Interference self-adjusting carrier communication system based on complex environment

Abstract

The present invention provides an interference self-adjusting carrier communication system based on a complex environment, which relates to the field of communication data adjustment technology. The system architecture includes a quantum-assisted interference detection and protection layer, a multi-domain integrated dynamic anti-interference core layer, and an edge computing-driven real-time anti-interference optimization layer. The quantum-assisted interference detection and protection layer uses a quantum sensor array and a quantum signal processing unit to accurately capture the quantum state information of the interference signal and quickly determine its key parameters. The multi-domain integrated dynamic anti-interference core layer integrates time, space, frequency and energy multi-domain resources, predicts interference rules in advance in the time domain and reasonably schedules communications, accurately locates interference sources and terminal positions in the space domain to optimize antenna beams, perceives spectrum in real time in the frequency domain and optimizes parameters, and intelligently regulates energy consumption in the energy domain. The edge computing-driven real-time anti-interference optimization layer uses distributed edge computing nodes to realize rapid analysis and processing of local interference, effectively reducing data transmission delays and cloud processing pressure.

Description

Technical Field

[0001] The present invention relates to the technical field of communication data adjustment, in particular to an interference self-adjusting carrier communication system based on complex environments. Background Art

[0002] According to a carrier communication method for automatically adjusting the transmission power of an on-grid and off-grid switching system disclosed in China Publication No. "CN114268123B", the method includes connecting a carrier signal generating device to n carrier signal detecting devices; the carrier signal generating device transmits a power carrier signal to the n carrier signal detecting devices; the n carrier signal detecting devices reply to the carrier signal generating device; the carrier signal generating device waits for the receipt; if the carrier signal generating device receives n receipts, the transmission power is reduced and the waiting receipt step is returned to complete the cycle; if the carrier signal generating device does not receive n receipts, the transmission power is increased and the waiting receipt step is returned to complete the cycle; if n receipts are still not received after the transmission power is increased, the address of the on-grid and off-grid response controller that has not received the receipt is recorded. Through the dynamic adjustment and optimization of the carrier signal transmission power, while ensuring the reliability of on-grid and off-grid switching, the impact on the power quality during the detection process is greatly reduced, and the minimum power value for on-grid and off-grid reliability can also be guaranteed.

[0003] The above patent documents and prior art have the following technical problems when used:

[0004] Problem 1: Existing communication systems mostly use conventional electromagnetic sensors and signal processing algorithms for interference detection, which makes it difficult to capture weak or complex interference signals, resulting in limited detection accuracy. At the same time, when facing high-intensity interference or malicious attacks, the encryption keys of traditional encrypted communications are easily cracked and the communication channels are easily damaged, making it impossible to guarantee the security and continuity of information transmission. For example, in some high-security military communications or financial data transmission scenarios, once the encrypted communication is breached, serious consequences will occur.

[0005] Problem 2: Current communication systems are often limited to a single domain when it comes to anti-interference. For example, they only perform simple frequency hopping operations in the frequency domain, and fail to fully integrate multi-domain resources such as time, space, frequency, and energy for coordinated anti-interference, resulting in poor anti-interference effects. In addition, they lack intuitive and efficient visual interaction methods, making it difficult for managers to fully grasp the interference situation in real time and make accurate decisions in a timely manner, resulting in low adaptability and adjustment efficiency of communication systems in complex interference environments.

[0006] Problem three: The application of edge computing in communication anti-interference in the existing technology is not in-depth enough, the interference analysis algorithm is relatively simple, and it is unable to accurately identify complex interference types and characteristics, and cannot generate effective carrier adjustment solutions in a timely manner. At the same time, the collaboration mechanism between edge computing nodes is imperfect, and information sharing is not timely. When local severe interference causes communication interruption, it is impossible to quickly transfer tasks to other suitable nodes and coordinate adjustments, which limits the reliability and flexibility of the entire communication network. Summary of the invention

[0007] Technical issues solved

[0008] In view of the shortcomings of the prior art, the present invention provides an interference self-adjusting carrier communication system based on a complex environment, which solves the following problems:

[0009] 1. The problem that the traditional communication system’s interference detection and protection are insufficient, resulting in the inability to ensure system security and continuity;

[0010] 2. The lack of multi-domain coordination and intelligent interaction in anti-interference makes it difficult to fully understand the interference situation in real time and make timely decisions;

[0011] 3. Address the problem of insufficient edge computing applications and weak collaboration capabilities that limit the reliability and flexibility of the entire communication network.

[0012] Technical Solution

[0013] To achieve the above objectives, the present invention is implemented through the following technical solutions: an interference self-adjusting carrier communication system based on a complex environment, the system architecture includes a quantum-assisted interference detection and protection layer, a multi-domain fusion dynamic anti-interference core layer, and an edge computing-driven real-time anti-interference optimization layer, wherein:

[0014] The quantum-assisted interference detection and protection layer is used to achieve accurate detection of interference and encrypted communication protection through quantum technology. The quantum-assisted interference detection and protection layer includes a quantum state interference detection module and a quantum encryption anti-interference communication module. The quantum state interference detection module is used to collect quantum state information of interference signals using a quantum sensor array and determine interference key parameters with the help of a quantum signal processing unit. The quantum encryption anti-interference communication module is based on quantum key distribution QKD technology and uses the E91 protocol to build a secure key channel to encrypt information transmission. In the event of interference, it uses quantum teleportation technology and quantum entanglement exchange technology to adaptively adjust encryption algorithm parameters and switch communication channels to ensure uninterrupted and confidential communication.

[0015] The multi-domain fusion dynamic anti-interference core layer is used to integrate time, space, frequency and energy multi-domain resources and combine visual interaction to achieve dynamic response to interference. The multi-domain fusion dynamic anti-interference core layer includes a time, space, frequency and energy fusion control module and a VR and AR interference visualization and interaction platform module. The time, space, frequency and energy fusion control module is used for data fusion and control scheduling in the time domain, space domain, frequency domain and energy domain. The VR and AR interference visualization and interaction platform module is used to display interference information in a three-dimensional space heat map and realize interactive regulation between managers and systems through voice recognition and gesture tracking technology;

[0016] The edge computing-driven real-time anti-interference optimization layer is used to achieve local interference analysis and inter-regional collaborative anti-interference with the help of edge computing nodes. The edge computing-driven real-time anti-interference optimization layer includes an edge computing node deployment and architecture module, a local interference analysis and adjustment module, and an edge node collaboration and communication task transfer mechanism module. The edge computing node deployment and architecture module is used to deploy edge computing nodes using a distributed microservice architecture near communication terminals, and ensure data transmission and instruction feedback through MQTT protocol communication and high-speed, low-latency connections. The local interference analysis and adjustment module is used to run an interference analysis process that integrates principal component analysis PCA and support vector data description SVDD algorithms on edge computing nodes to determine interference characteristics and generate targeted carrier adjustment solutions. The edge node collaboration and communication task transfer mechanism module is used to build an inter-node collaboration framework based on a consistent hashing algorithm, and to achieve the transfer and collaborative adjustment of communication tasks between adjacent nodes when interference is severe, thereby ensuring the overall stability of the communication system.

[0017] Preferably, the quantum state interference detection module includes a quantum sensor array deployment unit and a quantum signal processing and analysis unit. The quantum sensor array deployment unit is used to place a quantum sensor array based on the principle of quantum entanglement and with a Josephson junction quantum interferometer JJI as a core detection element at a key node of the communication system to capture the magnetic field fluctuations caused by electromagnetic interference and the quantum state information of interference signal photons. The quantum signal processing and analysis unit is used to use a quantum Grover search algorithm to process the data collected by the quantum sensor and determine parameters such as the frequency and amplitude of the interference signal.

[0018] Preferably, the quantum encryption anti-interference communication module includes a quantum key distribution and encryption unit and an adaptive encryption and channel switching unit. The quantum key distribution and encryption unit builds a secure key channel for the communicating parties based on quantum key distribution QKD technology, specifically adopts the E91 protocol, and uses entangled photon pairs to achieve key generation and distribution. Before information transmission, the data is first encrypted using the quantum key, and then carrier modulated for transmission in the communication link. When encountering interference in the adaptive encryption and channel switching unit, the quantum encryption channel starts a self-protection and adaptive adjustment mechanism based on the principle that quantum states cannot be cloned. With the help of quantum teleportation technology, once the interference source is detected trying to crack the encrypted signal, the system will instantly change the parameters of the encryption algorithm, such as dynamically adjusting the key encoding method based on the quantum error correction code. Using quantum entanglement exchange technology, when the communication link is interfered with, it can quickly switch to the communication channel corresponding to the spare quantum entangled pair, thereby ensuring the uninterrupted communication and high confidentiality.

[0019] Preferably, the space-time frequency-energy fusion control module includes a time domain prediction and scheduling unit, a space and positioning beam control unit, a frequency domain perception and optimization unit, and an energy domain mobile phone and distribution unit. In the time domain, the hidden Markov model HMM is used to predict the time pattern of interference and schedule communication. In the space domain, the arrival time difference TDOA positioning algorithm and Bluetooth positioning technology are used to determine the location of the interference source and the communication terminal and control the smart antenna beam. In the frequency domain, the energy detection spectrum sensing algorithm is used to monitor the spectrum and the genetic algorithm is used to optimize the communication parameters. In the energy domain, the thermoelectric energy harvester is used to collect and store electrical energy and regulate the energy consumption of components according to the interference.

[0020] Preferably, the VR and AR interference visualization and interaction platform module includes an interference visualization presentation unit and an interpersonal interaction control unit. The interference visualization presentation unit uses virtual reality VR and augmented reality AR technology to carefully create an interference visualization interface. By equipping the communication equipment with special VR and AR display devices, the interference signals in a complex environment are vividly presented to the communication system manager in the form of an extremely intuitive three-dimensional space heat map. In the heat map, interferences of different intensities are clearly represented by bright color gradients, and the distribution location of the interference source and the impact range on the communication link are displayed in detail, so that the manager can have a clear view of the interference status of the entire communication environment. The interpersonal interaction control unit is used to allow the manager to make real-time and accurate adjustments to the carrier communication system through natural and smooth gestures, voice and other interactive methods. For example, the advanced voice recognition technology is used to accurately analyze the manager's voice instructions, and the communication power, modulation mode and other key parameters are flexibly adjusted through specific command words; the high-precision gesture tracking technology is used to accurately identify the manager's operating gestures in the VR environment, such as the carrier frequency range can be easily adjusted through a simple sliding gesture. This innovative human-machine fusion interaction method greatly improves the speed and accuracy of anti-interference decision-making, allowing managers to quickly and flexibly respond to complex and changeable interference environments according to actual conditions.

[0021] Preferably, the edge computing node deployment and architecture module includes a distributed node layout unit and a high-speed connection unit. The distributed node layout unit carefully deploys edge computing nodes at key locations close to communication terminals, such as base stations, access points or local servers, and adopts an advanced distributed microservice architecture. Each node is responsible for the management and anti-interference processing tasks of communication links in a specific area. The nodes communicate through the MQTT message transmission protocol to achieve rapid sharing and collaborative work of partial information and resource status. The high-speed connection unit controls the edge computing nodes and communication equipment to adopt a high-speed and low-latency connection method, such as 5G or direct fiber connection. This connection method ensures that the communication equipment can quickly transmit interference data to the edge computing node for timely processing. At the same time, the edge computing node can also feed back precise adjustment instructions to the communication equipment in a very short time, thereby achieving real-time response to interference and efficient adaptive adjustment.

[0022] Preferably, the local interference analysis and adjustment module includes an interference data intelligent analysis unit and a dynamic carrier adjustment unit. In the interference data intelligent analysis unit, principal component analysis PCA is first used to effectively reduce the dimension of high-dimensional interference data, and then the support vector data description SVDD algorithm is used to construct a single-class classification model for the reduced-dimensional data, so as to accurately determine the distribution range and characteristics of the interference data, and accurately identify the type of interference, such as pulse interference, multipath interference, etc., and key parameters such as intensity. The dynamic carrier adjustment unit generates a highly targeted carrier adjustment scheme based on the results of the interference analysis. For example, when encountering pulse interference, the adjustment module can use time-hopping spread spectrum technology and dynamically adjust the spread spectrum code chip rate according to the interference intensity to effectively avoid the influence of pulse interference; if multipath interference is detected, the minimum mean square error MMSE algorithm is used to accurately estimate the channel parameters, and then the equalizer coefficients are flexibly adjusted to optimize the quality of the received signal. The adjustment instruction is quickly sent to the communication equipment through a low-latency communication link and executed immediately, ensuring that the communication system can adapt to interference changes in a timely manner and maintain stable communication.

[0023] Preferably, the edge node collaboration and communication task transfer mechanism module includes a collaborative communication framework construction unit and a task transfer and collaborative adjustment unit. A close collaborative communication mechanism is established between multiple edge computing nodes in the collaborative communication framework construction unit. Distributed storage and rapid query of interference information and communication resource status between nodes are realized based on the consistent hashing algorithm, ensuring that each node can grasp the global information in a timely and accurate manner, laying a solid foundation for collaborative work. When the interference in the area where a certain edge computing node is located is so serious that it may cause communication interruption, the node sends a communication task transfer request to the adjacent edge computing node with less interference according to a pre-set intelligent collaboration strategy. After receiving the request, the adjacent node uses a resource allocation algorithm, such as a first adaptation algorithm, to comprehensively evaluate its own resource status and interference situation to determine whether it has the ability to receive the transferred task. If it can be received, the two parties determine the specific method and parameters of the communication task transfer through efficient negotiation, such as reasonably allocating communication time slots according to a proportional fairness algorithm, finely adjusting the carrier frequency, and collaboratively adjusting the carrier parameters of adjacent links to ensure that the entire communication system can maintain smooth and stable communication in a complex interference environment, effectively avoiding the paralysis of the entire communication network due to severe local interference.

[0024] Beneficial Effects

[0025] The present invention provides an interference self-adjusting carrier communication system based on complex environments. It has the following beneficial effects:

[0026] 1. The system used in the present invention has excellent interference detection, prevention and protection capabilities. The system uses quantum-assisted interference detection and protection layers through quantum sensor arrays and quantum signal processing units to accurately capture the quantum state information of interference signals and quickly determine their key parameters. Compared with traditional detection methods, it has higher sensitivity and accuracy. At the same time, based on quantum key distribution and encryption technology, it can adaptively adjust encryption algorithms and switch channels when interference occurs, effectively ensuring the confidentiality and uninterruptedness of communications, greatly enhancing the system's ability to resist malicious interference and information theft.

[0027] 2. The system adopted by the present invention has efficient multi-domain fusion anti-interference and intelligent interaction effects. The system uses the dynamic anti-interference core layer of multi-domain fusion to integrate multi-domain resources of time, space, frequency and energy, predicts the interference law in advance in the time domain and reasonably schedules communication, accurately locates the interference source and terminal position in the space domain to optimize the antenna beam, perceives the spectrum in real time and optimizes parameters in the frequency domain, and intelligently regulates energy consumption in the energy domain. In addition, the VR and AR interference visualization and interaction platform enable managers to intuitively understand the interference status and accurately adjust the system parameters in real time through convenient interactive methods. This multi-domain collaboration and intelligent interaction mechanism significantly improves the system's adaptability and anti-interference decision-making efficiency in complex interference environments.

[0028] 3. The system adopted in the present invention has flexible edge computing optimization and reliable collaboration capabilities. The real-time anti-interference optimization layer driven by edge computing uses distributed edge computing nodes to realize rapid analysis and processing of local interference. The interference analysis process integrating advanced algorithms can accurately identify interference characteristics and generate targeted adjustment solutions, effectively reducing data transmission delays and cloud processing pressure. Moreover, the edge node collaboration and communication task transfer mechanism can realize efficient collaboration and task transfer between nodes based on algorithms such as consistent hashing when local interference is severe, ensuring that the entire communication system maintains smooth and stable operation in complex and changeable interference environments, thereby improving the overall reliability and flexibility of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a system architecture diagram of the present invention;

[0030] Figure 2 It is a diagram of the system operation steps of the present invention;

[0031] Figure 3 It is a system data trend relationship diagram of the present invention;

[0032] Figure 4 A schematic diagram of the hidden Markov model (HMM) of the present invention for predicting the time rule of interference in the time domain;

[0033] Figure 5It is a schematic diagram of using the time difference of arrival (TDOA) positioning algorithm of the present invention for positioning interference sources and communication terminals in the space domain;

[0034] Figure 6 A spectrum diagram of the energy detection spectrum sensing algorithm of the present invention for frequency domain spectrum monitoring;

[0035] Figure 7 A variation curve diagram of the genetic algorithm of the present invention for optimizing communication parameters in the frequency domain;

[0036] Figure 8 A three-dimensional graph of the principal component analysis (PCA) of the present invention for interference data dimensionality reduction;

[0037] Fig. 9 A discrete graph for determining interference features using the support vector data description (SVDD) algorithm of the present invention to construct a single-class classification model;

[0038] Fig.10 This is a diagram of the consistent hashing algorithm data and node allocation of the present invention;

[0039] Fig.11 A bar chart showing the first adaptation algorithm of the present invention for edge node resource evaluation and task reception judgment;

[0040] Fig.12 A bar graph showing the proportional fairness algorithm of the present invention used for communication time slot allocation and carrier frequency adjustment. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Specific embodiment one:

[0043] like Figures 1 to 12 As shown, the interference self-adjusting carrier communication system based on complex environment includes a system architecture including a quantum-assisted interference detection and protection layer, a multi-domain fusion dynamic anti-interference core layer, and an edge computing-driven real-time anti-interference optimization layer, among which:

[0044] The quantum-assisted interference detection and protection layer is used to achieve accurate detection of interference and encrypted communication protection through quantum technology. The quantum-assisted interference detection and protection layer includes a quantum state interference detection module and a quantum encryption anti-interference communication module. The quantum state interference detection module is used to collect quantum state information of interference signals using a quantum sensor array and determine the key interference parameters with the help of a quantum signal processing unit. The quantum encryption anti-interference communication module is based on quantum key distribution QKD technology and uses the E91 protocol to build a secure key channel to encrypt information transmission. In the event of interference, it uses quantum teleportation technology and quantum entanglement exchange technology to adaptively adjust encryption algorithm parameters and switch communication channels to ensure that communication is not The quantum state interference detection module includes a quantum sensor array deployment unit and a quantum signal processing and analysis unit. The quantum sensor array deployment unit is used to place a quantum sensor array based on the principle of quantum entanglement and with the Josephson junction quantum interferometer JJI as the core detection element at the key nodes of the communication system to capture the magnetic field fluctuations caused by electromagnetic interference and the quantum state information of the interference signal photons. The quantum signal processing and analysis unit is used to process the data collected by the quantum sensor using the quantum Grover search algorithm to determine the interference signal frequency, amplitude and other parameters; the quantum encryption anti-interference communication module includes a quantum key distribution and encryption unit and an adaptive encryption and channel switching unit. The quantum key distribution and encryption unit builds a secure key channel between the two communicating parties based on quantum key distribution QKD technology. Specifically, the E91 protocol is used to generate and distribute keys with the help of entangled photon pairs. Before information transmission, the data is first encrypted using this quantum key, and then carrier modulated for transmission in the communication link. When encountering interference in the adaptive encryption and channel switching unit, the quantum encryption channel starts the self-protection and adaptive adjustment mechanism based on the principle that quantum states cannot be cloned. With the help of quantum teleportation technology, once the interference source is detected trying to crack the encrypted signal, the system will immediately change the parameters of the encryption algorithm, such as dynamically encoding the key based on the quantum error correction code. Adjustment, using quantum entanglement exchange technology, when the communication link is interfered with, it can quickly switch to the communication channel corresponding to the backup quantum entanglement pair, thereby ensuring the uninterrupted and highly confidential communication. The system uses quantum-assisted interference detection and protection layer through quantum sensor arrays and quantum signal processing units to accurately capture the quantum state information of the interference signal and quickly determine its key parameters. Compared with traditional detection methods, it has higher sensitivity and accuracy. At the same time, based on quantum key distribution and encryption technology, it can adaptively adjust the encryption algorithm and switch channels when interference occurs, effectively ensuring the confidentiality and uninterrupted communication, and greatly enhancing the system's ability to resist malicious interference and information theft.

[0045] The multi-domain fusion dynamic anti-interference core layer is used to integrate the multi-domain resources of time, space, frequency and energy and combine visual interaction to realize dynamic response to interference. The multi-domain fusion dynamic anti-interference core layer includes the time, space, frequency and energy fusion control module and the VR and AR interference visualization and interaction platform module. The time, space, frequency and energy fusion control module is used for data fusion and control scheduling in the time domain, space domain, frequency domain and energy domain. The VR and AR interference visualization and interaction platform module is used to display interference information in a three-dimensional space heat map and realize interactive regulation between managers and systems through voice recognition and gesture tracking technology; the time, space, frequency and energy fusion control module includes a time domain prediction and scheduling unit, a space and positioning beam control unit, a frequency domain perception and optimization unit, and an energy domain mobile phone and allocation unit. In the time domain, the hidden Markov model HMM is used to predict the interference time law and schedule communication. In the space domain, the arrival time difference TDOA positioning algorithm and Bluetooth positioning technology are used to determine the interference source and the communication terminal position and control the smart antenna beam. In the frequency domain, the energy detection spectrum sensing algorithm is used to monitor the spectrum and the genetic algorithm is used to optimize the communication. signal parameters, collects and stores electrical energy through thermoelectric energy harvesters in the energy domain and regulates component energy consumption according to interference; VR and AR interference visualization and interaction platform modules include interference visualization presentation units and interpersonal interaction regulation units. The interference visualization presentation unit uses virtual reality VR and augmented reality AR technology to carefully create an interference visualization interface. By equipping communication equipment with special VR and AR display devices, the interference signals in complex environments are vividly presented to communication system managers in the form of extremely intuitive three-dimensional space heat maps. In the heat map, interferences of different intensities are clearly represented by bright color gradients, and the distribution location of the interference source and the scope of influence on the communication link are displayed in detail, so that managers can see the interference status of the entire communication environment at a glance. The interpersonal interaction regulation unit is used to allow managers to make real-time and accurate adjustments to the carrier communication system through natural and smooth gestures, voice and other interactive methods. For example, advanced voice recognition technology is used to accurately parse the manager's voice commands, and flexible adjustments to key parameters such as communication power and modulation mode are achieved through specific command words;With the help of high-precision gesture tracking technology, the manager's operation gestures in the VR environment can be accurately identified. For example, the carrier frequency range can be easily adjusted through a simple sliding gesture. This innovative human-machine fusion interaction method greatly improves the speed and accuracy of anti-interference decision-making, enabling managers to quickly and flexibly respond to complex and changeable interference environments according to actual conditions. The system uses a multi-domain fusion dynamic anti-interference core layer to integrate time, space, frequency and energy multi-domain resources, predict interference rules in advance in the time domain and reasonably schedule communications, accurately locate interference sources and terminal positions in the space domain to optimize antenna beams, perceive spectrum in real time in the frequency domain and optimize parameters, and intelligently regulate energy consumption in the energy domain. In addition, the VR and AR interference visualization and interaction platform enables managers to intuitively understand the interference status and accurately adjust system parameters in real time through convenient interactive methods. This multi-domain collaboration and intelligent interaction mechanism significantly improves the system's adaptability and anti-interference decision-making efficiency in complex interference environments. ;

[0046] The real-time anti-interference optimization layer driven by edge computing is used to realize local interference analysis and inter-regional collaborative anti-interference with the help of edge computing nodes. The real-time anti-interference optimization layer driven by edge computing includes edge computing node deployment and architecture module, local interference analysis and adjustment module, edge node collaboration and communication task transfer mechanism module. The edge computing node deployment and architecture module is used to deploy edge computing nodes with distributed microservice architecture close to the communication terminal, and ensure data transmission and instruction feedback through MQTT protocol communication and high-speed and low-latency connection. The local interference analysis and adjustment module is used to run the interference analysis process of the fusion principal component analysis PCA and support vector data description SVDD algorithm on the edge computing node to determine the interference characteristics and generate targeted carrier modulation. The edge node collaboration and communication task transfer mechanism module is used to build a node collaboration framework based on the consistent hashing algorithm, which can realize the transfer and coordinated adjustment of communication tasks between adjacent nodes when interference is serious, and ensure the overall stability of the communication system; the edge computing node deployment and architecture module includes a distributed node layout unit and a high-speed connection unit. The distributed node layout unit is located at key locations close to the communication terminal, such as base stations, access points or local servers, and the edge computing nodes are carefully deployed. The advanced distributed microservice architecture is adopted. Each node is responsible for the management and anti-interference processing tasks of the communication link in a specific area. The nodes communicate with each other through the MQTT message transmission protocol to realize the rapid sharing and collaborative work of some information and resource status, and high-speed The connection unit controls the edge computing node and the communication equipment using a high-speed, low-latency connection, such as 5G or direct fiber connection. This connection ensures that the communication equipment can quickly transmit the interference data to the edge computing node for timely processing. At the same time, the edge computing node can also feed back precise adjustment instructions to the communication equipment in a very short time, thereby achieving real-time response to interference and efficient adaptive adjustment. The local interference analysis and adjustment module includes an interference data intelligent analysis unit and a dynamic carrier adjustment unit. In the interference data intelligent analysis unit, principal component analysis PCA is first used to effectively reduce the dimensionality of high-dimensional interference data, and then the support vector data description SVDD algorithm is used to construct a single-class classification model for the reduced-dimensional data, thereby accurately determining the interference. The distribution range and characteristics of the data are used to accurately identify the type of interference, such as pulse interference, multipath interference, and key parameters such as intensity. The dynamic carrier adjustment unit uses the results of the interference analysis, and the adjustment module of the edge computing node quickly generates a highly targeted carrier adjustment scheme. For example, when encountering pulse interference, the adjustment module can use time-hopping spread spectrum technology and dynamically adjust the spread spectrum code chip rate according to the interference intensity to effectively avoid the impact of pulse interference; if multipath interference is detected, the minimum mean square error MMSE algorithm is used to accurately estimate the channel parameters, and then the equalizer coefficients are flexibly adjusted to optimize the quality of the received signal. The adjustment command is quickly sent to the communication equipment through a low-latency communication link and executed immediately, ensuring that the communication system can adapt to interference changes in a timely manner and maintain stable communication;The edge node collaboration and communication task transfer mechanism module includes a collaborative communication framework construction unit and a task transfer and collaborative adjustment unit. A close collaborative communication mechanism is established between multiple edge computing nodes in the collaborative communication framework construction unit. Distributed storage and fast query of interference information and communication resource status between nodes are realized based on the consistent hashing algorithm, ensuring that each node can grasp the global information in a timely and accurate manner, laying a solid foundation for collaborative work. When the interference in the area where a certain edge computing node is located is so serious that it may cause communication interruption, the node sends a communication task transfer request to the adjacent edge computing node with less interference based on the pre-set intelligent collaboration strategy. After receiving the request, the adjacent node uses a resource allocation algorithm, such as the first adaptation algorithm, to comprehensively evaluate its own resource status and interference situation to determine whether it has the ability to receive the transferred task. If it can be received, the two parties determine the specific method and parameters for communication task transfer through efficient negotiation. For example, it can reasonably allocate communication time slots according to the proportional fairness algorithm, finely adjust the carrier frequency, and coordinately adjust the carrier parameters of adjacent links to ensure that the entire communication system can maintain smooth and stable communication in a complex interference environment, effectively avoiding the paralysis of the entire communication network due to local severe interference. The system has flexible edge computing optimization and reliable collaboration capabilities. The real-time anti-interference optimization layer driven by edge computing uses distributed edge computing nodes to achieve rapid analysis and processing of local interference. The interference analysis process integrating advanced algorithms can accurately identify interference characteristics and generate targeted adjustment solutions, effectively reducing data transmission delays and cloud processing pressure. In addition, the edge node collaboration and communication task transfer mechanism can achieve efficient collaboration and task transfer between nodes based on algorithms such as consistent hashing when local interference is severe, ensuring that the entire communication system maintains smooth and stable operation in a complex and changeable interference environment, and improving the overall reliability and flexibility of the system. ; Specific embodiment 2:

[0048] like Figures 1 to 12 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0049] The operation steps of the entire system are as follows:

[0050] Sp1: The quantum-assisted interference detection and protection layer is started and operated. The quantum state interference detection module uses the quantum sensor array to collect quantum state information of the interference signal, and uses the quantum signal processing unit to process the collected data using the quantum Grover search algorithm to determine the key parameters such as the frequency and amplitude of the interference signal. At the same time, the quantum encryption anti-interference communication module uses the E91 protocol based on the quantum key distribution QKD technology to build a secure key channel for information encryption transmission;

[0051] Sp2: When interference is detected, the adaptive encryption and channel switching unit in the quantum encryption anti-interference communication module starts the self-protection and adaptive adjustment mechanism based on the principle that quantum states cannot be cloned, changes the encryption algorithm parameters with the help of quantum teleportation technology, and quickly switches to the communication channel corresponding to the backup quantum entanglement pair using quantum entanglement exchange technology to ensure uninterrupted communication and confidentiality;

[0052] Sp3: The dynamic anti-interference core layer of multi-domain fusion begins to operate. The time-space frequency-energy fusion control module uses the hidden Markov model HMM in the time domain to predict the time pattern of interference and schedule communication. In the space domain, the arrival time difference TDOA positioning algorithm and Bluetooth positioning technology are used to determine the location of the interference source and the communication terminal and control the smart antenna beam. In the frequency domain, the energy detection spectrum sensing algorithm is used to monitor the spectrum and the genetic algorithm is used to optimize the communication parameters. In the energy domain, the thermoelectric energy harvester is used to collect and store electrical energy and adjust the energy consumption of components according to the interference.

[0053] Sp4: VR and AR interference visualization and interactive platform module presents interference information to managers in the form of three-dimensional space heat maps by equipping communication equipment with special VR and AR display devices, and uses voice recognition and gesture tracking technology to achieve interactive control between managers and the system. Managers can use voice commands or gestures to accurately adjust key parameters such as communication power and modulation mode in real time;

[0054] Sp5: The real-time anti-interference optimization layer driven by edge computing is put into operation. The edge computing node deployment and architecture module deploys edge computing nodes using a distributed microservice architecture close to the communication terminal, and ensures data transmission and command feedback through MQTT protocol communication and high-speed, low-latency connections;

[0055] Sp6: The local interference analysis and adjustment module runs the interference analysis process of the fusion principal component analysis PCA and support vector data description SVDD algorithm on the edge computing node. PCA is first used to reduce the dimension of high-dimensional interference data, and then the SVDD algorithm is used to build a single-class classification model to accurately determine the distribution range and characteristics of interference data, accurately identify key parameters such as interference type and intensity, and generate targeted carrier adjustment solutions;

[0056] Sp7: When encountering pulse interference, the adjustment unit of the local interference analysis and adjustment module can use time-hopping spread spectrum technology and dynamically adjust the spread spectrum code rate according to the interference intensity to effectively avoid the influence of pulse interference; if multipath interference is detected, the minimum mean square error MMSE algorithm is used to accurately estimate the channel parameters, and then the equalizer coefficients are flexibly adjusted to optimize the quality of the received signal. The adjustment instructions are quickly sent to the communication equipment through a low-latency communication link and executed immediately;

[0057] Sp8: When the interference in the area where a certain edge computing node is located is so serious that it may cause communication interruption, the collaborative communication framework construction unit in the edge node collaboration and communication task transfer mechanism module implements distributed storage and fast query of interference information and communication resource status between nodes based on the consistent hashing algorithm. The node sends a communication task transfer request to the adjacent edge computing node with less interference based on the pre-set intelligent collaboration strategy;

[0058] Sp9: After receiving the request, the adjacent node uses a resource allocation algorithm, such as the first adaptation algorithm, to comprehensively evaluate its own resource status and interference situation to determine whether it has the ability to receive the transferred task. If it can be received, the two parties determine the specific method and parameters of the communication task transfer through efficient negotiation, such as reasonably allocating communication time slots based on the proportional fairness algorithm, fine-tuning the carrier frequency, and collaboratively adjusting the carrier parameters of adjacent links to ensure that the entire communication system maintains smooth and stable communication in a complex interference environment. Specific embodiment three:

[0060] like Figures 1 to 12 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0061] The algorithm contents of each module and unit in the whole system are as follows:

[0062] The algorithm formula in the quantum-assisted interference detection and protection layer is as follows:

[0063] The core iterative formula of the quantum Grover search algorithm can be expressed as:

[0064]

[0065] in:

[0066] Indicates that the algorithm The state vector of the quantum system at the iteration is a complex vector, and each element in the vector corresponds to the probability amplitude of the quantum system being in a certain ground state. For example, In a system of quantum bits, it exists The ground state, It describes the situation where the system is in a superposition state of these ground states. The square of the modulus of the probability amplitude represents the probability of obtaining the corresponding ground state during measurement.

[0067] is the vector corresponding to the target state, that is, the quantum state representation corresponding to the state we expect to find through the search algorithm. For example, if we want to find a specific state related to the interference signal from many possible data states, this specific state is represented by It means that it is also a complex vector and it satisfies the normalization condition (the modulus of the vector is 1).

[0068] Yes Conjugate transpose, both together Construct a projection operator, which is used to project other states to the target state That is, to measure the "degree of overlap" between other states and the target state;

[0069] is the unit operator, and its size is consistent with the Hilbert space dimension of the quantum system, e.g. The quantum bit system corresponds to The unit matrix of , whose function is to keep the quantum state unchanged, participates in the operation in the formula to play the role of comparison, adjustment, etc.;

[0070] It is a diffusion operator. Its function is to perform a "diffusion" operation on the quantum state, so that the probability amplitude of the target state in the quantum state is enhanced and the probability amplitude of the non-target state is correspondingly weakened. Its specific form is related to factors such as the specific representation of the quantum system, but its overall function is to achieve this diffusion effect on the state.

[0071] Implementation steps:

[0072] Sp1: Initialization: First, prepare the quantum system to a uniform superposition state ,exist In a quantum bit system, the probability amplitude of each ground state is equal, and its mathematical expression is

[0073]

[0074] here Represent different ground states;

[0075] Sp2: Iteration process: According to the above core iteration formula, multiple iterations are performed. Each iteration will make the probability amplitude of the target state increase relative to other states. Generally, the number of iterations is Approx. Second-rate( is the number of quantum bits), which is derived through theoretical analysis and related mathematical derivation. The purpose is to highlight the target state to the greatest extent and facilitate subsequent measurement;

[0076] Sp3: Measurement: After a set number of iterations, the quantum system is measured to obtain the final state. Since the probability amplitude of the target state has been enhanced after the previous iterations, there is a greater probability of measuring the target state, that is, finding the result we expect to search for (such as state information related to the interference signal).

[0077] Beneficial effect: Accelerate the search process: Compared with the classical search algorithm, when faced with a large amount of data (such as complex interference signal related data collected by a large amount of quantum sensors), its time complexity is significantly improved. elements usually require time, while the Grover algorithm can achieve The time complexity of the method is greatly accelerated, which greatly speeds up the speed of finding the relevant parameters of the target interference signal from complex data; taking advantage of quantum characteristics: giving full play to the characteristics of quantum superposition and quantum state operation, it can more efficiently process the interference signal information collected at the quantum level, lay the foundation for the subsequent accurate analysis of key parameters such as the frequency and amplitude of the interference signal, and improve the efficiency and accuracy of the entire interference detection link.

[0078] Quantum error correction code related coding adjustments:

[0079] For a single bit of information ,in and is plural and satisfies , representing and The probability amplitude of the state is encoded using quantum repetition code, and the encoded state is:

[0080]

[0081] here represents the tensor product, that is, the single-bit quantum state is copied multiple times (here 3 copies, that is, 3-repeat code, the number of repetitions can actually be expanded according to demand), and the encoded state becomes the entangled state of multiple quantum bits. For example, under this 3-repeat code, it becomes a state of 3 quantum bits, and is expressed as express Repeat 3 times, using express The state is repeated 3 times, etc. The specific encoded state is expanded as follows:

[0082]

[0083] Implementation steps:

[0084] Sp1: Information preparation: Obtain the original quantum information to be encoded, that is, the single-bit or multi-bit quantum state related to the quantum key, in the form mentioned above;

[0085] Sp2: Encoding operation: Encode the original quantum state according to the selected quantum error correction code rules (such as the tensor product operation of the above quantum repetitive code), convert it into an entangled state of multiple quantum bits, and obtain the encoded quantum state ;

[0086] Sp3: Error correction and adjustment (when errors occur during transmission, etc.): During subsequent transmission or use, if an error is detected (for example, due to interference causing a change in the quantum state, etc.), the error is discovered and corrected through corresponding measurement and error correction logic (for quantum repetition codes, the bit states at different repetition positions can be compared, and if most of them are the same, they are correct, etc.). For example, if the received state becomes , we found that the 1 in the middle is inconsistent with the 0s on both sides. According to the majority rule, the correct answer should be , and then adjust the quantum state accordingly to restore it to the correct state. At the same time, the subsequent encoding method can be dynamically adjusted according to the error situation (such as changing the number of repetitions, etc. If there are many errors, the number of repetitions can be increased to enhance the error correction capability).

[0087] It should be noted that the article only mentions the use of quantum error correction codes for dynamic adjustment, but the specific code types used and the detailed and complex adjustment logic may vary due to different actual system designs and application scenarios. Here, the common quantum repetition codes are used as an example to illustrate the basic principles and related characteristics. The above is a detailed analysis based on the main algorithms mentioned in the article. Different actual application scenarios and system implementations may vary in specific details, but the overall core ideas and effects are similar.

[0088] Hidden Markov Model (HMM) for predicting temporal regularity of interference in time domain

[0089] The hidden Markov model is mainly composed of the following elements, which can be expressed in mathematical form as follows:

[0090] State transition probability matrix: in, , represents different hidden states, Represents at the moment The hidden state of Indicates from time Status Transfer to the moment Status The probability of That is, the sum of the probabilities of moving from one state to all possible states is 1;

[0091] Observation probability matrix: ,in, Indicates at time The observed value of Represents different observation results, Indicates that in the hidden state The results were observed probability;

[0092] Initial state probability vector: ,in, , indicating that the model is at the initial moment In state The probability of ;

[0093] For predicting the temporal pattern of interference, it is assumed that the hidden state of interference (e.g., the potential state of interference of different intensities, types, etc.) is The observed values ​​of the interference occurrence time and other conditions are species, then:

[0094]

[0095] in, represents the observation sequence (i.e., the interference-related time information sequence observed over a period of time), represents the parameters of the entire hidden Markov model, represents the corresponding hidden state sequence, Indicates the length of the observation sequence (the number of observations corresponding to the time length);

[0096] Implementation steps:

[0097] Sp1: Model initialization: Determine the number of states of the hidden Markov model based on previous interference time data records, etc. (For example, divide the states corresponding to different interference intensities) and the types of observation values (For example, interference occurs at different time intervals as different observation values), and then the initial state probability vector is estimated , state transition probability matrix And the observation probability matrix ,The estimation of these parameters can be obtained by counting the occurrence of interference in historical data, the frequency of different state transitions, and the probability of the corresponding observation results;

[0098] Sp2: Data input: Take new real-time or recent interference time-related observation data (such as recording whether interference occurs at regular intervals) as the observation sequence Input into the initialized model;

[0099] Sp3: Probability calculation and prediction: Using the above probability calculation formula, calculate the probability of given model parameters. The observation sequence appears below Probability , and use some algorithms (such as the Viterbi algorithm) to find the most likely hidden state sequence ,This hidden state sequence can help infer the possible time pattern of interference in the future (for example, what intensity of interference may occur next and at what time, etc.), and then be used to schedule communications.

[0100] It is able to mine potential time patterns from complex and seemingly disordered interference time-related data, and taking into account the hidden state changes of interference, it can more accurately grasp the dynamic change trend of interference than simple traditional prediction methods such as statistical averages, providing a more reliable basis for communication scheduling; planning communications in advance, predicting the time patterns of interference in advance, so that the communication system can make adjustments in advance.

[0101] Time Difference of Arrival (TDOA) positioning algorithm is used for locating interference sources and communication terminals in the spatial domain

[0102] Assume existence Base stations, whose coordinates are , , the coordinates of the interference source or communication terminal to be located are , for two of the base stations and , the time difference of the signal propagating from the target point to them (i.e., the arrival time difference) is recorded as , the signal propagation speed is (usually the speed of light in air), then:

[0103]

[0104] We can get the unknown coordinates The system of equations, generally Base stations can be listed There are such independent equations, because have There are several combinations, but there is a certain linear relationship, and finally we can get The coordinate position of the target is determined by solving these equations together.

[0105] When positioning in a two-dimensional plane (assuming The coordinates are fixed, only considering and coordinates), the equation can be simplified to:

[0106]

[0107] Operation logic: Utilizing the time difference when signals propagate to different base stations, a set of equations containing the coordinates of the unknown target position is established based on the signal propagation speed and the known position of the base station. The specific position of the interference source or communication terminal in space is determined by solving the set of equations. The multiple sets of time difference information provided by multiple base stations work together to improve the accuracy and reliability of positioning.

[0108] Energy detection spectrum sensing algorithm for frequency domain spectrum monitoring

[0109] Assume the received signal is , under discrete sampling, the sampling sequence is obtained , the sampling interval is , the signal observation time window length is sampling points (i.e., the observation time is ), then the discrete Fourier transform (DFT) of the received signal is expressed as:

[0110]

[0111] in, It represents the frequency domain sequence after discrete Fourier transform, reflecting the amplitude and phase information of the signal at different frequency components;

[0112] For the energy detection spectrum sensing algorithm, its core is to calculate the distribution of signal energy in the frequency domain and define the detection statistic for:

[0113]

[0114] here Represents a frequency domain sequence The square of the modulus, that is, the energy on the corresponding frequency component, is obtained by summing the energy on all frequency components and taking the average to obtain the detection statistic ,Will With the pre-set threshold Compare to determine whether there is a signal (and spectrum occupancy, etc.), that is:

[0115] if , determine whether a signal exists (the spectrum is occupied);

[0116] if , it is determined that there is no signal (the spectrum is idle).

[0117] Operation logic: First, convert the received time domain signal into a frequency domain signal, calculate the energy of each frequency component in the frequency domain and summarize it to obtain a statistic, which is then compared with the set threshold to distinguish the spectrum part with signal (i.e. occupied spectrum) and the spectrum part without signal (idle spectrum), so as to realize real-time monitoring of the spectrum, understand the usage status of frequency resources in the current communication environment, and whether there is abnormal spectrum occupancy such as interference.

[0118] Genetic Algorithm for Frequency Domain Communication Parameter Optimization

[0119] Coding: Let the communication parameters to be optimized (such as carrier frequency, parameters corresponding to the modulation method, etc.) be vector Indicates that it is encoded into a chromosome form. Commonly used encoding methods include binary encoding, real number encoding, etc. Under binary encoding, each parameter Use a certain length The entire chromosome is a binary string of length A binary string such as ,in is a binary bit (0 or 1);

[0120] Fitness function: Define the fitness function To measure the quality of the chromosome (that is, the corresponding set of communication parameters), the fitness function is constructed according to the performance indicators of the communication system (such as bit error rate, communication capacity, etc.). The higher the fitness value, the better the corresponding communication parameter combination. Assuming that the bit error rate of the communication system is as low as possible, the fitness function can be expressed as ( Indicates the corresponding parameters bit error rate under );

[0121] Selection operation: Use a certain selection strategy to select some chromosomes from the current population (a set of chromosomes) to enter the next generation population. Common selection methods include roulette selection, etc. The probability calculation formula for roulette selection is:

[0122]

[0123] in, It is Chromosomes The probability of being selected, is the number of chromosomes in the current population, that is, the probability of each chromosome being selected is determined based on the proportion of its fitness value to the total fitness value of the population. The higher the fitness, the greater the probability of being selected.

[0124] Crossover operation: For the two selected chromosomes, let and Perform crossover operations, such as single-point crossover. Randomly select a crossover point k (1 < k < nm), and after crossover, obtain a new chromosome and , e.g.:

[0125]

[0126]

[0127] Mutation operation: With a certain mutation probability perform mutation operations on certain bits in the chromosome, such as inverting the binary bits (changing 0 to 1 and 1 to 0). Assume that the th bit in the chromosome is mutated, and the mutated chromosome is:

[0128]

[0129] Among them, represents inverted;

[0130] Operation logic: By simulating the genetic, mutation, and selection mechanisms in the biological evolution process, encode the communication parameters into a form similar to biological chromosomes, use the fitness function to measure their quality, retain the better individuals through the selection operation, combine the characteristics of different individuals through the crossover operation to explore more possible parameter combinations, introduce new changes through the mutation operation to avoid falling into local optima, and continuously optimize the population through multiple rounds of iteration, so that finally a communication parameter combination that can make the performance of the communication system reach a better state can be found, realizing the effective optimization of the communication parameters in the frequency domain.

[0131] Principal component analysis (PCA) is used for interference data dimensionality reduction

[0132] Let the original high-dimensional interference data matrix be matrix be matrix, where represents the number of samples, that is, the number of interference data samples collected at different times or different positions, represents the feature dimension of each sample (such as multiple frequency components of the interference signal, amplitude change and other feature dimensions;

[0133] First, calculate the covariance matrix, and the formula is:

[0134]

[0135] Among them, represents the transpose of matrix ;

[0136] Then solve the covariance matrix The eigenvalue of and the corresponding eigenvector , that is, satisfy , sort by eigenvalue from large to small, select the first The eigenvectors corresponding to the eigenvalues ​​form the projection matrix ;

[0137] Finally, the original data is projected and reduced in dimension to obtain the low-dimensional data matrix data matrix after dimension reduction :

[0138] Operation logic: The core idea of ​​PCA is to find the direction of the greatest data change (i.e., the direction of the principal components) through the covariance matrix. These principal components are linear combinations of the original features. By solving the eigenvalues ​​and eigenvectors of the covariance matrix, the importance of each principal component (indicated by the size of the eigenvalue) is determined, and then the principal components with higher importance are selected to form a projection matrix to project the original high-dimensional data into a low-dimensional space. In this process, the main information of the data is retained as much as possible, noise and redundant information are removed, and data dimensionality reduction is achieved.

[0139] The support vector data description (SVDD) algorithm is used to build a single-class classification model to determine the interference characteristics.

[0140] The goal of SVDD is to find a hypersphere such that as many sample data points as possible are inside the hypersphere and the radius of the hypersphere is as small as possible;

[0141] Assume that the training data sample set ( is the number of samples), the center of the hypersphere is , the radius is ;

[0142] Define the optimization objective function:

[0143]

[0144] The constraints are:

[0145] in, is a penalty parameter used to balance the penalty degree of the hypersphere volume and misclassified samples; is a slack variable used to handle the situation where the sample points are outside the hypersphere.

[0146] Introducing Lagrange multipliers , construct the Lagrangian function:

[0147]

[0148] right Find the partial derivatives and set them to 0, and get a series of equations, such as: wait;

[0149] By solving the above optimization problem, we can get the value of Lagrange multiplier and then determine the center of the hypersphere. and radius , for a new test sample , to determine whether it belongs to this category (i.e., whether it is inside the hypersphere), by calculating ,like ,but Belongs to this category, otherwise it does not.

[0150] Operation logic: SVDD attempts to find a minimum hypersphere boundary that can contain most normal (or specific class) data samples. By optimizing the objective function, the center and radius of the hypersphere are determined while allowing a certain number of misclassified samples (controlled by slack variables and penalty parameters). In the process of building the model, the constrained optimization problem is converted into an unconstrained problem using the Lagrange multiplier method to solve the hypersphere parameters. For new samples, the relationship between the distance to the center of the hypersphere and the radius is calculated to determine whether it belongs to the class, thereby determining whether it is interference data and the characteristics of the interference data.

[0151] Consistent hashing algorithm is used to build edge node collaboration framework

[0152] The consistent hashing algorithm usually uses the keyword of data (which can be understood as interference information or communication resource status in this scenario) Through hash function Mapped to a fixed range hash ring, assuming the hash ring range is 0 to ( is the number of bits generated by the hash function);

[0153] For each edge computing node , also through the hash function Mapped to a position on the hash ring;

[0154] When you want to store or find a piece of data When , then go clockwise along the hash ring to find the first Nodes;

[0155] When nodes are added or removed, for example, a new node is added , only the hash ring needs to be redistributed After that, some data, not all data, assuming that the original node The data scope responsible is (This means from arrive half-open interval), add After that, if exist In the interval, then the original Part of the data will be reallocated to , the new allocation interval becomes Depend on Responsible, Depend on Responsible.

[0156] Operation logic: The consistent hashing algorithm maps nodes and data to the same hash ring and uses the clockwise search rule of the hash ring to determine the correspondence between data and nodes. This method requires only partial data redistribution when the number of nodes changes, rather than large-scale data migration, reducing the impact of node changes on the data storage and query efficiency of the entire system, ensuring the stability and scalability of the system, and is suitable for distributed storage and fast query scenarios of interference information and communication resource status between edge computing nodes.

[0157] First adaptation algorithm for edge node resource evaluation and task reception judgment

[0158] Set up edge computing nodes The resource vector is ,in Representation Node In the The available amount of various resources, such as computing resources, storage resources, bandwidth resources, etc. is the number of resource types;

[0159] When a communication task transfer request is received, the task The required resource vector is ;

[0160] For each node , determine whether the conditions for receiving the task are met: , that is, the available amount of each resource of the node must be greater than or equal to the corresponding resource amount required by the task.

[0161] Operation logic: The first adaptation algorithm determines whether the edge computing node has enough resources to receive the communication task transfer based on resource comparison. It simply and directly compares the available resources of the node with the resources required for the task, starting from the first node and checking in sequence. Once a node that meets the resource conditions is found, it is determined that the node can receive the task. This method can quickly screen out nodes that may receive tasks from multiple edge computing nodes, improving the efficiency of task transfer decisions.

[0162] Proportional Fairness Algorithm for Communication Time Slot Allocation and Carrier Frequency Adjustment

[0163] Mathematical formula:

[0164] Shared edge computing nodes participate in communication task collaboration, The instantaneous data transmission rate of each node is , and its average data transmission rate over a period of time is ( is the time window length;

[0165] For communication time slot allocation, nodes At the moment The proportion of allocated time slots Calculated according to the following formula:

[0166]

[0167] For carrier frequency adjustment, assume that the total available carrier frequency range ,node Assigned carrier frequency It can be adjusted according to its weight (related to factors such as the communication time slot allocation ratio), for example: ,in For Node The weight can be based on Determined by factors such as Or obtained through further weighted calculation.

[0168] Operation logic: The proportional fairness algorithm comprehensively considers the instantaneous data transmission rate of the node and the average data transmission rate over a period of time in terms of communication time slot allocation. By calculating the time slot ratio, nodes with higher data transmission rates and relatively lower average rates can obtain relatively more time slot resources to balance the resource allocation of each node and avoid some nodes occupying a large amount of resources for a long time and causing other nodes to be "starved". In terms of carrier frequency adjustment, the weight of the node is determined based on factors such as the time slot allocation ratio, and then the appropriate carrier frequency is allocated to the node within the total available carrier frequency range, so as to achieve coordinated allocation of frequency resources between nodes, so as to adapt to the communication needs and interference conditions of different nodes, and improve the anti-interference ability and communication efficiency of the entire communication system.

[0169] like Figure 4As shown in the figure, it is a schematic diagram of the hidden Markov model (HMM) used to predict the time law of interference in the time domain. The figure contains two sub-figures, the upper sub-figure shows the change of the observation sequence generated by simulation (using 0 and 1 to indicate whether interference occurs) with the time step, and connects each observation point with a broken line to intuitively present the time law of interference occurrence; the lower sub-figure shows the interference hidden state sequence predicted by the Viterbi algorithm, represented by different state numbers, and connects each state point with a broken line, reflecting the model's speculation on the change law of the hidden state behind the interference, which can assist subsequent operations such as communication scheduling decisions based on time laws.

[0170] like Figure 5 As shown in the figure, the time difference of arrival (TDOA) positioning algorithm is used for locating interference sources and communication terminals in the spatial domain. The blue discrete points represent the position distribution of 4 base stations in the plane, the red discrete points represent the position coordinates of the simulated real interference sources, and the green discrete points represent the interference source position coordinates estimated by the time difference of arrival positioning algorithm combined with the nonlinear least squares method. The degree of proximity between the estimated position and the real position can be compared intuitively, and the accuracy of the positioning algorithm in the current simulation scenario can be evaluated. In actual applications, the communication resource allocation, antenna beam control and other operations in the spatial domain can be optimized according to the positioning conditions of multiple interference sources and communication terminals.

[0171] Figure 6 The spectrum diagram of the energy detection spectrum sensing algorithm for frequency domain spectrum monitoring is drawn. The spectrum diagram of the simulated mixed signal (including communication signal and interference signal) is drawn. The horizontal axis is frequency and the vertical axis is amplitude. It intuitively shows the energy distribution of the signal at different frequencies. It can be seen that two obvious spectrum peaks correspond to the simulated signal frequencies (communication and interference frequencies). If the detection statistic is greater than the set threshold, the threshold line will be drawn in a red dotted line in the figure, and "Spectrum Diagram (Signal Detected)" will be displayed in the title, indicating that a signal is detected in the spectrum (that is, interference and other occupied spectrum situations are found); if it is less than the threshold, the title will display "Spectrum Diagram (No Signal Detected)", which is used to monitor the occupancy status of the spectrum so that the corresponding frequency domain anti-interference measures can be taken later, such as adjusting the communication frequency to avoid the interference frequency band.

[0172] like Figure 7As shown in the figure, the change curve diagram of the genetic algorithm for frequency domain communication parameter optimization outputs the communication parameter value corresponding to the optimal chromosome finally found, indicating the better communication parameter combination found after the iterative optimization of the genetic algorithm, and shows the change curve of the population fitness value with the increase of the number of iterations. The horizontal axis is the number of iterations and the vertical axis is the fitness value. It can be observed that as the iterations proceed, the fitness value shows an overall upward trend (under ideal conditions and when the parameters are set reasonably), indicating that the population is constantly evolving and searching towards a better communication parameter combination. It helps to intuitively understand the optimization process and convergence of the genetic algorithm, and provide a reference for reasonably adjusting the algorithm parameters and determining the appropriate number of iterations in practical applications, so as to better optimize the performance of the communication system in the frequency domain and cope with interference.

[0173] like Figure 8 As shown in the figure, principal component analysis (PCA) is used for a three-dimensional graph of interference data dimensionality reduction, which includes two sub-graphs on the left and right: the left sub-graph shows the scatter distribution of the original interference data in some dimensions (here the first three dimensions are taken), and the distribution pattern of the original high-dimensional data in the three-dimensional space can be intuitively seen. Due to factors such as correlation between features, the data presents a certain distribution pattern. The right sub-graph shows the scatter distribution of the data after PCA dimensionality reduction in the space composed of the selected principal components. Compared with the original data dimensionality reduction, some redundant information is removed, which is more convenient for subsequent further analysis of interference data characteristics and other operations, and to a certain extent retains the main distribution patterns and information of the original data.

[0174] like Fig. 9 As shown in the figure, the support vector data description (SVDD) algorithm is used to construct a single-class classification model to determine the discrete graph of interference features. The blue discrete points represent the distribution of the training data (i.e., the interference data after PCA dimensionality reduction) in the two-dimensional space, showing the approximate distribution of the interference data as a whole. The red discrete points highlight the support vectors, which are data points that play a key role in determining the boundary of the hypersphere and are distributed at key positions such as the edge of the data. The green discrete points represent the part of the newly generated test data that is judged to belong to the interference data class (i.e., within the hypersphere), and the purple discrete points represent the part that is judged not to belong to the interference data class (outside the hypersphere). Through this visual display, we can intuitively see the classification effect of the single-class classification model constructed by the SVDD algorithm on the new data, and then reflect the recognition of the interference features, such as judging whether the newly emerging data conforms to the distribution characteristics of the interference data, etc., to assist in the formulation of subsequent targeted anti-interference measures such as carrier adjustment.

[0175] like Fig.10As shown in the figure, the distribution diagram of the consistent hashing algorithm used to build the edge node collaboration framework. The black horizontal straight line represents the constructed hash ring, which is the basic reference for the entire data distribution and node mapping. The blue discrete points represent the corresponding positions of each edge computing node on the hash ring, showing the distribution of the nodes in the hash space. The red discrete points represent the hash value positions of different data on the hash ring. The green lines intuitively show the corresponding edge computing nodes to which each data is assigned according to the consistent hashing algorithm rules. This chart format can clearly show the distribution of data among edge computing nodes, reflecting how the consistent hashing algorithm realizes distributed storage and the characteristics of data redistribution when nodes change (for example, after adding or reducing nodes, the data redistribution can be simulated according to similar rules), which helps to understand its role in building the edge node collaboration framework.

[0176] like Fig.11 As shown in the figure, the first adaptation algorithm is used for edge node resource evaluation and task reception judgment. The available amount of different resource types (blue, green, and red represent different resources) of each edge computing node is displayed through bar charts of different colors, which intuitively presents the resource reserve differences of each node in different resource dimensions. The purple bar chart (corresponding to the task resource requirements) at the node position indicates whether the node can receive tasks. If the purple bar chart is displayed at the corresponding position of the node (and the height corresponds to the task requirements), it means that all resources of the node meet the task requirements and can receive task transfer; if it is not displayed, it means that the task requirements are not met and cannot be received. Through this visual comparison, we can clearly see the matching of each node's resources and task requirements, which is convenient for making task allocation decisions and other operations.

[0177] like Fig.12 As shown in the figure, the proportional fairness algorithm is used for the bar chart of communication time slot allocation and carrier frequency adjustment, which includes two sub-graphs on the left and right: the left sub-graph shows the communication time slot allocation ratio of each edge computing node calculated by the proportional fairness algorithm. By comparing the time slot resource ratios occupied by different nodes through the bar chart, it can be intuitively seen which nodes can obtain more communication time slots under the comprehensive consideration of the current instantaneous transmission rate and the average rate, which reflects the fairness adjustment mechanism of the algorithm for resource allocation, and avoids some nodes from occupying too many time slot resources for a long time; the right sub-graph shows the carrier frequency allocated to each edge computing node based on the time slot allocation ratio, and presents the carrier frequency values ​​corresponding to each node in a bar chart, showing how to reasonably allocate appropriate carrier frequencies within the total available frequency range according to the resource allocation weights of different nodes, which helps nodes to arrange communication tasks according to the allocated frequency resources, optimize the anti-interference performance of the communication system in the frequency domain and the overall communication efficiency, and adapt to different interference environments and communication requirements. Specific embodiment four:

[0179] like Figures 1 to 12 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0180] The movement of data during the operation of the entire system is as follows:

[0181] Quantum-assisted interference detection and protection layer

[0182] Quantum state interference detection module:

[0183] Data source: interference signals in the communication environment;

[0184] Input type: magnetic field fluctuations caused by electromagnetic interference and quantum state information of interference signal photons;

[0185] Output type: key parameters of interference signal, such as frequency, amplitude, etc.;

[0186] Output flow: Output to the quantum encryption anti-interference communication module, providing a basis for subsequent adaptive adjustment of encryption algorithm parameters and switching of communication channels, and also for the overall system to evaluate interference conditions;

[0187] Quantum encryption anti-interference communication module:

[0188] Data sources: On the one hand, it comes from the interference key parameters output by the quantum state interference detection module; on the other hand, it comes from the information data to be transmitted;

[0189] Input type: The interference key parameters are quantifiable data such as numerical types, and the information to be transmitted is various types of communication data that need to be transmitted (such as encoded binary data such as text, voice, and images);

[0190] Output type: encrypted and modulated communication data, output the adjusted encryption algorithm parameters and the switched communication channel related information during interference adjustment;

[0191] Output flow: The encrypted and modulated communication data flow is transmitted to the communication link. The adjusted encryption algorithm parameters and channel-related information are fed back to the relevant processing units within the system to ensure the uninterrupted and confidentiality of communication.

[0192] Dynamic anti-interference core layer of multi-domain fusion

[0193] Space-time frequency-energy fusion control module:

[0194] Data sources: relevant data in the time domain, space domain, frequency domain and energy domain, including communication history data, positioning related data, spectrum monitoring data, energy collection related data, etc.

[0195] Input type: The time domain is data related to time, such as historical communication records (possibly time series data, etc.); the spatial domain is signal data that can be used for positioning (such as arrival time difference TDOA related data, Bluetooth positioning related data, etc.); the frequency domain is spectrum data obtained by spectrum monitoring (such as frequency, power and other related data); the energy domain is electric energy related data collected by the thermoelectric energy harvester and energy consumption related data of the interference control component.

[0196] Output type: Time domain outputs interference time pattern prediction results and communication scheduling instructions; space domain outputs interference source and communication terminal location information and intelligent antenna beam control instructions; frequency domain outputs optimized communication parameters; energy domain outputs energy consumption control instructions.

[0197] Output flow: The relevant instructions and information output by each domain flow to the corresponding equipment and modules in the communication system, such as antenna control equipment, communication parameter adjustment module, etc., to achieve optimization adjustment of the communication system in each domain.

[0198] VR and AR interference visualization and interaction platform module:

[0199] Data sources: mainly from the interference source and communication terminal location information, interference intensity and other related data output by the time-space frequency-energy fusion control module, as well as the interactive command data input by the system administrator through the interactive device;

[0200] Input type: interference-related location, intensity and other data are coordinate, numerical and other types of data; manager interaction command data are voice commands, gesture operation data, etc.;

[0201] Output type: The visualization unit outputs interference visualization interface display data in the form of a three-dimensional heat map; the interpersonal interaction control unit outputs the parsed manager's instructions to clarify the manager's intention to adjust key parameters such as communication power and modulation mode;

[0202] Output flow: The visual interface displays data output to the display device at the communication system manager end for viewing; the parsed manager instructions flow to the corresponding parameter adjustment module in the communication system to achieve real-time and accurate adjustment of the communication system parameters.

[0203] Real-time anti-interference optimization layer driven by edge computing

[0204] Edge computing node deployment and architecture module:

[0205] Data source: interference data generated by communication terminals and data related to the overall system architecture configuration.

[0206] Input type: Interference data refers to various interference-related data detected by the communication terminal during operation (such as interference intensity, type, and other related data); architecture configuration data refers to data such as setting parameters for the distributed microservice architecture.

[0207] Output type: Output node deployment related information and connection status information to ensure data transmission and command feedback.

[0208] Output flow: Node deployment related information flows to the system overall architecture management module for monitoring and management; connection status information is transmitted between nodes and between nodes and communication devices to ensure normal data transmission and command feedback.

[0209] Local interference analysis and adjustment module:

[0210] Data source: interference data from communication terminals received by edge computing nodes;

[0211] Input type: high-dimensional interference data, including various feature-related data of interference (such as intensity, type, duration, etc.);

[0212] Output type: identification results such as the characteristics and types of interference data, and targeted carrier adjustment solutions;

[0213] Output flow: The identification results can flow to the overall interference status assessment module of the system for further analysis; the carrier adjustment scheme flows to the communication device, is quickly sent to the communication device through a low-latency communication link and is immediately executed to achieve adjustment of the communication system.

[0214] Edge node collaboration and communication task transfer mechanism module:

[0215] Data sources: data related to the interference status of each edge computing node itself, data related to the resource status of adjacent nodes, and data related to communication tasks.

[0216] Input type: Interference status related data refers to the interference intensity, type and other related data of the area where the node is located; resource status related data refers to the computing resources, storage resources and other related data of the adjacent nodes; communication task related data refers to the relevant information of the ongoing communication task (such as communication time slot, carrier frequency and other related information).

[0217] Output type: storage and query results of interference information and communication resource status between output nodes, as well as relevant decision information on communication task transfer (such as whether to transfer, transfer method, transfer parameters, etc.).

[0218] Output flow: Storage and query results are shared between nodes for reference in collaborative work; relevant decision information on the transfer of communication tasks flows to relevant edge computing nodes and communication equipment to achieve the transfer and coordinated adjustment of communication tasks between nodes, ensuring that the entire communication system maintains smooth and stable communication in a complex interference environment.

[0219] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0220] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An interference self-adjusting carrier communication system based on complex environments, characterized by: The system includes a quantum-assisted interference detection and protection layer, a multi-domain fusion dynamic anti-interference core layer, and an edge computing-driven real-time anti-interference optimization layer, wherein: The quantum-assisted interference detection and protection layer is used to achieve accurate detection of interference and encrypted communication protection through quantum technology. The quantum-assisted interference detection and protection layer includes a quantum state interference detection module and a quantum encryption anti-interference communication module. The quantum state interference detection module is used to collect quantum state information of interference signals using a quantum sensor array and determine key interference parameters with the help of a quantum signal processing unit. The quantum state interference detection module includes a quantum sensor array deployment unit and a quantum signal processing and analysis unit. The quantum sensor array deployment unit is used to place a quantum sensor array based on the principle of quantum entanglement and with a Josephson junction quantum interferometer JJI as a core detection element at a key node of the communication system to capture the magnetic field fluctuations caused by electromagnetic interference and the quantum state information of interference signal photons. The quantum signal processing and analysis unit is used to process the data collected by the quantum sensor using a quantum Grover search algorithm to determine the frequency, amplitude and other parameters of the interference signal. The quantum encryption anti-interference communication module is based on quantum key distribution QKD technology and uses the E91 protocol to build a secure key channel to encrypt information. The invention relates to a quantum encryption anti-interference communication module, which is a quantum encryption anti-interference communication module. The invention relates to a quantum encryption anti-interference communication module, which is a quantum encryption anti-interference communication module. The quantum encryption anti-interference communication module comprises a quantum key distribution and encryption unit and an adaptive encryption and channel switching unit. The quantum key distribution and encryption unit constructs a secure key channel for both parties of the communication based on the quantum key distribution QKD technology. Specifically, the E91 protocol is adopted to realize the generation and distribution of keys by means of entangled photon pairs. Before information transmission, the data is first encrypted by means of the quantum key, and then carrier modulation is performed to facilitate transmission in the communication link. When interference is encountered in the adaptive encryption and channel switching unit, the quantum encryption channel starts the self-protection and adaptive adjustment mechanism according to the principle that quantum states cannot be cloned. With the help of quantum teleportation technology, once the interference source is detected to try to crack the encrypted signal, the system will immediately change the parameters of the encryption algorithm. With the help of quantum entanglement exchange technology, when the communication link is interfered, it can quickly switch to the communication channel corresponding to the standby quantum entanglement pair, thereby ensuring the uninterrupted communication and high confidentiality. The multi-domain fusion dynamic anti-interference core layer is used to integrate time, space, frequency and energy multi-domain resources and combine visual interaction to achieve dynamic response to interference. The multi-domain fusion dynamic anti-interference core layer includes a time, space, frequency and energy fusion control module and a VR and AR interference visualization and interaction platform module; The edge computing-driven real-time anti-interference optimization layer is used to achieve local interference analysis and inter-regional collaborative anti-interference with the help of edge computing nodes. The edge computing-driven real-time anti-interference optimization layer includes an edge computing node deployment and architecture module, a local interference analysis and adjustment module, and an edge node collaboration and communication task transfer mechanism module.

2. The interference self-adjusting carrier communication system based on complex environment according to claim 1, characterized in that: The space-time frequency-energy fusion control module is used for data fusion and control scheduling in the time domain, space domain, frequency domain and energy domain. In the space-time frequency-energy fusion control module, the hidden Markov model HMM is used in the time domain to predict the time pattern of interference and schedule communication. In the space domain, the arrival time difference TDOA positioning algorithm and Bluetooth positioning technology are used to determine the interference source and the communication terminal position and control the smart antenna beam. In the frequency domain, the energy detection spectrum sensing algorithm is used to monitor the spectrum and the genetic algorithm is used to optimize the communication parameters. In the energy domain, the thermoelectric energy harvester is used to collect and store electrical energy and the energy consumption of the components is adjusted according to the interference.

3. The interference self-adjusting carrier communication system based on complex environment according to claim 1, characterized in that: The VR and AR interference visualization and interaction platform module is used to display interference information in the form of a three-dimensional space heat map and realize interactive regulation between the manager and the system through voice recognition and gesture tracking technology. The VR and AR interference visualization and interaction platform module includes an interference visualization presentation unit and an interpersonal interaction regulation unit. The interference visualization presentation unit uses virtual reality VR and augmented reality AR technology to carefully create an interference visualization interface. By equipping the communication equipment with special VR and AR display devices, the interference signals in a complex environment are vividly presented to the communication system manager in the form of an extremely intuitive three-dimensional space heat map. The interpersonal interaction regulation unit is used to allow the manager to make real-time and accurate adjustments to the carrier communication system through natural and smooth gestures, voice and other interactive methods.

4. The interference self-adjusting carrier communication system based on complex environment according to claim 1, characterized in that: The edge computing node deployment and architecture module is used to deploy edge computing nodes using a distributed microservice architecture near the communication terminal, and ensure data transmission and instruction feedback through MQTT protocol communication and high-speed, low-latency connection. The edge computing node deployment and architecture module includes a distributed node layout unit and a high-speed connection unit. The distributed node layout unit is located at a key position close to the communication terminal and adopts an advanced distributed microservice architecture. Each node is responsible for the management and anti-interference processing tasks of the communication link in a specific area. The nodes communicate through the MQTT message transmission protocol to achieve rapid sharing and collaborative work of some information and resource status.

5. The interference self-adjusting carrier communication system based on complex environment according to claim 1, characterized in that: The local interference analysis and adjustment module is used to run the interference analysis process of integrating principal component analysis PCA and support vector data description SVDD algorithm at the edge computing node to determine the interference characteristics and generate a targeted carrier adjustment scheme. The local interference analysis and adjustment module includes an interference data intelligent analysis unit and a dynamic carrier adjustment unit. The interference data intelligent analysis unit first uses principal component analysis PCA to effectively reduce the dimension of high-dimensional interference data, and then uses the support vector data description SVDD algorithm to construct a single-class classification model for the reduced-dimensional data, so as to accurately determine the distribution range and characteristics of the interference data and accurately identify the type of interference. The dynamic carrier adjustment unit, based on the results of the interference analysis, the adjustment module of the edge computing node quickly generates a highly targeted carrier adjustment scheme.

6. The interference self-adjusting carrier communication system based on complex environment according to claim 1, characterized in that: The edge node collaboration and communication task transfer mechanism module is used to build a node-to-node collaboration framework based on the consistent hashing algorithm, and realize the transfer and coordinated adjustment of communication tasks between adjacent nodes when interference is serious, so as to ensure the overall stability of the communication system. The edge node collaboration and communication task transfer mechanism module includes a collaborative communication framework construction unit and a task transfer and coordinated adjustment unit. When the interference in the area where a certain edge computing node is located is serious enough to cause communication interruption, the node sends a communication task transfer request to the adjacent edge computing node with less interference according to a pre-set intelligent collaboration strategy. After receiving the request, the adjacent node uses the resource allocation algorithm to comprehensively evaluate its own resource status and interference situation to determine whether it has the ability to receive the transferred task. If it can be received, the two parties determine the specific method and parameters of the communication task transfer through efficient negotiation to ensure that the entire communication system can maintain smooth and stable communication in a complex interference environment, and effectively avoid the paralysis of the entire communication network due to local severe interference.

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