Electric power Internet of Things covert communication method and system and control master station

Abstract

The invention provides an electric power Internet of Things covert communication method and system and a control master station, and relates to the technical field of electric power wireless communication security. The method comprises the following steps: sending a pilot signal to each controlled node in the power Internet of Things to obtain a current channel state parameter estimated by each controlled node based on the pilot signal; receiving communication data sent by each controlled node under the interference signal of the corresponding interference node, and dynamically adjusting the interference transmission power of the interference node based on the current channel state parameter and the current transmission power; optimizing a transmitting time window by minimizing the signal detection probability of a monitoring node to a communication channel under different time windows; dynamically optimizing the current transmitting power of the controlled node by maximizing the channel transmission rate; and controlling to carry out covert communication of the controlled node in the current communication period according to the optimization result of the transmitting time window and the transmitting power. According to the invention, the problem of hidden failure caused by easy monitoring and identification in the wireless communication process of the power Internet of Things is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power wireless communication security, in particular to a power internet of things hidden communication method and system and a control master station. BACKGROUND

[0002] With the continuous improvement of the network and intelligence of the power system, the communication security demand of the power system presents a trend of changing from "content encryption" to "behavior concealment". Especially in the current complex power operation environment, electromagnetic interference is frequent, and the risk of external monitoring is increasing, which brings hidden dangers such as information hijacking and link blocking. The communication concealment has become a key bottleneck restricting the stable operation and the improvement of the security protection ability. The power internet of things realizes real-time monitoring of the device state in the power system, improvement of operation and maintenance efficiency, and intelligent decision support through digital technology. Therefore, the overall anti-interference and communication security level of the power internet of things have important practical significance and engineering application value for the safe and stable operation of the power system.

[0003] The existing communication concealment method includes encrypted transmission, frequency hopping or spread spectrum scrambling code strategy, etc. The probability of being recognized by the attacker is reduced by changing the signal characteristics or content coding mode. For this kind of hidden communication mode, the attacker can realize the reverse identification and tracking of the communication link through multi-dimensional observation, resulting in the failure of concealment. For example, the wireless communication of the power internet of things usually uses frequency hopping spread spectrum to issue control instructions to key devices. Although the signal frequency is not fixed, the power mutation is obvious, and the transmission window can still be locked by the monitoring device through spectrum focusing, causing link interference and control failure. SUMMARY

[0004] In order to overcome the problem that the wireless communication process of the power internet of things is easy to be monitored and recognized, resulting in the failure of concealment, the present application provides a power internet of things hidden communication method and system and a control master station.

[0005] In one aspect, the present application provides a power internet of things hidden communication method, applied to a control master station in the power internet of things, the method comprising: sending a pilot signal to each controlled node in the power internet of things according to a pilot period to obtain the current channel state parameter estimated by each controlled node based on the pilot signal; receiving the communication data sent by each controlled node under the interference signal of the corresponding interference node, and dynamically adjusting the interference transmission power of the interference node corresponding to the controlled node based on the current channel state parameter estimated by each controlled node and the current transmission power of each controlled node; for each controlled node, based on the current channel state parameter estimated by the controlled node, the current transmission power of the controlled node and the interference transmission power of the interference node corresponding to the controlled node, optimizing the transmission time window of the controlled node in the current communication period by minimizing the signal detection probability of the corresponding communication channel by the monitoring node in different time windows; based on the current channel state parameter estimated by the controlled node, dynamically optimizing the current transmission power of the controlled node by maximizing the channel transmission rate of the corresponding communication channel; controlling the stealthy communication of the controlled node in the current communication period with the optimization results of the transmission time window and the transmission power; wherein the channel transmission rate of the communication channel is determined based on the channel state parameter of the communication channel and the transmission power of the corresponding controlled node of the communication channel.

[0006] Optionally, based on the current channel state parameter estimated by each controlled node and the current transmission power of each controlled node, dynamically adjusting the interference transmission power of the interference node corresponding to the controlled node, comprising: based on the current channel state parameter estimated by each controlled node and the current transmission power of each controlled node, determining the current channel quality corresponding to each controlled node; based on the current channel quality corresponding to each controlled node, dynamically adjusting the interference transmission power of the interference node corresponding to the controlled node.

[0007] Optionally, the channel state parameter includes channel gain and channel noise power; the current channel quality corresponding to the controlled node is determined by channel signal-to-noise ratio, and the channel signal-to-noise ratio of the corresponding communication channel of the controlled node is as follows: ; wherein, is the channel signal-to-noise ratio of the communication channel corresponding to the controlled node at time t, is the channel gain of the communication channel corresponding to the controlled node at time t, is the channel noise power of the communication channel corresponding to the controlled node at time t, is the transmission power of the controlled node at time t.

[0008] Optionally, based on the channel quality corresponding to each controlled node, dynamically adjusting the interference transmission power of the interference node associated with the controlled node, comprising: if the channel signal-to-noise ratio is greater than a preset threshold, controlling to increase the interference transmission power of the interference node corresponding to the controlled node; if the channel signal-to-noise ratio is less than a preset threshold, controlling to reduce the interference transmission power of the interference node corresponding to the controlled node.

[0009] Optionally, the formula for calculating the interference transmit power of the interference node corresponding to the controlled node is as follows: ; in, This represents the interference transmit power of the interfering node corresponding to the controlled node. This represents the maximum allowable interference power for the interfering node corresponding to the controlled node. This is the preset channel signal-to-noise ratio threshold. Indicates a non-negative constraint.

[0010] Optionally, for each controlled node, based on the current channel state parameters estimated by the controlled node, the current transmit power of the controlled node, and the interference transmit power of the corresponding interfering node, the transmit time window of the controlled node in the current communication cycle is optimized by minimizing the signal detection probability of the listening node on the corresponding communication channel under different time windows, including: For each controlled node, based on the historical channel state parameters estimated by the controlled node, the historical interference transmission power of the interference node corresponding to the controlled node, and the historical transmission power of the controlled node, the relationship between the signal detection probability of the monitoring node on the communication channel corresponding to the controlled node and the channel environment is fitted. The channel detection theoretical model is modified by using the relationship between the fitted signal detection probability and the channel environment to obtain the signal detection probability model of the communication channel corresponding to the controlled node. Based on the current channel state parameters estimated by the controlled node, the interference transmission power of the interference node associated with the controlled node, and the current transmission power of the controlled node, the signal detection probability of the listening node on the communication channel under different time windows of the current communication cycle is calculated using the corresponding signal detection probability model. The time window corresponding to the lowest detection probability is selected as the optimized transmission time window result of the controlled node in the current communication cycle.

[0011] Optionally, based on the current channel state parameters estimated by the controlled node, the current transmit power of the controlled node is dynamically optimized by maximizing the channel transmission rate of the corresponding communication channel, including: For each controlled node's corresponding communication channel, a channel transmission rate calculation model for the communication channel is constructed using Shannon's formula based on the channel state parameters of the communication channel. Based on the channel transmission rate calculation model, an objective function is constructed with the goal of maximizing the channel transmission rate of the communication channel; the listening detection probability constraint of the communication channel and the transmit power constraint of the controlled node are used as the constraints of the objective function. Based on the objective function and the constraints, a power optimization model for the controlled node is constructed. Based on the current channel state parameters estimated by the controlled node and the current transmit power of the controlled node, the power optimization model is solved to obtain the transmit power optimization result of the controlled node in the current communication cycle; The transmit power constraint is constructed based on the channel state parameters of the detected listening node's listening channel and the length of the listening detection window.

[0012] Optionally, the expression for the power optimization model is: ; ; in, Let be the channel transmission rate of the communication channel at time t; Let t be the transmit power of the controlled node at time t, which is the quantity to be optimized. For the transmission power is The probability of detecting the listener's signal under the corresponding conditions. To detect the safety threshold, This represents the upper limit of the transmit power of the controlled node. This serves as an indicator of the stealth of the monitoring nodes. This refers to the length of the listening detection window for the listening node. This represents the noise power of the monitoring channel corresponding to the monitoring node.

[0013] Optionally, the process of controlling the covert communication of the controlled node in the current communication cycle based on the optimized results of the transmission time window and transmission power includes: For each controlled node and its corresponding communication channel, based on the self-interference response of the communication channel, interference cancellation processing is performed on the communication data received from the communication channel to obtain the corresponding residual signal. Based on the residual signal, the channel state parameters of the communication channel in the next communication cycle are estimated; The received self-interference response is obtained by interference modeling based on the transmission waveform of the controlled node corresponding to the communication channel.

[0014] Optionally, the process of controlling the covert communication of the controlled node in the current communication cycle based on the optimized results of the transmission time window and transmission power includes: Receive key communication parameters and monitoring status during the current communication cycle; The stealth performance of key communication parameters and monitoring status in the current communication cycle is evaluated. Based on the evaluation results and preset target values, a loss function value is calculated, and the target communication parameters for the next communication cycle are adjusted based on the loss function value.

[0015] Optionally, the key communication parameters include the signal detection probability of the monitoring node and the channel transmission rate of each channel; the concealment performance evaluation is calculated according to the following formula: ; in, For the concealment performance evaluation results, , These are the weighting coefficients corresponding to concealment and channel transmission rate, respectively. Let be the probability of the listening node detecting the signal at time t. Let be the channel transmission rate of the communication channel at time t; This represents the theoretical maximum channel transmission rate of the communication channel.

[0016] On the other hand, the present invention also provides a control master station for a power Internet of Things, comprising: The channel estimation module is used to send pilot signals to each controlled node in the power Internet of Things according to the pilot period, so as to obtain the current channel state parameters estimated by each controlled node based on the pilot signals. The interference adjustment module is used to receive the communication data sent by each controlled node under the interference signal of the corresponding interference node, and dynamically adjust the interference transmission power of the interference node corresponding to the controlled node based on the current channel state parameters estimated by each controlled node and the current transmission power of each controlled node. The parameter optimization module is used to optimize the transmission time window of the controlled node in the current communication cycle for each controlled node by minimizing the signal detection probability of the listening node to the corresponding communication channel under different time windows, based on the estimated current channel state parameters of the controlled node, the current transmission power of the controlled node, and the interference transmission power of the corresponding interference node. Based on the estimated current channel state parameters of the controlled node, the module also dynamically optimizes the current transmission power of the controlled node by maximizing the channel transmission rate of the corresponding communication channel. A covert communication module is used to control the covert communication of the controlled node in the current communication cycle based on the optimized results of the transmission time window and transmission power. The channel transmission rate of the communication channel is determined based on the channel state parameters of the communication channel and the transmit power of the controlled node corresponding to the communication channel.

[0017] Optionally, the interference adjustment module includes: The channel quality determination submodule is used to determine the current channel quality for each controlled node based on the estimated current channel state parameters and the current transmit power of each controlled node. The interference adjustment submodule is used to dynamically adjust the interference transmit power of the interference node corresponding to the controlled node based on the current channel quality of each controlled node.

[0018] Optionally, the channel state parameters include channel gain and channel noise power; the current channel quality corresponding to the controlled node is determined by the channel signal-to-noise ratio (SNR), and the SNR of the communication channel corresponding to the controlled node is as follows: ; in, Let be the channel signal-to-noise ratio (SNR) of the communication channel corresponding to the controlled node at time t. Let be the channel gain of the communication channel corresponding to the controlled node at time t. Let be the channel noise power of the communication channel corresponding to the controlled node at time t. Let be the transmit power of the controlled node at time t.

[0019] Optionally, the interference adjustment submodule is specifically used for: If the channel signal-to-noise ratio is greater than a preset threshold, the interference transmission power of the interference node corresponding to the controlled node is increased. If the channel signal-to-noise ratio is less than a preset threshold, the interference transmission power of the interference node corresponding to the controlled node is reduced.

[0020] Optionally, the formula for calculating the interference transmit power of the interference node corresponding to the controlled node is as follows: ; in, This represents the interference transmit power of the interfering node corresponding to the controlled node. This represents the maximum allowable interference power for the interfering node corresponding to the controlled node. This is the preset channel signal-to-noise ratio threshold. Indicates a non-negative constraint.

[0021] Optionally, the parameter optimization module includes a time window optimization submodule, which includes: The relationship fitting subunit is used to fit the relationship between the signal detection probability of the monitoring node on the communication channel corresponding to the controlled node and the channel environment for each controlled node, based on the historical channel state parameters estimated by the controlled node, the historical interference transmission power of the interference node corresponding to the controlled node, and the historical transmission power of the controlled node. The correction subunit is used to correct the channel detection theoretical model by using the relationship between the fitted signal detection probability and the channel environment, so as to obtain the signal detection probability model of the communication channel corresponding to the controlled node. The probability calculation subunit is used to calculate the signal detection probability of the listening node on the communication channel under different time windows of the current communication cycle based on the current channel state parameters estimated by the controlled node, the interference transmission power of the interference node associated with the controlled node, and the current transmission power of the controlled node, using the corresponding signal detection probability model. The filtering subunit is used to select the time window corresponding to the minimum detection probability as the optimized transmission time window result of the controlled node in the current communication cycle.

[0022] Optionally, the parameter optimization module includes a power optimization submodule, which includes: The calculation model construction subunit is used to construct a channel transmission rate calculation model for each controlled node's corresponding communication channel based on the channel state parameters of the communication channel using Shannon's formula. An optimization model construction subunit is used to construct an objective function based on the channel transmission rate calculation model, with the goal of maximizing the channel transmission rate of the communication channel; the eavesdropping detection probability constraint of the communication channel and the transmit power constraint of the controlled node are used as constraints on the objective function; and a power optimization model of the controlled node is constructed based on the objective function and the constraints. The power optimization subunit is used to solve the power optimization model based on the current channel state parameters estimated by the controlled node and the current transmit power of the controlled node, so as to obtain the transmit power optimization result of the controlled node in the current communication cycle. The transmit power constraint is constructed based on the channel state parameters of the detected listening node's listening channel and the length of the listening detection window.

[0023] Optionally, the expression for the power optimization model is: ; ; in, Let be the channel transmission rate of the communication channel at time t; Let t be the transmit power of the controlled node at time t, which is the quantity to be optimized. For the transmission power is The probability of detecting the listener's signal under the corresponding conditions. To detect the safety threshold, This represents the upper limit of the transmit power of the controlled node. This serves as an indicator of the stealth of the monitoring nodes. This refers to the length of the listening detection window for the listening node. This represents the noise power of the monitoring channel corresponding to the monitoring node.

[0024] Optionally, it also includes an interference cancellation module, the interference cancellation module being used for: For each controlled node and its corresponding communication channel, based on the self-interference response of the communication channel, interference cancellation processing is performed on the communication data received from the communication channel to obtain the corresponding residual signal. Based on the residual signal, the channel state parameters of the communication channel in the next communication cycle are estimated; The received self-interference response is obtained by interference modeling based on the transmission waveform of the controlled node corresponding to the communication channel.

[0025] Optionally, it also includes a performance evaluation module, which is used for: Receive key communication parameters and monitoring status during the current communication cycle; The stealth performance of key communication parameters and monitoring status in the current communication cycle is evaluated. Based on the evaluation results and preset target values, a loss function value is calculated, and the target communication parameters for the next communication cycle are adjusted based on the loss function value.

[0026] Optionally, the key communication parameters include the signal detection probability of the monitoring node and the channel transmission rate of each channel; the concealment performance evaluation is calculated according to the following formula: ; in, For the concealment performance evaluation results, , These are the weighting coefficients corresponding to concealment and channel transmission rate, respectively. Let be the probability of the listening node detecting the signal at time t. Let be the channel transmission rate of the communication channel at time t; This represents the theoretical maximum channel transmission rate of the communication channel.

[0027] On the other hand, the present invention also provides a covert communication system for the Internet of Things (IoT) of power, comprising: the control master station of the IoT of power as described in any of the above claims.

[0028] On the other hand, the present invention also provides a communication device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the method described in any of the foregoing is implemented.

[0029] On the other hand, the present invention also provides a readable storage medium having an executable program stored thereon, wherein when the executable program is executed, it implements the method described in any one of the above.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a covert communication method and system for the power Internet of Things (IoT). By sending periodic pilot signals to each controlled node, the current channel state parameters estimated by each controlled node based on the pilot signals are obtained, achieving real-time and accurate estimation of the communication channel state. During interference communication between the control master station and each controlled node, the interference transmission power of the interfering nodes associated with each controlled node is dynamically adjusted based on the estimated current channel state parameters and the current transmission power of each controlled node. Furthermore, based on the estimated current channel state parameters and the current transmission power of each controlled node, the transmission power of the controlled node is dynamically optimized by maximizing the channel transmission rate of the corresponding communication channel. This allows for dynamic adjustment of transmission power and interference intensity using channel state parameter feedback, controlling the energy distribution of the transmitted signals of the controlled nodes in the spectrum, achieving "imperceptible" communication behavior at the physical layer, and improving communication covertness and security.

[0031] This invention targets the communication channel corresponding to each controlled node. By minimizing the signal detection probability of the monitoring node on that communication channel under different time windows, it optimizes the transmission time window of the controlled node corresponding to the current communication cycle. Through a behavior scheduling mechanism that integrates temporal risk awareness and channel spatial location correlation, it intelligently selects the optimal transmission time window, avoiding sensitive periods and improving behavior-level stealth. Furthermore, this invention requires no additional spectrum resources or frequency hopping system, has a lightweight overall structure, reduces deployment complexity and system power consumption, and improves overall communication efficiency. Attached Figure Description

[0032] Figure 1 This is a flowchart illustrating a covert communication method for the power Internet of Things according to the present invention. Figure 2 The simulation results of the method of this invention and two existing communication strategies under different communication task load conditions are shown in the comparison curves. Figure 3 This is a schematic diagram of a covert communication system architecture for the power Internet of Things according to the present invention; Figure 4 This is a structural block diagram of a communication device according to the present invention. Detailed Implementation

[0033] While existing communication covert methods theoretically possess a certain degree of concealment, in the highly dynamic and reliable power environment, they often suffer from problems such as detectable encryption behavior, complex frequency scheduling, and imprecise interference control, making it difficult to meet the higher security goal of "unperceptible communication behavior." Power wireless communication typically uses frequency hopping spread spectrum to send control commands to critical equipment. Although the signal frequency is not fixed, significant power fluctuations can still allow eavesdropping devices to lock onto the transmission window through spectrum focusing, causing link interference and control failure. Existing methods lack comprehensive perception and strategic coordination of multi-source information such as channel state, temporal patterns, and spatial layout. In other words, covert communication technology must not only consider the impact of time scheduling and spectrum resources but also be closely related to the spatial location, load status, and control logic of equipment within the station. Attackers can establish implicit mapping relationships between devices, behaviors, and signals through multi-dimensional observation, enabling reverse identification and tracking of communication links, thus rendering traditional solutions ineffective.

[0034] To address the aforementioned problems, this invention provides a novel stealth communication scheme with time-frequency sensing capabilities, adaptive power control capabilities, and behavioral obfuscation mechanisms, enabling precise adjustment and dynamic camouflage of communication strategies. Details are as follows: (1) To address the problems of easily detectable encryption behavior and complex frequency scheduling, this invention introduces pilot sensing and full-duplex structure to achieve real-time and accurate estimation of communication channel status. At the same time, it adopts interference cancellation mechanism to avoid affecting the sensing accuracy due to self-excited interference, thereby effectively improving the system's ability to control covert behavior and reducing communication exposure caused by traditional encryption features.

[0035] (2) In view of the problem that the power change of the frequency hopping spread spectrum method is obvious and easily identified by spectrum focusing, the present invention establishes a listening party detection model and a power dynamic control mechanism. By using channel state feedback to dynamically adjust the transmission power and interference intensity, the energy distribution of the transmitted signal in the spectrum is controlled, so as to realize the communication behavior at the physical layer is "imperceptible".

[0036] (3) In view of the lack of comprehensive perception of multi-source information and lack of strategic linkage in existing covert communication technologies, this invention proposes a behavior scheduling mechanism that integrates temporal risk perception and spatial location association. It can intelligently select the optimal transmission time window based on the energy detection characteristics of the eavesdropping party and the prediction of the communication link status, avoid sensitive periods, and improve behavior-level covertness.

[0037] (4) In view of the problem that traditional solutions cannot adapt to dynamic environmental changes and lack evolution capabilities, this invention constructs an adaptive closed-loop control mechanism for communication parameters. By periodically collecting communication effectiveness and monitoring misjudgment information, power, scheduling and interference control strategies are updated in real time to realize continuous optimization and long-term evolution of communication strategies and enhance the system's adaptability to complex industrial wireless environments.

[0038] (5) In view of the problem that existing solutions rely on high-overhead spectrum resources or third-party encryption protocols, the present invention adopts a lightweight structure and achieves covert control based solely on local sensing and feedback mechanisms. It does not require additional spectrum resources or frequency hopping systems, thereby reducing deployment complexity and system power consumption and improving overall communication efficiency.

[0039] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0040] Example 1 The present invention provides a covert communication method for the power Internet of Things, the schematic diagram of which is shown below. Figure 1 As shown, the method includes: Step S110: Send pilot signals to each controlled node in the power Internet of Things according to the pilot period to obtain the current channel state parameters estimated by each controlled node based on the pilot signals; Step S120: Receive communication data sent by each controlled node under the interference signal of the corresponding interference node, and dynamically adjust the interference transmission power of the interference node corresponding to the controlled node based on the current channel state parameters estimated by each controlled node and the current transmission power of each controlled node. Step S130: For each controlled node, based on the current channel state parameters estimated by the controlled node, the current transmission power of the controlled node, and the interference transmission power of the corresponding interference node of the controlled node, the transmission time window of the controlled node in the current communication cycle is optimized by minimizing the signal detection probability of the listening node to the corresponding communication channel under different time windows. Step S140: Based on the current channel state parameters estimated by the controlled node, the current transmit power of the controlled node is dynamically optimized by maximizing the channel transmission rate of the corresponding communication channel; Step S150: Control the covert communication of the controlled node in the current communication cycle based on the optimized results of the transmission time window and transmission power.

[0041] In this example implementation, the executing entity is the control master station / control platform within the power Internet of Things (IoT), such as the control master station in a digital converter station. Each controlled node can be a different controlled object within the converter station (e.g., a relay station, various sensors, etc.). The system addresses the signal transmission process within the power IoT formed by the control master station and various controlled objects, including the issuance of control commands and the reporting of various parameters such as voltage, current, and fault commands. Each controlled object can be considered a controlled node, and each controlled node can form a communication channel with the control master station. That is, each controlled node corresponds to a communication channel, with the control master station as the receiver and the controlled node as the sender. Interference nodes can be one or more communication interference devices set up for each controlled node. They can be integrated as functional modules into the controlled node or set up independently near the controlled node. Interference nodes can interfere with the communication process of the corresponding controlled node. For example, auxiliary interference nodes can be deployed in the network to construct artificial interference signals with Gaussian characteristics to confuse the listening nodes' perception of critical status data (such as voltage, current, and fault commands). The monitoring node can be a monitoring party set up at the control master station, serving the covert communication process of the control master station. Interference nodes, monitoring nodes, and controlled nodes can all communicate with the control master station. The channel transmission rate of a communication channel is determined based on the channel state parameters of the communication channel and the transmit power of the corresponding controlled node. For example, a channel interaction model between the transmitter, receiver, and monitoring party is constructed, and a full-duplex receiving structure is deployed. Integrated unit interference cancellation technology is used to obtain real-time and accurate estimates of state parameters such as channel gain and noise power. A communication module with full-duplex capability is deployed at the signal receiving end (control master station). This module supports the periodic transmission of pilot signals through its transmission channel while receiving data. The pilot signals are known structure symbol sequences used to assist in robust channel state estimation under complex electromagnetic interference environments. Channel state parameters may include channel gain and channel noise power. This example performs a transmission time window and transmission power optimization process at the transmitter after the controlled node sends communication data to the control master station for the first time. The initial communication process can be used for channel quality assessment, i.e., calculating the channel signal-to-noise ratio. Subsequent communication processes can optimize the transmission power and transmission time window of the controlled node according to the communication cycle. The channel state parameters and other physical quantities that depend on the channel state parameters can be dynamically updated and calculated according to the pilot period, which is another shorter sub-cycle independent of the communication cycle.

[0042] For example, the receiving end (control master station) begins to periodically transmit pilot signals. Simultaneously, the data reception function of the receiving end is activated. The transmitting end receives the reference signal composed of pilot signals within the specified time slot. It then performs channel state parameter estimation. For example, it uses the least squares method to estimate the link complex gain, with the following estimation formula: ; in, Let be the channel gain estimate at time t. The channel noise power can be further estimated using the statistical variance of the residual signal. ; in, Let Var represent the channel noise power, and Var represent the variance. This invention is based on a full-duplex channel state awareness mechanism, which differs from the traditional unidirectional pilot transmission method. It periodically emits pilot signals at the receiving end and simultaneously models and cancels self-interference in the receiving path. It obtains the channel complex gain and noise power through least squares estimation, effectively improving the estimation accuracy under weak channel conditions and providing high-confidence data support for subsequent covert control.

[0043] In some implementations, to improve estimation accuracy, the pilot signal is embedded in the main communication structure using frequency division modulation. The channel estimation module is preferably implemented using a high-speed digital processing unit composed of a Field-Programmable Gate Array (FPGA). After completing the channel estimation, the listening node modeling module is activated. The listening node has energy detection capabilities, and the listening party determines whether there is transmission behavior by calculating the average power of the signal within a sliding window. By acquiring the channel gain and noise power information, the transmission power of the interference signal is dynamically adjusted to mislead the listening party while avoiding interference damage to the legitimate receiver. Based on the completion of channel state acquisition and interference control, the detection probability of the listening party is further modeled, and the risk of communication behavior being detected at different times is dynamically calculated by combining the sliding window detection probability prediction model. By estimating the decision probability of the listening party within a continuous time period, the time window in which the communication behavior is least likely to be detected is selected as the transmission opportunity for the current period, and the transmission power is dynamically optimized and allocated. Under the premise of ensuring that the communication behavior is not detected, the transmission rate of the communication link is maximized to achieve joint control of concealment and performance. The optimized power value is used as the transmission power control signal for the current communication cycle for power modulation. The modulator adjusts the output level according to the control input to ensure the concealment of physical layer transmission, while utilizing the current channel resources to achieve the maximum transmission rate.

[0044] In some implementations, S120 dynamically adjusts the interference transmit power of the interfering node corresponding to the controlled node based on the estimated current channel state parameters of each controlled node and the current transmit power of each controlled node, including: Based on the estimated current channel state parameters of each controlled node and the current transmit power of each controlled node, the current channel quality corresponding to each controlled node is determined; The interference transmit power of the interfering node corresponding to the controlled node is dynamically adjusted based on the current channel quality of each controlled node.

[0045] In this example implementation, for each communication channel, the channel quality can be characterized by the channel signal-to-noise ratio (SNR). The current channel state parameters can include the current channel gain and the current channel noise power. The current channel SNR can be determined based on the current channel gain, the current channel noise power, and the current transmit power of the corresponding transmitter (controlled node). For example, the formula for calculating the channel SNR of the communication channel corresponding to the controlled node is as follows: ; in, Let be the channel signal-to-noise ratio (SNR) of the communication channel corresponding to the controlled node at time t. Let be the channel gain of the communication channel corresponding to the controlled node at time t. Let be the channel noise power of the communication channel corresponding to the controlled node at time t. Let t be the transmit power of the controlled node at time t. To ensure that the interference signal has statistically suspicious characteristics at the receiving end of the monitoring node, the interference signal can be controlled to partially overlap with the original communication signal in time, and its power spectral density can be controlled to approximate white noise in the spectrum. The modulation method can be random spread spectrum or random phase coding to enhance the deception capability of the monitoring detector.

[0046] In some implementations, the interference transmit power of the interfering nodes associated with the controlled node is dynamically adjusted based on the channel quality corresponding to each controlled node, including: If the channel signal-to-noise ratio is greater than a preset threshold, the interference transmission power of the interference node corresponding to the controlled node is increased. If the channel signal-to-noise ratio is less than a preset threshold, the interference transmission power of the interference node corresponding to the controlled node is reduced.

[0047] In this example implementation, a concealment control target can be preset. When the communication signal is too strong (i.e., the SNR is too high), the interference power is increased to enhance the misleading effect on the eavesdropping party; conversely, when the channel quality is poor, the interference power is actively reduced to avoid blocking legitimate signals. Finally, a linear feedback model is used to determine the interference transmission power, the expression of which is: ; in, This represents the interference transmit power of the interfering node corresponding to the controlled node. This represents the maximum allowable interference power for the interfering node corresponding to the controlled node. This is the preset channel signal-to-noise ratio threshold. Indicates a non-negative constraint.

[0048] This example dynamically adjusts the interference transmission power of the interfering nodes to ensure the interference signal strength always matches the current communication state, thereby improving the effectiveness of the jamming while maximizing reception reliability. The power control strategy for the interference signal in this example employs an interference control method adaptively coupled with the communication signal-to-noise ratio. This method enhances the misleading nature of the interference when the communication link quality is good, and automatically reduces the interference power to avoid false blocking when link conditions deteriorate. This ensures that the interference signal is both deceptive and does not affect the legitimate receiver throughout the communication process, achieving a two-way dynamic balance between misleading jamming and transmission reliability.

[0049] In some implementations, for each controlled node in S130, based on the current channel state parameters estimated by the controlled node, the current transmit power of the controlled node, and the interference transmit power of the corresponding interfering node, the transmit time window of the controlled node in the current communication period is optimized by minimizing the signal detection probability of the listening node on the corresponding communication channel under different time windows, including: For each controlled node, based on the historical channel state parameters estimated by the controlled node, the historical interference transmission power of the interference node corresponding to the controlled node, and the historical transmission power of the controlled node, the relationship between the signal detection probability of the monitoring node on the communication channel corresponding to the controlled node and the channel environment is fitted. The channel detection theoretical model is modified by using the relationship between the fitted signal detection probability and the channel environment to obtain the signal detection probability model of the communication channel corresponding to the controlled node. Based on the current channel state parameters estimated by the controlled node, the interference transmission power of the interference node associated with the controlled node, and the current transmission power of the controlled node, the signal detection probability of the listening node on the communication channel under different time windows of the current communication cycle is calculated using the corresponding signal detection probability model. The time window corresponding to the lowest detection probability is selected as the optimized transmission time window result of the controlled node in the current communication cycle.

[0050] In this example implementation, an energy detector modeled by the eavesdropper can detect communication activities in the power Internet of Things (such as wireless communication networks within digital converter stations), specifically detecting whether the average energy of the eavesdropped communication signal exceeds a preset threshold over a period of time. The channel detection theoretical model is a pre-constructed detection probability model of the eavesdropper based on statistical probability theory. Specifically, it assumes a no-signal assumption... Under the condition that the monitored communication signal contains only Gaussian white noise, the energy statistics within the sliding window follow a chi-square distribution with 2n degrees of freedom; and with the signal assumption... The monitored communication signal contains both useful signal and noise, and its energy statistics follow a non-central chi-square distribution; the preset false alarm probability threshold for the monitoring party is... The inverse function of the chi-square distribution can be used. Calculate the threshold value that satisfies the false alarm constraint. Therefore, the detection probability of the eavesdropper can be estimated based on the current pilot feedback channel state and interference power. This probability represents the likelihood that the eavesdropper will correctly detect the communication behavior at time t. The theoretical model expression for channel detection is as follows: ; in, As a generalized Marcum Q function, its degrees of freedom are n. Therefore, its explicit input consists of only two variables: the signal strength term and the threshold term. This is the current transmission power. Let represent the inverse cumulative distribution function of the chi-square distribution with 2n degrees of freedom. Its function is to transform the false alarm probability constraint into a detection threshold, such that the probability of exceeding this threshold under the no-signal assumption is exactly . This calculation method is based on historical data fitting. Specifically, it uses historical channel gain, historical noise power, historical transmit power, historical interference power, and corresponding historical detection probability results recorded within a historical period to fit the relationship between the eavesdropper's detection probability and the channel environment state. Based on this relationship, the channel detection theoretical model is modified to obtain the signal detection probability model of the communication channel. The theoretical model is further modified using historical data to improve prediction accuracy. The average detection probability under different time windows is obtained based on the signal detection probability model as follows: ; in, In the time window The average detection probability under the following conditions Let i be the start time of the i-th time window. The time window length, Let be the probability of the listening node detecting the signal at time t. Then, select the time period with the lowest detection probability from all time windows, denoted as . This serves as the transmission time window for the next communication cycle. ; This example dynamically calculates the risk of communication behavior being detected at different times using a sliding window detection probability prediction model. By estimating the eavesdropper's decision probability over a continuous time period, the system selects the time window (the optimized transmission time window result) where the communication behavior is least likely to be detected as the transmission timing for the current communication cycle. Based on the eavesdropper detection probability model, the dynamic communication time slot selection mechanism constructs a false positive probability distribution within the sliding time window. This allows the system to select the period with the lowest detection risk to perform the transmission operation in each communication cycle, achieving minimum identifiability of the communication behavior in the time domain. This significantly improves the temporal concealment level compared to existing fixed time slot or random access mechanisms.

[0051] In some implementations, S140 dynamically optimizes the current transmit power of the controlled node based on the current channel state parameters estimated by the controlled node by maximizing the channel transmission rate of the corresponding communication channel, including: For each controlled node's corresponding communication channel, a channel transmission rate calculation model for the communication channel is constructed using Shannon's formula based on the channel state parameters of the communication channel. Based on the channel transmission rate calculation model, an objective function is constructed with the goal of maximizing the channel transmission rate of the communication channel; the listening detection probability constraint of the communication channel and the transmit power constraint of the controlled node are used as the constraints of the objective function. Based on the objective function and the constraints, a power optimization model for the controlled node is constructed. Based on the current channel state parameters estimated by the controlled node and the current transmit power of the controlled node, the power optimization model is solved to obtain the transmit power optimization result of the controlled node in the current communication cycle; The transmit power constraint is constructed based on the channel state parameters of the detected listening node's listening channel and the length of the listening detection window.

[0052] In this example implementation, the instantaneous capacity of the communication channel is calculated using Shannon's formula based on the channel state parameters, thereby constructing the channel transmission rate calculation model as follows: ; in, Let be the channel transmission rate of the communication channel at time t.

[0053] Based on this channel transmission rate calculation model, the current channel transmission rate is evaluated using the current channel gain and current noise power estimated in real time in step 1. Based on the signal detection probability model established in step 3, the transmission rate at the current transmit power is obtained. The corresponding signal detection probability of the listening party under the given conditions Since the probability of detecting the eavesdropper's signal increases with increasing transmission power, a detection probability constraint is established to maintain secrecy and ensure that the eavesdropper's signal detection probability does not exceed a set safety threshold. .set up For the transmit power to be optimized, the corresponding power optimization model is as follows: ; in, Let be the channel transmission rate of the communication channel at time t; Let be the transmit power of the controlled node at time t, which is the quantity to be optimized. By dynamically optimizing the allocation of transmit power, the transmission rate of the communication link is maximized while ensuring that the communication behavior is not detected, thus achieving joint control of concealment and communication performance.

[0054] In this example, the eavesdropping party possesses energy detection capabilities. It can determine the presence of transmission activity by calculating the average signal power within a sliding window. To prevent communication from being detected by the eavesdropping party, the maximum permissible transmission power for a legitimate transmitter can be calculated based on the energy detector's identifiability. This power limit satisfies the following constraints: ; in, This represents the upper limit of the transmit power of the controlled node. This serves as an indicator of the stealth of the monitoring nodes. This refers to the length of the listening detection window for the listening node. This represents the noise power of the monitoring channel corresponding to the monitoring node. The result of this formula serves as a constraint for power optimization, guiding power adjustment strategies in covert communication processes.

[0055] The power optimization model described above is convex, and the optimal power solution can be obtained using the Lagrange multiplier method. The optimal power value is used as the transmit power control signal for the current communication cycle for power modulation. This power optimization model can be deployed in the local control chip of the control module in a power Internet of Things (such as a wireless communication network within a digital converter station). The modulator adjusts the output level according to the control quantity to ensure that the physical layer transmission meets the concealment constraint, while utilizing the current channel resources to achieve the maximum transmission rate. Furthermore, considering the severe channel fluctuations, complex electromagnetic interference, and high sensitivity of control information in the converter station environment, the actual information transmission success rate and the eavesdropping detection probability are recorded after each communication cycle, and the key parameter record table is updated to provide data support for power regulation in the next communication cycle. Although the communication transmit power optimization strategy of this invention uses the Lagrange multiplier method to solve for power allocation, its uniqueness lies in introducing the eavesdropping detection probability as an explicit constraint term into the optimization objective function, considering it in parallel with maximizing channel capacity. This achieves for the first time the inclusion of "behavioral undetectability" in the physical layer power scheduling model, distinguishing it from traditional power control algorithms that only target rate or energy consumption.

[0056] In one example implementation, the process of controlling the covert communication of the controlled node during the current communication cycle, based on the optimized results of the transmission time window and transmission power, includes: For each controlled node and its corresponding communication channel, based on the self-interference response of the communication channel, interference cancellation processing is performed on the communication data received from the communication channel to obtain the corresponding residual signal. Based on the residual signal, the channel state parameters of the communication channel in the current communication period are estimated; The received self-interference response is obtained by interference modeling based on the transmission waveform of the controlled node corresponding to the communication channel.

[0057] In this example implementation, to prevent interference signals from the deployed interference nodes from affecting the normal operation of the receiver, an interference cancellation module can be integrated into the receiving path. This interference cancellation module is based on the transmitted waveform of the controlled node (transmitter). Model its interference contribution and estimate the received self-interference response. and receive signals from the original source in real time. The residual signal after removing interference terms is obtained. : ; After interference cancellation, the signal enters the channel estimation module and serves as purified input data to further enhance the accuracy of channel parameter identification. This operation can significantly improve the identification accuracy of weak signals and background conditions, ensuring that the system can still obtain effective CSI (Channel State Information) feedback even in the presence of a full-duplex structure.

[0058] In some implementations, step S130, in the process of controlling the covert communication of the controlled node in the current communication cycle based on the optimized results of the transmission time window and transmission power, further includes: Receive key communication parameters and monitoring status during the current communication cycle; The stealth performance of key communication parameters and monitoring status in the current communication cycle is evaluated. Based on the evaluation results and preset target values, a loss function value is calculated, and the target communication parameters for the next communication cycle are adjusted based on the loss function value.

[0059] In this example implementation, key communication parameters are recorded at the end of each communication cycle. These key communication parameters may include the signal detection probability of the monitoring node. and the channel transmission rate of each channel (Reception success rate) can also include the signal-to-noise ratio at the receiving end (control master station). The transmit power of the controlled node Etc. Monitoring status can be information regarding whether communication data has been interfered with by the monitoring party. These parameters can be packaged and sent via the station control network to the receiving end's feedback analysis unit for multi-period sliding statistics and trend analysis, and combined with an evaluation function for concealment performance evaluation. The evaluation function is as follows: ; in, For the concealment performance evaluation results, , These are the weighting coefficients corresponding to concealment and channel transmission rate, respectively. Let be the probability of the listening node detecting the signal at time t. Let be the channel transmission rate of the communication channel at time t; This represents the theoretical maximum channel transmission rate of the communication channel. A loss function value can be calculated based on the current evaluation function value and the preset target value. The loss function value is then used to determine whether the performance of the current covert communication strategy has met the set target. When it is determined that the current strategy is mismatched with the environment, the target communication parameters can be updated using gradient descent and automatically loaded in the next communication cycle, forming a closed-loop adaptive process of perception-optimization-execution. The target communication parameters may include... , The parameters related to the transmission time window and signal detection probability model are considered; therefore, updating the target communication parameters may include adjusting the threshold in the interference power control model. Correct the detection probability constraints in the power optimization module. The system employs a new transmission window selection strategy, prioritizing time periods with historically low false positive rates and updating relevant parameters of the eavesdropper's signal detection probability model to adapt to current noise fluctuations or enhanced eavesdropping capabilities. This example demonstrates a closed-loop adaptive mechanism for communication behavior. In each communication cycle, the system collects core metrics such as receiver SNR, eavesdropper detection false positive rate, and data reception success rate. It constructs a joint loss function and dynamically updates the interference threshold, transmission power control parameters, and time selection strategy via gradient descent, forming a communication evolution system with long-term learning capabilities. This significantly improves the system's adaptability and stability in complex electromagnetic environments.

[0060] Experimental verification Taking ±800kV digital converter station domain control communication as a typical application scenario, the stealthy air interface communication system described in this invention is deployed for the wireless transmission of key commands and status feedback between the converter valve control unit and the measurement and control protection device. This system is particularly suitable for remotely isolated areas (such as DC high-voltage platforms) with strong electromagnetic interference, limited physical links, and high concealment requirements. During implementation, the converter station receiver deploys an FPGA module supporting full-duplex communication to transmit pilot sequences while receiving data. The transmitter receives the pilots in real time and performs least-squares estimation to obtain the channel state. The system introduces two high-power auxiliary interference nodes within the communication frequency band to simulate distributed interference sources. The spectrum is perturbed by controllable Gaussian random noise, and the interference power is adjusted in real time by the main control module based on the communication channel quality.

[0061] When a converter valve control command needs to be issued, the system first predicts the detection probability of the current time period based on the eavesdropper's energy detection model, and prioritizes the 3ms time slot with the lowest historical false positive rate as the covert transmission window. Within this time slot, the power control module dynamically plans the transmission power using the Lagrange method based on the Shannon rate formula and detection probability constraints, ensuring that the maximum rate is achieved without being detected. During the communication process, indicators such as SNR, bit error rate, and eavesdropping interference feedback are continuously monitored. If the probability of the transmission window being identified increases, the interference power is automatically increased or the transmission time period is switched. The system also continuously optimizes the eavesdropping detector parameters through a sliding window learning model, forming a low-identifiability and high-robustness operation mechanism for communication control behavior.

[0062] Covert communication simulation experiments were conducted using the method of this invention (Strategy 1), the traditional frequency-hopping spread spectrum communication method (Strategy 2), and the low-speed transmission scheme with fixed interference and spread spectrum masking (Strategy 3). In the simulation, the false alarm probability threshold of the eavesdropping party was set to... The detection window length is N=100; the maximum power output of the legitimate transmitter is denoted as... In the simulation, typical values ​​supported by the system are used for comparison without affecting the applicability of the method itself. Simulation results are as follows: Figure 2 As shown, Figure 2The figure shows performance comparison curves of the proposed stealth air interface communication strategy (Strategy 1) and two existing communication strategies (Strategy 2 and Strategy 3) under different communication task load conditions. In the figure, the horizontal axis represents the communication task volume per hour, simulating the number of control commands that the converter station needs to complete per unit time; the vertical axis represents the effective stealth communication rate, used to measure the actual data transmission capability that the system can achieve under the condition of satisfying communication stealth constraints; the green curve represents the result of the dynamic sensing and power control mechanism adopted by the proposed scheme, the blue curve represents the result of the traditional frequency hopping spread spectrum communication method, and the orange curve represents the result of the low-speed transmission scheme with fixed interference and spread spectrum masking. Figure 2 As can be seen, under low communication task intensity, both Strategy 1 and Strategy 2 exhibit good initial response and rapid increase in communication rate. However, Strategy 3, limited by low power and constant spread spectrum processing, consistently restricts its communication capacity, resulting in slow rate increases. In the medium load range, the covert communication rate of Strategy 1 continuously increases, reaching a peak of 2.45 bit / symbol at approximately 1,800 tasks, significantly outperforming Strategy 2's 1.95 bit / symbol and Strategy 3's less than 0.6 bit / symbol. This verifies the communication capacity advantage of this invention under covert constraints, demonstrating efficient utilization of communication resources. It is particularly suitable for scenarios such as converter stations that are sensitive to both timeliness and security.

[0063] Further analysis shows that Strategy 1 exhibits a slow decline in communication rate during high-load periods (exceeding 2,500 tasks), maintaining an overall rate above 1.2 bits / symbol. This indicates that it retains good covert transmission capabilities and unidentifiable behavior even under high task intensity. In contrast, Strategy 2 experiences rapid rate decay, suggesting a lack of effective protection mechanisms when the risk of eavesdropping increases. Furthermore, Strategy 1 does not exhibit the "rate collapse" phenomenon seen in Strategy 2, demonstrating strong channel adaptability and anti-interference robustness. Within high-load periods, the communication rate decline of Strategy 1 is gradual, indicating that the system's built-in closed-loop control mechanism effectively senses channel pressure and dynamically adjusts power and scheduling strategies, showcasing excellent adaptability and long-term evolution capabilities. In comparison, Strategy 2 suffers significant performance degradation, with communication behavior easily identifiable, resulting in a loss of both covertness and rate. Strategy 3, limited by its transmission method design, consistently maintains a low-speed level, unable to support high-density command issuance.

[0064] Furthermore, simulation tests show that the proposed solution can be directly integrated into existing wireless communication platforms without adding extra synchronization modules or hardware structures. All control strategies are implemented on general-purpose embedded chips, significantly reducing deployment complexity and maintenance costs. Regarding task scheduling capabilities, when the communication task priority ratio is set to 3:7 (high-priority tasks account for 30%), Strategy 1 can maintain the transmission rate of high-priority tasks at no less than 85% of the overall average, demonstrating the ability to differentiate tasks and allocate resources for real-time control scenarios in converter stations. In summary, the comprehensive performance advantages of the proposed strategy in terms of covert control, resource allocation, and load response are verified, demonstrating its engineering feasibility and application value in complex dynamic environments.

[0065] This invention considers that the power Internet of Things (such as the wireless communication network in a digital converter station) has accumulated a large knowledge base of communication, power, and control characteristics during long-term operation, which provides a solid foundation for constructing a covert communication strategy based on data-driven optimization. Furthermore, with the continuous evolution of physical layer security theory, deep learning modeling, and interference management strategies, intelligent covert air interface communication technology based on a perception-control-optimization closed-loop mechanism is showing great potential in the field of industrial wireless communication, providing a new solution for ensuring the covert and stable transmission of core power control signaling. Therefore, this invention addresses the problem of easily detectable and easily interfered communication behavior in the power Internet of Things by proposing a covert air interface communication method based on time-frequency intelligent perception and dynamic power optimization, constructing a perception-control-optimization closed-loop system for communication behavior. Through innovative design in system structure, control mechanism, and strategy evolution, this invention achieves significant results in improving communication security, covertness, and resource utilization efficiency, mainly reflected in the following five aspects: (1) Enhance communication concealment A power and time joint control mechanism based on the eavesdropper detection model was constructed, which makes the communication behavior exhibit noise-like characteristics in the energy domain, significantly reducing the probability of being identified by the eavesdropper and enhancing the physical layer concealment of the communication process.

[0066] (2) Enhance anti-interference capability By deploying Gaussian artificial interference nodes and adjusting the interference transmission intensity in conjunction with real-time channel status feedback, external interference can be guided to misjudge target behavior, significantly reducing the control signaling transmission failure rate and improving system robustness.

[0067] (3) Optimize the efficiency of communication resource utilization By using the Lagrange multiplier method to constrain and optimize the transmission power, the communication capacity per unit time can be increased while meeting the concealment requirements, avoiding resource waste, improving transmission efficiency, and adapting to the needs of high-density control command transmission.

[0068] (4) Reduce the complexity of system deployment and operation and maintenance This method can be directly integrated into existing wireless communication modules without the need for new hardware support or external synchronization systems. It is flexible in deployment and easy to operate and maintain, making it suitable for large-scale, low-cost application in digital converter stations.

[0069] (5) Possessing long-term evolution and adaptation capabilities A parameter update mechanism based on communication behavior feedback and detection misjudgment data was constructed. The system can continuously optimize interference strategies, power adjustment and transmission scheduling logic according to environmental changes, and has good scene adaptability and communication stability.

[0070] Example 2 Based on the same inventive concept, the present invention also provides a control master station for a power Internet of Things, comprising: The channel estimation module is used to send pilot signals to each controlled node in the power Internet of Things according to the pilot period, so as to obtain the current channel state parameters estimated by each controlled node based on the pilot signals. The interference adjustment module is used to receive the communication data sent by each controlled node under the interference signal of the corresponding interference node, and dynamically adjust the interference transmission power of the interference node corresponding to the controlled node based on the current channel state parameters estimated by each controlled node and the current transmission power of each controlled node. The parameter optimization module is used to optimize the transmission time window of the controlled node in the current communication cycle for each controlled node by minimizing the signal detection probability of the listening node to the corresponding communication channel under different time windows, based on the estimated current channel state parameters of the controlled node, the current transmission power of the controlled node, and the interference transmission power of the corresponding interference node. Based on the estimated current channel state parameters of the controlled node, the module also dynamically optimizes the current transmission power of the controlled node by maximizing the channel transmission rate of the corresponding communication channel. A covert communication module is used to control the covert communication of the controlled node in the current communication cycle based on the optimized results of the transmission time window and transmission power. The channel transmission rate of the communication channel is determined based on the channel state parameters of the communication channel and the transmit power of the controlled node corresponding to the communication channel.

[0071] In one possible implementation, the interference adjustment module includes: The channel quality determination submodule is used to determine the current channel quality for each controlled node based on the estimated current channel state parameters and the current transmit power of each controlled node. The interference adjustment submodule is used to dynamically adjust the interference transmit power of the interference node corresponding to the controlled node based on the current channel quality of each controlled node.

[0072] In one possible implementation, the channel state parameters include channel gain and channel noise power; the current channel quality corresponding to the controlled node is determined by the channel signal-to-noise ratio (SNR), and the SNR of the communication channel corresponding to the controlled node is as follows: ; in, Let be the channel signal-to-noise ratio (SNR) of the communication channel corresponding to the controlled node at time t. Let be the channel gain of the communication channel corresponding to the controlled node at time t. Let be the channel noise power of the communication channel corresponding to the controlled node at time t. Let be the transmit power of the controlled node at time t.

[0073] In one possible implementation, the interference adjustment submodule is specifically used for: If the channel signal-to-noise ratio is greater than a preset threshold, the interference transmission power of the interference node corresponding to the controlled node is increased. If the channel signal-to-noise ratio is less than a preset threshold, the interference transmission power of the interference node corresponding to the controlled node is reduced.

[0074] In one possible implementation, the formula for calculating the interference transmission power of the interference node corresponding to the controlled node is as follows: ; in, This represents the interference transmit power of the interfering node corresponding to the controlled node. This represents the maximum allowable interference power for the interfering node corresponding to the controlled node. This is the preset channel signal-to-noise ratio threshold. Indicates a non-negative constraint.

[0075] In one possible implementation, the parameter optimization module includes a time window optimization submodule, which includes: The relationship fitting subunit is used to fit the relationship between the signal detection probability of the monitoring node on the communication channel corresponding to the controlled node and the channel environment for each controlled node, based on the historical channel state parameters estimated by the controlled node, the historical interference transmission power of the interference node corresponding to the controlled node, and the historical transmission power of the controlled node. The correction subunit is used to correct the channel detection theoretical model by using the relationship between the fitted signal detection probability and the channel environment, so as to obtain the signal detection probability model of the communication channel corresponding to the controlled node. The probability calculation subunit is used to calculate the signal detection probability of the listening node on the communication channel under different time windows of the current communication cycle based on the current channel state parameters estimated by the controlled node, the interference transmission power of the interference node associated with the controlled node, and the current transmission power of the controlled node, using the corresponding signal detection probability model. The filtering subunit is used to select the time window corresponding to the minimum detection probability as the optimized transmission time window result of the controlled node in the current communication cycle.

[0076] In one possible implementation, the parameter optimization module includes a power optimization submodule, which includes: The calculation model construction subunit is used to construct a channel transmission rate calculation model for each controlled node's corresponding communication channel based on the channel state parameters of the communication channel using Shannon's formula. An optimization model construction subunit is used to construct an objective function based on the channel transmission rate calculation model, with the goal of maximizing the channel transmission rate of the communication channel; the eavesdropping detection probability constraint of the communication channel and the transmit power constraint of the controlled node are used as constraints on the objective function; and a power optimization model of the controlled node is constructed based on the objective function and the constraints. The power optimization subunit is used to solve the power optimization model based on the current channel state parameters estimated by the controlled node and the current transmit power of the controlled node, so as to obtain the transmit power optimization result of the controlled node in the current communication cycle. The transmit power constraint is constructed based on the channel state parameters of the detected listening node's listening channel and the length of the listening detection window.

[0077] In one possible implementation, the expression for the power optimization model is: ; ; in, Let be the channel transmission rate of the communication channel at time t; Let t be the transmit power of the controlled node at time t, which is the quantity to be optimized. For the transmission power is The probability of detecting the listener's signal under the corresponding conditions. To detect the safety threshold, This represents the upper limit of the transmit power of the controlled node. This serves as an indicator of the stealth of the monitoring nodes. This refers to the length of the listening detection window for the listening node. This represents the noise power of the monitoring channel corresponding to the monitoring node.

[0078] In one possible implementation, an interference cancellation module is further included, the interference cancellation module being used for: For each controlled node and its corresponding communication channel, based on the self-interference response of the communication channel, interference cancellation processing is performed on the communication data received from the communication channel to obtain the corresponding residual signal. Based on the residual signal, the channel state parameters of the communication channel in the next communication cycle are estimated; The received self-interference response is obtained by interference modeling based on the transmission waveform of the controlled node corresponding to the communication channel.

[0079] In one possible implementation, a performance evaluation module is also included, the performance evaluation module being used for: Receive key communication parameters and monitoring status during the current communication cycle; The stealth performance of key communication parameters and monitoring status in the current communication cycle is evaluated. Based on the evaluation results and preset target values, a loss function value is calculated, and the target communication parameters for the next communication cycle are adjusted based on the loss function value.

[0080] In one possible implementation, the key communication parameters include the signal detection probability of the listening node and the channel transmission rate of each channel; the stealth performance evaluation is calculated according to the following formula: ; in, For the concealment performance evaluation results, , These are the weighting coefficients corresponding to concealment and channel transmission rate, respectively. Let be the probability of the listening node detecting the signal at time t. Let be the channel transmission rate of the communication channel at time t; This represents the theoretical maximum channel transmission rate of the communication channel.

[0081] Example 3 Based on the same inventive concept, the present invention also provides a covert communication system for the Internet of Things for power, including: a control master station in any of the embodiments of Example 2.

[0082] In some implementations, the system may also include multiple controlled nodes within the power Internet of Things, such as various controlled devices (sensors), relay stations, etc.; it may also include interference nodes for interfering with the transmitted signals of the controlled nodes, and monitoring nodes for monitoring the transmission signals of various communication channels.

[0083] For example, a concealed communication system for the Internet of Things in the power industry according to the present invention, such as Figure 3As shown, the system includes a transmitter (controlled node), a receiver (control master station), a monitoring terminal (listening node), and multiple interference nodes (distributed interference nodes). A full-duplex receiver module with pilot transmission capability is deployed at the receiver. At the transmitter, real-time perception of uplink and downlink Channel State Information (CSI) is achieved, and self-excited interference is eliminated through interference cancellation technology to improve perception accuracy. To address the artificial interference nodes introduced into the communication link at the transmitter, Gaussian distributed pseudo-random noise is used to dynamically mask the transmitted signal. The receiver calculates the interference power based on the real-time feedback CSI value and dynamically adjusts the interference power to enhance its ability to mislead the detector. The receiver has a transmission time window decision module and a transmission power optimization calculation module. The receiver introduces an adaptive time-domain control mechanism through the transmission time window decision module, combining a detection probability model and a channel state prediction algorithm to select the optimal transmission time window to avoid eavesdropping risks. Simultaneously, the transmission power optimization calculation module adopts a power control strategy based on the Lagrange multiplier method, comprehensively considering channel capacity, received signal-to-noise ratio, and concealment constraints to calculate the optimal transmission power allocation strategy. Ultimately, the system continuously monitors concealment performance indicators within a complete closed loop, dynamically adjusting transmission strategies based on environmental changes to achieve noise-like modeling and low-feature camouflage of communication behavior. Relying on a full-duplex receiver module and distributed jamming nodes, combined with core modules such as real-time channel state perception, jamming-guided transmission, adaptive power regulation, and dynamic transmission strategy planning, the system achieves joint control of the time and power domains of the communication process. This significantly enhances the concealment and robustness of communication behavior at the physical layer, ensuring the safe transmission of critical control information in high magnetic field environments such as digital converter stations.

[0084] Example 4 like Figure 4 As shown, the present invention also provides a communication device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0085] The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and it is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to realize the corresponding method flow or corresponding function, so as to realize the steps of the power Internet of Things covert communication method in the above embodiments.

[0086] Example 5 Based on the same inventive concept, this invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). An electronic device readable storage medium is a memory device within an electronic device used to store programs and data. It is understood that the storage medium here can include both the built-in storage medium within the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Loading and executing one or more instructions stored in the storage medium by the processor can implement the steps of the covert communication method for the power Internet of Things described in the above embodiments.

[0087] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the claims pending approval.

Claims

1. A power internet of things covert communication method, characterized in that, The method is applied to a control master station in a power internet of things, and comprises the following steps: sending a pilot signal to each controlled node in the power internet of things according to a pilot period to obtain a current channel state parameter estimated by each controlled node based on the pilot signal; receiving communication data sent by each controlled node under the interference signal of a corresponding interference node, and dynamically adjusting the interference transmission power of the interference node corresponding to each controlled node based on the current channel state parameter estimated by each controlled node and the current transmission power of each controlled node; for each controlled node, optimizing the transmission time window of the controlled node in the current communication period by minimizing the signal detection probability of a listening node on a corresponding communication channel under different time windows based on the current channel state parameter estimated by the controlled node, the current transmission power of the controlled node and the interference transmission power of the interference node corresponding to the controlled node; dynamically optimizing the current transmission power of the controlled node by maximizing the channel transmission rate of a corresponding communication channel based on the current channel state parameter estimated by the controlled node; controlling the concealed communication of the controlled node in the current communication period according to the optimization results of the transmission time window and the transmission power; wherein the channel transmission rate of the communication channel is determined based on the channel state parameter of the communication channel and the transmission power of the corresponding controlled node.

2. The method of claim 1, wherein, dynamically adjusting the interference transmission power of the interference node corresponding to each controlled node based on the current channel state parameter estimated by each controlled node and the current transmission power of each controlled node comprises: determining the current channel quality corresponding to each controlled node based on the current channel state parameter estimated by each controlled node and the current transmission power of each controlled node; dynamically adjusting the interference transmission power of the interference node corresponding to each controlled node based on the current channel quality corresponding to each controlled node.

3. The method of claim 2, wherein, The channel state parameter comprises a channel gain and a channel noise power; the current channel quality corresponding to each controlled node is determined by a channel signal-to-noise ratio, and the channel signal-to-noise ratio of the communication channel corresponding to each controlled node is as follows: ； wherein, is the channel signal-to-noise ratio of the communication channel corresponding to the controlled node at time t, is the channel gain of the communication channel corresponding to the controlled node at time t, is the channel noise power of the communication channel corresponding to the controlled node at time t, is the transmit power of the controlled node at time t.

4. The method of claim 3, wherein, dynamically adjusting the interference transmission power of the interference node associated with each controlled node based on the channel quality corresponding to each controlled node comprises: if the channel signal-to-noise ratio is greater than a preset threshold, controlling to increase the interference transmission power of the interference node corresponding to the controlled node; if the channel signal-to-noise ratio is less than a preset threshold, controlling to decrease the interference transmission power of the interference node corresponding to the controlled node.

5. The method of claim 4, wherein, The calculation formula of the interference transmission power of the interference node corresponding to each controlled node is as follows: ； wherein, is the interference transmit power of the interference node corresponding to the controlled node, is the maximum interference power allowed by the interference node corresponding to the controlled node, is a preset channel signal-to-noise ratio threshold, denotes a non-negative constraint.

6. The method of claim 1, wherein, for each controlled node, optimizing the transmission time window of the controlled node in the current communication period by minimizing the signal detection probability of a listening node on a corresponding communication channel under different time windows based on the current channel state parameter estimated by the controlled node, the current transmission power of the controlled node and the interference transmission power of the interference node corresponding to the controlled node comprises: fitting out the relationship between the signal detection probability of the monitoring node to the communication channel corresponding to the controlled node and the channel environment based on the historical channel state parameter estimated by the controlled node, the historical interference transmission power of the interference node corresponding to the controlled node and the historical transmission power of the controlled node; correcting the channel detection theoretical model by using the fitted relationship between the signal detection probability and the channel environment, to obtain the signal detection probability model of the communication channel corresponding to the controlled node; calculating the signal detection probability of the monitoring node to the communication channel in different time windows of the current communication period by using the corresponding signal detection probability model based on the current channel state parameter estimated by the controlled node, the interference transmission power of the interference node associated with the controlled node and the current transmission power of the controlled node; screening out the time window corresponding to the minimum detection probability as the transmission time window optimization result of the controlled node in the current communication period.

7. The method of claim 1, wherein, dynamically optimizing the current transmission power of the controlled node by maximizing the channel transmission rate of the corresponding communication channel based on the current channel state parameter estimated by the controlled node, including: for each communication channel corresponding to a controlled node, constructing a channel transmission rate calculation model of the communication channel by using Shannon formula based on the channel state parameter of the communication channel; constructing an objective function with the goal of maximizing the channel transmission rate of the communication channel based on the channel transmission rate calculation model; taking the monitoring detection probability constraint of the communication channel and the transmission power constraint of the controlled node as the constraint conditions of the objective function; constructing a power optimization model of the controlled node based on the objective function and the constraint conditions; solving the power optimization model based on the current channel state parameter estimated by the controlled node and the current transmission power of the controlled node, to obtain the transmission power optimization result of the controlled node in the current communication period; wherein the transmission power constraint is constructed based on the channel state parameter of the monitoring channel of the monitoring node detected and the monitoring detection window length.

8. The method of claim 7, wherein, The expression of the power optimization model is: ； ； wherein, is the channel transmission rate of the communication channel at time t; is the channel gain of the communication channel corresponding to the controlled node at time t, is the transmission power of the controlled node at time t, i.e., the quantity to be optimized; is the transmission power of the controlled node at time t, is the corresponding signal detection probability of the monitoring side under the condition, is the detection safety threshold, is the upper limit of the transmission power of the controlled node, is the concealment index of the monitoring node, is the monitoring detection window length of the monitoring node, is the noise power of the monitoring channel corresponding to the monitoring node.

9. The method of claim 1, wherein, in the process of controlling the covert communication of the controlled node in the current communication period with the optimization results of the transmission time window and the transmission power, including: for each communication channel corresponding to a controlled node, performing interference cancellation processing on the communication data received from the communication channel based on the received self-interference response of the communication channel, to obtain the corresponding residual signal; performing channel state parameter estimation of the communication channel in the next communication period based on the residual signal; wherein the received self-interference response is obtained by interference modeling based on the transmission waveform of the corresponding controlled node of the communication channel.

10. The method of claim 1, wherein, in the process of controlling the covert communication of the controlled node in the current communication period with the optimization results of the transmission time window and the transmission power, including: receiving key communication parameters and monitoring conditions in the communication process of the current communication period; The key communication parameters and the monitoring situation of the current communication period are evaluated, a loss function value is calculated based on the evaluation result and a preset target value, and a target communication parameter of a next communication period is adjusted based on the loss function value.

11. The method of claim 10, wherein, The key communication parameters include a signal detection probability of a monitoring node and a channel transmission rate of each passing channel; and the stealth performance evaluation is calculated according to the following formula: ； wherein, is the concealment performance evaluation result, , are the weighting coefficients corresponding to the concealment and the channel transmission rate, respectively; is the signal detection probability of the monitoring node at time t, is the channel transmission rate of the communication channel at time t; is the theoretical maximum channel transmission rate of the communication channel.

12. A control master station of a power internet of things, characterized by, The key communication parameters include a signal detection probability of a monitoring node and a channel transmission rate of each passing channel; and the stealth performance evaluation is calculated according to the following formula: The channel estimation module is configured to send a pilot signal to each controlled node in the power internet of things according to a pilot period, so as to obtain a current channel state parameter estimated by each controlled node based on the pilot signal; The interference adjustment module is configured to receive communication data sent by each controlled node under the interference signal of a corresponding interference node, and dynamically adjust the interference transmission power of the interference node corresponding to the controlled node based on the current channel state parameter estimated by each controlled node and the current transmission power of each controlled node; The parameter optimization module is configured to, for each controlled node, optimize a transmission time window of the controlled node in the current communication period by minimizing a signal detection probability of a monitoring node on a corresponding communication channel in different time windows based on the current channel state parameter estimated by the controlled node, the current transmission power of the controlled node and the interference transmission power of the interference node corresponding to the controlled node; The parameter optimization module is configured to, for each controlled node, optimize a transmission time window of the controlled node in the current communication period by minimizing a signal detection probability of a monitoring node on a corresponding communication channel in different time windows based on the current channel state parameter estimated by the controlled node, the current transmission power of the controlled node and the interference transmission power of the interference node corresponding to the controlled node; The channel transmission rate of the communication channel is determined based on a channel state parameter of the communication channel and a transmission power of the corresponding controlled node. The interference adjustment module includes:

13. A control master station according to claim 12, characterised in that, The channel quality determination submodule is configured to determine a current channel quality corresponding to each controlled node based on the current channel state parameter estimated by each controlled node and the current transmission power of each controlled node. The interference adjustment submodule is configured to dynamically adjust the interference transmission power of the interference node corresponding to each controlled node based on the current channel quality corresponding to each controlled node. The channel state parameter includes a channel gain and a channel noise power; the current channel quality corresponding to the controlled node is determined by a channel signal-to-noise ratio, and the channel signal-to-noise ratio of the communication channel corresponding to the controlled node is as follows:

14. A control master station according to claim 13, characterised in that, The interference adjustment submodule is specifically configured to: ； wherein, is the channel signal-to-noise ratio of the communication channel corresponding to the controlled node at time t, is the channel gain of the communication channel corresponding to the controlled node at time t, is the channel noise power of the communication channel corresponding to the controlled node at time t, is the transmission power of the controlled node at time t.

15. A control master station according to claim 14, characterised in that, If the channel signal-to-noise ratio is greater than a preset threshold, the interference transmission power of the interference node corresponding to the controlled node is increased; If the channel signal-to-noise ratio is less than a preset threshold, the interference transmission power of the interference node corresponding to the controlled node is decreased. The calculation formula of the interference transmission power of the interference node corresponding to the controlled node is as follows:

16. A control master station according to claim 15, characterised in that, The parameter optimization module includes a time window optimization submodule, and the time window optimization submodule includes: ； wherein, is the interference transmit power of the interference node corresponding to the controlled node, is the maximum interference power allowed by the interference node corresponding to the controlled node, is a preset channel signal-to-noise ratio threshold, denotes a non-negative constraint.

17. The control master station of claim 12, wherein, ​ The relationship fitting subunit is configured to, for each controlled node, fit a relationship between a signal detection probability of the communication channel corresponding to the controlled node and a channel environment based on a historical channel state parameter estimated by the controlled node, a historical interference transmission power of an interference node corresponding to the controlled node, and a historical transmission power of the controlled node. The correction subunit is configured to correct a channel detection theoretical model by using the fitted relationship between the signal detection probability and the channel environment, to obtain a signal detection probability model of the communication channel corresponding to the controlled node. The probability calculation subunit is configured to calculate, by using the corresponding signal detection probability model, the signal detection probability of the communication channel by the monitoring node at different time windows in a current communication period, based on a current channel state parameter estimated by the controlled node, an interference transmission power of an interference node associated with the controlled node, and a current transmission power of the controlled node. The screening subunit is configured to screen a time window corresponding to a minimum detection probability as a transmission time window optimization result of the controlled node in the current communication period.

18. The control master station of claim 12, wherein, The parameter optimization module includes a power optimization sub-module, and the power optimization sub-module includes: The calculation model construction subunit is configured to, for each communication channel corresponding to a controlled node, construct a channel transmission rate calculation model of the communication channel by using a Shannon formula based on a channel state parameter of the communication channel. The optimization model construction subunit is configured to construct a power optimization model of the controlled node based on the channel transmission rate calculation model, construct a target function with a maximum channel transmission rate of the communication channel as a target, and use a monitoring detection probability constraint of the communication channel and a transmission power constraint of the controlled node as constraint conditions of the target function. The power optimization subunit is configured to solve the power optimization model based on a current channel state parameter estimated by the controlled node and a current transmission power of the controlled node, to obtain a transmission power optimization result of the controlled node in a current communication period. The transmission power constraint is constructed based on a channel state parameter of a monitoring channel of a detected monitoring node and a monitoring detection window length.

19. A control master station according to claim 18, characterised in that, The expression of the power optimization model is as follows: ； ； wherein, is the channel transmission rate of the communication channel at time t; is the channel gain of the communication channel corresponding to the controlled node at time t, is the transmission power of the controlled node at time t, i.e., the quantity to be optimized; is the transmission power of the controlled node at time t, i.e., the quantity to be optimized; is the corresponding signal detection probability of the monitoring party under the condition, is the detection safety threshold, is the upper limit of the transmission power of the controlled node, is the concealment index of the monitoring node, is the monitoring detection window length of the monitoring node, is the noise power of the monitoring channel corresponding to the monitoring node.

20. The control master station of claim 12, wherein, The interference elimination module is configured to: For each communication channel corresponding to a controlled node, perform interference elimination processing on communication data received from the communication channel based on a received self-interference response of the communication channel, to obtain a corresponding residual signal. Perform channel state parameter estimation of the communication channel in a next communication period based on the residual signal. The received self-interference response is obtained based on interference modeling of a transmission waveform of the controlled node corresponding to the communication channel.

21. The control master station of claim 12, wherein, The performance evaluation module is configured to: Receive key communication parameters and monitoring conditions in a current communication process. Perform covert performance evaluation on the key communication parameters and monitoring conditions in the current communication period, calculate a loss function value based on an evaluation result and a preset target value, and adjust target communication parameters in a next communication period based on the loss function value.

22. A control master station according to claim 21, characterised in that, The key communication parameters include a signal detection probability of the monitoring node and a channel transmission rate of each passing channel; and the concealment performance evaluation is calculated according to the following formula: ； wherein, is the concealment performance evaluation result, , are the weighting coefficients corresponding to the concealment and the channel transmission rate, respectively; is the signal detection probability of the monitoring node at time t, is the channel transmission rate of the communication channel at time t; is the theoretical maximum channel transmission rate of the communication channel.

23. A power internet of things covert communication system, comprising: Comprising: The control master station of the power internet of things according to any one of claims 12-22.

24. A communications device, comprising: Comprising: At least one processor and a memory; The memory and the processor are connected through a bus; The memory is used for storing one or more programs; When the one or more programs are executed by the at least one processor, the method according to any one of claims 1 to 11 is implemented.

25. A readable storage medium characterized by, A computer program product, having stored thereon a program, which program is executed by a computer to implement the method according to any one of claims 1 to 11.

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