Communication antenna intelligent regulation and control method and system based on Internet of Things

Abstract

The invention provides a communication antenna intelligent regulation and control method and system based on the Internet of Things, and relates to the technical field of communication antennae. And analyzing the parameter data to obtain user tendency data, determining an antenna control target in combination with the environment characteristic data, and formulating an antenna control strategy according to the antenna control target. And establishing an enhanced neural Turing machine model, and analyzing the parameter data and the electromagnetic characteristic data to obtain balance control parameters. And adjusting the antenna control strategy by using the balance control parameter according to a regulation and control target decision method to obtain an antenna regulation and control decision. According to the invention, the parameter data is analyzed by using a social network analysis method to obtain the user tendency data, the antenna control strategy is formulated in combination with the environmental characteristic data, the antenna control target and strategy can be accurately determined according to the individual demands of the users and environmental factors, the adaptability of the communication antenna to different user groups and environments is improved, and the user experience is improved. And the communication quality is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of communication antennas, and in particular to an intelligent control method and system for communication antennas based on the Internet of Things. Background Art

[0002] In today's rapidly developing Internet of Things (IoT), intelligent control of communication antennas is crucial to ensuring efficient and stable communications. However, existing communication antenna adjustment strategies have many problems and are unable to meet the increasingly complex communication needs.

[0003] On the one hand, with the increasing diversity of user behavior and the continuous increase in environmental interference, adjustment strategies are unable to cope with the coupled effects of complex user behavior and environmental interference. Traditional antenna control methods are often based on fixed rules or simple models. These methods may perform well in a single, stable environment. However, in reality, user mobility, changes in usage habits, and the presence of various electromagnetic interference sources in the surrounding environment cause user behavior and environmental interference to intertwine, forming a complex coupling effect. Existing adjustment strategies are unable to accurately capture this coupling relationship, resulting in poor antenna control and reduced communication quality. Problems such as unstable signal strength and reduced data transmission rate frequently occur.

[0004] On the other hand, traditional models suffer from slow convergence when processing antenna control data. In the IoT, a large number of sensors and devices generate massive amounts of data in real time. Traditional models take a long time to analyze and process this data and converge to an optimal or near-optimal solution. This results in delayed updates to antenna control strategies and an inability to quickly adapt to changes in the environment and user needs. For example, when strong electromagnetic interference suddenly occurs in an environment, traditional models may take a long time to adjust antenna parameters to maintain communication quality, potentially severely impacting communications during this period.

[0005] In order to solve the above-mentioned defects in the prior art, this technical solution proposes an intelligent control method for communication antennas based on the Internet of Things. Summary of the Invention

[0006] The present invention provides an intelligent control method and system for communication antennas based on the Internet of Things, which is used to solve the defects of the existing adjustment strategies in coping with the coupling influence of complex user behavior and environmental interference and the slow convergence speed of traditional models.

[0007] In one aspect, the present invention provides a method for intelligently controlling a communication antenna based on the Internet of Things, comprising: Collect parameter data of IoT devices and electromagnetic characteristic data of communication antennas, and collect environmental characteristic data in the target area at preset time intervals.

[0008] The parameter data is analyzed according to the social network analysis method to obtain user tendency data, the antenna control target is determined according to the user tendency data and combined with the environmental characteristic data, and the antenna control strategy is formulated according to the antenna control target.

[0009] A neural Turing machine model is established, and the sand cat swarm optimization algorithm is used to optimize the neural Turing machine model to obtain an enhanced neural Turing machine model. Based on the enhanced neural Turing machine model, the parameter data and electromagnetic characteristic data are analyzed to obtain the balance control parameters.

[0010] According to the control target decision method, the antenna control strategy is adjusted using the balance control parameters to obtain the antenna control decision.

[0011] According to the method for intelligently controlling a communication antenna based on the Internet of Things provided by the present invention, the step of analyzing and obtaining user tendency data includes: Various parameter data of multiple IoT devices are integrated and normalized to obtain a device parameter set.

[0012] Each IoT device in the device parameter set is regarded as a device node, and the corresponding user using the IoT device is regarded as a user node, and the connection length is determined according to the relationship between the IoT devices and between the IoT devices and the corresponding users.

[0013] The degree, betweenness, closeness centrality and clustering coefficient of each device node and each user node are calculated to determine the network indicators, and key nodes are selected according to preset values.

[0014] User preference data is obtained by analyzing key nodes based on preferred device types, usage time patterns, and device interaction preferences.

[0015] According to the method for intelligently controlling a communication antenna based on the Internet of Things provided by the present invention, the step of determining an antenna control target includes: Users are divided into different user groups according to their personalized needs, and corresponding needs are summarized for different user groups.

[0016] The influence of different climatic conditions on the performance of communication antennas is analyzed based on the environmental characteristic data to obtain the climate impact, and the influence of different building materials on signal propagation is analyzed to obtain the building impact.

[0017] For different user groups, the ridge regression analysis method is used to correlate climate impact with building impact to obtain multiple influencing factors, from which the factors that have the greatest impact on demand in the current environment are determined to obtain the key influencing factors.

[0018] Determine the antenna control target for each user group based on different key influencing factors.

[0019] According to the method for intelligently controlling a communication antenna based on the Internet of Things provided by the present invention, the step of obtaining key influencing factors includes: The climate impact and building impact are standardized and used as independent variables, each climate and building is formed into a matrix, and demand is used as the dependent variable.

[0020] Introduce the regularization parameter and construct the objective function of ridge regression.

[0021] Initialize the regression coefficients, calculate the partial derivatives of the objective function with respect to each regression coefficient, and obtain the gradient vector.

[0022] The objective function is updated according to the gradient vector until the preset number of times is reached to obtain the coefficient value.

[0023] Sort the independent variables according to the absolute value of the coefficient, and select the independent variable with the largest absolute value as the key influencing factor.

[0024] According to the method for intelligently controlling a communication antenna based on the Internet of Things provided by the present invention, the steps of formulating an antenna control strategy include: A plurality of candidate control strategies are specified according to the antenna control objectives and the adjustable parameters of the communication antenna.

[0025] According to the antenna control target, the evaluation index of the candidate control strategy is determined. According to the evaluation index, multiple candidate control strategies are evaluated to obtain corresponding simulation scores, and the highest score is selected as the antenna control strategy.

[0026] According to the method for intelligent control of communication antennas based on the Internet of Things provided by the present invention, the steps of optimizing and obtaining the enhanced neural Turing machine model include: Initialize the population size of the sand cat group, where each sand cat represents a set of parameters of the neural Turing machine model.

[0027] The parameters corresponding to each sand cat are applied to the neural Turing machine model, and the inverse of the mean squared error loss is used as the fitness function.

[0028] Each sand cat moves randomly based on its current position according to a preset probability to obtain a corresponding new moving position.

[0029] The fitness value of each new position is calculated according to the fitness function, and the one with the highest fitness value is selected as the global optimal sand cat.

[0030] Each sand cat moves towards the global optimal sand cat and updates its latest position.

[0031] The fitness value of each sand cat's latest position is calculated, and the one with the highest fitness value is selected as the optimization parameter. The optimized parameters are used to construct a neural Turing machine model to obtain an enhanced neural Turing machine model.

[0032] According to the method for intelligently controlling a communication antenna based on the Internet of Things provided by the present invention, the step of analyzing and obtaining a balance control parameter includes: The parameter data and electromagnetic characteristic data are integrated and outliers and error values ​​are removed to obtain a fused data set.

[0033] The fused data set is encoded and input into the enhanced neural Turing machine model according to the input requirements to perform calculations and generate control signals.

[0034] Read and write operations are performed according to the control signal, a decision result is output, and the communication antenna is adjusted according to the decision result to obtain a balance control parameter.

[0035] According to the method for intelligently controlling a communication antenna based on the Internet of Things provided by the present invention, the steps of adjusting and obtaining an antenna control decision include: The influence of the balance control parameters on the communication antenna performance and the preset system goals is analyzed, and the goals and performance indicators of the currently used antenna control strategy are evaluated to obtain the evaluation results.

[0036] Based on the evaluation results, the gap between the current antenna control strategy and the preset system goals is analyzed, and adjustment targets are set.

[0037] A balance control adjustment strategy is formulated based on the adjustment target, multiple adjustment schemes are generated, and the one with the closest performance is selected from each adjustment scheme based on the performance indicators as the antenna control decision.

[0038] According to the method for intelligently controlling a communication antenna based on the Internet of Things provided by the present invention, the step of setting an adjustment target includes: The data obtained from the evaluation of the goals and performance indicators of the current antenna control strategy are summarized to obtain the total performance data, which are compared one by one with the preset system goals. The difference between the actual value and the target value of each indicator is calculated and the gap is summarized.

[0039] The key gaps are selected from the gaps based on the size of the difference and the degree of impact on performance. Based on the key gaps, the feasibility of the goals is considered and the goals are adjusted to be consistent with the preset system goals.

[0040] On the other hand, the present invention also provides a communication antenna intelligent control system based on the Internet of Things, comprising: The characteristic data acquisition module is used to collect parameter data of IoT devices and electromagnetic characteristic data of communication antennas, and collect environmental characteristic data in the target area at preset time intervals.

[0041] The target strategy formulation module is used to analyze the parameter data according to the social network analysis method to obtain user tendency data, determine the antenna control target according to the user tendency data and the environmental characteristic data, and formulate the antenna control strategy according to the antenna control target.

[0042] The Sand Cat optimization modeling module is used to establish a neural Turing machine model and use the Sand Cat swarm optimization algorithm to optimize the neural Turing machine model to obtain an enhanced neural Turing machine model. The parameter data and electromagnetic characteristic data are analyzed according to the enhanced neural Turing machine model to obtain the balance control parameters.

[0043] The parameter strategy adjustment module is used to adjust the antenna control strategy according to the balance control parameters to obtain the antenna control decision.

[0044] The IoT-based communication antenna intelligent control method and system provided by the present invention uses social network analysis methods to analyze parameter data to obtain user tendency data, combines environmental feature data to determine antenna control targets, and formulates antenna control strategies based on this data. It can accurately determine antenna control targets and strategies based on the user's personalized needs and environmental factors, thereby improving the adaptability of communication antennas to different user groups and environments, optimizing user experience, and enhancing communication quality.

[0045] The present invention provides an intelligent control method and system for communication antennas based on the Internet of Things. By establishing a neural Turing machine model and optimizing it using a sand cat swarm optimization algorithm, an enhanced neural Turing machine model is obtained. This model is then used to analyze parameter data and electromagnetic characteristic data to obtain balance control parameters. The sand cat swarm optimization algorithm has strong global search capabilities and a fast convergence rate. By optimizing the neural Turing machine model using this algorithm, more optimal model parameters can be obtained, enabling the enhanced neural Turing machine model to more accurately analyze data and obtain more reasonable balance control parameters, providing a more scientific basis for antenna control. Furthermore, the antenna control strategy is adjusted based on the balance control parameters to obtain an antenna control decision, achieving dynamic adjustment of the antenna control strategy. This allows the antenna control decision to be continuously optimized and improved based on actual conditions, thereby improving the performance and stability of the communication antenna and better meeting the system's preset goals and user needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1This is one of the flow diagrams of the method for intelligently controlling a communication antenna based on the Internet of Things provided by an embodiment of the present invention; Figure 2 This is the second flow chart of the method for intelligently controlling a communication antenna based on the Internet of Things provided by an embodiment of the present invention; Figure 3 This is a structural diagram of an Internet of Things-based communication antenna intelligent control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0049] The following combination Figure 1-Figure 3 The present invention describes the method and system for intelligently controlling communication antennas based on the Internet of Things.

[0050] like Figure 1 and Figure 2 As shown, the embodiment of the present invention provides an intelligent control method and system for a communication antenna based on the Internet of Things. The execution subject may be an intelligent control method for a communication antenna based on the Internet of Things, including: Collect parameter data of IoT devices and electromagnetic characteristic data of communication antennas, and collect environmental characteristic data in the target area at preset time intervals.

[0051] IoT device parameter data can include device type (such as temperature sensors, smart appliances), device operating status (operating, dormant, faulty, etc.), operating frequency band, transmit power, data transmission rate, signal strength, and more. Environmental characteristic data can include topography, landforms, buildings, and climate conditions. Electromagnetic characteristic data can include impedance, radiation pattern, polarization, and more. For high-density deployment scenarios, distributed edge computing nodes are used to locally pre-process parameter data, filter redundant data, and compress transmission bandwidth. A dynamic sampling mechanism is introduced to automatically adjust the collection frequency based on device activity (e.g., every 5 seconds for active devices and every 30 minutes for dormant devices).

[0052] The parameter data is analyzed according to the social network analysis method to obtain user tendency data, the antenna control target is determined according to the user tendency data and combined with the environmental characteristic data, and the antenna control strategy is formulated according to the antenna control target.

[0053] The steps to analyze and obtain user trend data include: The various parameter data from multiple IoT devices is integrated and normalized to create a device parameter set. During the integration process, parameter data from different sources and formats must be cleaned and preprocessed to remove noise and invalid data, ensuring data accuracy and consistency. Normalization allows parameter data of different magnitudes and ranges to be compared and analyzed on the same scale, improving the effectiveness of data analysis. For example, the ranges and magnitudes of data transmission rate and signal strength can vary significantly; normalization converts these values ​​into comparable values.

[0054] Each IoT device in the device parameter set is regarded as a device node, and the corresponding user using the IoT device is regarded as a user node, and the connection length is determined according to the relationship between the IoT devices and between the IoT devices and the corresponding users.

[0055] Different device nodes are distinguished by the device's unique identifier (such as device ID), and user nodes are distinguished by account and device binding information.

[0056] Device-Device Relationship: When two devices interact with each other (e.g., data transmission), an edge is established between the corresponding nodes. The weight of the edge can be determined based on factors such as the frequency of interaction and the amount of data.

[0057] User-Device Relationship: When a user uses a device, an edge is established between the user node and the device node. The weight of the edge can be determined based on the duration and frequency of use.

[0058] The degree, betweenness, closeness centrality, and clustering coefficient of each device and user node are calculated to determine network metrics, and key nodes are screened based on preset values. Degree reflects the number of connections a node has within the network, betweenness reflects the importance of information transfer within the network, closeness centrality measures the average distance between a node and other nodes, and the clustering coefficient indicates the closeness between neighboring nodes. Comprehensive analysis of these metrics accurately identifies key nodes with significant influence within the network. For example, a device node with a high degree indicates frequent data exchange with other devices and may be a core device in the entire IoT system. A user node with a high betweenness indicates that the user plays a key role in bridging information transfer. In-depth analysis of key nodes based on preferred device types, usage patterns, and device interaction preferences can uncover potential user needs and behavioral patterns when using IoT devices, thereby generating accurate user preference data.

[0059] User preference data is obtained by analyzing key nodes based on preferred device types, usage time patterns, and device interaction preferences.

[0060] The steps for determining the antenna control target include: Users are divided into different user groups according to their personalized needs, and corresponding needs are summarized for different user groups.

[0061] Personalized needs may include users' device usage habits (such as usage frequency, duration, and device type preference), communication needs (such as data transmission rate and signal stability requirements), usage scenarios (such as indoor, outdoor, and mobile status), etc.

[0062] Users with similar needs and behavior patterns can be grouped together based on their individual needs. For example, users who frequently use IoT devices for video calls while on the move can be categorized as mobile video call users; users who primarily use smart office devices for file transfer and processing in indoor office environments can be categorized as indoor office users. A deeper understanding of the specific needs of different user groups can help develop more precise antenna control targets. For example, mobile video call users have higher requirements for signal stability and data transmission rates, while indoor office users may be more concerned with signal coverage and interference resistance.

[0063] The influence of different climatic conditions on the performance of communication antennas is analyzed based on the environmental characteristic data to obtain the climate impact, and the influence of different building materials on signal propagation is analyzed to obtain the building impact.

[0064] For different user groups, the ridge regression analysis method is used to correlate climate impact with building impact to obtain multiple influencing factors, from which the factors that have the greatest impact on demand in the current environment are determined to obtain the key influencing factors.

[0065] The steps to obtain key influencing factors include: The climate impact and building impact are standardized and used as independent variables, each climate and building is formed into a matrix, and demand is used as the dependent variable.

[0066] Introducing the regularization parameter, constructing the objective function of ridge regression, the formula is expressed as:

[0067] Where, is the sample size, is the number of independent variables, It is The dependent variable value of the sample, It is The first sample independent variable values, It is The regression coefficients of the independent variables, is the regularization parameter, yes The square of is used to calculate the penalty strength of the regularization term.

[0068] Initialize the regression coefficients, calculate the partial derivatives of the objective function with respect to each regression coefficient, and obtain the gradient vector.

[0069] According to the chain rule Find the partial derivatives with respect to the regression coefficients: make ,but .

[0070] . \ Where, is an intermediate variable, indicating the The difference between the predicted and actual values ​​of the dependent variable for a sample, Yes Find the partial derivative.

[0071] In the pair Find the partial derivatives with respect to the regression coefficients:

[0072] That is, the formula of the gradient vector is expressed as:

[0073] Where, is the gradient vector.

[0074] The objective function is updated according to the gradient vector until the preset number of times is reached to obtain the coefficient value.

[0075] Sort the independent variables by the absolute value of their coefficients and select the variable with the largest absolute value as the key influencing factor. For mobile office users, signal attenuation in rainy weather can be a key factor affecting communication. In densely populated commercial areas, signal obstruction and handover issues can be key factors.

[0076] Determine the antenna control target for each user group based on different key influencing factors.

[0077] The steps to developing an antenna control strategy include: Multiple candidate control strategies are specified based on the antenna control objectives and the adjustable parameters of the communication antenna. The adjustable parameters of a communication antenna may include transmit power, operating frequency band, polarization mode, directional angle, gain, and so on. When specifying candidate control strategies, it is necessary to comprehensively consider the relationship between the antenna control objectives and these adjustable parameters. For example, if the antenna control objective is to improve signal coverage, then strategies such as increasing transmit power, adjusting the antenna directional angle, or optimizing the antenna gain can be considered; if the objective is to reduce interference, it may be necessary to switch the operating frequency band or adjust the polarization mode. By combining these adjustable parameters in different ways, multiple candidate control strategies can be generated, providing more possibilities for subsequent optimization selection.

[0078] According to the antenna control target, the evaluation index of the candidate control strategy is determined. According to the evaluation index, multiple candidate control strategies are evaluated to obtain corresponding simulation scores, and the highest score is selected as the antenna control strategy.

[0079] A neural Turing machine model is established, and the sand cat swarm optimization algorithm is used to optimize the neural Turing machine model to obtain an enhanced neural Turing machine model. Based on the enhanced neural Turing machine model, the parameter data and electromagnetic characteristic data are analyzed to obtain the balance control parameters.

[0080] The steps to optimize the enhanced neural Turing machine model include: Initialize the population size of the sand cat group, where each sand cat represents a set of parameters of the neural Turing machine model.

[0081] The parameters corresponding to each sand cat are applied to the neural Turing machine model, and the inverse of the mean square error loss is used as the fitness function. The formula is expressed as:

[0082] Where is the sample size, is the fitness function, It is The true output value of the sample, In the parameter The following model The predicted value of the sample.

[0083] Each sand cat moves randomly based on its current position according to the preset probability to obtain the corresponding new position. The formula is expressed as:

[0084] Where, It is the current location of Sand Cat. is the exploration step length, is a random number that follows a standard normal distribution, Move to a new location.

[0085] The fitness value of each new position is calculated according to the fitness function, and the one with the highest fitness value is selected as the global optimal sand cat.

[0086] Each sand cat moves towards the global optimal sand cat and updates its latest position. The formula is:

[0087] Where is the latest position of each sand cat, is the learning factor, It is the globally optimal sand cat.

[0088] The fitness value of each sand cat's latest position is calculated, and the one with the highest fitness value is selected as the optimization parameter. The optimized parameters are used to construct a neural Turing machine model to obtain an enhanced neural Turing machine model.

[0089] The steps of analyzing and obtaining the balance control parameters include: The parameter data and electromagnetic characteristic data are integrated and outliers and errors are removed to obtain a fused data set. Statistical methods, such as the 3σ principle, can be used to identify and remove outliers. Errors can be corrected or deleted through data validation and error correction algorithms.

[0090] The fused dataset is encoded and fed into the reinforcement neural Turing machine model according to the input requirements, which generates control signals for calculation. The control signals can include the attention distribution of the read / write head, the erase vector, the add vector, etc.

[0091] Read and write operations are performed based on control signals, outputting decision results and adjusting the communication antenna accordingly to obtain balance control parameters. Read operations retrieve relevant information from memory based on the attention distribution and feed it back to the controller. Write operations update memory contents based on erase and add vectors to store intermediate results and knowledge generated during the model's computation.

[0092] According to the control target decision method, the antenna control strategy is adjusted using the balance control parameters to obtain the antenna control decision.

[0093] The steps to adjust the antenna control decision include: The influence of the balance control parameters on the communication antenna performance and the preset system goals is analyzed, and the goals and performance indicators of the currently used antenna control strategy are evaluated to obtain the evaluation results.

[0094] Based on the evaluation results, the gap between the current antenna control strategy and the preset system goals is analyzed, and adjustment targets are set.

[0095] The steps to setting adjustment goals include: The data obtained from the evaluation of the goals and performance indicators of the current antenna control strategy are summarized to obtain the total performance data, which are compared one by one with the preset system goals. The difference between the actual value and the target value of each indicator is calculated and the gap is summarized.

[0096] The key gaps are selected from the gaps based on the size of the difference and the degree of impact on performance. Based on the key gaps, the feasibility of the goals is considered and the goals are adjusted to be consistent with the preset system goals.

[0097] Adjustment goals may include: Signal coverage: Improve signal coverage in a specific area, increasing the signal coverage in the city center from the current 80% to 90% within a certain period of time (such as one month), or achieving signal coverage of more than 95% within a large building.

[0098] Expand the geographical scope of signal coverage, such as extending the signal coverage in remote suburbs a certain distance (such as 5 kilometers) to cover more users or areas.

[0099] Signal strength: Improves the average signal strength within a specific area, for example, increasing the average signal strength in a commercial area from the current -80dBm to -70dBm, improving the user experience. This ensures signal strength stability and reduces fluctuations, ensuring that signal strength fluctuations within a certain threshold (e.g., ±5dBm) do not exceed a certain threshold (e.g., ±5dBm) within a specified timeframe (e.g., 24 hours).

[0100] Data transmission rate: Improve the average data transmission rate. For example, increase the average data transmission rate for users in a cell from the current 10 Mbps to 20 Mbps to meet user needs for services such as high-definition video and large file transfers. Reduce data transmission latency, reducing the average latency from the current 50 ms to less than 30 ms, improving the quality of real-time services such as video calls and online gaming.

[0101] Bit Error Rate: Reduce the system's bit error rate, for example, from the current 1% to below 0.1%, improving data transmission accuracy. Ensure the bit error rate remains within an acceptable range under various environmental conditions (such as severe weather and high electromagnetic interference). For example, the bit error rate should not exceed 0.5% during heavy rain.

[0102] Energy consumption: Reduce antenna system energy consumption without compromising communication performance. For example, reduce antenna transmit power by 20% while still meeting signal coverage and transmission requirements to save energy costs. Optimize antenna energy management to enable intelligent adjustment of antenna energy consumption under varying workloads, achieving a balance between energy consumption and performance. For example, automatically reduce transmit power during periods with fewer users.

[0103] Interference mitigation: Enhances the antenna system's ability to resist interference. For example, in areas with co-channel interference, the operating frequency band or polarization mode can be adjusted to reduce the interference signal strength by more than 30dB. Improves the antenna's ability to resist multipath interference by employing appropriate signal processing algorithms or antenna technologies to minimize the impact of multipath interference on signal quality to a negligible level.

[0104] Based on the adjustment objectives, a balance control adjustment strategy is formulated. This strategy may include changing transmit power, switching operating frequency bands, adjusting antenna polarization and directional angles, and optimizing antenna gain. Multiple adjustment plans are generated, and the one with the closest performance is selected from each adjustment plan based on performance indicators as the antenna control decision. When formulating a balance control adjustment strategy, it is necessary to comprehensively consider the different adjustment objectives and the adjustable parameters of the communication antenna. Different adjustment strategies or combinations of strategies may be required for different adjustment objectives. For example, to improve signal strength, strategies such as increasing transmit power, optimizing antenna gain, or adjusting antenna polarization can be considered. To reduce energy consumption, strategies such as reducing transmit power and optimizing the antenna operating frequency band can be used. By generating multiple adjustment plans and evaluating and comparing them based on performance indicators, the antenna control decision that best suits the current situation can be selected, thereby achieving precise control of the communication antenna.

[0105] This embodiment provides an IoT-based intelligent control method for communication antennas. By analyzing user preference data and dividing user groups, it can perform antenna control based on the personalized needs of different users, avoiding a "one-size-fits-all" control approach and better meeting diverse user needs. It also considers the impact of environmental factors such as different climatic conditions and building materials on communication antenna performance, enabling antenna control to adapt to complex and changing environments and reduce interference and obstruction of environmental factors on signal propagation. The Neural Turing Machine model is also optimized using a sand cat swarm optimization algorithm, improving the model's accuracy and adaptability. This allows the analyzed balance control parameters to more accurately reflect actual conditions, providing a more reliable basis for antenna control. This method can adapt to different user needs, environmental changes, and system objectives, making the communication antenna system more adaptable and flexible, and better able to cope with various complex situations.

[0106] Based on the same general inventive concept, the present invention also protects an intelligent control system for a communication antenna based on the Internet of Things. The intelligent control system for a communication antenna based on the Internet of Things provided by the present invention is described below. The intelligent control system for a communication antenna based on the Internet of Things described below and the intelligent control method for a communication antenna based on the Internet of Things described above can be referred to each other.

[0107] like Figure 3As shown in FIG, the communication antenna intelligent control system based on the Internet of Things includes: The characteristic data acquisition module is used to collect parameter data of IoT devices and electromagnetic characteristic data of communication antennas, and collect environmental characteristic data in the target area at preset time intervals.

[0108] The target strategy formulation module is used to analyze the parameter data according to the social network analysis method to obtain user tendency data, determine the antenna control target according to the user tendency data and the environmental characteristic data, and formulate the antenna control strategy according to the antenna control target.

[0109] The Sand Cat optimization modeling module is used to establish a neural Turing machine model and use the Sand Cat swarm optimization algorithm to optimize the neural Turing machine model to obtain an enhanced neural Turing machine model. The parameter data and electromagnetic characteristic data are analyzed according to the enhanced neural Turing machine model to obtain the balance control parameters.

[0110] The parameter strategy adjustment module is used to adjust the antenna control strategy using the balance control parameters according to the control target decision method to obtain the antenna control decision.

[0111] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A communication antenna intelligent control method based on the Internet of Things, characterized in that: include: Collect parameter data of IoT devices and electromagnetic characteristics data of communication antennas, and collect environmental characteristic data in the target area at preset time intervals; Analyzing the parameter data according to a social network analysis method to obtain user tendency data, determining an antenna control target based on the user tendency data and in combination with the environmental characteristic data, and formulating an antenna control strategy based on the antenna control target; Establishing a neural Turing machine model, and optimizing the neural Turing machine model using a sand cat swarm optimization algorithm to obtain an enhanced neural Turing machine model, and analyzing the parameter data and the electromagnetic characteristic data according to the enhanced neural Turing machine model to obtain a balance control parameter; The antenna control strategy is adjusted using the balance control parameters according to the control target decision method to obtain an antenna control decision.

2. The method for intelligently controlling a communication antenna based on the Internet of Things according to claim 1, wherein: The steps of analyzing and obtaining the user tendency data include: Integrate and normalize various parameter data of multiple IoT devices to obtain a device parameter set; Treating each IoT device in the device parameter set as a device node and a corresponding user using the IoT device as a user node, and determining a connection length based on relationships between IoT devices and between IoT devices and corresponding users; Calculate the degree, betweenness, closeness centrality and clustering coefficient of each device node and each user node to determine the network index, and select key nodes according to the preset values; The user tendency data is obtained by analyzing the key nodes based on the preferred device type, usage time pattern and device interaction preference.

3. The method for intelligently controlling a communication antenna based on the Internet of Things according to claim 1, wherein: The step of determining the antenna control target includes: Divide users into different user groups according to their personalized needs, and summarize the corresponding needs for different user groups; Analyzing the effects of different climate conditions on the performance of the communication antenna according to the environmental characteristic data to obtain climate impact, and analyzing the effects of different building materials on signal propagation to obtain building impact; For different user groups, the ridge regression analysis method is used to correlate the climate impact with the building impact to obtain multiple influencing factors, from which the factors that have the greatest impact on demand in the current environment are determined to obtain key influencing factors; The antenna control target for each user group is determined according to different key influencing factors.

4. The method for intelligently controlling a communication antenna based on the Internet of Things according to claim 3, wherein: The steps of obtaining the key influencing factors include: The climate impact and the building impact are normalized and used as independent variables, the individual climates and buildings are combined into a matrix, and the demand is used as the dependent variable; Introduce regularization parameters and construct the objective function of ridge regression; Initialize the regression coefficients, calculate the partial derivative of the objective function with respect to each regression coefficient, and obtain a gradient vector; The objective function is updated according to the gradient vector until a preset number of times is reached to obtain a coefficient value; The independent variables are sorted according to the absolute values ​​of the coefficient values, and the independent variable with the largest absolute value is selected as the key influencing factor.

5. The method for intelligently controlling a communication antenna based on the Internet of Things according to claim 4, characterized in that: The steps of formulating the antenna control strategy include: specifying a plurality of candidate control strategies according to the antenna control target and in combination with adjustable parameters of the communication antenna; According to the antenna control target, an evaluation index of the candidate control strategy is determined, and multiple candidate control strategies are evaluated according to the evaluation index to obtain corresponding simulation scores, and the highest score is selected as the antenna control strategy.

6. The method for intelligently controlling a communication antenna based on the Internet of Things according to claim 1, wherein: The steps of optimizing and obtaining the enhanced neural Turing machine model include: Initializing the population size of the sand cat group, where each sand cat represents a set of parameters of the neural Turing machine model; Apply the parameters corresponding to each sand cat to the neural Turing machine model, and use the inverse of the mean square error loss as the fitness function; Each sand cat moves randomly based on its current position according to the preset probability to obtain the corresponding new position; Calculate the fitness value of each new position of the movement according to the fitness function, and select the one with the highest fitness value as the global optimal sand cat; Each sand cat moves toward the global optimal sand cat and updates the latest position; The fitness value of the latest position of each sand cat is calculated, and the fitness value with the highest value is selected as the optimization parameter. The neural Turing machine model is constructed using the optimization parameter to obtain the enhanced neural Turing machine model.

7. The method for intelligently controlling a communication antenna based on the Internet of Things according to claim 1, wherein: The step of analyzing and obtaining the balance control parameters includes: Integrating the parameter data and the electromagnetic characteristic data, and removing outliers and error values ​​to obtain a fused data set; Encoding the fused data set, inputting it into the enhanced neural Turing machine model according to input requirements, and performing calculations to generate control signals; Reading and writing operations are performed according to the control signal, a decision result is output, and the communication antenna is adjusted according to the decision result to obtain the balance control parameter.

8. The method for intelligently controlling a communication antenna based on the Internet of Things according to claim 1, wherein: The steps of adjusting and obtaining the antenna control decision include: Analyzing the influence of the balance control parameters on the communication antenna performance and the preset system objectives, and evaluating the objectives and performance indicators of the currently used antenna control strategy to obtain an evaluation result; Analyzing the gap between the current antenna control strategy and the preset system target based on the evaluation results, and setting an adjustment target; A balance control adjustment strategy is formulated according to the adjustment target, multiple adjustment schemes are generated, and the one with the closest performance is selected from each adjustment scheme according to the performance index as the antenna control decision.

9. The method for intelligently controlling a communication antenna based on the Internet of Things according to claim 8, characterized in that: The steps of setting the adjustment target include: Summarize the data obtained from the evaluation of the goals and performance indicators of the current antenna control strategy to obtain total performance data, compare them one by one with the preset system goals, calculate the difference between the actual value and the target value of each indicator, and summarize the gap; The key gaps are screened out from the gaps according to the size of the difference and the degree of impact on the performance, and the adjustment target is obtained based on the key gaps, considering the feasibility of the target and keeping it consistent with the preset system target.

10. An intelligent control system for communication antennas based on the Internet of Things, which adopts the intelligent control method for communication antennas based on the Internet of Things according to any one of claims 1 to 9, characterized in that: The control system comprises: A characteristic data acquisition module is used to collect parameter data of IoT devices and electromagnetic characteristic data of communication antennas, and to collect environmental characteristic data in the target area at preset time intervals; a target strategy formulation module, configured to analyze the parameter data according to a social network analysis method to obtain user tendency data, determine an antenna control target based on the user tendency data and the environmental feature data, and formulate an antenna control strategy based on the antenna control target; A sand cat optimization modeling module is used to establish a neural Turing machine model, and use a sand cat swarm optimization algorithm to optimize the neural Turing machine model to obtain an enhanced neural Turing machine model, and analyze the parameter data and the electromagnetic characteristic data according to the enhanced neural Turing machine model to obtain balance control parameters; The parameter strategy adjustment module is used to adjust the antenna control strategy using the balance control parameter according to the control target decision method to obtain an antenna control decision.

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