is obtained.

[0089] The prediction process of the deep learning model is:

[0090] Using the trained deep learning model, input the current real-time feature vector X real (t), predict the channel quality in the future

[0091] Then the predicted channel quality result is Feedback is fed back into the system for further decision-making.

[0092] Step 3: Establish an intelligent service priority mechanism, analyze the transmitted data packets through the intelligent service identification module, identify different types of services, and dynamically adjust the data flow priority of different services based on the service classification results and real-time channel status prediction.

[0093] Specifically:

[0094] S301: Construct a priority function P(T i ,t), determine the priority of each service based on the service quality requirements and channel status prediction;

[0095] The priority function P(T i ,t) is:

[0096]

[0097] Where Z is a normalization factor used to ensure that the sum of the priorities is 1. This item represents the service quality requirement S(T i ) and the current channel bandwidth W(T i ), and the weights of the two are adjusted by an exponential function, where α is the adjustment parameter;

[0098] The expression of the normalization factor Z for all services is:

[0099]

[0100] S302: Calculate the normalized priorities of all services so as to allocate bandwidth later;

[0101] S303: Dynamically adjust the data flow bandwidth allocation for each service based on the calculated priority.

[0102] A multi-service fusion system for low-voltage power line carrier communication includes the following modules: a spectrum sensing module, which scans all available frequency bands in the power line in real time, detects the channel status of each frequency band, and evaluates the communication quality; a channel status prediction module, which uses a deep learning model to predict future channel status change trends based on historical channel status data and real-time spectrum sensing data; an intelligent service classification module, which analyzes transmitted data packets, identifies different types of services, and classifies services according to service requirements and channel status prediction results; a priority dynamic adjustment module, which dynamically adjusts the data flow priority of different services according to the results of intelligent service classification and channel status prediction; a bandwidth allocation module, which dynamically allocates bandwidth resources according to service priority and current channel conditions to optimize transmission efficiency.

[0103] In summary, the method of the present invention realizes the multi-service integration of low-voltage power line carrier communication through intelligent spectrum sensing, deep learning channel prediction and intelligent service identification. In a complex channel environment, it can dynamically adjust communication parameters and service priorities to ensure the communication quality of key services, while optimizing resource utilization. Further, it improves the utilization rate of spectrum resources: through the real-time spectrum sensing mechanism, the optimal communication channel is dynamically selected, which effectively improves the utilization efficiency of spectrum resources; enhances communication stability: through the deep learning model, the channel state change trend is predicted, and possible communication quality fluctuations are responded to in advance, which enhances the stability and reliability of the system; optimizes service transmission: through intelligent service classification and dynamic priority adjustment mechanism, it ensures the priority transmission of key services in complex environments, and improves the transmission efficiency and service quality of the overall network.

[0104] This embodiment also provides a computer device, which is applicable to a multi-service fusion method and system for low-voltage power line carrier communication, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a multi-service fusion method and system for low-voltage power line carrier communication as proposed in the above embodiment.

[0105] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0106] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a multi-service fusion method and system for low-voltage power line carrier communication as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0107] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-service fusion method for low-voltage power line carrier communication, characterized in that: The method comprises the following steps: Step 1: Build a spectrum sensing mechanism and use it to scan all available frequency bands in the power line in real time, detect the channel status of each frequency band, and evaluate the communication quality of each frequency band based on the spectrum sensing results, and select the frequency band with the best channel status as the initial communication channel; Step 2: Use historical channel status data and real-time spectrum sensing data to train a deep learning model, which is used to predict the changing trend of channel status in the future and generate prediction results. Step 3: Establish an intelligent service priority mechanism, analyze the transmitted data packets through the intelligent service identification module, identify different types of services, and dynamically adjust the data flow priority of different services based on the service classification results and real-time channel status prediction.

2. A multi-service integration method for low-voltage power line carrier communication according to claim 1, characterized in that: The spectrum sensing mechanism comprises the following steps: S101: All available frequency bands f in the power line i Perform real-time scanning to obtain the signal strength S(f i , t) and the noise level N(f i , t); S102: Calculate the channel interference metric, that is, for each frequency band f i Calculate its interference measure D(f i ), which reflects the degree of external interference to the frequency band; S103: According to the signal strength, noise level and interference measurement data, each frequency band f i Evaluate the channel quality Q(f i ), and in all evaluated channel quality values ​​Q(f i ), select the frequency band with the largest value as the initial communication channel.

3. A multi-service integration method for low-voltage power line carrier communication according to claim 2, characterized in that: The calculation formula of the signal strength is: S(f i ,t)=P t ·H(f i ,t); Among them, P t is the transmitting power, H(f i , t) is the channel gain, which means that in the frequency band f i and signal attenuation and gain at time t; The noise level is calculated as follows: N(f i ,t)=N0·(1+γ(f i ,t)); Where N0 is the reference noise power, γ(f i , t) is the noise gain of the external dry silver enterprise; The calculation formula of the channel interference metric is: Among them, I(f i , t) is at time t, frequency band f i The interference signal strength on i , t) is the spectrum weight function of the interference signal, which indicates the influence of the interference signal.

4. A multi-service integration method for low-voltage power line carrier communication according to claim 3, characterized in that: The channel quality Q(f i ) is calculated as: Among them, λ represents the noise impact factor, which represents the attenuation effect of noise on channel quality. The attenuation exponential function representing the noise level is used to reduce the impact of noise on the signal. i ) The value range represents the relative quality of the channel. The larger the value, the better the channel status.

5. A multi-service integration method for low-voltage power line carrier communication according to claim 4, characterized in that: In the step 2, the training process of the deep learning model is as follows: S201: Construct a feature vector X(t), including but not limited to the following features: X(t)=[Q(f i ),S(f i ,t),N(f i ,t),D(f i )]; S202: Select a convolutional neural network as the deep learning model structure, use the feature vector X(t) and the corresponding target value Y(t+Δt) as training data, and the target value is expressed as: Y(t+Δt)=Q(f i , t+Δt); The loss function L is defined to measure the prediction error of the model, and its expression is: Among them, θ is the parameter of the model; Y k is the actual channel quality; is the channel quality predicted by the model; N is the number of samples. The model parameter θ is continuously adjusted through the SGD optimizer to minimize the loss function L(θ), and finally a trained deep learning model is obtained.

6. A multi-service integration method for low-voltage power line carrier communication according to claim 5, characterized in that: The intelligent service classification mechanism in step 3 includes the following steps: S301: Construct a priority function P(T i ,t),determine the priority of each service based on the service quality requirements and channel status prediction; S302: Calculate the normalized priorities of all services so as to allocate bandwidth later; S303: Dynamically adjust the data flow bandwidth allocation for each service based on the calculated priority.

7. A multi-service integration method for low-voltage power line carrier communication according to claim 6, characterized in that: The priority function P(T i , t) is expressed as: Where Z is a normalization factor used to ensure that the sum of the priorities is 1. This item represents the service quality requirement S(T i ) and the current channel bandwidth w(T i ), and the weights of the two are adjusted by an exponential function, where α is the adjustment parameter; The expression of the normalization factor Z for all services is:

8. A multi-service fusion system for low-voltage power line carrier communication, based on a multi-service fusion method for low-voltage power line carrier communication according to claim 7, characterized in that: The system includes the following modules: The spectrum sensing module scans all available frequency bands in the power line in real time, detects the channel status of each frequency band, and evaluates the communication quality; The channel state prediction module uses a deep learning model to predict future channel state change trends based on historical channel state data and real-time spectrum sensing data; Intelligent service classification module, which analyzes transmitted data packets, identifies different types of services, and classifies services based on service requirements and channel status prediction results; The priority dynamic adjustment module dynamically adjusts the data flow priority of different services based on the results of intelligent service classification and channel status prediction; The bandwidth allocation module dynamically allocates bandwidth resources and optimizes transmission efficiency based on service priorities and current channel conditions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a multi-service integration method for low-voltage power line carrier communication described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a multi-service integration method for low-voltage power line carrier communication described in any one of claims 1 to 7 are implemented.

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