HPLC and HRF dual-mode communication method and system for smart power grid

Abstract

The invention discloses an HPLC and HRF dual-mode communication method and system for an intelligent power grid, and the method comprises the steps: enabling a source communication node to transmit power grid service data through an HPLC and HRF dual channel, and forming a dual-mode data package; after receiving the data, the target communication node demodulates and recovers service data from the HPLC and HRF signals, synchronously measures and calculates the two paths of signals, and extracts real-time cable fingerprints reflecting power line medium characteristics and real-time space fingerprints reflecting space wireless characteristics; further, performing similarity comparison on the real-time dual-mode fingerprint and a pre-stored source node reference dual-mode fingerprint; and finally, judging the security state of the communication link and the validity of the data in a combined manner according to whether the comparison result meets a credible threshold value or not and by combining the integrity verification of the service data, and executing subsequent network operation accordingly. According to the invention, the physical layer fingerprints are invisibly extracted in normal service communication, so that real-time link authentication without special detection signals is realized, and man-in-the-middle attack and equipment counterfeit are effectively defended.

Description

Technical Field

[0001] This invention relates to the field of power system communication technology, and in particular to a dual-mode communication method and system for smart grids using HPLC and HRF. Background Technology

[0002] As smart grids evolve towards deeper digitalization and intelligence, the communication reliability of the "last mile" in low-voltage distribution substations is crucial. High-speed power line carrier (HPLC) and high-speed radio (HRF) dual-mode communication technology, leveraging the complementary coverage advantages of power line media and wireless space, has become the mainstream solution for building highly reliable local communication networks and is widely used in core operations such as electricity consumption information collection, distributed energy monitoring, and precise load control.

[0003] To ensure the secure transmission of the aforementioned critical business data, existing technologies primarily rely on traditional cryptographic authentication mechanisms. At the network or application layer, the logical identity of access devices is authenticated using digital certificates, pre-shared keys, etc., and the transmitted data is encrypted. However, in complex scenarios involving massive deployments of distribution network equipment, open physical environments, and dynamically changing network topologies, these cryptographic-based "logical security" methods reveal inherent flaws: First, traditional authentication cannot defend against physical layer "man-in-the-middle" attacks and device spoofing. Attackers can illegally access malicious devices on power lines or wireless channels. Once this device obtains legitimate logical identity credentials through password cracking or key theft, it can successfully impersonate a legitimate node to access the network, eavesdropping on, tampering with, or forging business data (such as faking power outages or stealing electricity). Because cryptographic authentication only occurs when a connection is established, once successful, all subsequent data streams are considered trustworthy, allowing attackers to remain lurking within this "trust window" for an extended period.

[0004] Secondly, the active probe-based physical layer authentication introduced to enhance security significantly compromises communication efficiency. To compensate for the shortcomings of cryptography, some improved schemes attempt to utilize the physical characteristics of the channel for device identification. However, the common practice is to require devices to periodically interrupt normal business communication and send dedicated probe signals (such as specific training sequences), with the receiver verifying the device by analyzing the characteristics of these signals. While this method provides a degree of physical binding, it introduces significant additional communication overhead, energy consumption, and processing latency, contradicting the real-time and efficiency requirements of smart grid services and making large-scale application difficult.

[0005] In summary, existing dual-mode communication in smart grids faces a fundamental technical contradiction: logical security relying on cryptography cannot defend against physical access attacks, while the introduction of physical layer authentication inevitably interferes with normal business communication, increasing system complexity and resource consumption.

[0006] Therefore, there is an urgent need for an innovative dual-mode communication security method that can perform real-time and covert verification of the physical identity of communication devices and the integrity of the transmission link without interrupting or affecting normal business data transmission. This would fundamentally solve the core technical problem of "how to achieve real-time link-level security authentication against man-in-the-middle attacks and device spoofing without introducing dedicated detection signals and additional equipment". Summary of the Invention

[0007] In view of the aforementioned deficiencies of the prior art, the technical problem to be solved by the present invention is to provide a dual-mode communication method and system for smart grids using HPLC and HRF, aiming to achieve secure authentication of communication devices and improve communication security without introducing additional equipment.

[0008] To achieve the above objectives, the first aspect of this invention discloses a dual-mode communication method for smart grids using HPLC and HRF, the method comprising: Step S1: The source communication node transmits the power grid service data to be transmitted simultaneously or in time-division multiplexing through the high-speed power line carrier channel and the high-speed wireless channel to form a dual-mode data packet carrying the same service content. Step S2: The target communication node synchronously receives the HPLC signal and HRF signal in the dual-mode data packet, and demodulates and decodes them respectively to recover the power grid service data; Step S3: The target communication node measures and calculates the HPLC signal, extracting a first type of physical layer feature characterizing the characteristics of the power line transmission medium as a real-time cable fingerprint; the target communication node measures and calculates the HRF signal, extracting a second type of physical layer feature characterizing the characteristics of spatial wireless transmission as a real-time spatial fingerprint; wherein, the first type of physical layer feature includes channel impulse response or channel frequency response features determined by line topology, impedance and noise distribution, and the second type of physical layer feature includes angle of arrival, multipath delay distribution or carrier features determined by signal propagation path, multipath structure and spatial orientation; Step S4: The target communication node compares the real-time cable fingerprint and real-time spatial fingerprint with the pre-stored benchmark dual-mode fingerprint corresponding to the source communication node to obtain the dual-mode fingerprint similarity. Step S5: The target communication node determines the security status of the current communication link and the validity of the service data based on whether the dual-mode fingerprint similarity meets the preset trust threshold and the integrity verification result of the power grid service data. Based on the determination result corresponding to the security status of the current communication link and the validity of the service data, the target communication node performs corresponding network coordination or security handling operations.

[0009] Optionally, the reference dual-mode fingerprint is dynamically updated, and the update steps include: The target communication node establishes a dynamic trusted model of the source communication node, and the dynamic trusted model represents the benchmark dual-mode fingerprint as a feature vector that is allowed to fluctuate within a confidence interval. When the security status of multiple consecutive communications is determined to be trustworthy, the parameters of the dynamic trustworthy model are updated with a preset learning rate using the real-time cable fingerprint and the real-time spatial fingerprint, thereby realizing the adaptive progressive update of the baseline dual-mode fingerprint.

[0010] Optionally, the dynamic trustworthy model decomposes the feature vector to achieve precise separation and differentiated management of the sources of change, specifically including: The feature vector of the benchmark dual-mode fingerprint is decomposed into steady-state components, slowly varying components, and transient components. The steady-state components are used to characterize the invariant features that are strongly bound to the physical topology of the power grid and the spatial location of the equipment, and a steady-state confidence interval is configured for them to prevent physical migration attacks. The slowly varying components are used to characterize the features that drift slowly with environmental temperature and humidity and line load, and a dynamic confidence interval based on time series prediction is established for them to achieve the inclusion of legitimate environmental changes.

[0011] Optionally, the similarity comparison in step S4 specifically employs an enhanced decision rule based on association analysis, including: The target communication node calculates the deviation between the real-time cable fingerprint and the cable fingerprint in the reference dual-mode fingerprint, and the deviation between the real-time spatial fingerprint and the spatial fingerprint in the reference dual-mode fingerprint. The target communication node analyzes the physical correlation between the deviation of the real-time cable fingerprint and the deviation of the real-time spatial fingerprint; if the analysis shows that the two deviations exhibit an abnormal decoupling pattern of being uncorrelated or negatively correlated, then it is directly determined that the dual-mode fingerprint similarity does not meet the confidence threshold.

[0012] Optionally, step S3 includes: The target communication node performs time-domain channel estimation on the HPLC signal to obtain the channel impulse response; from the channel impulse response, the number of multipaths, the delay of the main path, and the relative amplitude and delay spread features of each path are extracted to obtain the real-time cable fingerprint; The target communication node receives the HRF signal through an antenna array and performs spatial signal processing on it; using a direction-of-arrival estimation algorithm, the angle of arrival of the HRF signal is extracted as the core feature of the spatial fingerprint; by performing power delay spectrum analysis on the HRF signal, multipath structure features are extracted as auxiliary features of the spatial fingerprint; thus, the real-time spatial fingerprint is obtained.

[0013] Optionally, step S5 includes: If the dual-mode fingerprint similarity meets the trust threshold and the power grid business data passes the integrity verification, then the link is determined to be secure and the data is valid, and the data is allowed to be used for subsequent power grid business. If the dual-mode fingerprint similarity does not meet the trust threshold, but the power grid business data passes the integrity verification, it is determined that the link is at high risk of being attacked by a man in the middle, a security alarm is generated and an isolation procedure is initiated. If the power grid business data fails the integrity check, it is determined to be a communication failure, triggering the data retransmission or path switching mechanism of the source communication node.

[0014] The second aspect of this invention discloses a dual-mode communication system for smart grids using HPLC and HRF. The system includes a source communication node device and a target communication node device. The source communication node device includes a transmitting module, and the target communication node device includes a receiving module, a fingerprint feature extraction module, a similarity comparison module, and a communication processing module. The transmitting module is used to transmit the power grid service data to be transmitted simultaneously or in time-division multiplexing through a high-speed power line carrier channel and a high-speed wireless channel to form a dual-mode data packet carrying the same service content. The receiving module is used to synchronously receive the HPLC signal and HRF signal in the dual-mode data packet, and demodulate and decode them respectively to recover the power grid service data; The fingerprint feature extraction module is used to measure and calculate the HPLC signal to extract a first type of physical layer feature characterizing the characteristics of the power line transmission medium as a real-time cable fingerprint; and to measure and calculate the HRF signal to extract a second type of physical layer feature characterizing the characteristics of spatial wireless transmission as a real-time spatial fingerprint; wherein, the first type of physical layer feature includes channel impulse response or channel frequency response features determined by line topology, impedance and noise distribution, and the second type of physical layer feature includes angle of arrival, multipath delay distribution or carrier features determined by signal propagation path, multipath structure and spatial orientation; The similarity comparison module is used to compare the real-time cable fingerprint and real-time spatial fingerprint with a pre-stored reference dual-mode fingerprint corresponding to the source communication node to obtain the dual-mode fingerprint similarity. The communication processing module is used to jointly determine the security status of the current communication link and the validity of the service data based on whether the dual-mode fingerprint similarity meets a preset trust threshold and in combination with the integrity verification result of the power grid service data, and to perform corresponding network coordination or security processing operations based on the determination result corresponding to the security status of the current communication link and the validity of the service data.

[0015] Optionally, the reference dual-mode fingerprint is dynamically updated, and the update steps include: The target communication node establishes a dynamic trusted model of the source communication node, and the dynamic trusted model represents the benchmark dual-mode fingerprint as a feature vector that is allowed to fluctuate within a confidence interval. When the security status of multiple consecutive communications is determined to be trustworthy, the parameters of the dynamic trustworthy model are updated with a preset learning rate using the real-time cable fingerprint and the real-time spatial fingerprint, thereby realizing the adaptive progressive update of the baseline dual-mode fingerprint.

[0016] Optionally, the dynamic trustworthy model decomposes the feature vector to achieve precise separation and differentiated management of the sources of change, specifically including: The feature vector of the benchmark dual-mode fingerprint is decomposed into steady-state components, slowly varying components, and transient components. The steady-state components are used to characterize the invariant features that are strongly bound to the physical topology of the power grid and the spatial location of the equipment, and a steady-state confidence interval is configured for them to prevent physical migration attacks. The slowly varying components are used to characterize the features that drift slowly with environmental temperature and humidity and line load, and a dynamic confidence interval based on time series prediction is established for them to achieve the inclusion of legitimate environmental changes.

[0017] Optionally, the similarity comparison module is based on enhanced decision rules for association analysis, including: The similarity comparison module calculates the deviation between the real-time cable fingerprint and the cable fingerprint in the reference dual-mode fingerprint, and the deviation between the real-time spatial fingerprint and the spatial fingerprint in the reference dual-mode fingerprint. The similarity comparison module analyzes the physical correlation between the deviation of the real-time cable fingerprint and the deviation of the real-time spatial fingerprint; if the analysis shows that the two deviations exhibit an abnormal decoupling pattern of being uncorrelated or negatively correlated, then it is directly determined that the dual-mode fingerprint similarity does not meet the confidence threshold.

[0018] Optionally, the fingerprint feature extraction module is specifically used for: The target communication node performs time-domain channel estimation on the HPLC signal to obtain the channel impulse response; from the channel impulse response, the number of multipaths, the delay of the main path, and the relative amplitude and delay spread features of each path are extracted to obtain the real-time cable fingerprint; The target communication node receives the HRF signal through an antenna array and performs spatial signal processing on it; using a direction-of-arrival estimation algorithm, the angle of arrival of the HRF signal is extracted as the core feature of the spatial fingerprint; by performing power delay spectrum analysis on the HRF signal, multipath structure features are extracted as auxiliary features of the spatial fingerprint; thus, the real-time spatial fingerprint is obtained.

[0019] Optionally, the communication processing module is specifically used for: If the dual-mode fingerprint similarity meets the trust threshold and the power grid business data passes the integrity verification, then the link is determined to be secure and the data is valid, and the data is allowed to be used for subsequent power grid business. If the dual-mode fingerprint similarity does not meet the trust threshold, but the power grid business data passes the integrity verification, it is determined that the link is at high risk of being attacked by a man in the middle, a security alarm is generated and an isolation procedure is initiated. If the power grid business data fails the integrity check, it is determined to be a communication failure, triggering the data retransmission or path switching mechanism of the source communication node.

[0020] The beneficial effects of this invention are as follows: 1. This invention achieves a fundamental unity of security and efficiency, constructing a physical layer security defense without affecting business communication. This invention uses normal business data packets as "invisible probes," extracting fingerprints by analyzing the inherent distortion (decay) after passing through the dual-mode channel at the receiving end, thereby completely eliminating the bandwidth occupation, increased latency, and energy consumption caused by periodically sending dedicated probe signals. This allows high-strength physical layer authentication to operate routinely, resolving the core contradiction between security mechanisms and communication efficiency in traditional solutions. 2. This invention provides a disruptive means to resist "man-in-the-middle attacks" and physical spoofing, resulting in a significant leap in security. Traditional cryptographic authentication cannot defend against malicious devices accessing the physical link. This invention verifies the physical characteristics of the signal transmission path itself (cable fingerprints are bound to power line topology, spatial fingerprints are bound to three-dimensional coordinates), ensuring that any intermediate device inserted outside the original legitimate path immediately changes the channel characteristics and is detected. In particular, the dual-mode fingerprint association analysis rules can effectively identify collaborative deception attacks where the dual-mode association is broken even if the single-mode fingerprint change is not obvious, resulting in a security level far exceeding that of single-mode authentication schemes. 3. This invention possesses strong environmental adaptability and system robustness, ensuring long-term reliable practical application. By introducing a dynamic trust model and separating and managing the steady-state / gradually varying components of fingerprint features, the system can intelligently distinguish between "legitimate drift" such as equipment aging and changes in environmental temperature and humidity, and "malicious attacks" such as unauthorized equipment movement and line tampering. This significantly reduces the false alarm rate, enabling the security mechanism to operate stably in real and complex power grid operating environments, achieving adaptive and long-term effectiveness of the security strategy.

[0021] In summary, this invention transforms the physical characteristics of dual-mode communication from a technical feature into a security resource, achieving deep integration of the communication process and security authentication in a near-zero-overhead manner, thus providing technical support for building a highly reliable, secure, and efficient smart grid communication system. Attached Figure Description

[0022] Figure 1This is a flowchart illustrating a dual-mode communication method for smart grids using HPLC and HRF, provided in a specific embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a smart grid HPLC and HRF dual-mode communication system provided in a specific embodiment of the present invention. Detailed Implementation

[0023] This invention discloses a dual-mode communication method and system for smart grids using HPLC and HRF. Those skilled in the art can refer to the content of this document and appropriately modify the technical details to implement it. It should be particularly noted that all similar substitutions and modifications are obvious to those skilled in the art and are considered to be included in this invention. The methods and applications of this invention have been described through preferred embodiments. Those skilled in the art can clearly modify or appropriately change and combine the methods and applications described herein without departing from the content, spirit, and scope of this invention to implement and apply the technology of this invention.

[0024] The applicant's research revealed a fundamental technical contradiction in existing smart grid dual-mode communication: the logical security based on cryptography cannot defend against physical access attacks, while the introduction of physical layer authentication inevitably interferes with normal business communication, increasing system complexity and resource consumption.

[0025] Therefore, embodiments of the present invention provide a dual-mode communication method for smart grids using HPLC and HRF, such as... Figure 1 As shown, the method includes: Step S1: The source communication node transmits the power grid service data to be transmitted simultaneously or in time-division multiplexing via a high-speed power line carrier channel and a high-speed wireless channel, forming a dual-mode data packet carrying the same service content.

[0026] It should be noted that step S1 is the starting point of this embodiment of the invention, and its core lies in the concept of "business data as probe". The source communication node (such as a smart meter or concentrator) transmits the same set of power grid business data (such as voltage value, switch status) on both HPLC (power line) and HRF (wireless) physical media through scheduling. This does not send two independent copies, but rather constitutes a "logically related bimodal signal pair". The purpose is to provide two physical channel observation samples originating from the same moment and the same event for subsequent steps, which is the foundation for achieving stealth authentication.

[0027] It is worth mentioning that in specific applications, if only a single model is used, only the single model can be verified.

[0028] Step S2: The target communication node synchronously receives the HPLC signal and HRF signal in the dual-mode data packet, and demodulates and decodes them respectively to recover the power grid business data.

[0029] It should be noted that the target communication node (such as a concentrator or adjacent node) receives both signals simultaneously. The term "synchronous" here emphasizes capturing the temporal correlation for subsequent joint analysis. By demodulating and decoding separately, the basic communication function—recovering the original service data—is first achieved. This step ensures that the method's fundamental communication functions are unaffected; security authentication is an enhancement layer superimposed on top of this.

[0030] Step S3: The target communication node measures and calculates the HPLC signal, and extracts the first type of physical layer features characterizing the characteristics of the power line transmission medium as a real-time cable fingerprint; the target communication node measures and calculates the HRF signal, and extracts the second type of physical layer features characterizing the characteristics of spatial wireless transmission as a real-time spatial fingerprint.

[0031] The first type of physical layer features includes channel impulse response or channel frequency response features determined by line topology, impedance, and noise distribution. The second type of physical layer features includes angle of arrival, multipath delay distribution, or carrier features determined by signal propagation path, multipath structure, and spatial orientation.

[0032] It should be noted that: Step S3 is the core step of this embodiment of the invention. The target communication node is not satisfied with decoding data, but takes the received raw HPLC and HRF signal waveforms as input and performs high-precision physical layer signal processing.

[0033] For HPLC signals, their "channel impulse response" or "channel frequency response" is extracted through measurement and calculation (such as correlation processing and Fourier transform). These characteristics are like the "acoustic fingerprint" of power lines, uniquely determined by the specific line impedance, branch topology, and noise environment through which the signal travels, thus forming a "real-time cable fingerprint" that binds the physical connection point between the device and the power line.

[0034] For HRF signals, spatial processing (such as using an antenna array) is used to extract their "angle of arrival" and "multipath delay distribution". These characteristics are like the device's "spatial coordinates" and "environmental profile", uniquely determined by the signal's propagation path and the reflection and scattering characteristics of surrounding objects, thus forming a "real-time spatial fingerprint" that binds the device's physical location in three-dimensional space.

[0035] This step simultaneously transforms the business communication process into a precise detection of the physical characteristics of the communication channel.

[0036] In this specific embodiment, step S3 includes: The target communication node performs time-domain channel estimation on the HPLC signal to obtain the channel impulse response; from the channel impulse response, the number of multipaths, the delay of the main path, and the relative amplitude and delay spread features of each path are extracted to obtain the real-time cable fingerprint. The target communication node receives HRF signals through an antenna array and performs spatial signal processing on them. Using a direction-of-arrival estimation algorithm, the angle of arrival of the HRF signal is extracted as the core feature of the spatial fingerprint. By performing power delay spectrum analysis on the HRF signal, multipath structure features are extracted as auxiliary features of the spatial fingerprint. Thus, a real-time spatial fingerprint is obtained.

[0037] It should be noted that this embodiment describes the detailed technical means for extracting dual-mode fingerprints in step S3. For cable fingerprints, the specific path for obtaining the channel impulse response through "time-domain channel estimation" and extracting multipath parameters from it is specified. For spatial fingerprints, it is specified that they need to be received through an "antenna array" and that "direction of arrival estimation" and "power delay spectrum analysis" are used in combination to obtain spatial orientation and multipath structure characteristics. These limitations transform the abstract concept of "feature extraction" into a concrete operation that can be implemented in the field, defining a specific physical layer signal processing method for achieving the core function optimization.

[0038] Step S4: The target communication node compares the real-time cable fingerprint and real-time spatial fingerprint with the pre-stored benchmark dual-mode fingerprint corresponding to the source communication node to obtain the dual-mode fingerprint similarity.

[0039] It should be noted that step S4 completes the transformation from feature to decision. The target node calculates the similarity between the real-time extracted dual-mode fingerprint and the baseline dual-mode fingerprint of the source node, which has been pre-learned and stored in a secure state. The comparison is not a simple "equality" judgment, but rather a similarity score calculated based on a model (such as a dynamic trust model). The core function of this step is to quantify the difference between the current physical link and the expected legitimate link, providing a quantitative basis for security decisions.

[0040] In this specific embodiment, the baseline dual-mode fingerprint is dynamically updated, and the update steps include: The target communication node establishes a dynamic trust model of the source communication node. The dynamic trust model represents the benchmark dual-mode fingerprint as a feature vector that is allowed to fluctuate within the confidence interval. When the security status of multiple consecutive communications is determined to be trustworthy, the parameters of the dynamic trustworthy model are updated with a preset learning rate using real-time cable fingerprints and real-time spatial fingerprints, thereby achieving adaptive progressive update of the baseline dual-mode fingerprint.

[0041] It should be noted that this embodiment establishes a "dynamic trust model," transforming fingerprints from static reference values ​​into statistically significant feature vectors that are allowed to fluctuate within a reasonable "confidence interval." When a communication link is continuously determined to be trustworthy, the system cautiously incorporates new real-time fingerprint data into the model using a controlled parameter—the "learning rate"—thus progressively updating the baseline. This solves the core problem of fixed fingerprints failing due to slow environmental changes, enabling the authentication system to possess self-learning and adaptive capabilities, ensuring long-term availability.

[0042] Furthermore, the dynamic trust model in this specific embodiment achieves precise separation and differentiated management of sources of change by decomposing feature vectors, specifically including: The feature vector of the benchmark dual-mode fingerprint is decomposed into steady-state components, slowly varying components, and transient components. The steady-state components are used to characterize the invariant features that are strongly bound to the physical topology of the power grid and the spatial location of the equipment, and a steady-state confidence interval is configured for them to prevent physical migration attacks. The slowly varying components are used to characterize the features that drift slowly with the ambient temperature and humidity and the line load, and a dynamic confidence interval based on time series prediction is established for them to achieve the inclusion of legitimate environmental changes.

[0043] It should be noted that this embodiment further defines the core implementation method of the dynamic trust model—feature decomposition and differential management. It intelligently decomposes complex fingerprint feature vectors into "steady-state components," "gradually varying components," and "transient components." Based on the physical meaning of different components (e.g., steady-state components are bound to hard physical topology, while gradually varying components reflect soft environmental changes), the system implements differentiated management strategies, particularly establishing dynamic confidence intervals based on time series predictions for the gradually varying components. This precise separation management enables the system to strictly prevent illegal physical migration while accommodating legitimate environmental drift, significantly improving the accuracy and robustness of authentication.

[0044] In this specific embodiment, the similarity comparison in step S4 specifically adopts an enhanced decision rule based on association analysis, including: The target communication node calculates the deviation between the real-time cable fingerprint and the reference dual-mode fingerprint, as well as the deviation between the real-time spatial fingerprint and the reference dual-mode fingerprint. The target communication node analyzes the physical correlation between the deviation of the real-time cable fingerprint and the deviation of the real-time spatial fingerprint. If the analysis shows that the two deviations exhibit an abnormal decoupling pattern of being uncorrelated or negatively correlated, then it is directly determined that the dual-mode fingerprint similarity does not meet the confidence threshold.

[0045] It should be noted that this embodiment defines an advanced similarity comparison rule, namely "enhanced decision based on correlation analysis." It goes beyond simply judging whether two fingerprints are valid; instead, it innovatively calculates and analyzes the "physical correlation" between the deviations of the cable fingerprint and the spatial fingerprint. If the changes in the dual-mode fingerprints, which should have a physical correlation, exhibit an "abnormal decoupling pattern" that is uncorrelated or negatively correlated, it is directly judged as abnormal, even if neither exceeds a threshold. This rule can effectively identify complex deception attacks that attempt to collaboratively forge fingerprints on both modes but cannot simulate genuine physical correlation, greatly enhancing the system's anti-counterfeiting capabilities.

[0046] Step S5: The target communication node determines the security status of the current communication link and the validity of the business data based on whether the dual-mode fingerprint similarity meets the preset trust threshold and the integrity verification result of the power grid business data. Based on the determination result corresponding to the security status of the current communication link and the validity of the business data, the target communication node performs corresponding network coordination or security handling operations.

[0047] It should be noted that step S5 is the output and closed loop of this embodiment of the invention. The decision logic integrates "physical layer evidence" (dual-mode fingerprint similarity) and "data layer evidence" (verification of business data such as CRC). Only when both pass is the link secure and the data completely trustworthy determined. If the fingerprint is abnormal but the data is correct, it strongly suggests the existence of a "transparent" man-in-the-middle attack, and the system will trigger an alarm and isolation. This dual-evidence chain decision mechanism enables this embodiment of the invention to detect both data tampering and link hijacking that has not been tampered with but has been eavesdropped on, achieving a more profound proactive security defense than traditional methods. Finally, the decision result drives network coordination or security handling operations, forming a complete closed loop of perception-decision-execution.

[0048] In this specific embodiment, step S5 includes: If the similarity between the two-mode fingerprints meets the trust threshold and the power grid business data passes the integrity verification, then the link is deemed secure and the data is valid, and the data is allowed to be used for subsequent power grid business. If the similarity of the dual-mode fingerprint does not meet the trust threshold, but the power grid business data passes the integrity verification, it is determined that the link is at high risk of being attacked by a man in the middle, a security alarm is generated and the isolation procedure is initiated. If the power grid business data fails the integrity check, it is determined as a communication failure, triggering the data retransmission or path switching mechanism of the source communication node.

[0049] It should be noted that this embodiment clearly defines the specific logical output and corresponding handling of the "joint determination" in step S5. It clarifies three typical scenarios and their conclusions: if both verifications pass, normal processing is performed; if the fingerprint is abnormal but the data is intact, it is determined to be a high-risk man-in-the-middle attack and isolation is initiated; if only the data is corrupted, it is considered a normal communication failure and retransmission is triggered. This design makes the system's security response refined and closed-loop, ensuring that security threats can be correctly classified and handled in a timely and appropriate manner, forming a complete security decision-making and execution chain.

[0050] The core principle of this invention is to simultaneously use normal power grid service data packets as "invisible probes." At the receiving end, by analyzing the inherent signal distortions (i.e., channel characteristics) generated during transmission in two different physical media, HPLC and HRF, which are strongly correlated with line topology and spatial location, the "cable fingerprint" and "spatial fingerprint," which are difficult to clone, are extracted in real time. By dynamically comparing and correlating the real-time dual-mode fingerprint with a legitimate benchmark, the physical identity of the communication equipment and the integrity of the transmission link can be continuously and covertly verified without interrupting services or adding dedicated signaling, thereby achieving essential defense against physical layer security threats such as man-in-the-middle attacks.

[0051] This invention achieves a fundamental balance between security and efficiency, constructing a physical layer security defense without affecting business communication. This invention uses normal business data packets as "stealth probes," extracting fingerprints by analyzing the inherent distortion (decay) of the data packets after passing through a dual-mode channel at the receiving end. This completely eliminates the bandwidth consumption, increased latency, and energy consumption caused by periodically sending dedicated probe signals. This allows high-strength physical layer authentication to operate routinely, resolving the core contradiction between security mechanisms and communication efficiency in traditional solutions.

[0052] This invention provides disruptive measures against "man-in-the-middle" attacks and physical spoofing, resulting in a significant leap in security. Traditional cryptographic authentication cannot defend against malicious devices that connect to the physical link. This invention verifies the physical characteristics of the signal transmission path itself (cable fingerprints are bound to power line topology, and spatial fingerprints are bound to three-dimensional coordinates), ensuring that any intermediate device inserted outside the original legitimate path will immediately alter channel characteristics and be detected. In particular, the dual-mode fingerprint association analysis rules can effectively identify collaborative spoofing attacks where the dual-mode association is broken even if the single-mode fingerprint changes are not obvious, achieving a security level far exceeding that of single-mode authentication schemes.

[0053] The embodiments of this invention possess strong environmental adaptability and system robustness, ensuring long-term reliable practical application. By introducing a dynamic trust model and separating and managing the steady-state / gradually varying components of fingerprint features, the system can intelligently distinguish between "legitimate drift" such as equipment aging and changes in environmental temperature and humidity, and "malicious attacks" such as unauthorized equipment movement and line tampering. This significantly reduces the false alarm rate, enabling the security mechanism to operate stably in real and complex power grid operating environments, achieving adaptive and long-term effectiveness of the security strategy.

[0054] In summary, the embodiments of the present invention transform the physical characteristics of dual-mode communication from a technical feature into a security resource, achieving deep integration of the communication process and security authentication in a near-zero-overhead manner, and providing technical support for building a highly reliable, secure, and efficient smart grid communication system.

[0055] Based on the above-described HPLC and HRF dual-mode communication method for smart grids, this invention also provides an HPLC and HRF dual-mode communication system for smart grids, such as... Figure 2 As shown, the system includes: a source communication node device 21 and a target communication node device 31. The source communication node device 21 includes a sending module 211, and the target communication node device 31 includes a receiving module 311, a fingerprint feature extraction module 312, a similarity comparison module 313, and a communication processing module 314. The transmitting module 211 is used to transmit the power grid service data to be transmitted simultaneously or in time-division multiplexing through a high-speed power line carrier channel and a high-speed wireless channel to form a dual-mode data packet carrying the same service content. The receiving module 311 is used to synchronously receive the HPLC signal and HRF signal in the dual-mode data packet, and demodulate and decode them respectively to recover the power grid service data; The fingerprint feature extraction module 312 is used to measure and calculate HPLC signals to extract first-type physical layer features characterizing the characteristics of power line transmission media as real-time cable fingerprints; and to measure and calculate HRF signals to extract second-type physical layer features characterizing spatial wireless transmission characteristics as real-time spatial fingerprints. The first-type physical layer features include channel impulse response or channel frequency response features determined by line topology, impedance, and noise distribution; and the second-type physical layer features include angle of arrival, multipath delay distribution, or carrier features determined by signal propagation path, multipath structure, and spatial orientation. The similarity comparison module 313 is used to compare the real-time cable fingerprint and the real-time spatial fingerprint with the pre-stored benchmark dual-mode fingerprint corresponding to the source communication node to obtain the dual-mode fingerprint similarity. The communication processing module 314 is used to jointly determine the security status of the current communication link and the validity of the business data based on whether the similarity of the dual-mode fingerprint meets the preset trust threshold and in combination with the integrity verification result of the power grid business data, and to perform corresponding network coordination or security processing operations based on the determination result corresponding to the security status of the current communication link and the validity of the business data.

[0056] Optionally, the baseline dual-mode fingerprint is dynamically updated, and the update steps include: The target communication node establishes a dynamic trust model of the source communication node. The dynamic trust model represents the benchmark dual-mode fingerprint as a feature vector that is allowed to fluctuate within the confidence interval. When the security status of multiple consecutive communications is determined to be trustworthy, the parameters of the dynamic trustworthy model are updated with a preset learning rate using real-time cable fingerprints and real-time spatial fingerprints, thereby achieving adaptive progressive update of the baseline dual-mode fingerprint.

[0057] Optionally, the dynamic trust model decomposes the feature vector to achieve precise separation and differentiated management of the sources of change, specifically including: The feature vector of the benchmark dual-mode fingerprint is decomposed into steady-state components, slowly varying components, and transient components. The steady-state components are used to characterize the invariant features that are strongly bound to the physical topology of the power grid and the spatial location of the equipment, and a steady-state confidence interval is configured for them to prevent physical migration attacks. The slowly varying components are used to characterize the features that drift slowly with the ambient temperature and humidity and the line load, and a dynamic confidence interval based on time series prediction is established for them to achieve the inclusion of legitimate environmental changes.

[0058] Optionally, the similarity comparison module 313 includes enhanced decision rules based on association analysis, including: The similarity comparison module 313 calculates the deviation between the real-time cable fingerprint and the reference dual-mode fingerprint, as well as the deviation between the real-time spatial fingerprint and the reference dual-mode fingerprint. The similarity comparison module 313 analyzes the physical correlation between the deviation of the real-time cable fingerprint and the deviation of the real-time spatial fingerprint; if the analysis shows that the two deviations exhibit an abnormal decoupling pattern of being uncorrelated or negatively correlated, then it is directly determined that the dual-mode fingerprint similarity does not meet the confidence threshold.

[0059] Optionally, the fingerprint feature extraction module 312 is specifically used for: The target communication node performs time-domain channel estimation on the HPLC signal to obtain the channel impulse response; from the channel impulse response, the number of multipaths, the delay of the main path, and the relative amplitude and delay spread features of each path are extracted to obtain the real-time cable fingerprint. The target communication node receives HRF signals through an antenna array and performs spatial signal processing on them. Using a direction-of-arrival estimation algorithm, the angle of arrival of the HRF signal is extracted as the core feature of the spatial fingerprint. By performing power delay spectrum analysis on the HRF signal, multipath structure features are extracted as auxiliary features of the spatial fingerprint. Thus, a real-time spatial fingerprint is obtained.

[0060] Optionally, the communication processing module 314 is specifically used for: If the similarity between the two-mode fingerprints meets the trust threshold and the power grid business data passes the integrity verification, then the link is deemed secure and the data is valid, and the data is allowed to be used for subsequent power grid business. If the similarity of the dual-mode fingerprint does not meet the trust threshold, but the power grid business data passes the integrity verification, it is determined that the link is at high risk of being attacked by a man in the middle, a security alarm is generated and the isolation procedure is initiated. If the power grid business data fails the integrity check, it is determined as a communication failure, triggering the data retransmission or path switching mechanism of the source communication node.

[0061] This invention utilizes normally transmitted power grid service data packets as stealth probes. At the receiving end, it extracts and compares the "cable fingerprint" of the HPLC signal with the "spatial fingerprint" of the HRF signal in real time. This achieves continuous and covert authentication of the physical identity of communication devices and the integrity of the transmission link without introducing any dedicated probe signals or additional overhead. This invention fundamentally solves the problem that traditional cryptographic security cannot defend against physical layer man-in-the-middle attacks. It also overcomes the drawback of active probe authentication that impairs communication efficiency. Furthermore, it intelligently adapts to environmental changes with a dynamic trust model, significantly improving the inherent security, transmission reliability, and overall operational resilience of the smart grid dual-mode communication system.

[0062] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0063] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0064] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A dual-mode communication method using HPLC and HRF for smart grids, characterized in that, The method includes: Step S1: The source communication node transmits the power grid service data to be transmitted simultaneously or in time-division multiplexing through the high-speed power line carrier channel and the high-speed wireless channel to form a dual-mode data packet carrying the same service content. Step S2: The target communication node synchronously receives the HPLC signal and HRF signal in the dual-mode data packet, and demodulates and decodes them respectively to recover the power grid service data; Step S3: The target communication node measures and calculates the HPLC signal, extracting a first type of physical layer feature characterizing the characteristics of the power line transmission medium as a real-time cable fingerprint; the target communication node measures and calculates the HRF signal, extracting a second type of physical layer feature characterizing the characteristics of spatial wireless transmission as a real-time spatial fingerprint; wherein, the first type of physical layer feature includes channel impulse response or channel frequency response features determined by line topology, impedance and noise distribution, and the second type of physical layer feature includes angle of arrival, multipath delay distribution or carrier features determined by signal propagation path, multipath structure and spatial orientation; Step S4: The target communication node compares the real-time cable fingerprint and real-time spatial fingerprint with the pre-stored benchmark dual-mode fingerprint corresponding to the source communication node to obtain the dual-mode fingerprint similarity. Step S5: The target communication node determines the security status of the current communication link and the validity of the service data based on whether the dual-mode fingerprint similarity meets the preset trust threshold and the integrity verification result of the power grid service data. Based on the determination result corresponding to the security status of the current communication link and the validity of the service data, the target communication node performs corresponding network coordination or security handling operations.

2. The HPLC and HRF dual-mode communication method for smart grids according to claim 1, characterized in that, The baseline dual-mode fingerprint is dynamically updated, and the update steps include: The target communication node establishes a dynamic trusted model of the source communication node, and the dynamic trusted model represents the benchmark dual-mode fingerprint as a feature vector that is allowed to fluctuate within a confidence interval. When the security status of multiple consecutive communications is determined to be trustworthy, the parameters of the dynamic trustworthy model are updated with a preset learning rate using the real-time cable fingerprint and the real-time spatial fingerprint, thereby realizing the adaptive progressive update of the baseline dual-mode fingerprint.

3. The HPLC and HRF dual-mode communication method for smart grids according to claim 2, characterized in that, The dynamic trustworthy model achieves precise separation and differentiated management of sources of change by decomposing the feature vector, specifically including: The feature vector of the benchmark dual-mode fingerprint is decomposed into steady-state components, slowly varying components, and transient components. The steady-state components are used to characterize the invariant features that are strongly bound to the physical topology of the power grid and the spatial location of the equipment, and a steady-state confidence interval is configured for them to prevent physical migration attacks. The slowly varying components are used to characterize the features that drift slowly with environmental temperature and humidity and line load, and a dynamic confidence interval based on time series prediction is established for them to achieve the inclusion of legitimate environmental changes.

4. The HPLC and HRF dual-mode communication method for smart grids according to claim 1, characterized in that, The similarity comparison in step S4 specifically employs an enhanced decision rule based on association analysis, including: The target communication node calculates the deviation between the real-time cable fingerprint and the cable fingerprint in the reference dual-mode fingerprint, and the deviation between the real-time spatial fingerprint and the spatial fingerprint in the reference dual-mode fingerprint. The target communication node analyzes the physical correlation between the deviation of the real-time cable fingerprint and the deviation of the real-time spatial fingerprint; if the analysis shows that the two deviations exhibit an abnormal decoupling pattern of being uncorrelated or negatively correlated, then it is directly determined that the dual-mode fingerprint similarity does not meet the confidence threshold.

5. The HPLC and HRF dual-mode communication method for smart grids according to claim 1, characterized in that, Step S3 includes: The target communication node performs time-domain channel estimation on the HPLC signal to obtain the channel impulse response; from the channel impulse response, the number of multipaths, the delay of the main path, and the relative amplitude and delay spread features of each path are extracted to obtain the real-time cable fingerprint; The target communication node receives the HRF signal through an antenna array and performs spatial signal processing on it; using a direction-of-arrival estimation algorithm, the angle of arrival of the HRF signal is extracted as the core feature of the spatial fingerprint; by performing power delay spectrum analysis on the HRF signal, multipath structure features are extracted as auxiliary features of the spatial fingerprint; thus, the real-time spatial fingerprint is obtained.

6. The HPLC and HRF dual-mode communication method for smart grids according to claim 1, characterized in that, Step S5 includes: If the dual-mode fingerprint similarity meets the trust threshold and the power grid business data passes the integrity verification, then the link is determined to be secure and the data is valid, and the data is allowed to be used for subsequent power grid business. If the dual-mode fingerprint similarity does not meet the trust threshold, but the power grid business data passes the integrity verification, it is determined that the link is at high risk of being attacked by a man in the middle, a security alarm is generated and an isolation procedure is initiated. If the power grid business data fails the integrity check, it is determined to be a communication failure, triggering the data retransmission or path switching mechanism of the source communication node.

7. A dual-mode communication system for smart grids using HPLC and HRF, characterized in that, The system includes: a source communication node device and a target communication node device. The source communication node device includes a sending module, and the target communication node device includes a receiving module, a fingerprint feature extraction module, a similarity comparison module, and a communication processing module. The transmitting module is used to transmit the power grid service data to be transmitted simultaneously or in time-division multiplexing through a high-speed power line carrier channel and a high-speed wireless channel to form a dual-mode data packet carrying the same service content. The receiving module is used to synchronously receive the HPLC signal and HRF signal in the dual-mode data packet, and demodulate and decode them respectively to recover the power grid service data; The fingerprint feature extraction module is used to measure and calculate the HPLC signal to extract a first type of physical layer feature characterizing the characteristics of the power line transmission medium as a real-time cable fingerprint; and to measure and calculate the HRF signal to extract a second type of physical layer feature characterizing the characteristics of spatial wireless transmission as a real-time spatial fingerprint; wherein, the first type of physical layer feature includes channel impulse response or channel frequency response features determined by line topology, impedance and noise distribution, and the second type of physical layer feature includes angle of arrival, multipath delay distribution or carrier features determined by signal propagation path, multipath structure and spatial orientation; The similarity comparison module is used to compare the real-time cable fingerprint and real-time spatial fingerprint with a pre-stored reference dual-mode fingerprint corresponding to the source communication node to obtain the dual-mode fingerprint similarity. The communication processing module is used to jointly determine the security status of the current communication link and the validity of the service data based on whether the dual-mode fingerprint similarity meets a preset trust threshold and in combination with the integrity verification result of the power grid service data, and to perform corresponding network coordination or security processing operations based on the determination result corresponding to the security status of the current communication link and the validity of the service data.

8. The HPLC and HRF dual-mode communication system for smart grids according to claim 7, characterized in that, The baseline dual-mode fingerprint is dynamically updated, and the update steps include: The target communication node establishes a dynamic trusted model of the source communication node, and the dynamic trusted model represents the benchmark dual-mode fingerprint as a feature vector that is allowed to fluctuate within a confidence interval. When the security status of multiple consecutive communications is determined to be trustworthy, the parameters of the dynamic trustworthy model are updated with a preset learning rate using the real-time cable fingerprint and the real-time spatial fingerprint, thereby realizing the adaptive progressive update of the baseline dual-mode fingerprint.

9. The HPLC and HRF dual-mode communication system for smart grids according to claim 8, characterized in that, The dynamic trustworthy model achieves precise separation and differentiated management of sources of change by decomposing the feature vector, specifically including: The feature vector of the benchmark dual-mode fingerprint is decomposed into steady-state components, slowly varying components, and transient components. The steady-state components are used to characterize the invariant features that are strongly bound to the physical topology of the power grid and the spatial location of the equipment, and a steady-state confidence interval is configured for them to prevent physical migration attacks. The slowly varying components are used to characterize the features that drift slowly with environmental temperature and humidity and line load, and a dynamic confidence interval based on time series prediction is established for them to achieve the inclusion of legitimate environmental changes.

10. The HPLC and HRF dual-mode communication system for smart grids according to claim 7, characterized in that, The similarity comparison module is based on enhanced decision rules for association analysis, including: The similarity comparison module calculates the deviation between the real-time cable fingerprint and the cable fingerprint in the reference dual-mode fingerprint, and the deviation between the real-time spatial fingerprint and the spatial fingerprint in the reference dual-mode fingerprint. The similarity comparison module analyzes the physical correlation between the deviation of the real-time cable fingerprint and the deviation of the real-time spatial fingerprint; if the analysis shows that the two deviations exhibit an abnormal decoupling pattern of being uncorrelated or negatively correlated, then it is directly determined that the dual-mode fingerprint similarity does not meet the confidence threshold.

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