Method and apparatus for interference suppression in smart communication device communications

Abstract

The application relates to the technical field of signal transmission and interference suppression, and discloses an interference suppression method and device in intelligent communication equipment communication, which comprises the following steps: filling in a data monitoring blind area according to real-time communication data of the intelligent communication equipment, identifying interference data, and constructing an interference heat map; identifying interference rules and predicting interference change trends through a preset interference analysis model based on the interference heat map; matching a plurality of communication adjustment strategies, simulating and analyzing the communication adjustment strategies, and optimizing the communication adjustment strategies to obtain an interference suppression strategy; and generating equipment control instructions according to the interference suppression strategy and performing real-time control on the equipment. The application can predict the interference change trends, quickly match corresponding communication adjustment strategies, take interference suppression measures in advance, reduce communication interruption time, simulate and analyze the strategies, and optimize the strategies, so that resource competition can be avoided, the optimized interference suppression strategy can improve the interference suppression effect, and data loss and communication failure can be reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of signal transmission and interference suppression, and more particularly to an interference suppression method and device for smart communication equipment. BACKGROUND

[0002] At present, with the rapid development of smart grid, smart communication equipment, as a key data acquisition node, widely uses dual-mode communication of power line carrier communication and wireless communication to realize functions such as remote meter reading, load control and fault detection. Smart communication equipment includes but is not limited to smart meter dual-mode equipment, wireless cellular communication equipment, WiFi communication equipment, Internet of Things communication equipment, Sub-1G wireless communication equipment, Bluetooth communication equipment and zigbee communication equipment. However, in actual deployment, the communication environment is complex and is easily affected by various interference sources, such as electromagnetic interference, signal attenuation, multipath effect, environmental noise and equipment failure. These interferences will cause unstable communication link, high data packet loss rate and rising bit error rate, thereby affecting the real-time monitoring and decision-making efficiency of the smart grid.

[0003] The prior art has the following problems: it cannot comprehensively cover the communication area, especially in the network topology edge or signal weak area, there is data missing, leading to missing of interference data, and misjudgment or missed judgment of the interference source; after the interference occurs, the interference is adjusted and suppressed, lacking analysis and trend prediction of the interference law, leading to delayed response and inability to suppress the interference in advance; when multiple interference sources exist at the same time, there will be competition for communication resources, and a single interference suppression strategy lacks optimization mechanism, leading to low communication performance; in order to solve at least one of the above problems, the present application provides an interference suppression method and device for smart communication equipment communication. SUMMARY

[0004] In view of the deficiencies of the prior art, the purpose of the present application is to provide an interference suppression method and device for smart communication equipment communication, which can effectively solve the problems in the background art. The specific technical solution of the present application is as follows:

[0005] The interference suppression method for smart communication equipment communication comprises:

[0006] According to the real-time communication data of the smart communication equipment, the communication entropy value of each equipment is calculated, the communication link reliability is analyzed and the communication reliability map is constructed, the data monitoring blind area is filled in the communication reliability map through data fusion and interpolation, the interference data is identified, the correlation between the real-time interference data and the predicted interference data is analyzed, and the interference heat map is constructed;

[0007] Based on the interference heat map, the interference law is identified through a preset interference analysis model, and the interference change trend is predicted;

[0008] The interference suppression strategy is obtained by establishing a composite interference field of multiple interference sources in the interference change trend, weighting and fusing corresponding strategies, matching multiple communication adjustment strategies with the strategy of a single interference source, simulating and analyzing the communication adjustment strategies through a preset strategy analysis model, and optimizing the communication adjustment strategies.

[0009] According to the interference suppression strategy, a device control instruction is generated to control the device in real time to suppress interference in the communication of the intelligent communication device.

[0010] Specifically, the communication entropy value of each device is calculated according to the real-time communication data of the intelligent communication device, the communication link reliability is analyzed, and a communication reliability map is constructed. In the communication reliability map, data monitoring blind areas are identified by data fusion and interpolation filling, interference data is identified, the correlation between real-time interference data and predicted interference data is analyzed, and an interference heat map is constructed, including:

[0011] According to the real-time communication data of the intelligent communication device, the communication entropy value of each device is calculated in combination with the pre-acquired network topology data, the communication link reliability is analyzed, and a communication reliability map is constructed to identify data monitoring blind areas.

[0012] The communication data of the adjacent area of the data monitoring blind area in the communication reliability map is subjected to data fusion and interpolation filling to obtain blind area communication data.

[0013] Interference data is extracted from the real-time communication data and the blind area communication data, the correlation between real-time interference data and predicted interference data is analyzed, and an interference heat map is constructed.

[0014] Specifically, the communication entropy value of each device is calculated according to the real-time communication data of the intelligent communication device in combination with the pre-acquired network topology data, the communication link reliability is analyzed, a communication reliability map is constructed, and data monitoring blind areas are identified, including:

[0015] According to the real-time communication data of the intelligent communication device and the pre-acquired network topology data, the communication state of each intelligent communication device is analyzed, and the corresponding communication entropy value is calculated.

[0016] In combination with the communication entropy value and the topological importance of the intelligent communication device in the network topology, the communication link reliability is analyzed, and the corresponding communication reliability value is calculated.

[0017] Based on the communication reliability value, a communication reliability map reflecting the communication situation between the intelligent communication devices is constructed.

[0018] In the communication reliability map, the area with a communication reliability value less than a preset reliability threshold is screened out as a data monitoring blind area.

[0019] Specifically, the interference data is extracted from the real-time communication data and the blind area communication data, the correlation between the real-time interference data and the predicted interference data is analyzed, and an interference heat map is constructed, including:

[0020] The real-time communication data and the blind area communication data are feature extracted by a preset feature extraction model to obtain a feature vector;

[0021] The feature vector is compressed and mapped to a low-dimensional space to obtain a corresponding feature code;

[0022] Based on the feature code, the interference data is extracted by a preset interference extraction model and the interference of different interference sources is analyzed, the corresponding interference data is predicted, and an interference analysis result is obtained;

[0023] According to the interference analysis result, the correlation between the real-time interference data and the predicted interference data is analyzed, and an interference heat map is constructed.

[0024] Specifically, based on the interference heat map, the interference law is identified by a preset interference analysis model, and the interference change trend is predicted, including:

[0025] Based on the interference heat map, the interference correlation between different interference sources is analyzed to obtain an interference correlation degree;

[0026] Combined with the interference correlation degree, the interference law is identified by a preset interference analysis model, and the interference change trend is predicted.

[0027] Specifically, the multiple interference sources in the interference change trend are established to form a composite interference field, and the corresponding strategy is weighted and fused to match the strategy of a single interference source to match multiple communication adjustment strategies, the communication adjustment strategy is simulated and optimized by a preset strategy analysis model, and an interference suppression strategy is obtained, including:

[0028] The strategy matching is performed on the interference change trend, a composite interference field is established for multiple interference sources, and the corresponding strategy is weighted and fused to match the strategy of a single interference source to multiple communication adjustment strategies to obtain a corresponding initial strategy set;

[0029] The effect of the communication adjustment strategy in the initial strategy set is simulated by a preset strategy analysis model, and the strategy is optimized according to the simulation result to obtain an interference suppression strategy.

[0030] Specifically, the strategy matching is performed on the interference change trend, a composite interference field is established for multiple interference sources, and the corresponding strategy is weighted and fused to match the strategy of a single interference source to multiple communication adjustment strategies to obtain a corresponding initial strategy set, including:

[0031] constructing an interference strategy mapping matrix based on the interference change trend, wherein a row vector in the interference strategy mapping matrix represents an interference source type identified by the interference change trend, a column vector represents a matched communication adjustment strategy, and a matrix element represents an inhibition degree value of the communication adjustment strategy on a corresponding interference source;

[0032] For multiple interference sources in the interference change trend, a composite interference field is established, the corresponding strategies in the interference strategy mapping matrix are weighted and fused to obtain a corresponding composite strategy.

[0033] The communication adjustment strategies matched by the single interference sources and the composite strategy are integrated to obtain a corresponding initial strategy set.

[0034] Specifically, the execution effect of the communication adjustment strategies in the initial strategy set is simulated by a preset strategy analysis model, and the strategies are optimized according to the simulation result to obtain an interference suppression strategy, including:

[0035] The execution effect of the communication adjustment strategies in the initial strategy set is simulated by a preset strategy analysis model, and the resource competition between the communication adjustment strategies and the influence degree on the communication performance are analyzed to obtain a simulation result.

[0036] A strategy mutual exclusion graph is constructed based on the simulation result, wherein a node represents a communication adjustment strategy, and an edge weight represents the resource competition intensity between the strategies.

[0037] In the strategy mutual exclusion graph, the communication adjustment strategies with resource competition are screened, the strategies with a communication performance influence degree greater than a preset communication performance influence degree threshold are retained, and candidate strategies without resource competition and with the same interference suppression efficiency are matched from the strategy mutual exclusion graph for strategy optimization to obtain an interference suppression strategy.

[0038] Specifically, according to the interference suppression strategy, a device control instruction is generated to control the device in real time to suppress the interference in the communication of the intelligent communication device, including:

[0039] According to the interference suppression strategy, a device control instruction is generated by matching in a pre-constructed instruction mapping table.

[0040] According to the device control instruction, the device is controlled in real time, and the bit error rate of the communication link is monitored in real time during the control process. If the bit error rate change rate is less than a preset change threshold, the control process is feedback optimized to suppress the interference in the communication of the intelligent communication device.

[0041] An intelligent communication device communication interference suppression device is used to implement the intelligent communication device communication interference suppression method, including:

[0042] An interference heat map construction module calculates a communication entropy value of each device according to real-time communication data of the intelligent communication device, analyzes communication link reliability, and constructs a communication credibility map, in which data monitoring blind spots are filled by data fusion and interpolation, interference data is identified, the correlation between real-time interference data and predicted interference data is analyzed, and an interference heat map is constructed;

[0043] An interference analysis module identifies interference rules and predicts interference trends by a preset interference analysis model based on the interference heat map;

[0044] An interference suppression strategy matching module matches multiple communication adjustment strategies by establishing a composite interference field for multiple interference sources in the interference trend and weighting and fusing corresponding strategies in combination with the strategy matching of a single interference source, and obtains an interference suppression strategy by simulating and analyzing the communication adjustment strategies by a preset strategy analysis model and optimizing them.

[0045] An interference suppression module generates device control instructions according to the interference suppression strategy to control the device in real time to suppress interference in the communication of the intelligent communication device.

[0046] The application has the following beneficial effects: data monitoring blind spots are filled by data fusion and interpolation, an interference heat map is constructed, interference correlation is analyzed based on the interference heat map, interference rules are identified, interference trends are predicted, an interference strategy mapping matrix and a composite interference field are constructed, corresponding communication adjustment strategies are matched, resource competition and communication performance influence are simulated by a strategy analysis model, strategies are optimized, and the device is controlled in real time by the optimized interference suppression strategy. The interference heat map is constructed by data fusion and interpolation, providing comprehensive data reference for the interference analysis process, predicting interference trends and quickly matching corresponding communication adjustment strategies, so that interference suppression measures can be taken in advance, communication interruption time is reduced, resource competition can be avoided by simulating and analyzing the strategies and optimizing them, the optimized interference suppression strategy can improve the interference suppression effect, and data loss and communication failure can be reduced. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 The working flowchart of the interference suppression method for the intelligent communication device in the embodiments of the application is shown in the figure;

[0048] Figure 2 The schematic diagram of the communication credibility map in the embodiments of the application is shown in the figure;

[0049] Figure 3 The schematic diagram of the strategy mutual exclusion map in the embodiments of the application is shown in the figure;

[0050] Figure 4 The structural schematic diagram of the interference suppression device for the intelligent communication device in the embodiments of the application is shown in the figure. DETAILED DESCRIPTION

[0051] The application will be further described below in details with reference to the drawings and embodiments. Identical parts are denoted by identical reference numerals in the description. It should be noted that the words "front", "back", "left", "right", "up" and "down" used in the following description refer to the directions in the drawings, and the words "bottom" and "top", "inner" and "outer" refer to the directions towards or away from the geometric center of a particular part.

[0052] In the embodiments of the present application, the words "exemplary" or "for example" are used to mean serving as an example, instance, or illustration, in no way limiting. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application is not necessarily to be construed as preferred or advantageous over other embodiments or designs. In fact, any embodiment or design described as "exemplary" or "for example" is intended to present concepts in a concrete manner.

[0053] Hereinafter, the terms "first", "second", and the like are used generically and are only for the purpose of description, and should not be construed as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise specified.

[0054] Reference Figure 1 As shown, the specific implementation of the interference suppression method for the communication of the intelligent communication device in the present application includes:

[0055] S101, according to the real-time communication data of the intelligent communication device, the data monitoring blind area is filled by data fusion and interpolation, the interference data is identified, and the interference heat map is constructed;

[0056] S102, based on the interference heat map, the interference law is identified by a preset interference analysis model, and the interference change trend is predicted;

[0057] S103, matching a plurality of communication adjustment strategies to the interference change trend, simulating and analyzing the communication adjustment strategies by a preset strategy analysis model and optimizing to obtain an interference suppression strategy;

[0058] S104, according to the interference suppression strategy, generating a device control instruction to control the device in real time to suppress the interference in the communication of the intelligent communication device.

[0059] The stable operation of the smart grid depends on high-quality data communication, and interference in the communication process can affect the communication quality. With the expansion of the deployment scale of smart communication devices and the increase of communication demand, the existing interference suppression methods cannot meet the requirements of high reliability and low delay. The smart communication devices include, but are not limited to, smart meter dual-mode devices, wireless cellular communication devices, WiFi communication devices, Internet of Things communication devices, Sub-1G wireless communication devices, Bluetooth communication devices, and zigbee communication devices.

[0060] In the embodiment, the data monitoring blind area is identified according to the real-time communication data of the smart communication device and the position of the communication device in the network topology, the data in the data monitoring blind area is filled by data fusion and interpolation, the interference data is identified, and an interference heat map is constructed. By identifying the detection blind area and filling the data by interpolation, the existing data can be used to infer the blind area condition, the problem of incomplete information caused by missing monitoring points can be avoided, and a complete data basis is provided for the interference identification and suppression process. By identifying the interference data to construct the interference heat map, the accuracy of the interference identification result can be improved, and accurate data support is provided for interference analysis and suppression.

[0061] Specifically, based on the interference heat map, the spatial distribution relationship of different interference sources in the heat map is analyzed by a preset interference analysis model, the periodic fluctuation, trend enhancement or random burst interference law is identified in combination with the space-time correlation characteristics of the interference, and the interference change trend is predicted. By analyzing the interference correlation, the influence or propagation mechanism between the interference sources can be determined in combination with the internal relationship between the interference events; by predicting the interference change trend, the development trend can be predicted in advance before the interference causes substantial and serious damage to the communication quality, a clear direction is provided for taking interference suppression strategies, and thus the interference prevention capability and interference suppression effect of the communication process are improved.

[0062] According to the predicted interference change trend, strategy matching is performed, the interference strategy mapping matrix is constructed by analyzing the suppression efficiency degree value of each strategy on a specific interference source, in the case that multiple interference sources exist and interact with each other, a composite interference field is constructed by superimposing the interference sources, the strategies corresponding to each interference source are weighted and fused according to their suppression degree values to obtain a composite strategy, the single interference source strategy and the composite strategy matched are integrated to obtain an initial strategy set; the communication adjustment strategy is simulated and executed by a preset strategy analysis model, the resource competition between strategies and the influence degree on the overall communication performance are analyzed to optimize the strategy, and an interference suppression strategy is obtained. By constructing the interference strategy mapping matrix, the corresponding strategy can be quickly matched for interference, the efficiency and accuracy of strategy matching are improved, the strategy simulation is performed by the strategy analysis model, the system performance consumption caused by resource competition between strategies can be avoided, the interference suppression strategy obtained by optimizing the strategy can effectively suppress interference while improving the feasibility, synergy and resource efficiency of the strategy, thereby ensuring the stable and efficient operation of the overall communication system in a complex interference environment.

[0063] Specifically, according to the interference suppression strategy, a device control instruction is generated by matching in a pre-defined instruction mapping table, after the instruction is generated, the instruction is issued to the corresponding intelligent communication device through a communication channel, and the device is controlled in real time to suppress interference in the communication of the intelligent communication device. Through the pre-constructed instruction mapping table, the corresponding device control instruction can be quickly matched, the response speed and operation accuracy of the interference suppression process are improved, the robustness and adaptability of the interference suppression measure are improved, and finally the communication link of the intelligent communication device can be quickly restored and maintained in a high-quality state after being interfered.

[0064] The present application fills in the data monitoring blind area through data fusion and interpolation, constructs an interference heat map, analyzes the interference correlation degree based on the interference heat map, identifies the interference law, predicts the interference change trend, constructs the interference strategy mapping matrix and the composite interference field, matches the corresponding communication adjustment strategy, simulates the resource competition and communication performance influence through the strategy analysis model, optimizes the strategy, and controls the device in real time using the optimized interference suppression strategy. Through data fusion and interpolation, an interference heat map is constructed to provide comprehensive data reference for the interference analysis process, the interference change trend is predicted and the corresponding communication adjustment strategy is quickly matched, so that interference suppression measures can be taken in advance, the communication interruption time is reduced, the resource competition can be avoided through simulation analysis and optimization of the strategy, and the optimized interference suppression strategy can improve the interference suppression effect and reduce data loss and communication failure.

[0065] Further, according to the real-time communication data of the intelligent communication device, the data monitoring blind area is filled in through data fusion and interpolation, the interference data is identified, and an interference heat map is constructed, including:

[0066] S201, analyze the communication link reliability according to the real-time communication data of the intelligent communication device in combination with the pre-acquired network topology data, construct a communication reliability map, and identify a data monitoring blind area;

[0067] S202, perform data fusion and interpolation filling on the communication data of the adjacent area of the data monitoring blind area in the communication reliability map to obtain blind area communication data;

[0068] S203, extract interference data from the real-time communication data and the blind area communication data to construct an interference heat map.

[0069] In this embodiment, according to the real-time communication data of the intelligent communication device, the real-time communication data includes but is not limited to signal strength, signal-to-noise ratio, and data packet success rate and other dynamic parameters, in combination with the pre-acquired network topology data, the network topology data includes the connection relationship, hierarchical structure and relay dependency relationship between the intelligent communication device nodes, the communication link reliability is analyzed, the communication reliability map is constructed, and the data monitoring blind area is identified. By identifying the data monitoring blind area, the dynamic fluctuation and potential instability of the communication state are analyzed, and the unreliable link is identified. By introducing the topological importance factor, in combination with the entire network topology structure, the blind area on the key node which has a great influence on network connectivity after failure can be preferentially identified, the blind area identification efficiency and accuracy are improved, and data support is provided for data filling and interference analysis of the data blind area.

[0070] In the communication reliability map, the identified data monitoring blind area is located, all nodes with reliability values higher than the threshold value directly connected to the blind area boundary are extracted as reliable reference data; the data from multiple adjacent reliable nodes is fused through data fusion, the reliable data is used as a known sample point through spatial interpolation, the communication data value of any position point inside the blind area is estimated, the inverse distance weighted interpolation method is used, the known sample point closer to the estimated point in the blind area has a larger corresponding weight, the data is interpolated and calculated to obtain the blind area communication data. The blind area communication data is calculated by interpolation, which can avoid the data blanking problem caused by the lack of physical monitoring points, improve the integrity and accuracy of the interference analysis data. Through the data fusion and interpolation process, the integrity of the data is improved, which provides an accurate data basis for identifying and locating the interference source, and avoids the interference misjudgment or misjudgment caused by the existence of the blind area.

[0071] Specifically, the interference in the communication data will be manifested as abnormal characteristics different from the normal communication mode. By extracting the data characteristics and analyzing, the interference data is extracted from the real-time communication data and the blind area communication data, and the interference heat map is constructed. Through feature extraction and interference identification, the normal communication fluctuation and the real interference can be accurately distinguished, and the type of interference can be judged, which improves the efficiency and accuracy of the interference identification result. By constructing the interference heat map, accurate data support is provided for interference analysis, and the accuracy of the interference analysis process and the effectiveness of the suppression strategy are improved.

[0072] Further, according to the real-time communication data of the intelligent communication device, in combination with the pre-acquired network topology data, the communication link reliability is analyzed, the communication reliability map is constructed, and the data monitoring blind area is identified, including:

[0073] S301, according to the real-time communication data of the intelligent communication device and the pre-acquired network topology data, the communication state of each intelligent communication device is analyzed, and the corresponding communication entropy value is calculated;

[0074] S302, in combination with the communication entropy value and the topological importance of the intelligent communication device in the network topology, the communication link reliability is analyzed, and the corresponding communication reliability value is calculated;

[0075] S303, based on the communication reliability value, a communication reliability map reflecting the communication situation between the intelligent communication devices is constructed;

[0076] S304, in the communication reliability map, the area with a communication reliability value less than a preset reliability threshold is screened out as a data monitoring blind area.

[0077] In this embodiment, according to the real-time communication data of the intelligent communication device and the pre-acquired network topology data, the fluctuation of the communication state in a period of time is taken as a random event sequence, the entropy value is calculated by analyzing the probability distribution of each state value, and the communication entropy value of each node is calculated. If the communication parameter fluctuation of a node is severe, it is excellent at times and poor at times, showing high unpredictability, the probability distribution of its state is more uniform, and the calculated entropy value is higher. If the communication state of a node is always stable at a good or poor level, the predictability is strong, the probability distribution is concentrated, and the entropy value is lower. By calculating the communication entropy value, the instability of the communication link can be analyzed, the dynamic evolution of the communication quality with time can be reflected, and the communication link with poor communication effect can be identified, thereby providing a data basis for blind area identification.

[0078] Specifically, after obtaining the communication entropy value of each node, the topological importance of each node is calculated based on the pre-acquired network topology data, and the topological importance is calculated by combining the betweenness centrality, closeness centrality and eigenvector centrality of the node; the communication entropy value and the topological importance are weighted and fused according to the respective weights of the self-stability and network structure role of each node, and the communication credibility value of each node is calculated. By combining the communication entropy value reflecting the dynamic stability of the node itself and the topological importance reflecting the static structure status of the node in the network, the communication credibility value calculated can avoid the one-sidedness of single-dimensional evaluation, provide data support for network maintenance and monitoring priority analysis under the condition of limited resources, avoid excessive attention to high-entropy nodes with little impact on global performance, and improve the accuracy of communication state analysis results.

[0079] As shown in Figure 2 Based on the pre-acquired network topology graph, the nodes in the network topology graph represent intelligent communication device nodes, and the edges represent communication links. The communication credibility value of each node calculated is added to the corresponding node as the weight of the edge to construct a communication credibility graph. The construction of the communication credibility graph provides accurate and clear data support for data communication process analysis, and improves the accuracy and efficiency of the communication analysis process.

[0080] In the communication credibility graph, the area with a communication credibility value less than a preset credibility threshold is screened out. The credibility threshold is set according to historical operation and maintenance experience and network service quality requirements. All nodes in the credibility graph are traversed, and the communication credibility value of each node is compared with the preset credibility threshold. The nodes with a communication credibility value less than the preset credibility threshold and the communication links directly associated with the nodes are identified as data monitoring blind areas. By using the threshold, nodes with unreliable communication status, frequent interruption and loss risk are quickly and accurately identified, the corresponding monitoring blind areas are determined, accurate target range is provided for the interference analysis process, resource waste and blindness are avoided, the most critical problems are solved preferentially, and the reliability and communication effect of the intelligent communication device communication network are improved.

[0081] Further, interference data is extracted from real-time communication data and blind area communication data to construct an interference heat map, including:

[0082] S401, feature extraction is performed on the real-time communication data and the blind area communication data by using a preset feature extraction model to obtain a feature vector;

[0083] S402, the feature vector is compressed and mapped to a low-dimensional space to obtain a corresponding feature code;

[0084] S403, based on the feature code, interference data is extracted and the interference situation of different interference sources is analyzed by using a preset interference extraction model, corresponding interference data is predicted, and an interference analysis result is obtained.

[0085] S404, according to the interference analysis result, the real-time interference data is associated with the predicted interference data, and an interference heat map is constructed.

[0086] In the embodiment, the real-time communication data and the blind area communication data are subjected to feature extraction by a preset feature extraction model, which includes but is not limited to a deep neural network model. The deep neural network model is trained by using a large amount of historical communication data to obtain a pre-trained deep neural network model. The real-time communication data and the blind area communication data are respectively input into the pre-trained deep neural network model, and the model extracts corresponding features to construct a feature vector. The corresponding features are quickly extracted by the model, which provides a data basis for interference analysis and prediction, and improves the accuracy and reliability of the interference identification process.

[0087] Specifically, after obtaining the high-dimensional feature vector, a low-dimensional feature code is generated by a dimension reduction mapping process. The feature vector is projected onto the orthogonal principal component with the largest variance by principal component analysis for data compression to obtain the corresponding low-dimensional feature code. By compressing and mapping the high-dimensional feature vector, the redundant information and irrelevant noise in the feature can be removed, the key discriminant features are focused on in the interference analysis process, overfitting is avoided, the generalization ability is improved, the low-dimensional feature code can reduce the data amount in the analysis process, and the analysis efficiency is improved.

[0088] Based on the feature code, the interference data is extracted by a preset interference extraction model, and the interference situation of different interference sources is analyzed. The interference extraction model includes but is not limited to a support vector machine model. A large amount of historical feature code data is used to train the support vector machine model to obtain a pre-trained support vector machine model. The feature code is input into the pre-trained support vector machine model, the model predicts the interference situation in the future time interval, and outputs the interference analysis result containing the current state and the prediction. The model can quickly identify and predict the interference data and the interference situation, provide a suppression direction for interference suppression, improve the interference response speed and the suppression effect.

[0089] Specifically, according to the interference analysis result, the geographic coordinate information of the intelligent communication device or its position in the network topology graph is associated between the real-time interference data and the predicted interference data. For the current real-time interference data, the spatial position and the interference intensity are directly mapped. For the predicted interference data, the corresponding predictive spatial distribution is generated according to the prediction result. The real-time interference data and the predicted interference data are integrated, the color gradient is used to reflect the interference intensity level, the deeper the color, the higher the interference intensity, and the interference heat map is constructed. By constructing the interference heat map, the interference distribution, the severity and the evolution trend can be quickly and intuitively analyzed, data support is provided for the interference suppression strategy making, and the analysis accuracy and the suppression effect of the interference analysis process and the suppression process are improved.

[0090] Further, based on the interference heat map, an interference law is identified through a preset interference analysis model, and an interference change trend is predicted, including:

[0091] S501, based on the interference heat map, interference correlation between different interference sources is analyzed, and an interference correlation degree is obtained;

[0092] S502, in combination with the interference correlation degree, an interference law is identified through a preset interference analysis model, and an interference change trend is predicted.

[0093] In this embodiment, based on the interference heat map, interference correlation between different interference sources is analyzed, and an interference correlation degree is obtained. The spatial distribution profile of each interference source and its intensity time sequence change data are extracted from the interference heat map. In the spatial dimension, the Pearson correlation coefficient of the interference intensity between different interference source regions is calculated, reflecting the correlation between the interference sources in space that presents synergistic enhancement or weakening. In the time dimension, time-lag cross-correlation analysis is performed between the interference sources to determine whether the change of one interference source leads the change of another interference source, and the causal or propagation relationship is identified. The interference correlation degree between different interference source signals is calculated in combination with the spatial correlation and the time correlation. By calculating the interference correlation degree, the interference propagation path and the synergistic action mode between different interference sources can be analyzed, data basis is provided for predicting the interference evolution trend, and the interference analysis and prediction efficiency and accuracy are improved.

[0094] Specifically, the interference data and the calculated interference correlation degree are input into a preset interference analysis model, the interference analysis model includes but is not limited to a graph attention recurrent neural network model, a large amount of historical interference data is used to train the graph attention recurrent neural network model to obtain a pre-trained graph attention recurrent neural network model, the model analyzes the interference propagation according to the interference data and the interference correlation degree, predicts the spatial distribution change of the interference intensity in a future period of time, and obtains the interference change trend. By predicting the interference change trend through the model, the propagation path of the interference, the range expansion, and the resonance effect between multiple interference sources are reflected, so that interference suppression or communication strategy adjustment can be performed in advance before the interference reaches the key area or reaches a serious intensity, the timeliness of interference suppression is improved, early prevention is realized, and the communication quality and effect of the communication network are improved.

[0095] Further, the interference change trend is matched with a plurality of communication adjustment strategies, the communication adjustment strategies in the initial strategy set are simulated and optimized by a preset strategy analysis model to obtain the interference suppression strategy, including:

[0096] S601, strategy matching is performed on the interference change trend, a plurality of communication adjustment strategies are matched, and a corresponding initial strategy set is obtained;

[0097] S602, the simulation of the execution effect of the communication adjustment strategies in the initial strategy set is performed by a preset strategy analysis model, the strategy is optimized according to the simulation result, and the interference suppression strategy is obtained.

[0098] In this embodiment, a mapping relationship from the interference feature to the countermeasures is established, the interference change trend is matched with a plurality of communication adjustment strategies, and a corresponding initial strategy set is obtained. By matching the corresponding communication adjustment strategies for the interference change trend, the rationality and matching efficiency of the strategy matching process can be improved.

[0099] Specifically, the execution effect of the communication adjustment strategies in the initial strategy set is simulated by a preset strategy analysis model, the interaction and resource competition among the strategies are analyzed, the strategy is synergistically optimized according to the simulation result, and the interference suppression strategy is obtained; through strategy simulation and synergistic optimization, the resource conflict and performance contradiction problem in the parallel execution of multiple strategies can be avoided, the execution effect of the strategy combination is simulated and judged, the system performance decline caused by resource competition among the strategies is avoided, the interference suppression efficiency is improved, the feasibility and effectiveness of the interference suppression strategy are improved, the optimized strategy can not only effectively suppress the interference, but also maintain the overall performance stability of the communication system, and the synergistic optimization of interference suppression and system performance is realized.

[0100] Further, the interference change trend is matched with a plurality of communication adjustment strategies, a corresponding initial strategy set is obtained, including:

[0101] S701, constructing an interference strategy mapping matrix based on the interference change trend, wherein a row vector in the interference strategy mapping matrix represents an interference source type identified by the interference change trend, a column vector represents a matched communication adjustment strategy, and a matrix element represents an inhibition degree value of the communication adjustment strategy on a corresponding interference source;

[0102] S702, for multiple interference sources in the interference change trend, establishing a composite interference field, and weighting and fusing corresponding strategies in the interference strategy mapping matrix to obtain a corresponding composite strategy;

[0103] S703, integrating the communication adjustment strategy matched by a single interference source and the composite strategy to obtain a corresponding initial strategy set.

[0104] In this embodiment, based on the predicted interference change trend, the types of interference sources that need to be dealt with are extracted, including but not limited to persistent narrowband interference, burst impulse noise, and time-varying channel fading. A communication adjustment strategy library is set according to historical communication adjustment modes. Each strategy in the communication adjustment strategy library is mapped to a column vector of a matrix. According to statistical analysis of historical application data, the inhibition degree value of each strategy on interference is determined. The interference strategy mapping matrix is constructed according to the inhibition degree value. By constructing the interference strategy mapping matrix, the strategy decision process is converted into a data analysis process. Based on the interference strategy mapping matrix, the identified interference types can be quickly and accurately mapped to the corresponding strategies, improving the efficiency and consistency of strategy matching and providing a data basis for subsequent strategy fusion and optimization in complex interference scenarios.

[0105] Specifically, when the interference change trend indicates that multiple interference sources exist on the communication link at the same time, a composite interference field is constructed according to the mutual superposition of the interference. For the composite interference field, a corresponding weight is assigned to each interference source according to the interference intensity, the influence degree on the communication quality, and the time and space urgency of the interference source. For each communication adjustment strategy in the strategy mapping matrix, the inhibition degree value of each single interference source in the composite interference field is combined with the weight of the corresponding interference source for weighted calculation. The weighted results of all interference sources are fused to calculate the comprehensive inhibition performance score of the strategy on the entire composite interference field, and a corresponding composite strategy is obtained. By establishing a composite interference field model and weighting and fusing the strategies, the strategy conflict or resource competition problem caused by simply superimposing multiple interference sources in the traditional method can be avoided. From a holistic perspective, the comprehensive potential of each strategy to deal with complex situations is evaluated, and the adaptability and solving ability of the interference suppression system to the composite interference scenario are enhanced, thereby improving the interference suppression effect.

[0106] Specifically, based on the interference strategy mapping matrix, for each single interference source in the interference change trend, a corresponding communication adjustment strategy is directly matched according to the suppression degree value, and a corresponding composite strategy is generated for the composite interference field; the communication adjustment strategy matched by the single interference source and the composite strategy are integrated to obtain a corresponding initial strategy set. By integrating the strategy for a single interference source and the composite strategy for the composite interference field, both local interference problems and global interference problems can be considered, the quality and reliability of the interference suppression strategy are improved, and the interference suppression effect and the data transmission quality of the notification process are improved.

[0107] Further, the execution effect of the communication adjustment strategy in the initial strategy set is simulated by a preset strategy analysis model, the strategy is optimized according to the simulation result, and the interference suppression strategy is obtained, including:

[0108] S801, the execution effect of the communication adjustment strategy in the initial strategy set is simulated by a preset strategy analysis model, the resource competition between the communication adjustment strategies and the influence degree on the communication performance are analyzed, and a simulation result is obtained;

[0109] S802, a strategy mutual exclusion graph is constructed based on the simulation result, wherein a node represents a communication adjustment strategy, and an edge weight represents the resource competition intensity between strategies;

[0110] S803, the communication adjustment strategies with resource competition are screened in the strategy mutual exclusion graph, the strategies with a communication performance influence degree greater than a preset communication performance influence degree threshold are retained, and candidate strategies without resource competition and with the same interference suppression efficiency are matched from the strategy mutual exclusion graph for strategy optimization, and the interference suppression strategy is obtained.

[0111] In the embodiment, the execution effect of the communication adjustment strategy in the initial strategy set is simulated by a preset strategy analysis model, the strategy analysis model includes but is not limited to a discrete event simulation and a multi-agent reinforcement learning model, the occupation of the communication resources such as spectrum, power and time slot during the strategy execution process is simulated by the discrete event simulation, each strategy is regarded as an agent by the multi-agent reinforcement learning, each agent influences other strategies when making a decision, and the interaction between the strategies is simulated; in the strategy simulation process, the concurrent request conflict frequency of multiple strategies for the same resource instance is monitored in real time, the resource competition of the strategies is quantified, and the influence degree of the strategies on the communication performance is quantified by calculating the change of the key performance indicators such as network overall throughput, delay and bit error rate before and after the strategy execution; the model outputs a resource competition matrix and a performance influence evaluation result as the simulation result. By simulating and analyzing the strategies, the implementation effect of the strategies can be evaluated in advance, the problems of resource conflict and performance degradation in the actual deployment of the strategies can be avoided, a data basis is provided for the strategy optimization, the strategy combination that will cause network performance degradation is excluded, and the safety and reliability of the interference suppression process are improved.

[0112] As shown in Figure 3 Based on the simulation results, a strategy mutual exclusion graph is constructed, wherein each node corresponds to a communication adjustment strategy, the connection of edges represents that there is a resource competition relationship between two strategies, and the edge weight represents the resource competition intensity between strategies. The resource competition intensity between strategies is calculated by weighted summation according to the monitored resource conflict frequency, conflict duration, and resource utilization rate reduction degree caused by conflict and the like in the simulation results. By constructing the strategy mutual exclusion graph, the resource competition relationship between strategies is clearly reflected, which provides a basis for the optimization and selection of strategies, avoids the resource competition conflict problem between strategies, and improves the running performance in the strategy implementation process.

[0113] In the strategy mutual exclusion graph, the communication adjustment strategies with resource competition are screened, the strategies with high communication performance influence degree are retained, and the candidate strategies without resource competition and with the same interference suppression effectiveness are matched from the strategy mutual exclusion graph for strategy optimization to obtain an interference suppression strategy. All edges with a weight exceeding a preset competition threshold in the strategy mutual exclusion graph are traversed, the competition threshold can be set according to the system operation performance requirement, and the strategy pairs with strong resource competition are screened out. The communication performance influence degree of each strategy in the strategy pair is compared in combination with the simulation results, the communication performance influence degree threshold is set according to the communication process accuracy requirement, the strategies with an influence degree higher than the communication performance influence degree threshold are retained, and the strategies with a low influence degree are used as the strategies to be replaced. The candidate strategies without edge connection and with the same or similar interference suppression effectiveness are found in the strategy mutual exclusion graph for replacement, and the absence of edge connection indicates that there is no resource competition between the strategies. The strategies are continuously iteratively optimized, and finally a set of interference suppression strategies with minimized resource conflict, maximized interference suppression effectiveness, and optimal communication performance influence is obtained.

[0114] It should be noted that by screening the resource competition strategies and optimization, the performance of the interference suppression strategies can be optimized, and the feasibility and efficiency of the interference suppression strategies can be improved. Based on the strategy mutual exclusion graph, the resource conflict between strategies can be quickly eliminated while ensuring that the overall interference suppression effectiveness is not reduced. In combination with the effectiveness of individual strategies and the synergy between strategies, the optimized strategy set can achieve the best interference suppression effect under the condition of limited network resources, and the overall communication performance and communication quality are improved.

[0115] Further, according to the interference suppression strategy, a device control instruction is generated to control the device in real time to suppress interference in intelligent communication device communication, including:

[0116] S901, according to the interference suppression strategy, matching in the pre-constructed instruction mapping table to generate a device control instruction;

[0117] S902, real-time control the device according to the device control instruction, and real-time monitor the error code rate of the communication link in the control process, if the error code rate change rate is less than a preset change threshold, feedback optimization is performed on the control process to suppress the interference in the communication of the intelligent communication device.

[0118] In this embodiment, according to the interference suppression strategy, a device control instruction is generated by matching in a pre-constructed instruction mapping table. In the instruction mapping table, a determined corresponding relationship between each interference suppression strategy and a specific and operable device control instruction is set according to an operation manual or priori knowledge. For example, the strategy "switch from power line carrier communication to wireless communication mode" corresponds to a set of instruction sequences including mode switching instructions and corresponding parameter initialization settings. According to the interference suppression strategy, the corresponding instruction template is searched in the instruction mapping table to generate the corresponding device control instruction. Through the instruction mapping table, the device control instruction corresponding to the interference suppression strategy can be quickly matched, the decision execution time is shortened, the efficiency and accuracy of the system in responding and processing real-time interference are improved, and the accuracy and reliability of the control action are ensured.

[0119] Specifically, real-time control is performed on the device according to the device control instruction, and real-time monitoring is performed on the error code rate of the communication link in the control process. If the error code rate change rate is less than a preset change threshold, feedback optimization is performed on the control process to suppress the interference in the communication of the intelligent communication device. The system generates the device control instruction and sends it to the target intelligent communication device through a communication channel to drive it to perform corresponding parameter adjustment or mode switching operation for real-time control. At the same time, the system continuously and real-time monitors the performance indicators of the controlled communication link, including but not limited to the error code rate, the absolute value and the change rate of the error code rate. According to the network service quality requirement and historical experience, the change threshold is set. If it is found through monitoring that the error code rate change rate is less than the change threshold, it indicates that the control effect is not as expected, and there is a problem of slow interference suppression and insignificant effect. The feedback optimization mechanism is triggered immediately, including but not limited to fine-tuning the parameters of the current control instruction, switching to a backup interference suppression strategy, and feeding back the poor effect information to the upstream strategy analysis module to trigger re-evaluation and decision. Through real-time monitoring of the strategy implementation process and optimization of the closed-loop control, deviations can be dynamically corrected, and the interference suppression effect can be improved.

[0120] It should be noted that, by real-time monitoring and feedback optimization based on the error code rate change rate in the control process, the system can dynamically optimize according to the dynamic response of the actual environment, can effectively cope with individual differences in device response characteristics, time-varying nature of environmental interference, and inherent errors of model prediction and other uncertain factors, and can timely adjust the control action to improve the suppression effect. Thus, the accuracy and environmental adaptability of the interference suppression measures are improved, and the stability of the communication link of the intelligent communication device in a complex and variable interference environment is ensured.

[0121] AsFigure 4 The intelligent communication device communication interference suppression device shown in the figure is used to implement the intelligent communication device communication interference suppression method, and comprises:

[0122] An interference heat map construction module constructs an interference heat map according to real-time communication data of the intelligent communication device, fills in data monitoring blind areas through data fusion and interpolation, and identifies interference data.

[0123] An interference analysis module identifies interference rules and predicts interference change trends through a preset interference analysis model based on the interference heat map.

[0124] An interference suppression strategy matching module matches a plurality of communication adjustment strategies to the interference change trends, simulates and analyzes the communication adjustment strategies through a preset strategy analysis model, and optimizes the communication adjustment strategies to obtain an interference suppression strategy.

[0125] An interference suppression module generates device control instructions according to the interference suppression strategy, and performs real-time control on the device to suppress interference in intelligent communication device communication.

[0126] In this embodiment, the interference heat map construction module accesses real-time communication data streams, calls pre-stored network topology structure information, quantitatively evaluates the reliability of communication links, identifies data monitoring blind areas formed due to poor signal quality or node failure, performs consistency processing on multi-source data around the blind areas using a data fusion algorithm, estimates the communication state inside the blind areas using a spatial interpolation technique, fills in data blanks, locates interference data from complete global data through feature extraction and pattern recognition models, and constructs an interference heat map. By constructing the interference heat map, the problem of missed interference judgment due to monitoring blind areas is avoided, complete and accurate data basis is provided for the interference suppression process, and the interference suppression effect is improved.

[0127] The interference analysis module analyzes the space-time information of the heat map through an interference analysis model, calculates the correlation between different interference sources, analyzes the mode of interference evolution over time, identifies the propagation path, periodic characteristics, and intensity change trend of the interference, and predicts the development trend of the interference in the future period of time. By predicting the interference change trend, the suppression strategy can be deployed in advance, the interference can be suppressed before it affects communication, the interference suppression effect is enhanced, and the communication effect is improved.

[0128] Specifically, the interference suppression strategy matching module matches a communication adjustment strategy from a predefined strategy library according to the interference type and characteristics to obtain an initial strategy set, including a composite strategy generated by weighted fusion to cope with composite interference; the initial strategy is simulated by a preset strategy analysis model to evaluate resource competition among the strategies and their impact on overall communication performance; based on the simulation results, a strategy mutual exclusion graph is constructed to filter out a strategy combination with good synergy and high resource efficiency to obtain an optimized interference suppression strategy. The strategy simulation analysis avoids resource competition among the strategies, ensures that the output suppression strategy is not only effective in theory but also feasible and efficient in implementation, improves the strategy implementation effect and reduces resource consumption.

[0129] The interference suppression module generates specific control instructions corresponding to the input interference suppression strategy and conforming to the device communication protocol in combination with a preset instruction mapping table, and issues the instructions to the target intelligent communication device or communication unit to perform real-time control such as power adjustment, mode switching or channel change; the change rate of key indicators such as the error rate of the controlled link is continuously monitored, and if the control effect is found to be not as expected, a feedback optimization mechanism is triggered immediately to dynamically adjust the control parameters or switch to a backup strategy. Through real-time device control and control process optimization, the interference suppression process can dynamically adapt to actual changes in the network environment, ensuring the accuracy, real-time performance and robustness of the interference suppression effect.

[0130] The above only describes the preferred embodiments of the present application, and the protection scope of the present application is not limited to the above-described embodiments. Any technical solutions falling within the scope of the present application shall be considered as falling within the protection scope of the present application. It should be noted that, for ordinary technical personnel in the technical field, some improvements and refinements without departing from the principles of the present application shall also be considered as falling within the protection scope of the present application.

Claims

1. A method for suppressing interference during communication in intelligent communication devices, characterized in that, include: The communication entropy value of each device is calculated based on the real-time communication data of the intelligent communication devices. The reliability of the communication link is analyzed and a communication credibility map is constructed. In the communication credibility map, data fusion and interpolation are used to fill the data monitoring blind spots, identify interference data, analyze the correlation between real-time interference data and predicted interference data, and construct an interference heat map. Based on the aforementioned interference heatmap, interference patterns are identified and interference trends are predicted using a preset interference analysis model. By establishing a composite interference field from multiple interference sources in the interference change trend and weighting and fusing the corresponding strategies, combined with the strategies of a single interference source, various communication adjustment strategies are matched. Through a preset strategy analysis model, the communication adjustment strategies are simulated, analyzed, and optimized to obtain an interference suppression strategy. Based on the interference suppression strategy, device control commands are generated to control the device in real time, thereby suppressing interference in the communication of intelligent communication devices.

2. The interference suppression method in intelligent communication device communication according to claim 1, characterized in that, The process involves calculating the communication entropy value of each device based on real-time communication data from intelligent communication devices, analyzing the reliability of communication links and constructing a communication reliability map. Within this reliability map, data fusion and interpolation are used to fill in data monitoring blind spots, identify interference data, analyze the correlation between real-time interference data and predicted interference data, and construct an interference heatmap. This includes: Based on the real-time communication data of intelligent communication devices, combined with the pre-acquired network topology data, the communication entropy value of each device is calculated, the reliability of the communication link is analyzed, a communication credibility map is constructed, and data monitoring blind spots are identified. Data fusion and interpolation are performed on the communication data of adjacent areas to the data monitoring blind zone in the communication reliability map to obtain the communication data of the blind zone. Interference data is extracted from real-time communication data and blind zone communication data. The correlation between real-time interference data and predicted interference data is analyzed, and an interference heatmap is constructed.

3. The interference suppression method in intelligent communication device communication according to claim 2, characterized in that, The process involves calculating the communication entropy value of each device based on real-time communication data from intelligent communication devices, combined with pre-acquired network topology data, analyzing the reliability of communication links, constructing a communication reliability map, and identifying data monitoring blind spots, including: Based on the real-time communication data of the intelligent communication devices and the pre-acquired network topology data, analyze the communication status of each intelligent communication device and calculate the corresponding communication entropy value. By combining the communication entropy value and the topological importance of the intelligent communication device in the network topology, the reliability of the communication link is analyzed, and the corresponding communication reliability value is calculated. Based on the communication reliability value, a communication reliability map reflecting the communication status between intelligent communication devices is constructed; In the communication credibility map, regions with communication credibility values ​​less than a preset credibility threshold are selected as data monitoring blind spots.

4. The interference suppression method in intelligent communication device communication according to claim 3, characterized in that, The process of extracting interference data from real-time communication data and blind zone communication data, analyzing the correlation between real-time interference data and predicted interference data, and constructing an interference heatmap includes: Feature vectors are obtained by extracting features from real-time communication data and blind zone communication data using a preset feature extraction model. The feature vector is compressed and mapped to a low-dimensional space to obtain the corresponding feature code; Based on the feature encoding, interference data is extracted through a preset interference extraction model and the interference situation of different interference sources is analyzed. The corresponding interference data is predicted to obtain the interference analysis results. Based on the interference analysis results, a correlation is established between the real-time interference data and the predicted interference data to construct an interference heatmap.

5. The interference suppression method in intelligent communication device communication according to claim 1, characterized in that, Based on the aforementioned interference heatmap, interference patterns are identified and interference trends are predicted using a pre-defined interference analysis model, including: Based on the interference heatmap, the interference correlation between different interference sources is analyzed to obtain the interference correlation degree. Based on the aforementioned interference correlation, interference patterns are identified and interference trends are predicted using a pre-defined interference analysis model.

6. The interference suppression method in intelligent communication device communication according to claim 1, characterized in that, The process involves establishing a composite interference field from multiple interference sources within the interference variation trend, weighting and fusing corresponding strategies, and combining these with strategies for individual interference sources to match various communication adjustment strategies. A preset strategy analysis model is then used to simulate, analyze, and optimize the communication adjustment strategies, resulting in an interference suppression strategy, including: Strategy matching is performed on the interference change trend. By establishing a composite interference field for multiple interference sources and weighting and fusing the corresponding strategies, combined with the strategy of a single interference source, multiple communication adjustment strategies are obtained, resulting in the corresponding initial strategy set. The execution effect of communication adjustment strategies in the initial strategy set is simulated by a pre-set strategy analysis model. Based on the simulation results, the strategies are optimized to obtain interference suppression strategies.

7. The interference suppression method in intelligent communication device communication according to claim 6, characterized in that, The strategy matching for interference change trends involves establishing a composite interference field for multiple interference sources, weighting and fusing the corresponding strategies, and combining this with the strategy matching for a single interference source to obtain multiple communication adjustment strategies, resulting in a corresponding initial strategy set, including: Based on the interference change trend, an interference strategy mapping matrix is ​​constructed. In the interference strategy mapping matrix, the row vectors represent the types of interference sources identified by the interference change trend, the column vectors represent the matching communication adjustment strategies, and the matrix elements represent the degree of suppression of the corresponding interference sources by the communication adjustment strategies. For multiple interference sources in the interference change trend, a composite interference field is established, and the corresponding strategies in the interference strategy mapping matrix are weighted and fused to obtain the corresponding composite strategy. The communication adjustment strategy matched for a single interference source and the composite strategy are integrated to obtain the corresponding initial strategy set.

8. The interference suppression method in intelligent communication device communication according to claim 7, characterized in that, The process involves simulating the execution effect of communication adjustment strategies in the initial strategy set using a preset strategy analysis model, optimizing the strategies based on the simulation results, and obtaining interference suppression strategies, including: The execution effect of communication adjustment strategies in the initial strategy set is simulated by a pre-set strategy analysis model. The resource competition among communication adjustment strategies and their impact on communication performance are analyzed to obtain simulation results. Based on the simulation results, a policy mutual exclusion graph is constructed, where nodes represent communication adjustment policies and edge weights represent the intensity of resource competition between policies. In the policy mutual exclusion graph, communication adjustment strategies with resource contention are screened, and strategies whose impact on communication performance is greater than the preset threshold are retained. Candidate strategies with no resource contention and the same interference suppression effectiveness are matched from the policy mutual exclusion graph for policy optimization to obtain the interference suppression strategy.

9. The interference suppression method in intelligent communication device communication according to claim 1, characterized in that, Based on the interference suppression strategy, device control commands are generated to control the device in real time, thereby suppressing interference in the communication of intelligent communication devices, including: Based on the interference suppression strategy, the device control commands are generated by matching in the pre-built command mapping table. The device is controlled in real time according to the device control instructions. During the control process, the bit error rate of the communication link is monitored in real time. If the rate of change of the bit error rate is less than the preset change threshold, the control process is optimized by feedback to suppress interference in the communication of the intelligent communication device.

10. An interference suppression device for intelligent communication equipment, characterized in that, An interference suppression method for intelligent communication devices as described in any one of claims 1 to 9, comprising: The interference heatmap construction module calculates the communication entropy value of each device based on the real-time communication data of the intelligent communication devices, analyzes the reliability of the communication link and constructs a communication credibility map. In the communication credibility map, it fills the data monitoring blind spots through data fusion and interpolation, identifies interference data, analyzes the correlation between real-time interference data and predicted interference data, and constructs an interference heatmap. The interference analysis module, based on the interference heatmap, identifies interference patterns and predicts interference trends through a preset interference analysis model. The interference suppression strategy matching module establishes a composite interference field for multiple interference sources in the interference change trend, weights and fuses the corresponding strategies, and combines the strategies of a single interference source to match multiple communication adjustment strategies. Through a preset strategy analysis model, the communication adjustment strategies are simulated, analyzed and optimized to obtain the interference suppression strategy. The interference suppression module generates device control commands based on the interference suppression strategy and performs real-time control of the device to suppress interference during communication of the smart communication device.

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