Intermediate wave signal transmission interference suppression method and system based on wireless radio and television

Abstract

The invention discloses a medium-short wave signal transmission interference suppression method and system based on wireless radio and television, and the method comprises the following steps: S1, collecting and preprocessing medium-short wave frequency band data, and constructing a frequency spectrum state data set; s2, constructing a spectrum evolution cognitive map model based on the spectrum state data set; s3, performing clustering analysis by using the spectrum evolution cognitive map model to generate a broadcast interference popularity map; s4, inputting the spectrum evolution cognitive map model and the broadcast interference heat map into an improved badger interference avoidance algorithm, and outputting a frequency hopping avoidance path; s5, the broadcast transmitting system executes frequency hopping scheduling control according to the frequency hopping avoiding path, and it is guaranteed that frequency point switching is completed under the condition that broadcast service is not interrupted; and S6, the broadcast receiving terminal collects signal quality parameters and transmits the signal quality parameters back to the central system, and the node state of the spectrum evolution cognitive graph model is updated in real time. According to the method, active avoidance of medium-short wave broadcast interference and stable frequency hopping scheduling are realized by combining frequency spectrum cognitive modeling and a swarm intelligent optimization algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of radio communication and broadcast interference suppression, and particularly to a method and system for suppressing interference in medium and short wave signal transmission based on wireless radio and television. Background Art

[0002] In a wireless radio and television system, the medium and short wave frequency band (usually referring to 3 MHz to 30 MHz) has the capabilities of long-distance propagation, compatibility of ground wave and sky wave, and support for ionospheric refraction. Therefore, it is widely used in rural areas, mountainous areas, border areas, and emergency communication scenarios. However, with the continuous improvement of the multiplexing degree of the medium and short wave frequency band, as well as the increasing spurious emissions brought by industrial electrical equipment, power systems, and communication facilities, the broadcast signal faces a complex electromagnetic interference environment during transmission. Especially in urban fringes, industrial parks, areas near high-voltage lines, and post-disaster reconstruction areas, the interference situation is more frequent and unpredictable. This poses a severe challenge to the traditional broadcast scheduling mode, resulting in a decline in frequency scheduling efficiency and frequent broadcast interruption events, greatly affecting the service continuity and reception quality of the broadcast service.

[0003] Most of the existing spectrum management methods for medium and short wave broadcast systems adopt a scheduling method that combines static frequency point allocation and manual experience intervention, lacking the ability of dynamic perception of the interference environment and adaptive regulation. In the process of formulating scheduling strategies, it mainly relies on historical statistical data or static frequency occupancy tables, and cannot effectively capture the temporal changes of the spectrum state and the dynamic evolution characteristics of the propagation environment. At the same time, the existing interference identification and avoidance mechanisms mainly focus on single-point interference detection, lacking the ability to systematically model the mutual interference relationship between frequencies and also unable to identify the spatial distribution characteristics of interference aggregation areas. Therefore, when interference breaks out, the broadcast system often cannot make a quick response and can only cope with it through inefficient methods such as frequency replacement and power increase, resulting in problems such as lagging scheduling response, blind frequency point switching, and low utilization rate of frequency resources.

[0004] In addition, the traditional frequency hopping control strategy lacks an intelligent optimization mechanism. The frequency point selection is mostly based on static rules or fixed priorities, ignoring the interference propagation trend and potential coupling relationship between frequency points in the frequency hopping path. For example, when a certain frequency point is suddenly interfered with, the frequency hopping strategy may schedule the signal to another high-risk frequency point, resulting in repeated interference, continuous fluctuation of signal quality, and even frequent interruptions and back jumps. More seriously, these strategies usually lack a closed-loop feedback mechanism with the terminal reception quality. After frequency hopping, the broadcast system cannot obtain the actual signal quality data at the receiving end in real time, so it cannot adjust the allocation method of spectrum resources according to the feedback results, nor can it dynamically update the spectrum usage status.

[0005] In recent years, with the development of cognitive radio, graph neural modeling, and swarm intelligence optimization algorithms, some technical routes have attempted to introduce sensing and graph structure modeling for the understanding and regulation of the spectrum environment. However, most of these studies focus on specific applications such as mobile communication and military communication, lacking customized modeling and scheduling control mechanisms for the characteristics of medium and short-wave broadcasting. Key parameters involved in medium and short-wave broadcasting, such as amplitude modulation characteristics, ionospheric propagation delay, and channel hopping window synchronization, are often not considered or inadequately modeled in existing technologies, making it difficult for these methods to be directly applicable to the broadcast signal transmission scenario. Especially under the requirements of low-cost and large-scale deployment, there are application barriers in terms of system complexity, execution real-time performance, and feedback response efficiency for existing methods.

[0006] Therefore, how to provide a method and system for suppressing interference in medium and short-wave signal transmission based on wireless radio and television is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0007] An object of the present invention is to propose a method and system for suppressing interference in medium and short-wave signal transmission based on wireless radio and television. The present invention combines a spectrum evolution cognitive graph modeling method with an improved honey badger interference avoidance algorithm, constructs a dynamic interference correlation relationship between broadcast frequency points, and guides the generation of frequency hopping paths. By fusing terminal feedback to achieve real-time model update, it comprehensively realizes active identification, autonomous avoidance, and frequency hopping scheduling control of medium and short-wave broadcast interference, with significant advantages of fast response, reasonable path optimization, strong anti-interference ability, high signal continuity, and applicability to complex propagation environments, especially suitable for typical scenarios with high requirements for medium and short-wave broadcast coverage such as rural areas, mountainous areas, borders, and emergency communications.

[0008] According to the method for suppressing interference in medium and short-wave signal transmission based on wireless radio and television according to an embodiment of the present invention, the following steps are included:

[0009] S1. Deploy spectrum monitoring terminals at the transmitting nodes of the wireless radio and television system, collect medium and short-wave band data and perform preprocessing to construct a spectrum status data set;

[0010] S2. Based on the spectrum status data set, construct a spectrum evolution cognitive graph model;

[0011] S3. Use the spectrum evolution cognitive graph model for clustering analysis, identify interference concentration areas, generate a broadcast interference heat map, and calibrate the interference risk level of frequency points;

[0012] S4. Input the spectrum evolution cognitive graph model and the broadcast interference heat map into the improved honey badger interference avoidance algorithm, and combine the frequency hopping migration strategy and the broadcast interference suppression memory library constructed by historical interference frequency points to output a frequency hopping avoidance path;

[0013] S5. The broadcast transmission system performs frequency hopping scheduling control according to the frequency hopping avoidance path, ensuring the completion of frequency point switching without interrupting the broadcast service, and comprehensively considering the hopping time window, ionospheric delay, and channel synchronization;

[0014] S6. The broadcast receiving terminal collects signal quality parameters and transmits them back to the central system to update the node status of the spectral evolution cognitive map model in real time.

[0015] Optionally, the medium and short wave band data includes frequency availability, interference intensity, signal stability, and the confidence level of amplitude modulation recognition.

[0016] Optionally, the preprocessing includes interference rejection, normalization, and filtering.

[0017] Optionally, S2 specifically includes:

[0018] S21. Structurally process the spectral state data set to form a frequency point state sequence arranged in chronological order. Each record in the frequency point state sequence includes frequency availability parameters, interference intensity parameters, signal stability parameters, and amplitude modulation recognition confidence parameters corresponding to the frequency point, which are used to describe the spectral behavior characteristics of the frequency point within the current time window;

[0019] S22. Use each frequency point in the spectral state data set as a graph node to construct a node set, and map the state vector of each node to the node attributes in the spectral evolution cognitive map model;

[0020] S23. Set judgment rules based on the interval distance relationship between different frequency points in the frequency proximity dimension and their behavioral similarity in the historical interference pattern. Establish directed edge connections between node pairs that meet the proximity threshold and similarity threshold. The weight of the edge is used to quantify the channel interference coupling degree and frequency hopping migration potential, forming an edge set;

[0021] S24. Construct an initial static spectral cognitive map based on the node set and edge set to describe the spectral usage relationship and interference propagation path between medium and short wave channels within the current time window;

[0022] S25. Set the evolution mechanism of the initial static spectral cognitive map to update the node status and edge weights over time, forming an evolution cognitive map sequence;

[0023] S26. Organize the evolution cognitive map sequence into a four-dimensional data structure, which includes the number of nodes, connections between nodes, time series, and state dimension, constituting the spectral evolution cognitive map model.

[0024] Optionally, S3 specifically includes:

[0025] S31. Extract the node states and edge weight relationships of each broadcast frequency point in the spectral evolution cognitive map model at different times, and construct a spectral feature set across time windows;

[0026] S32. Calculate the comprehensive interference weight of each frequency point within the current time window based on the spectral feature set:

[0027]

[0028] Among them, D i represents the comprehensive interference weight of the i-th frequency point, T represents the total number of time steps, ω t represents the time decay coefficient, I i,t represents the interference intensity of the i-th frequency point at the t-th time step, represents the change rate of the interference intensity, N(i) represents the set of adjacent nodes that have mutual interference edges with the i-th frequency point, represents the interference influence factor of the j-th frequency point on the i-th frequency point, I j,t represents the interference intensity of the j-th frequency point at the t-th time step, and α, β, and γ represent weight coefficients;

[0029] S33. Based on the graph structure distance and interference state similarity between frequency points in the spectral evolution cognitive map, use an adaptive graph density clustering algorithm to automatically identify interference concentration regions and form interference aggregation clusters;

[0030] S34. Combine the broadcast transmitter node location information with the geospatial reference data, map the interference aggregation clusters to the geographic coordinate system, and construct a broadcast interference spatial distribution layer;

[0031] S35. Based on the broadcast interference spatial distribution layer, generate a broadcast interference heat map, and use color gradients and numerical levels to represent the interference concentration degree of frequency points;

[0032] S36. According to the comprehensive interference weight D i calculated in step S32, set classification thresholds θ1 and θ2 to perform interference risk level classification on each frequency point:

[0033] If D i ≥ θ2, then determine that the i-th frequency point has a high interference risk;

[0034] If θ1 ≤ D i < θ2, then determine that the i-th frequency point has a medium interference risk;

[0035] If D i < θ1, then determine that the i-th frequency point has a low interference risk.

[0036] Optionally, the specific content of S4 includes:

[0037] S41. Initialize the improved honey badger interference avoidance algorithm using the spectrum evolution cognitive map model and the broadcast interference heat map, and construct an initial population of honey badger individuals. Each honey badger individual corresponds to a frequency point node in the spectrum cognitive map model, and each honey badger individual carries frequency point status information, including interference risk level and frequency availability status.

[0038] S42. Construct a broadcast interference suppression memory bank. Extract high-interference-risk frequency points from the broadcast interference heat map and historical frequency hopping failure data, mark these high-interference-risk frequency points as memory bank nodes, and record the interference times, interference occurrence moments, interference duration, and frequency hopping failure reasons of the nodes.

[0039] S43. Introduce the high-interference-risk frequency points in the broadcast interference suppression memory bank as soft-disabled nodes into the path evaluation link of the improved honey badger interference avoidance algorithm. When a honey badger individual migrates to a frequency point marked in the broadcast interference suppression memory bank, the path cost function automatically increases the memory penalty weight, reducing the path selection probability.

[0040] S44. Through multiple rounds of iterative updates, the improved honey badger interference avoidance algorithm continuously adjusts the honey badger individual migration path selection according to the path cost function, and finally outputs a frequency hopping avoidance path that meets the constraints of the lowest interference risk and uninterrupted service.

[0041] Optionally, the calculation formula for the memory penalty weight in the path cost function is:

[0042]

[0043] where P i represents the memory penalty weight of the i-th frequency point, n i represents the historical interference times of the i-th frequency point recorded in the broadcast interference suppression memory bank, N represents the maximum value of the historical interference times of all frequency points in the broadcast interference suppression memory bank, τ i represents the time interval after the i-th frequency point's most recent interference occurrence, and δ and η represent weight parameters.

[0044] The calculation result of the memory penalty weight is used to increase the frequency hopping migration cost of high-interference-risk frequency points.

[0045] Optionally, the specific content of S5 includes:

[0046] S51. The broadcast transmission system reads the frequency point switching order and hopping interval information in the frequency hopping avoidance path, and formulates a frequency hopping switching scheduling plan. The frequency hopping switching scheduling plan includes a frequency switching instruction set and a corresponding scheduling time sequence table.

[0047] S52. Set a hopping time window with a fixed length according to the modulation method and the device processing delay. The hopping time window is bound to each hopping instruction in the scheduling time sequence table to form a frequency hopping window control sequence.

[0048] S53. Conduct ionospheric state analysis to obtain the predicted refraction delay data of each frequency point in the current time period, adjust the hopping start time and the duration period in the frequency hopping window control sequence, and correct the transmission time parameters during the handover process.

[0049] S54. Extract the modulation configuration files of adjacent frequency points in the frequency hopping path, calculate the difference in carrier parameter changes between the frequency points, and correct the amplitude modulation parameters of the transmitting end according to the difference in carrier parameter changes. The amplitude modulation parameters of the transmitting end include frequency offset, amplitude gain factor, and initial carrier phase value.

[0050] S55. During the frequency hopping execution stage, read the frequency output value, amplitude of the amplitude modulation signal, and error rate index collected by the monitoring device at the transmitting end, compare the index with the preset standard in the frequency hopping handover scheduling plan, and judge the frequency hopping stability state.

[0051] S56. Under the condition that the frequency hopping stability state does not meet the preset standard, call the standby frequency point instruction and switch to the standby path channel.

[0052] Optionally, the specific steps of S6 are as follows:

[0053] S61. Configure signal monitoring devices at the broadcast receiving terminals to perform real-time sampling on the decoding success rate, error rate, audio restoration clarity, and channel stability of the received signals. The sampling period is set synchronously with the frequency hopping scheduling period of the broadcast transmission system.

[0054] S62. Construct each signal quality parameter into a receiving end state vector. The receiving end state vector is bound to the unique identifier of the broadcast receiving terminal and the time stamp, and is uploaded and encapsulated through the auxiliary control channel, IP data link, or satellite feedback channel of the broadcast transmission system.

[0055] S63. After the central system receives the receiving end state vector data from multiple broadcast receiving terminals, perform data synchronization and terminal identifier matching operations, and establish a broadcast reception quality sequence set in chronological order.

[0056] S64. Extract the signal state data matching the corresponding frequency points in the current frequency hopping avoidance path from the broadcast reception quality sequence set, and calculate the mean value and volatility of the signal quality index of each frequency point in the current period.

[0057] S65. According to the mean and volatility of the signal quality indicators of each frequency point in the current period, the sliding average method is used to update the state vector of the corresponding frequency point node in the spectrum evolution cognitive map model. Specifically, the mean of the signal quality in the current period is extracted, and combined with the historical signal quality means of this frequency point in the previous several periods, the average level of this frequency point in multiple consecutive periods is calculated, and this result is used as the new node state vector to replace the original frequency point state information. At the same time, the edge weights between this node and adjacent nodes are updated.

[0058] The medium and short wave signal transmission interference suppression system based on wireless radio and television according to the embodiment of the present invention includes the following modules:

[0059] A spectrum monitoring module, configured to collect spectrum data in the medium and short wave frequency bands at a broadcast transmission node, and perform preprocessing to form a spectrum state data set;

[0060] A cognitive modeling module, configured to construct a spectrum evolution cognitive map model based on the spectrum state data set;

[0061] An interference analysis module, configured to perform clustering analysis on the spectrum evolution cognitive map model, identify interference concentration areas, generate a broadcast interference heat map, and calibrate the interference risk level of frequency points;

[0062] A path optimization module, configured to input the spectrum evolution cognitive map model and the broadcast interference heat map into an improved honey badger interference avoidance algorithm, and output a frequency hopping avoidance path;

[0063] A frequency hopping control module, configured to enable the broadcast transmission system to perform frequency point switching according to the frequency hopping avoidance path, and execute frequency hopping scheduling in combination with the hopping time window, ionospheric delay, and channel modulation parameters;

[0064] A feedback update module, configured to collect signal quality parameters by a broadcast receiving terminal and transmit them back, and use the sliding average method to update the state vector of the corresponding node in the spectrum evolution cognitive map model.

[0065] The beneficial effects of the present invention are:

[0066] First of all, by deploying spectrum monitoring terminals at wireless radio and television transmission nodes, the system of the present invention can collect key spectrum state data in the medium and short wave frequency bands in real time, and through preprocessing operations such as interference elimination, normalization, and filtering, form a structured and time-ordered spectrum state data set, laying an accurate data foundation for subsequent cognitive modeling. Compared with the traditional experience-based frequency allocation method, this process can dynamically reflect the interference evolution trend and the change of frequency point availability in the broadcast environment, providing support for the perception-driven scheduling mechanism.

[0067] Secondly, based on the spectrum evolution cognitive graph model constructed based on spectrum status data, the system can model each frequency point as a state node, and describe the frequency proximity, interference coupling relationship and frequency hopping accessibility between frequency points through graph structure. By introducing the graph structure evolution mechanism, the time perception capability of ionospheric changes, policy adjustments and interference behavior patterns is realized, which enhances the system's adaptability and predictability in practical applications. On this basis, the interference clustering area is further mined through the graph density clustering algorithm, and the broadcast interference heat map is generated in combination with the geographic location information of the broadcast transmission node, realizing the transition from single-point interference identification to spatial interference pattern identification, and providing spatial guidance for frequency hopping path avoidance.

[0068] In addition, the improved honey badger interference avoidance algorithm introduced in the present invention integrates the spectrum evolution cognitive map and broadcast interference heat map information under the swarm intelligence framework. It not only considers the interference risk level of the frequency points in the path, but also introduces the frequency hopping migration strategy and the broadcast interference suppression memory library to effectively avoid high-risk frequency points. By setting the memory penalty weight mechanism, the algorithm assigns a higher cost to historical high-interference frequency points in path selection, thereby realizing active avoidance of interference-sensitive areas during the path search process. Compared with the traditional frequency hopping scheduling method based on fixed rules or static priorities, the path optimization strategy of the present invention is more dynamic, robust and environmentally adaptable.

[0069] Finally, after the broadcast frequency hopping is executed, the signal quality parameters are collected in real time through the receiving end, and the decoding success rate, bit error rate, audio restoration clarity and other indicators are transmitted back to the central system. The system uses the sliding average method to update the node status in the cognitive graph, thereby realizing the closed-loop self-learning ability of the model. This feedback mechanism opens up the complete chain of modeling-scheduling-execution-feedback-update, enabling the system to have the ability of dynamic cognition and continuous optimization, effectively improving the utilization efficiency of spectrum resources and the continuity of broadcast services. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0071] Figure 1 This is an overall flow chart of the method for suppressing medium and short wave signal transmission interference based on wireless broadcasting and television proposed by the present invention;

[0072] Figure 2 The present invention is a schematic diagram of the structure of the medium and short wave signal transmission interference suppression system based on wireless broadcasting and television. DETAILED DESCRIPTION

[0073] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are all simplified schematic diagrams, only schematically showing the basic structure of the present invention, so they only show the components related to the present invention.

[0074] Reference Figure 1 , a method for suppressing interference in medium and short wave signal transmission based on wireless radio and television, includes the following steps:

[0075] S1. Deploy spectrum monitoring terminals at the transmitting nodes of the wireless radio and television system, collect medium and short wave band data and perform preprocessing, and construct a spectrum status data set;

[0076] S2. Based on the spectrum status data set, construct a spectrum evolution cognitive map model;

[0077] S3. Use the spectrum evolution cognitive map model to perform clustering analysis, identify interference concentration areas, generate a broadcast interference heat map, and calibrate the interference risk level of frequency points;

[0078] S4. Input the spectrum evolution cognitive map model and the broadcast interference heat map into an improved honey badger interference avoidance algorithm, and combine the frequency hopping migration strategy and a broadcast interference suppression memory bank constructed based on historical interference frequency points to output a frequency hopping avoidance path;

[0079] S5. The broadcast transmission system executes frequency hopping scheduling control according to the frequency hopping avoidance path, ensures the completion of frequency point switching without interrupting the broadcast service, and comprehensively considers the hopping time window, ionospheric delay, and channel synchronization;

[0080] S6. The broadcast receiving terminal collects signal quality parameters and transmits them back to the central system to update the node status of the spectrum evolution cognitive map model in real time.

[0081] By proposing a multi-step collaborative method for suppressing interference in medium and short wave signal transmission, the present invention realizes a full-process closed-loop scheduling mechanism from spectrum status acquisition, cognitive modeling, interference identification, path avoidance, frequency hopping control to feedback update. This method constructs a spectrum evolution cognitive map model, accurately describes the interference coupling relationship and evolution trend between frequency points, and improves the adaptability of the broadcast system to complex electromagnetic environments. At the same time, an improved honey badger algorithm and an interference suppression memory mechanism are introduced to enhance the intelligent avoidance effect of the frequency hopping path and avoid frequent interruptions and repeated frequency hopping. Through the feedback of the receiving terminal to drive model update, the system has the ability of continuous learning and dynamic adjustment, significantly improving the continuity, anti-interference ability of the broadcast service and the scheduling efficiency of spectrum resources.

[0082] In this embodiment, the medium and short wave band data includes frequency availability, interference intensity, signal stability, and the confidence level of amplitude modulation recognition.

[0083] The present invention makes the spectrum data structure more expressive by clarifying the specific composition of spectrum state data, including four types of indicators: frequency availability, interference intensity, signal stability, and AM modulation recognition confidence. It can comprehensively reflect the multi-dimensional transmission state characteristics of medium and short wave broadcast signals. Compared with the traditional spectrum data structure mainly based on power spectrum or single interference judgment, this parameter system is more suitable for fine frequency point modeling in the AM broadcast scenario, providing a more discriminative state basis for subsequent cognitive map construction, interference clustering analysis, and frequency hopping optimization, effectively improving the system's perception ability and modeling accuracy.

[0084] In this embodiment, the preprocessing includes interference rejection, normalization, and filtering. By defining the preprocessing as three operations: interference rejection, normalization, and filtering, the usability and structural degree of the spectrum state data set are improved, and the influence of pseudo-signal interference and data fluctuations on the modeling process is significantly reduced. This method can remove non-broadcast signal components, unify the signal amplitude scales of different frequency points, and eliminate high-frequency jitter components, making the construction of the subsequent spectrum evolution cognitive map model more stable and robust. Compared with the existing technologies that do not clearly define the data preprocessing steps, this solution effectively enhances the system's ability to depict the real signal state and provides a high-quality data basis for scheduling optimization.

[0085] In this embodiment, the specific steps of S2 are as follows:

[0086] S21. Structurally process the spectrum state data set to form a frequency point state sequence arranged in chronological order. Each record in the frequency point state sequence includes the frequency availability parameter, interference intensity parameter, signal stability parameter, and AM modulation recognition confidence parameter corresponding to the frequency point, which are used to describe the spectrum behavior characteristics of the frequency point within the current time window.

[0087] S22. Use each frequency point in the spectrum state data set as a graph node to construct a node set, and map the state vector of each node to the node attribute in the spectrum evolution cognitive map model.

[0088] S23. According to the interval distance relationship of different frequency points in the frequency proximity dimension and the behavior similarity in the historical interference pattern, set judgment rules, and establish directed edge connections between node pairs that meet the proximity threshold and similarity threshold. The weight of the edge is used to quantify the channel interference coupling degree and frequency hopping migration potential, forming an edge set.

[0089] S24. Based on the node set and the edge set, construct an initial static spectrum cognitive map to describe the spectrum usage relationship and interference propagation path between medium and short wave channels within the current time window.

[0090] S25. Set the evolution mechanism of the initial static spectrum cognitive map to update the node state and edge weight over time, forming an evolution cognitive map sequence.

[0091] S26. Organize the sequence of evolutionary cognitive maps into a four-dimensional data structure, which includes the number of nodes, connections between nodes, time series, and state dimension, constituting a spectral evolutionary cognitive map model.

[0092] By stepwise defining the construction process of the spectral evolutionary cognitive map model, including state sequence formation, graph node mapping, edge establishment rules, static graph generation, evolutionary mechanism setting, and four-dimensional structure encapsulation, the cognitive modeling is made executable and highly general. In particular, by establishing the edge weight relationship based on the dual criteria of proximity and similarity, the graph model's ability to depict the interference propagation mechanism between channels is enhanced. The four-dimensional structure uniformly manages the evolutionary state, enabling the model to be compatible with time dynamics, structural evolution, and state updates, breaking through the limitations of existing static graph models in medium and short wave applications, and improving the broadcast system's prediction and response capabilities to interference environment changes.

[0093] In this embodiment, the specific steps of S3 are as follows:

[0094] S31. Extract the node states and edge weight relationships of each broadcast frequency point in the spectral evolutionary cognitive map model at different times, and construct a spectral feature set across time windows;

[0095] S32. Calculate the comprehensive interference weight of each frequency point within the current time window based on the spectral feature set:

[0096]

[0097] where D i represents the comprehensive interference weight of the i-th frequency point, T represents the total number of time steps, ω t represents the time decay coefficient, I i,t represents the interference intensity of the i-th frequency point at the t-th time step, represents the change rate of the interference intensity, N(i) represents the set of adjacent nodes that have mutual interference edges with the i-th frequency point, represents the interference influence factor of the j-th frequency point on the i-th frequency point, I j,t represents the interference intensity of the j-th frequency point at the t-th time step, and α, β, and γ represent weight coefficients;

[0098] S33. Based on the graph structure distance and interference state similarity between frequency points in the spectral evolutionary cognitive map, use an adaptive graph density clustering algorithm to automatically identify interference concentration regions and form interference aggregation clusters;

[0099] S34. Combine the position information of the broadcast transmission nodes with the geospatial reference data, map the interference aggregation clusters to the geographic coordinate system, and construct a broadcast interference spatial distribution layer;

[0100] S35. Based on the broadcast interference spatial distribution layer, generate a broadcast interference heat map, and use color gradients and numerical levels to represent the degree of interference concentration of frequency points;

[0101] S36. According to the comprehensive interference weight D calculated in step S32 i , set classification thresholds θ1 and θ2 to perform interference risk level classification for each frequency point:

[0102] If D i ≥ θ2, then determine that the i-th frequency point has a high interference risk;

[0103] If θ1 ≤ D i < θ2, then determine that the i-th frequency point has a medium interference risk;

[0104] If D i < θ1, then determine that the i-th frequency point has a low interference risk.

[0105] By constructing a comprehensive interference weight index, introducing a graph density clustering algorithm to identify interference aggregation clusters, and then combining spatial geographic information to generate a broadcast interference heat map, it effectively realizes multi-level interference perception from frequency point status to spatial interference trend. The hierarchical interference risk assessment mechanism can accurately calibrate the interference intensity level of each frequency point, providing accuracy support for frequency hopping path avoidance. This solution overcomes the limitations of traditional interference identification based only on single-point detection or static thresholds, has the advantages of a wide interference identification range, strong spatial resolution, and clear level classification, and enhances the dynamic controllability of spectrum resources and the stability of broadcast services.

[0106] In this embodiment, the specific steps of S4 include:

[0107] S41. Use the spectrum evolution cognitive map model and the broadcast interference heat map to initialize the improved honey badger interference avoidance algorithm, construct an initial population of honey badger individuals. The honey badger individuals correspond to the frequency point nodes in the spectrum cognitive map model, and each honey badger individual carries frequency point status information, including interference risk level and frequency availability status;

[0108] S42. Construct a broadcast interference suppression memory bank, extract high-interference-risk frequency points from the broadcast interference heat map and historical frequency hopping failure data, mark the high-interference-risk frequency points as memory bank nodes, and record the interference times, interference occurrence moments, interference duration, and frequency hopping failure reasons of the nodes;

[0109] S43. Introduce the high-interference-risk frequency points in the broadcast interference suppression memory bank as soft-disabled nodes into the path evaluation link of the improved honey badger interference avoidance algorithm. When a honey badger individual migrates to the frequency points marked in the broadcast interference suppression memory bank, the path cost function automatically increases the memory penalty weight, reducing the path selection probability;

[0110] S44. The improved honey badger interference avoidance algorithm is updated through multiple rounds of iteration, and continuously adjusts the migration path selection of honey badger individuals according to the path cost function, and finally outputs a frequency hopping avoidance path that meets the constraints of the lowest interference risk and uninterrupted service.

[0111] By introducing the improved honey badger interference avoidance algorithm under the swarm intelligence framework and integrating the spectrum evolution cognitive map and the broadcast interference heat map, the search process of the frequency hopping path has the comprehensive avoidance ability for the current frequency point state, interference trend and historical high-risk frequency points. The broadcast interference suppression memory bank realizes the dynamic update of the frequency hopping path cost function by recording the historical behavior of interference frequency points, and avoids the path from repeatedly entering the high-risk area. Compared with the traditional frequency hopping strategy that relies on static priorities or exclusion lists, this solution has significant advantages such as strong path adaptability, reasonable dynamic penalty, and high scheduling efficiency.

[0112] In this embodiment, the calculation formula of the memory penalty weight in the path cost function is:

[0113]

[0114] where P i represents the memory penalty weight of the i-th frequency point, n i represents the historical interference times of the i-th frequency point recorded in the broadcast interference suppression memory bank, N represents the maximum value of the historical interference times of all frequency points in the broadcast interference suppression memory bank, τ i represents the time interval from the i-th frequency point to the most recent interference, and δ and η represent weight parameters;

[0115] The calculation result of the memory penalty weight is used to increase the frequency hopping migration cost of high interference risk frequency points.

[0116] By defining a specific memory penalty function and taking the historical interference occurrence frequency and the time distance from the most recent interference of the frequency point as the dynamic weighting factors of the path cost, the frequency hopping path search process has the time sensitivity and intensity penalty ability for high interference frequency points. This function introduces a decaying memory and a density-based suppression mechanism in the path cost evaluation, so that high-risk frequency points are gradually abandoned in the frequency hopping path, while still retaining short-term emergency availability, balancing the robustness of the frequency hopping path and resource utilization. This method improves the controllability of the algorithm avoidance strategy and the stability of the optimization result.

[0117] In this embodiment, the specific steps of S5 are as follows:

[0118] S51. The broadcast transmission system reads the frequency point switching sequence and hopping interval information in the frequency hopping avoidance path, and formulates a frequency hopping switching scheduling plan, where the frequency hopping switching scheduling plan includes a frequency switching instruction set and a corresponding scheduling time sequence table;

[0119] S52. Set a hopping time window with a fixed length according to the modulation method and the device processing delay. The hopping time window is bound to each hopping instruction in the scheduling time sequence table to form a frequency hopping window control sequence.

[0120] S53. Conduct ionospheric state analysis, obtain the predicted refraction delay data of each frequency point in the current time period, adjust the hopping start time and the duration period in the frequency hopping window control sequence, and correct the transmission time parameter during the handover process.

[0121] S54. Extract the modulation configuration files of adjacent frequency points in the frequency hopping path, calculate the difference in carrier parameter changes between the frequency points, and correct the amplitude modulation parameters of the transmitter according to the difference in carrier parameter changes. The amplitude modulation parameters of the transmitter include frequency offset, amplitude gain factor, and initial carrier phase.

[0122] S55. In the frequency hopping execution stage, read the frequency output value, the amplitude of the amplitude modulation signal, and the bit error rate index collected by the transmitter monitoring device, compare the index with the preset standard in the frequency hopping handover scheduling plan, and judge the frequency hopping stability state.

[0123] S56. Under the condition that the frequency hopping stability state does not meet the preset standard, call the standby frequency point instruction and switch to the standby path channel.

[0124] By refining the frequency hopping path execution process into six steps: frequency hopping scheduling plan generation, time window control, ionospheric delay correction, modulation parameter adjustment, execution monitoring, and standby path call, the frequency hopping scheduling process has engineering-level feasibility. Especially in combination with the hopping time window and ionospheric refraction correction, the handover process better conforms to the physical characteristics of medium and short wave propagation, significantly reducing the risk of synchronization instability during signal hopping. This solution constructs a complete control chain from scheduling to execution, breaking through the technical bottlenecks of blind frequency point switching and synchronization failure in traditional frequency hopping control.

[0125] In this embodiment, the specific content of S6 includes:

[0126] S61. Configure signal monitoring devices at the broadcast receiving terminal to perform real-time sampling on the decoding success rate, bit error rate, audio restoration clarity, and channel stability of the received signal. The sampling period is set synchronously with the frequency hopping scheduling period of the broadcast transmission system.

[0127] S62. Construct each signal quality parameter into a receiver status vector. The receiver status vector is bound to the unique identifier and timestamp of the broadcast receiving terminal, and is uploaded and encapsulated for transmission through the auxiliary control channel, IP data link, or satellite feedback channel of the broadcast transmission system.

[0128] After the central system receives the receiver status vector data from multiple broadcast receiving terminals, it performs data synchronization and terminal identification matching operations, and establishes a broadcast reception quality sequence set in chronological order.

[0129] S64. Extract the signal status data that matches the corresponding frequency points in the current frequency hopping avoidance path from the broadcast reception quality sequence set, and calculate the mean value and volatility of the signal quality index for each frequency point in the current period.

[0130] S65. According to the mean value and volatility of the signal quality index for each frequency point in the current period, use the moving average method to update the status vector of the corresponding frequency point node in the spectrum evolution cognitive map model. Specifically, extract the mean value of the signal quality in the current period, and combine it with the historical signal quality mean values of this frequency point in the previous several periods to calculate the average level of this frequency point in multiple consecutive periods. Use this result as the new node status vector to replace the original frequency point status information, and at the same time update the edge weight value between this node and adjacent nodes.

[0131] By configuring the terminal sampling device, constructing a status vector upload mechanism, establishing a reception quality data set, and performing moving average updates, a closed-loop mechanism for dynamic updates of the broadcast receiver feedback and the cognitive map model status is formed. This feedback update scheme can achieve the real-time response of the broadcast system to the actual reception quality, dynamically adjust the node status and edge weight relationship, and improve the fitting ability and prediction effect of the model for the real channel state. Compared with the problem of missing or lagging feedback paths in traditional systems, this mechanism enhances the real-time adaptation ability and scheduling adjustment accuracy of the system.

[0132] Reference Figure 2 , a medium and short wave signal transmission interference suppression system based on wireless radio and television, includes the following modules:

[0133] A spectrum monitoring module, which is used to collect spectrum data in the medium and short wave frequency bands at the broadcast transmission node and perform preprocessing to form a spectrum status data set.

[0134] A cognitive modeling module, which is used to construct a spectrum evolution cognitive map model based on the spectrum status data set.

[0135] An interference analysis module, which is used to perform clustering analysis on the spectrum evolution cognitive map model, identify interference concentration areas, generate a broadcast interference heat map, and calibrate the interference risk level of frequency points.

[0136] A path optimization module, which is used to input the spectrum evolution cognitive map model and the broadcast interference heat map into an improved honey badger interference avoidance algorithm and output a frequency hopping avoidance path.

[0137] A frequency hopping control module, which is used to make the broadcast transmission system perform frequency point switching according to the frequency hopping avoidance path, and execute frequency hopping scheduling in combination with the hopping time window, ionospheric delay, and channel modulation parameters.

[0138] A feedback update module is used to broadcast and receive signal quality parameters collected by the terminal and transmit them back, and the sliding average method is used to update the state vectors of the corresponding nodes in the spectrum evolution cognitive map model.

[0139] By integrating six modules of spectrum monitoring, cognitive modeling, interference analysis, path optimization, frequency hopping control and feedback update, a complete medium and short wave radio interference suppression system architecture is constructed. The system has the closed-loop ability of data acquisition - graph modeling - path output - terminal feedback - model update, and supports multi-source information fusion, multi-dimensional state evolution and adaptive path scheduling. The system structure is modular and the function division is clear, which is convenient for actual deployment and maintenance. Compared with the existing single-module radio scheduling equipment, it has significantly improved radio anti-interference ability, frequency hopping stability and spectrum utilization efficiency.

[0140] Embodiment 1:

[0141] In order to verify the feasibility of the present invention in implementation, the present invention is applied to the actual deployment scenario of the medium and short wave radio coverage task of a certain municipal radio and television station in the remote mountainous areas of the southwestern region. The terrain of this area is complex, with large altitude differences, serious valley shielding, and there are long-term high-voltage transmission lines, mining area equipment and other wireless communication interference sources, resulting in unstable, intermittent reception and even black screen phenomena of medium and short wave radio signals in multiple townships. The existing system uses a static frequency point configuration method, which cannot effectively avoid frequency hopping in the face of sudden interference, and also lacks the dynamic response ability to the changes in the propagation characteristics of the ionosphere, seriously affecting the coverage quality and persistence of radio signals.

[0142] In this embodiment, the radio and television station has deployed 8 transmitting nodes in this area, which are located in the main urban area, transportation hubs and six key villages and towns respectively. Spectrum monitoring terminals are installed at each transmitting node to conduct all-weather sampling of the availability, interference intensity, signal stability and amplitude modulation confidence of the 3MHz to 30MHz frequency band, and form a set of spectrum state data every 5 minutes and transmit it to the central control system. After continuously collecting data for 5 days, the interference relationship between frequency points is modeled through the spectrum evolution cognitive map modeling module of the present invention to construct a sequence of cognitive maps evolving with time. Each node in the map represents an available channel, and each edge represents the interference coupling intensity and frequency hopping migration potential.

[0143] After the cognitive modeling is completed, the interference clustering analysis mechanism of the present invention is used to analyze the data within 7 days, and 5 obvious interference aggregation regions are identified. Among them, 2 regions are long-term located near the high-voltage lines on the north side of the mining area, and the remaining 3 are located in the densely populated areas of electric equipment in the villages and towns. The system automatically generates a broadcast interference heat map, and calibrates the interference risks of each frequency point into three levels: high, medium, and low. Based on this heat map and the cognitive map model, the improved honey badger interference avoidance algorithm is activated, and combined with the frequency hopping migration strategy and the broadcast interference suppression memory bank, the frequency hopping path is optimized. Subsequently, the frequency hopping scheduling control system dynamically switches the frequency points according to the path output without interrupting the broadcast task, and corrects the hopping time window and modulation synchronization parameters according to the ionospheric delay prediction results.

[0144] During the execution of the frequency hopping scheduling, each receiving terminal continuously transmits back the signal quality parameters, including the audio clarity level, the bit error rate, and the decoding success rate. By using the moving average method to update the node states in the spectrum cognitive map, the system gradually realizes the dynamic adaptive perception of the interference trend. The entire broadcast system forms a closed-loop scheduling mechanism of perception-modeling-avoidance-frequency hopping-feedback-update.

[0145] The implementation results show that before the application of the present invention, the broadcast interruption events occurred 13 times per week on average, the average duration of each interruption was 37 seconds, and the proportion of the time period when the signal strength was lower than the standard value was as high as 22.8%. Within 30 consecutive days after the deployment of the present invention, the number of interruption events decreased to 2 times, the duration of each interruption was shortened to less than 5 seconds, and the signal coverage stability rate increased to 96.7%. Especially during the interference peak period (such as the equipment maintenance period in the mining area from March 22nd to March 25th), the system can automatically avoid the high-interference frequency points through the algorithm, ensuring that the key programs can still be normally covered in the high-interference environment, and the received clarity rating is improved from acceptable to clear and excellent levels.

[0146] The above embodiments verify the actual application effect of the present invention under complex terrain and multi-source interference conditions, which not only improves the signal continuity and stability of the broadcast system, but also significantly reduces the frequency of manual intervention and the scheduling response delay, providing a highly intelligent operation mechanism for medium and short-wave broadcasts in low-resource environments.

[0147] The above is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and all should be covered within the protection scope of the present invention.

Claims

1. A method for suppressing interference in medium and short wave signal transmission based on wireless broadcasting television, characterized in that: The steps include: S1. Deploy spectrum monitoring terminals at the transmitting nodes of the wireless broadcasting and television system, collect medium and short wave frequency band data and perform preprocessing to build a spectrum status data set; S2. constructing a spectrum evolution cognitive graph model based on the spectrum state data set; S3. Perform cluster analysis using the spectrum evolution cognitive graph model to identify interference concentration areas, generate a broadcast interference heat map, and calibrate the frequency interference risk level; S4, inputting the spectrum evolution cognitive graph model and the broadcast interference heat map into the improved honey badger interference avoidance algorithm, combining the frequency hopping migration strategy and the broadcast interference suppression memory library constructed by the historical interference frequency points, and outputting the frequency hopping avoidance path; S5. The broadcast transmission system performs frequency hopping scheduling control according to the frequency hopping avoidance path to ensure that the frequency switching is completed without interrupting the broadcast service, and comprehensively considers the hopping time window, ionospheric delay and channel synchronization; S6. The broadcast receiving terminal collects signal quality parameters and transmits them back to the central system, and updates the node status of the spectrum evolution cognitive graph model in real time.

2. The method for suppressing interference of medium and short wave signal transmission based on wireless broadcasting and television according to claim 1 is characterized in that: The medium and short wave frequency band data include frequency availability, interference strength, signal stability and amplitude modulation recognition confidence.

3. The method for suppressing interference of medium and short wave signal transmission based on wireless broadcasting and television according to claim 1 is characterized in that: The preprocessing includes interference removal, normalization and filtering.

4. The method for suppressing interference of medium and short wave signal transmission based on wireless broadcasting and television according to claim 1 is characterized in that: The S2 specifically includes: S21, structure the spectrum status data set to form a frequency point status sequence arranged in chronological order, wherein each record in the frequency point status sequence includes a frequency availability parameter, an interference intensity parameter, a signal stability parameter, and an amplitude modulation recognition confidence parameter of the corresponding frequency point, which are used to describe the spectrum behavior characteristics of the frequency point in the current time window; S22, taking each frequency point in the spectrum state data set as a graph node, constructing a node set, and mapping the state vector of each node to a node attribute in the spectrum evolution cognitive graph model; S23, according to the interval distance relationship of different frequency points in the frequency proximity dimension and the behavior similarity in the historical interference pattern, set the judgment rule, establish the directed edge connection between the node pairs that meet the proximity threshold and the similarity threshold, and the edge weight is used to quantify the channel interference coupling degree and the frequency hopping migration potential to form an edge set; S24, constructing an initial static spectrum cognitive graph based on the node set and the edge set, describing the spectrum usage relationship and interference propagation path between the medium and short wave channels in the current time window; S25, setting an evolution mechanism of the initial static spectrum cognitive graph so that the node states and edge weights are updated over time to form an evolution cognitive graph sequence; S26. Organizing the evolutionary cognitive graph sequence into a four-dimensional data structure, wherein the four-dimensional data structure includes the number of nodes, connections between nodes, time series, and state dimensions, to form a spectrum evolutionary cognitive graph model.

5. The method for suppressing interference of medium and short wave signal transmission based on wireless broadcasting and television according to claim 1, characterized in that: The S3 specifically includes: S31, extracting the node status and edge weight relationship of each broadcast frequency point at different times in the spectrum evolution cognitive graph model, and constructing a spectrum feature set across time windows; S32. Calculate the comprehensive interference weight of each frequency point in the current time window based on the spectrum feature set: Among them, D i represents the comprehensive interference weight of the ith frequency point, T represents the total number of time steps, ω t represents the time attenuation coefficient, I i,t represents the interference intensity of the ith frequency point at the tth time step, represents the rate of change of interference intensity, N(i) represents the set of adjacent nodes that have mutual interference edges with the i-th frequency point, It represents the interference factor of the jth frequency point on the ith frequency point, I j,t represents the interference intensity of the jth frequency point at the tth time step, α, β and γ represent weight coefficients; S33, based on the graph structure distance between frequency points in the spectrum evolution cognitive graph and the similarity of interference states, an adaptive graph density clustering algorithm is used to automatically identify interference concentration areas and form interference clusters; S34, combining the broadcast transmission node location information and the geographic spatial reference data, mapping the interference cluster to a geographic coordinate system, and constructing a broadcast interference spatial distribution layer; S35. Based on the broadcast interference spatial distribution layer, a broadcast interference heat map is generated, and a color gradient and a numerical level are used to indicate the interference concentration of the frequency points; S36: Comprehensive interference weight D calculated according to step S32 i , set the classification thresholds θ1, θ2 to perform interference risk level classification for each frequency point: If D i ≥θ2, the i-th frequency point is judged to have high interference risk; If θ1≤D i <θ2, the i-th frequency point is judged to have medium interference risk; If D i <θ1, the i-th frequency point is judged to have low interference risk.

6. The method for suppressing interference of medium and short wave signal transmission based on wireless broadcasting and television according to claim 1, characterized in that: The S4 specifically includes: S41, using the spectrum evolution cognitive graph model and the broadcast interference heat map, initialize the improved honey badger interference avoidance algorithm, and construct an initial honey badger individual group, wherein the honey badger individuals correspond to the frequency nodes in the spectrum cognitive graph model, and each honey badger individual carries frequency status information, including interference risk level and frequency availability status; S42, constructing a broadcast interference suppression memory library, extracting high interference risk frequencies from the broadcast interference heat map and historical frequency hopping failure data, marking the high interference risk frequencies as memory library nodes, and recording the number of interferences of the nodes, the time when the interference occurs, the duration of the interference, and the cause of the frequency hopping failure; S43, introducing the high interference risk frequency points in the broadcast interference suppression memory library as soft-disabled nodes into the path evaluation link of the improved honey badger interference avoidance algorithm, when the honey badger individual migrates to the frequency points marked in the broadcast interference suppression memory library, the path cost function automatically increases the memory penalty weight to reduce the probability of path selection; S44. The improved honey badger interference avoidance algorithm is updated through multiple rounds of iterations, and the individual migration path selection of honey badgers is continuously adjusted according to the path cost function, and finally outputs a frequency hopping avoidance path that meets the constraints of minimum interference risk and uninterrupted service.

7. The method for suppressing interference of medium and short wave signal transmission based on wireless broadcasting and television according to claim 6, characterized in that: The calculation formula of the memory penalty weight in the path cost function is: Among them, P i represents the memory penalty weight of the ith frequency point, n i represents the number of historical interferences recorded in the broadcast interference suppression memory bank for the ith frequency point, N represents the maximum number of historical interferences of all frequency points in the broadcast interference suppression memory bank, τ i represents the time interval between the ith frequency point and the most recent interference, δ and η represent weight parameters; The calculation result of the memory penalty weight is used to increase the frequency hopping migration cost of the high interference risk frequency point.

8. The method for suppressing interference of medium and short wave signal transmission based on wireless broadcasting and television according to claim 1, characterized in that: The S5 specifically includes: S51, the broadcast transmission system reads the frequency switching sequence and hopping interval information in the frequency hopping avoidance path, and formulates a frequency hopping switching scheduling plan, wherein the frequency hopping switching scheduling plan includes a frequency switching instruction set and a corresponding scheduling timing table; S52, according to the modulation mode and the device processing delay, set a fixed-length hopping time window, and bind the hopping time window to each hopping instruction in the scheduling timing table to form a frequency hopping window control sequence; S53, performing ionospheric state analysis, obtaining predicted refraction delay data of each frequency point in the current time period, adjusting the hopping start time and duration period in the frequency hopping window control sequence, and correcting the transmission time parameters in the switching process; S54, extracting the modulation configuration files of adjacent frequency points in the frequency hopping path, calculating the carrier parameter change difference between the frequency points, and correcting the transmitter amplitude modulation parameters according to the carrier parameter change difference, wherein the transmitter amplitude modulation parameters include frequency offset, amplitude gain factor and carrier phase initial value; S55, in the frequency hopping execution phase, reading the frequency output value, amplitude of the AM signal and the bit error rate index collected by the transmitter monitoring device, comparing the index with the preset standard in the frequency hopping switching scheduling plan, and judging the frequency hopping stability state; S56: When the frequency hopping stability state does not meet the preset standard, call the backup frequency point instruction and switch to the backup path channel.

9. The method for suppressing interference of medium and short wave signal transmission based on wireless broadcasting and television according to claim 1, characterized in that: The S6 specifically includes: S61. A signal monitoring device is configured at the broadcast receiving terminal to perform real-time sampling of the decoding success rate, bit error rate, audio restoration clarity and channel stability of the received signal. The sampling period is synchronously set with the frequency hopping scheduling period of the broadcast transmission system. S62, constructing various signal quality parameters into a receiving end state vector, wherein the receiving end state vector is bound to a unique identifier and a timestamp of a receiving broadcast receiving terminal, and is uploaded, packaged and transmitted through an auxiliary control channel of a broadcast transmission system, an IP data link or a satellite feedback channel; S63, after receiving the receiving terminal state vector data from multiple broadcast receiving terminals, the central system performs data synchronization and terminal identification matching operations, and establishes a broadcast receiving quality sequence set in chronological order; S64, extracting signal status data matching the corresponding frequency point in the current frequency hopping avoidance path from the broadcast reception quality sequence set, and calculating the mean value and volatility of the signal quality index of each frequency point in the current cycle; S65. According to the mean value and volatility of the signal quality index of each frequency point in the current cycle, a sliding average method is used to update the state vector of the corresponding frequency point node in the spectrum evolution cognitive graph model.

10. A medium- and short-wave signal transmission interference suppression system based on wireless broadcasting and television, which executes the medium- and short-wave signal transmission interference suppression method based on wireless broadcasting and television as claimed in any one of claims 1 to 9, characterized in that: Includes the following modules: The spectrum monitoring module is used to collect spectrum data of the medium and short wave frequency bands at the broadcast transmission node and perform preprocessing to form a spectrum status data set; A cognitive modeling module, used to build a spectrum evolution cognitive graph model based on the spectrum state dataset; Interference analysis module, used to perform cluster analysis on the spectrum evolution cognitive graph model, identify interference concentration areas, generate broadcast interference heat maps, and calibrate frequency interference risk levels; The path optimization module is used to input the spectrum evolution cognitive graph model and the broadcast interference heat map into the improved honey badger interference avoidance algorithm and output the frequency hopping avoidance path; The frequency hopping control module is used for the broadcast transmission system to switch the frequency points according to the frequency hopping avoidance path, and to perform frequency hopping scheduling in combination with the hopping time window, ionospheric delay and channel modulation parameters; The feedback update module is used for the broadcast receiving terminal to collect and transmit signal quality parameters, and uses the sliding average method to update the state vector of the corresponding node in the spectrum evolution cognitive graph model.

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