Signal interconnection optimization method and system for smart home network equipment

Abstract

The invention relates to the technical field of smart home interconnection, in particular to a signal interconnection optimization method and system for smart home network equipment. The method comprises the following steps: identifying all intelligent network equipment nodes in a home area, and obtaining real-time signal states of all intelligent home network equipment; carrying out inter-device topological dependency mining on the intelligent network device nodes, and constructing a network device topological dependency graph; carrying out inter-device signal interference analysis on a network device topology dependence graph based on the real-time signal state so as to obtain signal interference characteristics in the region; performing signal transmission frequency calculation on the intelligent network equipment node to obtain the signal transmission frequency of the network equipment; and performing dynamic frequency coordination optimization on the signal transmission frequency of the network equipment based on the signal interference characteristics in the region, and constructing a dynamic frequency optimization strategy. According to the invention, the signal interconnection quality and efficiency of the smart home are improved.

Description

Technical Field

[0001] The present invention relates to the field of smart home interconnection technology, and in particular to a signal interconnection optimization method and system for smart home network devices. Background Art

[0002] With the continuous development of smart home technology, more and more home devices have begun to have network interconnection functions, such as smart home appliances, smart lighting, smart door locks, etc. These smart home devices need to be interconnected and remotely controlled through wireless communication networks to provide a more convenient and intelligent home life experience.

[0003] However, in practical applications, there are many challenges in signal interconnection between smart home network devices. These devices often use different communication protocols, such as Wi-Fi, Bluetooth, Zigbee, etc., resulting in high heterogeneity in signal formats and frequency bands. At the same time, there are a large number of electromagnetic interference sources in the smart home environment, such as microwave ovens, televisions, etc., which make signal transmission between devices susceptible to interference and attenuation. In addition, with the increasing complexity of the home environment, the dynamic changes in the number and layout of devices have also brought huge challenges to the optimization of signal interconnection.

[0004] Traditional smart home network device signal interconnection optimization methods often rely on manual configuration and empirical rules, which are unable to perceive the complex signal transmission environment in real time and difficult to perform adaptive optimization for dynamically changing network topologies and load requirements. Therefore, it is urgent to develop a signal interconnection optimization technology based on intelligent algorithms that can fully perceive the signal status between devices, accurately analyze and predict interference problems, and actively adjust parameters such as operating frequency and transmission power to ensure stable interconnection between smart home network devices. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a signal interconnection optimization method and system for smart home network devices to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides a signal interconnection optimization method for smart home network devices, comprising the following steps: Step S1: Identify all smart network device nodes in the home area and obtain the real-time signal status of all smart home network devices; perform inter-device topology dependency mining on the smart network device nodes and construct a network device topology dependency graph; Step S2: performing inter-device signal interference analysis on the network device topology dependency graph based on the real-time signal status, thereby obtaining signal interference characteristics within the area; Step S3: Calculate the signal transmission frequency of the intelligent network device node to obtain the signal transmission frequency of the network device; Step S4: Dynamically coordinate and optimize the signal transmission frequency of network devices based on the signal interference characteristics in the area, and build a dynamic frequency optimization strategy; Step S5: performing logical virtual channel identification on the signal transmission frequency of the network device, and extracting interactive logical virtual channels between multiple devices; Step S6: Dynamically reconfigure the interactive logical virtual channels between multiple devices to generate a virtual channel reconfiguration strategy; and construct a smart home signal interconnection optimization model based on the dynamic frequency optimization strategy and the virtual channel reconfiguration strategy.

[0007] The present invention helps to fully understand the operation status of home network devices by identifying all intelligent network device nodes in the home area and obtaining their real-time signal status. By mining the topological dependency between devices and constructing a network device topological dependency graph, it helps to understand the dependency relationship and communication path between devices. Based on the real-time signal status, the signal interference analysis between devices is performed to obtain the signal interference characteristics in the area, which helps to identify and solve the signal interference problem and improve the communication quality. The signal transmission frequency of the intelligent network device nodes is calculated to obtain the signal transmission frequency of the network device, which helps to understand the communication performance of the device. Dynamic frequency coordination optimization is performed based on the signal interference characteristics, and a dynamic frequency optimization strategy is constructed to improve the signal transmission efficiency and stability. Logical virtual channels are identified between devices, and interactive logical virtual channels between multiple devices are extracted, which helps to optimize the setting of communication channels. Dynamic channel reconfiguration is performed on the interactive logical virtual channels, and a virtual channel reconfiguration strategy is generated to help optimize the signal transmission path and improve communication efficiency. A smart home signal interconnection optimization model is constructed based on the dynamic frequency optimization strategy and the virtual channel reconfiguration strategy to provide a more efficient solution for the signal interconnection of smart home network devices.

[0008] Preferably, step S1 comprises the following steps: Step S11: Identify all smart network device nodes in the home area and obtain the real-time signal status of all smart home network devices; Step S12: Calculate the spatial position of the intelligent network device node to obtain the node spatial position coordinates; Step S13: performing signal strength distribution calculation on the real-time signal status to obtain signal strength distribution data of each network device; Step S14: performing a regional three-dimensional topological correlation analysis on the signal strength distribution data of each network device based on the node spatial position coordinates to generate topological correlation data of the device node; Step S15: mining the topological dependencies between devices on the topological association data of the device nodes to construct a network device topological dependency graph.

[0009] The present invention provides a data basis for subsequent analysis by identifying all network device nodes in the smart home area and obtaining their real-time signal status. Obtaining the real-time signal status of smart home network devices helps to monitor the operating status and communication status of the devices. The spatial position of the device nodes is calculated to obtain the spatial position coordinates of the nodes, providing device location information for subsequent analysis. The signal strength distribution is calculated based on the real-time signal status to obtain the signal strength distribution data of each network device, which helps to understand the signal coverage and strength. The signal strength distribution data of the device is analyzed for regional three-dimensional topology based on the spatial position coordinates of the nodes to generate topology association data of the device nodes, which helps to understand the layout and mutual relationship of the devices in space. The topology association data of the device nodes is mined for topological dependencies between devices, and a network device topology dependency graph is constructed to help identify the dependencies and communication paths between devices, providing a basis for signal interconnection optimization.

[0010] Preferably, the specific steps of step S13 are: Performing a fast Fourier transform on the real-time signal state to obtain a plurality of signal spectrum features; Perform heterogeneous signal perception on multiple signal spectrum characteristics to obtain signal status data of various network devices; Signal strength distribution calculation is performed on the signal status data of multiple network devices to obtain signal strength distribution data of each network device.

[0011] The present invention processes the real-time signal state through fast Fourier transform to obtain multiple signal spectrum features, which is helpful to analyze the characteristics of the signal from the frequency domain perspective. By extracting the spectrum features, the distribution of the signal at different frequencies is obtained, providing a data basis for subsequent signal analysis and optimization. By sensing multiple signal spectrum features, the signal state data of multiple network devices are obtained, which helps to identify and distinguish the signal characteristics of different devices. The signal state data of multiple network devices are obtained to help understand the signal characteristics and working status of different devices, and provide more information for signal interconnection optimization. The signal strength distribution calculation is performed on the signal state data of multiple network devices to obtain the signal strength distribution data of each network device, which helps to understand the signal coverage and strength distribution between devices. Through the signal strength distribution data, the signal transmission quality between devices is evaluated, providing a basis for optimizing signal interconnection, and further improving the communication efficiency and stability of smart home network devices.

[0012] Preferably, the specific steps of step S2 are: Step S21: performing a timing channel state feature analysis on the real-time signal state to extract the timing channel state feature of each network device; Step S22: performing inter-device signal interference analysis on the timing channel state characteristics of each network device to obtain inter-device signal interference effect data; Step S23: performing device interaction logic analysis on the network device topology dependency graph to extract interaction paths between devices; Step S24: performing secondary interaction interference identification on the timing channel state characteristics of each network device based on the interaction path between devices to obtain secondary interaction interference data between devices; Step S25: Perform multi-level interactive transfer simulation on the signal interference effect data between devices and the secondary interactive interference data between devices, so as to obtain the signal interference characteristics within the area.

[0013] The present invention analyzes the timing channel state characteristics of the real-time signal state and extracts the timing channel state characteristics of each network device, which helps to understand the changing rules and characteristics of the channel. Extracting the timing channel state characteristics helps to identify the dynamic changes of the channel and provide basic data for subsequent analysis. The timing channel state characteristics of each network device are analyzed for signal interference between devices to obtain signal interference effect data between devices, which helps to evaluate the signal interference between devices. By analyzing the signal interference effect data between devices, the mutual influence between devices is understood, and a basis for signal optimization is provided. The network device topology dependency graph is analyzed for device interaction logic, and the interaction path between devices is extracted, which helps to understand the communication logic and path planning between devices. Extracting the interaction path between devices reveals the communication mode between devices and provides information for subsequent interference analysis and optimization. Based on the interaction path between devices, the timing channel state characteristics of each network device are identified for secondary interaction interference, and the secondary interaction interference data between devices is obtained, which helps to identify the secondary interference effect between devices. Obtaining the secondary interaction interference data between devices helps to more comprehensively understand the signal interaction between devices and provide a reference for interference optimization. Multi-level interactive transfer simulation is performed on the signal interference effect data between devices and the secondary interactive interference data between devices to obtain the signal interference characteristics in the area, which helps to comprehensively evaluate the signal interference situation. Through multi-level interactive transfer simulation, the signal interference effects under different situations are simulated to provide a deeper understanding for signal optimization and interference management.

[0014] Preferably, step S3 specifically comprises the following steps: Step S31: Calculate the signal transmission frequency of the intelligent network device node to obtain the signal transmission frequency of the network device; Step S32: The signal transmission frequencies of the network devices are subjected to frequency superposition identification between the devices to obtain the signal frequency overlap data between the devices; Step S33: Calculate the current frequency transmission quality of the intelligent network device nodes based on the inter-device signal frequency overlap data to obtain the current frequency transmission quality value of each network device.

[0015] The present invention calculates the signal transmission frequency of intelligent network device nodes to obtain the signal transmission frequency of each network device, which helps to understand the communication frequency situation between devices. Obtaining the signal transmission frequency of network devices helps to identify the frequency distribution and provide data support for subsequent frequency management and optimization. The signal transmission frequency of network devices is identified by frequency superposition between devices to obtain signal frequency overlapping data between devices, which helps to identify frequency conflicts and overlaps between devices. Analysis of signal frequency overlapping data between devices reveals the interference caused by frequency conflicts and provides a basis for frequency optimization and interference management. Based on the signal frequency overlapping data between devices, the current frequency transmission quality of intelligent network device nodes is calculated to obtain the current frequency transmission quality value of each network device, which helps to evaluate the current signal transmission situation of the device. The signal transmission quality between devices is evaluated by calculating the current frequency transmission quality value, providing guidance for optimizing signal transmission and frequency adjustment.

[0016] Preferably, the specific steps of step S4 are: Step S41: performing multiple iterative frequency adjustment simulations on the signal transmission frequency of the network device based on the signal interference characteristics in the area to obtain multiple frequency adjustment simulation data; Step S42: performing signal time-frequency isolation analysis on multiple frequency adjustment simulation data to obtain interference isolation data at each adjustment frequency; Step S43: performing frequency adjustment transmission quality quantification based on the interference isolation data at each adjustment frequency to extract the transmission quality value at the adjustment frequency; Step S44: performing an optimal interference isolation frequency analysis on the transmission quality value at the adjusted frequency based on the current frequency transmission quality value of each network device, and extracting the optimal adjustment frequency; Step S45: Perform dynamic frequency coordination optimization on the signal transmission frequency of the network device based on the optimal adjustment frequency, and construct a dynamic frequency optimization strategy.

[0017] The present invention performs multiple iterative frequency adjustment simulations on the signal transmission frequency of the network device based on the signal interference characteristics in the area to obtain multiple frequency adjustment simulation data, which is helpful to find the optimal frequency configuration scheme, evaluates the effects of different frequency configuration schemes through multiple iterative simulations, and provides a reference for frequency adjustment and optimization, performs signal time-frequency isolation analysis on the multiple frequency adjustment simulation data to obtain interference isolation data at each adjustment frequency, which is helpful to evaluate the interference situation under different frequency configurations, analyzes the interference isolation data to help determine the degree of influence of frequency adjustment on interference, and provides a basis for interference management and frequency optimization, and quantifies the frequency adjustment transmission quality based on the interference isolation data at each adjustment frequency to extract the transmission quality value at the adjustment frequency, which is helpful to evaluate the interference situation under different frequency configurations. Transmission effect: Quantifying the transmission quality value helps compare the signal transmission quality under different frequency configurations, provides data support for selecting the best frequency, and performs optimal interference isolation frequency analysis on the transmission quality value under the adjusted frequency based on the current frequency transmission quality value of each network device. Extracting the optimal adjustment frequency helps to find the best frequency adjustment solution, and determines the most suitable frequency configuration through optimal interference isolation frequency analysis to improve signal transmission quality and reduce interference. Based on the optimal adjustment frequency, the signal transmission frequency of the network device is dynamically coordinated and optimized, and a dynamic frequency optimization strategy is constructed to help achieve dynamic adjustment and optimization of signal transmission. The frequency configuration is adjusted in real time through dynamic frequency coordination optimization, which improves signal transmission efficiency and network stability, and provides users with a better smart home experience.

[0018] Preferably, the specific steps of step S5 are: Step S51: dividing the signal transmission frequency of the network device into multiple frequency bands to obtain multiple frequency sub-bands; Step S52: performing logical virtual channel identification on multiple frequency sub-bands, and extracting interactive logical virtual channels between multiple devices; Step S53: performing device virtual channel matching on intelligent network device nodes based on interactive logical virtual channels between multiple devices to obtain virtual channel data of the devices; Step S54: Calculate the signal delay state of the real-time signal state based on the virtual channel data of the device to obtain the virtual channel delay parameter of the device.

[0019] The present invention divides the signal transmission frequency of the network device into multiple frequency bands to obtain multiple frequency sub-bands, which helps to effectively manage and allocate spectrum resources and avoid frequency conflicts and interference. Dividing the frequency sub-bands helps optimize frequency utilization, improve signal transmission efficiency, and reduce signal interference. Logical virtual channel identification is performed on multiple frequency sub-bands, and interactive logical virtual channels between multiple devices are extracted, which helps to identify the communication mode and channel conditions between devices. Extracting interactive logical virtual channels helps optimize the communication process between devices and improve communication efficiency and stability. Based on the interactive logical virtual channels between multiple devices, the intelligent network device nodes are matched with device virtual channels to obtain the virtual channel data of the devices, which helps to establish virtual channel connections between devices. Device virtual channel matching helps establish effective communication paths between devices, improves communication efficiency and data transmission stability. Based on the virtual channel data of the device, the signal delay state of the real-time signal state is calculated to obtain the virtual channel delay parameter of the device, which helps to evaluate the communication delay and optimize the communication quality. By calculating the virtual channel delay parameter, it helps to adjust the communication parameters, improve the communication speed and stability, and provide a smoother interconnection experience for smart home devices.

[0020] Preferably, the specific steps of step S6 are: Step S61: Calculate the channel frequency load of the interactive logical virtual channels between multiple devices to obtain frequency load data of each virtual channel; Step S62: Optimizing the inter-channel delay based on the virtual channel delay parameter of the device and each virtual channel frequency load data, thereby obtaining virtual channel delay optimization data; Step S63: dynamically reconfigure the channel of the intelligent network device node based on the virtual channel delay optimization data to generate a virtual channel reconfiguration strategy; Step S64: Perform deep signal optimization mining based on dynamic frequency optimization strategy and virtual channel reconfiguration strategy to build a smart home signal interconnection optimization model.

[0021] The present invention calculates the channel frequency load of the interactive logical virtual channel between multiple devices to obtain the frequency load data of each virtual channel, which is helpful to evaluate the channel load and frequency utilization. By calculating the frequency load data of the virtual channel, it helps to balance the channel load and avoid frequency congestion and communication blocking. Based on the virtual channel delay parameters of the device and the frequency load data of each virtual channel, the inter-channel delay optimization is performed to obtain the virtual channel delay optimization data, which is helpful to optimize the communication delay and improve the communication efficiency. By optimizing the inter-channel delay, the communication speed and stability are improved, the data transmission delay is reduced, and the user experience is improved. Based on the virtual channel delay optimization data, the intelligent network device node is dynamically reconfigured to generate a virtual channel reconfiguration strategy, which is helpful to achieve real-time channel optimization and adjustment. Through dynamic channel reconfiguration, the communication channel is adjusted according to the real-time situation to improve the communication efficiency and network stability. Based on the dynamic frequency optimization strategy and the virtual channel reconfiguration strategy, deep signal optimization mining is carried out to build a smart home signal interconnection optimization model, which is helpful to comprehensively optimize the signal transmission effect. By building a signal interconnection optimization model, channel resources are better managed, communication efficiency and network performance are improved, and a more stable and intelligent signal interconnection experience is provided for smart homes.

[0022] In this specification, a signal interconnection optimization system for smart home network devices is provided, which is used to execute the signal interconnection optimization method for smart home network devices as described above, including: A topology dependency module is used to identify all smart network device nodes in the home area and obtain the real-time signal status of all smart home network devices; perform inter-device topology dependency mining on the smart network device nodes and construct a network device topology dependency graph; A signal interference module, used to perform inter-device signal interference analysis on a network device topology dependency graph based on the real-time signal status, thereby obtaining signal interference characteristics within the area; A signal transmission frequency module, used to calculate the signal transmission frequency of the intelligent network device node to obtain the signal transmission frequency of the network device; Dynamic frequency coordination module, used to dynamically coordinate and optimize the signal transmission frequency of network devices based on the signal interference characteristics in the area, and build a dynamic frequency optimization strategy; A virtual channel identification module is used to identify the logical virtual channels of the signal transmission frequencies of network devices and extract the interactive logical virtual channels between multiple devices; The signal interconnection optimization module is used to dynamically reconfigure the interactive logical virtual channels between multiple devices to generate a virtual channel reconfiguration strategy; a smart home signal interconnection optimization model is constructed based on the dynamic frequency optimization strategy and the virtual channel reconfiguration strategy.

[0023] The present invention identifies all smart network device nodes in a home area and obtains their real-time signal status, which helps to fully understand the working status and communication status of home smart devices. It mines the topological dependencies of device nodes and builds a topological dependency graph of network devices to help analyze the correlation and communication path between devices. It performs signal interference analysis on the device topological dependency graph based on the real-time signal status to obtain the signal interference characteristics in the area, which helps to identify and solve the signal interference problem between devices and improve the communication quality. It optimizes signal transmission by analyzing the signal interference characteristics, reduces the interference effect, and improves the communication stability and efficiency. It calculates the signal transmission frequency of smart network device nodes to obtain the signal transmission frequency of each device, which helps to optimize frequency utilization and avoid frequency conflicts. The calculated signal transmission frequency helps adjust communication parameters, improves communication speed and stability, and optimizes signal transmission effects. The signal interference characteristics dynamically coordinate and optimize the signal transmission frequency of the device and build a dynamic frequency optimization strategy, which helps to avoid interference and improve signal transmission efficiency. Dynamic frequency coordination optimizes frequency utilization, reduces interference, and improves communication quality and stability. Logical virtual channels are identified for the signal transmission frequency of the device, and interactive logical virtual channels between devices are extracted, which helps to optimize the communication process and improve communication efficiency. Identifying logical virtual channels helps to establish effective communication paths, optimize communication transmission, and improve data transmission stability. Dynamic channel reconfiguration is performed on the interactive logical virtual channels between devices, and a virtual channel reconfiguration strategy is generated, which helps to adjust the communication channel according to real-time conditions, improve communication efficiency and network stability. A smart home signal interconnection optimization model is built based on the dynamic frequency optimization strategy and the virtual channel reconfiguration strategy to provide a more stable and intelligent signal interconnection experience for smart homes. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the steps of a method for optimizing signal interconnection of smart home network devices according to the present invention; Figure 2 Detailed implementation flow chart of step S1; Figure 3 Detailed implementation flow chart of step S2; Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0025] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0026] The present application example provides a signal interconnection optimization method and system for smart home network devices. The execution subject of the signal interconnection optimization method and system for smart home network devices includes but is not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of the present application, and the data processing platform includes but is not limited to: at least one of an audio image management system, an information management system, and a cloud data management system.

[0027] See also Figures 1 to 4 The present invention provides a signal interconnection optimization method for smart home network devices, and the signal interconnection optimization method for smart home network devices comprises the following steps: Step S1: Identify all smart network device nodes in the home area and obtain the real-time signal status of all smart home network devices; perform inter-device topology dependency mining on the smart network device nodes and construct a network device topology dependency graph; Step S2: performing inter-device signal interference analysis on the network device topology dependency graph based on the real-time signal status, thereby obtaining signal interference characteristics within the area; Step S3: Calculate the signal transmission frequency of the intelligent network device node to obtain the signal transmission frequency of the network device; Step S4: Dynamically coordinate and optimize the signal transmission frequency of network devices based on the signal interference characteristics in the area, and build a dynamic frequency optimization strategy; Step S5: performing logical virtual channel identification on the signal transmission frequency of the network device, and extracting interactive logical virtual channels between multiple devices; Step S6: Dynamically reconfigure the interactive logical virtual channels between multiple devices to generate a virtual channel reconfiguration strategy; and construct a smart home signal interconnection optimization model based on the dynamic frequency optimization strategy and the virtual channel reconfiguration strategy.

[0028] The present invention helps to fully understand the operation status of home network devices by identifying all intelligent network device nodes in the home area and obtaining their real-time signal status. By mining the topological dependency between devices and constructing a network device topological dependency graph, it helps to understand the dependency relationship and communication path between devices. Based on the real-time signal status, the signal interference analysis between devices is performed to obtain the signal interference characteristics in the area, which helps to identify and solve the signal interference problem and improve the communication quality. The signal transmission frequency of the intelligent network device nodes is calculated to obtain the signal transmission frequency of the network device, which helps to understand the communication performance of the device. Dynamic frequency coordination optimization is performed based on the signal interference characteristics, and a dynamic frequency optimization strategy is constructed to improve the signal transmission efficiency and stability. Logical virtual channels are identified between devices, and interactive logical virtual channels between multiple devices are extracted, which helps to optimize the setting of communication channels. Dynamic channel reconfiguration is performed on the interactive logical virtual channels, and a virtual channel reconfiguration strategy is generated to help optimize the signal transmission path and improve communication efficiency. A smart home signal interconnection optimization model is constructed based on the dynamic frequency optimization strategy and the virtual channel reconfiguration strategy to provide a more efficient solution for the signal interconnection of smart home network devices.

[0029] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of a signal interconnection optimization method for smart home network devices of the present invention. In this example, the steps of the signal interconnection optimization method for smart home network devices include: Step S1: Identify all smart network device nodes in the home area and obtain the real-time signal status of all smart home network devices; perform inter-device topology dependency mining on the smart network device nodes and construct a network device topology dependency graph; in this embodiment, In this embodiment, a network scanning tool (such as Nmap or Fing) is used to scan the home network and identify all connected smart devices. This step includes: listing all active IP addresses. Identifying the device type (such as smart light bulbs, thermostats, cameras, etc.) based on the device's MAC address prefix or response information. For each identified device, collect the following information: Device identifier: such as MAC address, IP address. Device type: such as brand and model. Such as online / offline status. Obtain the real-time signal status information of the device through the device's API or SNMP (Simple Network Management Protocol) interface. Associate the acquired real-time signal status data with the device information to form a device status data set to ensure a unified data structure (such as JSON or CSV format). Define dependencies between devices, for example: Signal dependency: The function of a device depends on the signal quality of another device. Functional dependency: For example, a smart light bulb depends on the instructions of the smart home control center. Use graph theory methods (such as adjacency matrix or edge list) to construct a topological dependency graph. Each device is a node in the graph, and the dependency relationship is an edge in the graph: Node: network device. Edge: dependency, the weight of the edge represents the signal strength or degree of dependency.

[0030] Step S2: performing inter-device signal interference analysis on the network device topology dependency graph based on the real-time signal status, thereby obtaining signal interference characteristics within the area; In this embodiment, the real-time signal status data of each device is obtained from the previous steps, including signal strength, connection quality, delay and packet loss rate, etc. The dependency relationship between devices is extracted from the stored topological dependency graph to ensure that which devices are dependent on each other can be identified. The signal status data is integrated with the topological dependency graph to form a comprehensive data set. Each node (device) should contain its signal status and information about its dependence on other devices. The criteria for signal interference are defined, including: insufficient signal strength or signal of the interfering device, and decreased signal quality of the dependent device. An appropriate model is selected for interference analysis, for example: used to evaluate the relationship between the degree of interference and signal strength. Machine learning models (such as random forests and support vector machines): used to identify complex interference patterns. The selected model is trained using the collected signal status data to identify the interference characteristics between devices. The data is stored in the topological dependency graph. It is divided into training set and test set to ensure that the model can effectively learn and predict interference situations. The trained model is used to analyze the signal interference between devices. The evaluation includes: the signal status and topology information of the device, the degree of interference, the type of interference (such as signal attenuation or conflict), and the signal interference characteristics are extracted from the model analysis results, including: interference intensity: indicating the degree of influence of interference on the signal, interference device: identifying which devices are the main sources of interference, and organizing the results of the signal interference analysis into structured data, including device ID, interference intensity, interference source device and degree of influence. An analysis report is generated, including: an overview of the signal interference characteristics in the area, a list of the main interference sources and affected devices, and visual charts (such as interference intensity distribution map, interference source map) for decision-making reference. The signal interference analysis results are stored in the database for subsequent query and analysis.

[0031] Step S3: Calculate the signal transmission frequency of the intelligent network device node to obtain the signal transmission frequency of the network device; In this embodiment, the real-time signal status of each network device, including signal strength, connection quality, and device type, is obtained from the previous steps to ensure that each device can provide real-time data related to signal transmission. The collected signal data is organized into a unified format (such as CSV or JSON) to ensure the convenience of subsequent processing. The calculation method of the signal transmission frequency is selected according to actual needs. Commonly used methods include: Fourier transform: used for frequency domain analysis, extracting signal frequency components through fast Fourier transform (FFT), periodic analysis: analyzing the periodic behavior of the signal through the autocorrelation function, inputting the prepared signal data into the selected calculation method, preprocessing the signal, such as denoising and normalization, performing frequency calculation, for example, using fast Fourier transform (FFT) to analyze the signal: extracting frequency components with significant amplitudes, usually selecting the first few frequency components as the main signal transmission frequencies, storing the signal transmission frequency data of each device in a database, ensuring data structuring (such as creating a table in an SQL database), and recording the results, including device ID, signal transmission frequency, and calculation timestamp, for tracking and analysis. A preliminary analysis is performed on the calculation results to identify devices with abnormal frequencies or related to signal interference.

[0032] Step S4: Dynamically coordinate and optimize the signal transmission frequency of the network device based on the signal interference characteristics in the area, and build a dynamic frequency optimization strategy; In this embodiment, the signal interference feature data in the area is obtained from the previous analysis, including the interference source, interference intensity and affected devices. Signal transmission frequency data acquisition: the current signal transmission frequency data of each device is obtained to ensure the accuracy and timeliness of the data. The signal interference feature and signal transmission frequency data are integrated into a comprehensive data set for subsequent analysis, clarifying the optimization goals, reducing signal interference, improving signal transmission quality (such as signal-to-noise ratio, data transmission rate), and achieving reasonable frequency allocation. A suitable optimization algorithm is selected to find the optimal frequency combination. Particle swarm optimization (PSO): used to dynamically adjust the signal transmission frequency to adapt to the changing interference environment. Reinforcement learning: The frequency strategy is continuously optimized through real-time feedback. According to the previously integrated data, the optimal signal transmission frequency of each device is calculated. The relationship between the interference intensity and the device signal transmission frequency is calculated, and the selected optimization algorithm is applied Calculate the device frequency adjustment, generate the optimal frequency allocation for each device, apply the calculated optimal frequency allocation to the actual device, update the signal transmission frequency of the device, realize dynamic frequency adjustment through the device API or management interface, implement a real-time monitoring system, track the signal transmission quality and interference status of each device, including signal strength, interference strength and connection quality, collect feedback data after frequency adjustment, evaluate the optimization effect, for example, analyze the improvement of signal transmission rate and reduction of interference, adjust the optimization strategy according to the feedback results to ensure that it adapts to the changing network environment, update the optimization algorithm parameters in time to improve the optimization effect, record the implementation process of the dynamic frequency optimization strategy, including algorithm selection, frequency adjustment, monitoring results, etc., generate a detailed optimization report, compare the signal transmission quality before and after optimization, specific data and effect analysis of frequency adjustment, improvement suggestions and future optimization direction.

[0033] Step S5: performing logical virtual channel identification on the signal transmission frequency of the network device, and extracting interactive logical virtual channels between multiple devices; In this embodiment, the interaction information between devices is collected, including: the communication protocol between devices (such as Wi-Fi, Zigbee), the functions of the devices and their dependencies (for example, control devices, sensors, etc.), the signal transmission frequency data and the device interaction information are integrated to form a comprehensive data set for subsequent analysis, and the characteristics of the logical virtual channel are defined, such as: the signal transmission frequency range, the type of interactive device and its function, and a suitable recognition model is selected. The devices are regarded as nodes in the graph and the interaction relationships are regarded as edges, such as K-means or DBSCAN, to identify the interaction groups between devices. The integrated data is analyzed using the selected model, for example, a graph theory model is used to construct a graph structure between devices: the graph structure is analyzed by an algorithm to identify the logical virtual channel, and the effective channel combination is extracted according to the frequency and interaction relationship between the devices. The interaction information of each logical virtual channel is extracted, including: participating devices, the signal transmission frequency used, and the interaction type (such as data transmission, status update).

[0034] Step S6: Dynamically reconfigure the interactive logical virtual channels between multiple devices to generate a virtual channel reconfiguration strategy; and construct a smart home signal interconnection optimization model based on the dynamic frequency optimization strategy and the virtual channel reconfiguration strategy.

[0035] In this embodiment, information for identifying interactive logical virtual channels is obtained, including virtual channel ID, participating devices, used frequencies, etc. The dynamic frequency optimization strategy generated in the previous step is obtained, including the optimal signal transmission frequency and interference characteristics of each device. The virtual channel information is integrated with the dynamic frequency optimization strategy to form a comprehensive data set for subsequent processing. The specific goals of reconfiguration are determined, such as improving signal transmission quality, reducing signal interference, and achieving efficient use of channels between devices. A suitable dynamic reconfiguration algorithm is selected, such as: genetic algorithm: used for optimization between different channel configurations, simulated annealing: used to find the global optimal channel configuration, reinforcement learning: dynamically adjust the channel configuration according to real-time feedback, and apply the selected algorithm to the channel reconfiguration model. The input (device status, signal characteristics) and output (optimized channel configuration) are defined. The reconfiguration algorithm is applied to calculate the optimal channel configuration for each device based on the current virtual channel information and the dynamic frequency optimization strategy. The calculated new channel configuration is applied to the actual device and the channel parameters of the device are updated. This process is usually performed through the device management system. Interface implementation, implement real-time monitoring, track the signal status after channel reconfiguration, collect feedback data to evaluate the effect of reconfiguration, design the framework of the smart home signal interconnection optimization model, clarify the model's input (device status, signal characteristics, virtual channel information) and output (optimized signal transmission strategy), and implement the model algorithm according to the designed framework, which usually includes the following parts: Input module: collect and process device status and signal data, Processing module: apply dynamic frequency optimization and channel reconfiguration strategy to generate optimization results, Output module: generate optimized signal transmission strategy, Use historical data to train and verify the optimization model to ensure that the model can effectively adapt to the dynamically changing network environment, Evaluate the effect of the model, analyze the effectiveness of the optimization results, adjust model parameters when necessary to improve performance, Record the implementation process of dynamic channel reconfiguration, including algorithm selection, reconfiguration results and monitoring data, Generate a detailed optimization report, including: Comparison of signal transmission quality before and after channel reconfiguration, Design and implementation details of the optimization model, Visual charts (such as signal effect charts before and after optimization) for reference.

[0036] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Step S11: Identify all smart network device nodes in the home area and obtain the real-time signal status of all smart home network devices; Step S12: Calculate the spatial position of the intelligent network device node to obtain the node spatial position coordinates; Step S13: performing signal strength distribution calculation on the real-time signal status to obtain signal strength distribution data of each network device; Step S14: performing a regional three-dimensional topological correlation analysis on the signal strength distribution data of each network device based on the node spatial position coordinates to generate topological correlation data of the device node; Step S15: mining the topological dependencies between devices on the topological association data of the device nodes to construct a network device topological dependency graph.

[0037] In this embodiment, a network scanning tool (such as Nmap) is used to scan the home network to identify all connected smart devices, including smart bulbs, thermostats, cameras, etc. The unique identification information (such as MAC address, IP address) and device type of each device are recorded to form a device list. The real-time signal status of each device is obtained through the device API or SNMP protocol. The signal status includes switch status, working mode, fault status, etc. The obtained real-time signal status is organized into a structured data format (such as JSON, CSV) for subsequent processing and analysis. According to the device type, a suitable positioning method is selected, such as Wi-Fi signal strength positioning, Bluetooth positioning or sensor-based positioning (such as RFID). For the selected positioning method, relevant signal data is collected. For example, if Wi-Fi positioning is used, the signal strength between the device and multiple Wi-Fi access points needs to be measured. The spatial position coordinates of each device are calculated using triangulation or positioning algorithms (such as TOA, TDOA). These coordinates are usually expressed in the form of (x, y, z). Signal strength data is extracted from the real-time status of each smart device. These data are obtained through the device API. The collected signal strength data is statistically analyzed to calculate the average signal strength, minimum signal strength, and signal strength of each device. The signal strength is modeled using distribution models (such as normal distribution and uniform distribution), the signal strength distribution characteristics are analyzed, the signal strength distribution data is associated with the device identification information to form a signal strength distribution data set for subsequent analysis, and a suitable topological analysis method (such as K-nearest neighbor algorithm and graph theory method) is selected to analyze the correlation between devices, the spatial position coordinates and signal strength data of the device are mapped to a three-dimensional space model to form a three-dimensional layout of the device, the correlation between each device node is calculated, and the connection strength between devices is evaluated based on the relative distance between the signal strength and the spatial position. Save the topological association data (such as adjacency matrix or edge list) in a structured format for subsequent use, define the dependencies between devices, such as whether the functions of some devices depend on the signal strength or working status of other devices, use data mining algorithms (such as association rule mining, graph mining algorithms) to analyze the topological association data, identify the potential dependencies between devices, visualize the mined dependencies as a topological dependency graph, use graphics libraries (such as NetworkX, Graphviz) to generate graphical representations, analyze the topological dependency graph, identify key devices and potential failure points, form a report on the dependencies between devices, and record the structure of the topological dependency graph.

[0038] importnetworkxasnx defbuild_dependency_graph(topology_data): G = nx.Graph() fordevice, connectionsintopology_data.items(): forother_device,strengthinconnections: ifstrength>threshold:#Judge the dependency relationship based on the threshold G.add_edge(device, other_device, weight=strength) returnG dependency_graph=build_dependency_graph(topology_data) In this embodiment, the specific steps of step S13 are: Performing a fast Fourier transform on the real-time signal state to obtain a plurality of signal spectrum features; Perform heterogeneous signal perception on multiple signal spectrum characteristics to obtain signal status data of various network devices; Signal strength distribution calculation is performed on the signal status data of multiple network devices to obtain signal strength distribution data of each network device.

[0039] In this embodiment, real-time signal status data is obtained from the intelligent network device. These data include the signal strength, switch status, temperature, etc. of the device. It is necessary to ensure that the timestamps of data acquisition are consistent. The collected signal data is preprocessed, including denoising, mean filtering and normalization, to improve the accuracy and stability of FFT. The preprocessed time domain signal is converted into a frequency domain signal using an FFT algorithm (such as the numpy.fft.fft function in NumPy). The calculation complexity of FFT is low and it is suitable for real-time signal processing. Multiple spectrum features are extracted from the FFT result, including the main frequency, frequency amplitude, phase information, etc. The frequency domain characteristics of the signal are analyzed using the amplitude spectrum and the phase spectrum. The spectrum features from different devices are fused to form a multi-dimensional feature vector for subsequent analysis and classification. A suitable classification algorithm (such as support vector machine (SVM), Use random forest, K-means clustering, etc. to process the fused spectral features, use the labeled signal status data to train the classification model to ensure that the model can recognize and distinguish different types of signal states, input the newly acquired spectral features into the trained model, obtain the signal status data of each network device, record the status classification result of each device, extract the signal strength data from the results of heterogeneous signal perception to ensure data integrity, perform statistical analysis on the extracted signal strength data, including calculating the mean, variance, maximum and minimum values, etc., use the histogram or probability density function (PDF) method to model the signal strength, thereby generating a signal strength distribution diagram, organize the calculated signal strength distribution data into a structured format, and use visualization tools (such as Matplotlib and Seaborn) to draw the signal strength distribution diagram for easy analysis and decision-making.

[0040] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Step S21: performing a timing channel state feature analysis on the real-time signal state to extract the timing channel state feature of each network device; Step S22: performing inter-device signal interference analysis on the timing channel state characteristics of each network device to obtain inter-device signal interference effect data; Step S23: performing device interaction logic analysis on the network device topology dependency graph to extract interaction paths between devices; Step S24: performing secondary interaction interference identification on the timing channel state characteristics of each network device based on the interaction path between devices to obtain secondary interaction interference data between devices; Step S25: Perform multi-level interactive transfer simulation on the signal interference effect data between devices and the secondary interactive interference data between devices, so as to obtain the signal interference characteristics within the area.

[0041] In this embodiment, real-time signal status data within a certain time window is collected from each network device, including signal strength, delay, packet loss rate, etc. The collected time series data is denoised and normalized to eliminate the influence of external interference on the analysis results. Time series analysis methods (such as sliding window, time domain feature calculation) are used to extract time series channel state features, including average signal strength, signal change rate, fluctuation amplitude, etc. Select a suitable signal interference model, such as a linear regression model or a machine learning model (such as a support vector machine) to evaluate the interference effect. Extract relevant data from the time series channel state features, construct a feature set of signal interference between devices, and record the signal strength, location, etc. of the device. Apply the selected model to calculate the signal interference effect between devices, and evaluate the change in signal strength and its impact on device performance. The analysis results are organized into a signal interference effect data set between devices, recording information such as interference strength and impact range. Based on the signal interference effect data and topological relationship between devices, a topological dependency graph between network devices is constructed. Use graph theory methods (such as Dijkstra algorithm, A* pathfinding algorithm) to analyze the interaction logic between devices and extract effective interaction paths. Save the extracted interaction paths between devices as structured data, and identify key devices and interaction nodes. Analyze the changes in signal status during the interaction process for the extracted interaction paths between devices, with special attention to secondary interference sources. Use machine learning algorithms (such as random forests and decision trees) to identify secondary interference on the interaction paths and evaluate the interaction impact between devices. Organize the identified secondary interaction interference data into structured information, and record the interference type, impact range, etc. Select a suitable signal interference transfer model (such as MarkovChain, Agent-based model) to simulate the dynamic changes of signal interference. Use the signal interference effect data between devices and the secondary interaction interference data as model inputs to define state transition rules. Run the transfer model, perform multi-level signal interference simulation, and analyze the changes in the signal under different states. Analyze the simulation results, extract the signal interference features in the area, and use visualization tools (such as Matplotlib and Plotly) to generate signal interference feature maps.

[0042] Specific implementation steps code example: importpandas aspd defanalyze_time_series_signal(devices): time_series_features={} fordeviceindevices: signal_data=get_signal_history(device)#Get signal history data autocorr = signal_data.autocorr (lag = 1) # Calculate autocorrelation moving_avg = signal_data.rolling(window=10).mean() #Calculate moving average time_series_features[device]={ 'autocorr':autocorr, 'moving_avg':moving_avg } return time_series_features time_series_features=analyze_time_series_signal(devices) Calculate the signal interference effect between each pair of devices and record the interference effect data.

[0043] defanalyze_signal_interference(devices, time_series_features): interference_data={} fordevice1indevices: fordevice2indevices: if device1!=device2: interference=calculate_interference(device1, device2, time_series_features) interference_data[(device1, device2)]=interference returninterference_data interference_data=analyze_signal_interference(devices, time_series_features) Based on the topological dependency graph, identify the interaction paths and potential interaction logic between devices.

[0044] importnetworkxasnx def extract_interaction_paths(dependency_graph): interaction_paths = {} for device in dependency_graph.nodes: paths = nx.single_source_shortest_path(dependency_graph, device) interaction_paths[device] = paths return interaction_paths interaction_paths = extract_interaction_paths(dependency_graph) Evaluate the interference effect of secondary interaction on the signal according to the interaction paths between devices.

[0045] def identify_secondary_interference(interaction_paths, time_series_features): secondary_interference_data = {} for device, paths in interaction_paths.items(): for target_device in paths: interference = calculate_secondary_interference(device, target_device, time_series_features) secondary_interference_data[(device, target_device)] = interference return secondary_interference_data secondary_interference_data = identify_secondary_interference(interaction_paths, time_series_features) Based on the interference effect data and secondary interference data between devices, the signal interference characteristics in the area are simulated.

[0046] importnumpyasnp defsimulate_interference_transfer(interference_data, secondary_interference_data): signal_interference_features={} for(device1,device2),interferenceininterference_data.items(): # Combine secondary interference to perform transfer simulation signal_interference_features[(device1, device2)]=interference+secondary_interference_data.get((device1, device2), 0) returnsignal_interference_features signal_interference_features=simulate_interference_transfer(interference_data, secondary_interference_data) In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Step S31: Calculate the signal transmission frequency of the intelligent network device node to obtain the signal transmission frequency of the network device; Step S32: The signal transmission frequencies of the network devices are subjected to frequency superposition identification between the devices to obtain the signal frequency overlap data between the devices; Step S33: Calculate the current frequency transmission quality of the intelligent network device nodes based on the inter-device signal frequency overlap data to obtain the current frequency transmission quality value of each network device.

[0047] In this embodiment, signal status data is collected from intelligent network devices, including signal strength, transmission rate, and changes in time domain signals. The time window for signal acquisition is determined, such as every second, every minute, etc., to facilitate subsequent frequency calculation. Fourier transform or periodic analysis method (such as autocorrelation function) is used to calculate the transmission frequency of the signal. The numpy.fft function of NumPy and other libraries is used to implement fast Fourier transform (FFT). Signal transmission frequency data is extracted from each network device to form a unified data format, including device ID and corresponding frequency. The frequency data is analyzed using a threshold method or a clustering algorithm (such as K-means or DBSCAN) to identify devices with overlapping frequencies. The detected signal frequency overlap data between devices is organized into structured information, and the device ID, overlapping frequency, and overlapping degree are recorded. Determine the evaluation indicators of signal transmission quality, such as signal-to-noise ratio (SNR), bit error rate (BER), and packet loss rate. Collect frequency overlap data and signal status data related to each device to ensure data integrity. The quality calculation formula is applied: signal-to-noise ratio = 10*log10(P_signal / P_noise), bit error rate = number of error bits / total number of bits. The current frequency transmission quality value of each device is calculated based on the frequency overlap degree and signal quality index.

[0048] In this embodiment, step S4 includes the following steps: Step S41: performing multiple iterative frequency adjustment simulations on the signal transmission frequency of the network device based on the signal interference characteristics in the area to obtain multiple frequency adjustment simulation data; Step S42: performing signal time-frequency isolation analysis on multiple frequency adjustment simulation data to obtain interference isolation data at each adjustment frequency; Step S43: performing frequency adjustment transmission quality quantification based on the interference isolation data at each adjustment frequency to extract the transmission quality value at the adjustment frequency; Step S44: performing an optimal interference isolation frequency analysis on the transmission quality value at the adjusted frequency based on the current frequency transmission quality value of each network device, and extracting the optimal adjustment frequency; Step S45: Perform dynamic frequency coordination optimization on the signal transmission frequency of the network device based on the optimal adjustment frequency, and construct a dynamic frequency optimization strategy.

[0049] In this embodiment, a frequency adjustment model is selected and the signal interference characteristics are used for simulation. Genetic algorithms, particle swarm optimization and other methods are used for frequency adjustment. The signal transmission frequency of the network device is adjusted iteratively for multiple times, and the simulation data after each adjustment is recorded.

[0050] importnumpyasnp defsimulate_frequency_adjustment(initial_frequencies, interference_data): adjusted_frequencies=[] for_inrange(10): #Make 10 adjustments new_frequencies = [freq + np.random.uniform (-1, 1) forfreqin initial_frequencies] # small adjustment adjusted_frequencies.append(new_frequencies) returnadjusted_frequencies initial_frequencies = [2.4, 5.0] # Initial frequency (GHz) frequency_simulations=simulate_frequency_adjustment(initial_frequencies, interference_data) Perform time-frequency analysis on the signal using short-time Fourier transform (STFT) or wavelet transform to identify interference isolation features. Perform time-frequency analysis on the signal at each adjustment frequency and record the interference isolation data.

[0051] fromscipy.signalimportstft defanalyze_time_frequency_signals(frequency_simulations, signal_data): interference_isolation_data={} forfreqinfrequency_simulations: f, t, Zxx = stft(signal_data, fs = freq) # Perform STFT analysis isolation = np.abs(Zxx) #Calculate the absolute value as the interference isolation index interference_isolation_data[freq]=isolation returninterference_isolation_data signal_data=get_signal_data()#Get signal data isolation_data=analyze_time_frequency_signals(frequency_simulations,signal_data) Determine the transmission quality evaluation indicators (such as signal-to-noise ratio, bit error rate, etc.), and perform quantitative analysis based on the interference isolation data. Calculate the transmission quality value for the interference isolation data at each adjustment frequency.

[0052] defquantify_transmission_quality(isolation_data): quality_values={} forfreq, isolationinisolation_data.items(): snr = calculate_snr(isolation) #Calculate signal-to-noise ratio quality_values[freq]=snr#Save the signal-to-noise ratio as the transmission quality value returnquality_values transmission_quality_values=quantify_transmission_quality(isolation_data) By comparing the current frequency transmission quality value of each network device with the transmission quality value under different adjustment frequencies, the optimal frequency is extracted. Based on the transmission quality value, the optimal interference isolation frequency analysis is performed to determine the optimal adjustment frequency.

[0053] deffind_optimal_frequency(current_quality, transmission_quality_values): optimal_frequency=max(transmission_quality_values, key=transmission_quality_values.get) returnoptimal_frequency current_quality=get_current_quality()#Get the transmission quality value of the current frequency optimal_frequency=find_optimal_frequency(current_quality, transmission_quality_values) Based on the optimal adjustment frequency, a dynamic frequency coordination optimization strategy is designed to ensure that network equipment maintains optimal performance in a changing interference environment. Signal quality is monitored in real time, and the signal transmission frequency of network equipment is dynamically adjusted.

[0054] defoptimize_dynamic_frequency(network_devices, optimal_frequency): fordeviceinnetwork_devices: set_device_frequency(device, optimal_frequency)#Set the device frequency to the optimal frequency network_devices=get_network_devices()#Get the network device list optimize_dynamic_frequency(network_devices, optimal_frequency) In this embodiment, step S5 includes the following steps: Step S51: dividing the signal transmission frequency of the network device into multiple frequency bands to obtain multiple frequency sub-bands; Step S52: performing logical virtual channel identification on multiple frequency sub-bands, and extracting interactive logical virtual channels between multiple devices; Step S53: performing device virtual channel matching on intelligent network device nodes based on interactive logical virtual channels between multiple devices to obtain virtual channel data of the devices; Step S54: Calculate the signal delay state of the real-time signal state based on the virtual channel data of the device to obtain the virtual channel delay parameter of the device.

[0055] In this embodiment, the frequency is divided into multiple sub-bands according to the signal transmission frequency range of the device to facilitate more detailed signal analysis. A frequency range (eg, 2.4 GHz to 5 GHz) is set and divided into several frequency sub-bands (eg, each sub-band has a width of 20 MHz).

[0056] defdivide_frequency_bands(start_freq, end_freq, band_width): frequency_bands=[] current_freq=start_freq whilecurrent_freq <end_freq: frequency_bands.append((current_freq, current_freq+band_width)) current_freq+=band_width returnfrequency_bands frequency_bands=divide_frequency_bands(2.4, 5.0, 0.02)#20MHz subband By analyzing the signal interaction between devices, the logical virtual channels of the devices are identified. Mutual information measurement or correlation analysis is used. For each frequency subband, the mutual correlation of the signals between devices is calculated, and the device pairs with high correlation are identified as virtual channels.

[0057] fromscipy.statsimportpearsonr defidentify_virtual_channels(devices, signal_data, frequency_bands): virtual_channels={} forbandinfrequency_bands: fordevice1indevices: fordevice2indevices: if device1!=device2: correlation,_=pearsonr(signal_data[device1][band],signal_data[device2][band]) ifcorrelation>threshold:#determine as a virtual channel based on the threshold virtual_channels.setdefault(band,[]).append((device1,device2)) returnvirtual_channels virtual_channels=identify_virtual_channels(devices, signal_data, frequency_bands) Use a matching algorithm (such as the Hungarian algorithm or the greedy algorithm) to match virtual channels between devices to ensure optimal channel utilization. Based on the identified virtual channel data, match the channel of each network device.

[0058] fromscipy.optimizeimportlinear_sum_assignment defmatch_virtual_channels(virtual_channels): matches={} forband, pairsinvirtual_channels.items(): cost_matrix=create_cost_matrix(pairs)#Create cost matrix row_ind, col_ind = linear_sum_assignment(cost_matrix) #Hungarian algorithm matches[band]=[(pairs[row_ind[i]][0],pairs[col_ind[i]][1])foriinrange(len(row_ind))] returnmatches virtual_channel_matches=match_virtual_channels(virtual_channels) By recording the signal transmission delay of the device under different virtual channels, the virtual channel delay parameters of each device are calculated. The signal status of each device is timestamped, the delay is calculated and the delay parameters are extracted.

[0059] defcalculate_signal_delay(virtual_channel_matches, signal_data): delay_parameters={} forband, matchesinvirtual_channel_matches.items(): fordevice1, device2inmatches: delay = calculate_delay(signal_data[device1], signal_data[device2]) # calculate delay delay_parameters[(device1, device2)]=delay returndelay_parameters signal_delay_parameters=calculate_signal_delay(virtual_channel_matches, signal_data) In this embodiment, the specific steps of step S6 are: Step S61: Calculate the channel frequency load of the interactive logical virtual channels between multiple devices to obtain frequency load data of each virtual channel; Step S62: Optimizing the inter-channel delay based on the virtual channel delay parameter of the device and each virtual channel frequency load data, thereby obtaining virtual channel delay optimization data; Step S63: dynamically reconfigure the channel of the intelligent network device node based on the virtual channel delay optimization data to generate a virtual channel reconfiguration strategy; Step S64: Perform deep signal optimization mining based on dynamic frequency optimization strategy and virtual channel reconfiguration strategy to build a smart home signal interconnection optimization model.

[0060] In this embodiment, virtual channels between all devices in the network are determined. These channels are constructed based on the interaction logic between devices. Signal transmission data in each virtual channel is collected, including transmission frequency, bandwidth utilization, and data transmission rate. Using the load calculation formula, load = (currently used frequency / maximum supported frequency) × 100%, the frequency load data of each virtual channel is calculated, and the channels with excessive load are identified. The delay parameters of each virtual channel are collected, and the channel delay time and response time are recorded. Select a suitable optimization algorithm (such as genetic algorithm, particle swarm optimization (PSO)) to optimize the delay between channels. The delay parameters and frequency load data of the virtual channel are input into the optimization algorithm to perform delay optimization calculation between channels. According to the optimized virtual channel delay data, analyze which channels need to be reconfigured to reduce delay or improve performance. Design a dynamic channel reconfiguration strategy, including channel reallocation, frequency adjustment, and bandwidth allocation. Implement the reconfiguration strategy, update the channel parameters of the network device, and ensure that the new channel configuration can effectively reduce delay. Record the reconfigured channel information to form a virtual channel reconfiguration strategy document for subsequent monitoring and optimization. According to the dynamic frequency optimization strategy and virtual channel reconfiguration strategy, design a smart home signal interconnection optimization model and clarify the input and output of the model. Select appropriate algorithms (such as deep learning models, reinforcement learning) for signal optimization mining to ensure that the model can adapt to dynamic environments. Use historical signal data and real-time signal data to train and verify the optimization model, and adjust model parameters to improve accuracy. Apply the optimization model to the actual smart home system, monitor the signal status in real time, and continuously optimize the model based on feedback.

[0061] In this embodiment, a signal interconnection optimization system for smart home network devices is provided, which is used to execute the signal interconnection optimization method for smart home network devices as described above, including: A topology dependency module is used to identify all smart network device nodes in the home area and obtain the real-time signal status of all smart home network devices; perform inter-device topology dependency mining on the smart network device nodes and construct a network device topology dependency graph; A signal interference module, used to perform inter-device signal interference analysis on a network device topology dependency graph based on the real-time signal status, thereby obtaining signal interference characteristics within the area; A signal transmission frequency module, used to calculate the signal transmission frequency of the intelligent network device node to obtain the signal transmission frequency of the network device; Dynamic frequency coordination module, used to dynamically coordinate and optimize the signal transmission frequency of network devices based on the signal interference characteristics in the area, and build a dynamic frequency optimization strategy; A virtual channel identification module is used to identify the logical virtual channels of the signal transmission frequencies of network devices and extract the interactive logical virtual channels between multiple devices; The signal interconnection optimization module is used to dynamically reconfigure the interactive logical virtual channels between multiple devices to generate a virtual channel reconfiguration strategy; a smart home signal interconnection optimization model is constructed based on the dynamic frequency optimization strategy and the virtual channel reconfiguration strategy.

[0062] The present invention identifies all smart network device nodes in a home area and obtains their real-time signal status, which helps to fully understand the working status and communication status of home smart devices. It mines the topological dependencies of device nodes and builds a topological dependency graph of network devices to help analyze the correlation and communication path between devices. It performs signal interference analysis on the device topological dependency graph based on the real-time signal status to obtain the signal interference characteristics in the area, which helps to identify and solve the signal interference problem between devices and improve the communication quality. It optimizes signal transmission by analyzing the signal interference characteristics, reduces the interference effect, and improves the communication stability and efficiency. It calculates the signal transmission frequency of smart network device nodes to obtain the signal transmission frequency of each device, which helps to optimize frequency utilization and avoid frequency conflicts. The calculated signal transmission frequency helps adjust communication parameters, improves communication speed and stability, and optimizes signal transmission effects. The signal interference characteristics dynamically coordinate and optimize the signal transmission frequency of the device and build a dynamic frequency optimization strategy, which helps to avoid interference and improve signal transmission efficiency. Dynamic frequency coordination optimizes frequency utilization, reduces interference, and improves communication quality and stability. Logical virtual channels are identified for the signal transmission frequency of the device, and interactive logical virtual channels between devices are extracted, which helps to optimize the communication process and improve communication efficiency. Identifying logical virtual channels helps to establish effective communication paths, optimize communication transmission, and improve data transmission stability. Dynamic channel reconfiguration is performed on the interactive logical virtual channels between devices, and a virtual channel reconfiguration strategy is generated, which helps to adjust the communication channel according to real-time conditions, improve communication efficiency and network stability. A smart home signal interconnection optimization model is built based on the dynamic frequency optimization strategy and the virtual channel reconfiguration strategy to provide a more stable and intelligent signal interconnection experience for smart homes.

[0063] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0064] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A signal interconnection optimization method for smart home network devices, characterized in that: The following steps are involved: Step S1: Identify all smart network device nodes in the home area and obtain the real-time signal status of all smart home network devices; Performing inter-device topology dependency mining on the intelligent network device nodes to construct a network device topology dependency graph; Step S2: performing inter-device signal interference analysis on the network device topology dependency graph based on the real-time signal status, thereby obtaining signal interference characteristics within the area; Step S3: Calculate the signal transmission frequency of the intelligent network device node to obtain the signal transmission frequency of the network device; Step S4: Dynamically coordinate and optimize the signal transmission frequency of network devices based on the signal interference characteristics in the area, and build a dynamic frequency optimization strategy; Step S5: performing logical virtual channel identification on the signal transmission frequency of the network device, and extracting interactive logical virtual channels between multiple devices; Step S6: Dynamically reconfigure the interactive logical virtual channels between multiple devices to generate a virtual channel reconfiguration strategy; and construct a smart home signal interconnection optimization model based on the dynamic frequency optimization strategy and the virtual channel reconfiguration strategy.

2. The signal interconnection optimization method of smart home network devices according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: Identify all smart network device nodes in the home area and obtain the real-time signal status of all smart home network devices; Step S12: Calculate the spatial position of the intelligent network device node to obtain the node spatial position coordinates; Step S13: performing signal strength distribution calculation on the real-time signal status to obtain signal strength distribution data of each network device; Step S14: performing a regional three-dimensional topological correlation analysis on the signal strength distribution data of each network device based on the node spatial position coordinates to generate topological correlation data of the device node; Step S15: mining the topological dependencies between devices on the topological association data of the device nodes to construct a network device topological dependency graph.

3. The signal interconnection optimization method of smart home network devices according to claim 2, characterized in that: The specific steps of step S13 are: Performing a fast Fourier transform on the real-time signal state to obtain a plurality of signal spectrum features; Perform heterogeneous signal perception on multiple signal spectrum characteristics to obtain signal status data of various network devices; Signal strength distribution calculation is performed on the signal status data of multiple network devices to obtain signal strength distribution data of each network device.

4. The signal interconnection optimization method of smart home network devices according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: performing a timing channel state feature analysis on the real-time signal state to extract the timing channel state feature of each network device; Step S22: performing inter-device signal interference analysis on the timing channel state characteristics of each network device to obtain inter-device signal interference effect data; Step S23: performing device interaction logic analysis on the network device topology dependency graph to extract interaction paths between devices; Step S24: performing secondary interaction interference identification on the timing channel state characteristics of each network device based on the interaction path between devices to obtain secondary interaction interference data between devices; Step S25: Perform multi-level interactive transfer simulation on the signal interference effect data between devices and the secondary interactive interference data between devices, so as to obtain the signal interference characteristics within the area.

5. The signal interconnection optimization method of smart home network devices according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: Calculate the signal transmission frequency of the intelligent network device node to obtain the signal transmission frequency of the network device; Step S32: The signal transmission frequencies of the network devices are subjected to frequency superposition identification between the devices to obtain the signal frequency overlap data between the devices; Step S33: Calculate the current frequency transmission quality of the intelligent network device nodes based on the inter-device signal frequency overlap data to obtain the current frequency transmission quality value of each network device.

6. The signal interconnection optimization method of smart home network devices according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: performing multiple iterative frequency adjustment simulations on the signal transmission frequency of the network device based on the signal interference characteristics in the area to obtain multiple frequency adjustment simulation data; Step S42: performing signal time-frequency isolation analysis on multiple frequency adjustment simulation data to obtain interference isolation data at each adjustment frequency; Step S43: performing frequency adjustment transmission quality quantification based on the interference isolation data at each adjustment frequency to extract the transmission quality value at the adjustment frequency; Step S44: performing an optimal interference isolation frequency analysis on the transmission quality value at the adjusted frequency based on the current frequency transmission quality value of each network device, and extracting the optimal adjustment frequency; Step S45: Perform dynamic frequency coordination optimization on the signal transmission frequency of the network device based on the optimal adjustment frequency, and construct a dynamic frequency optimization strategy.

7. The signal interconnection optimization method of smart home network devices according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: dividing the signal transmission frequency of the network device into multiple frequency bands to obtain multiple frequency sub-bands; Step S52: performing logical virtual channel identification on multiple frequency sub-bands, and extracting interactive logical virtual channels between multiple devices; Step S53: performing device virtual channel matching on intelligent network device nodes based on interactive logical virtual channels between multiple devices to obtain virtual channel data of the devices; Step S54: Calculate the signal delay state of the real-time signal state based on the virtual channel data of the device to obtain the virtual channel delay parameter of the device.

8. The signal interconnection optimization method of smart home network devices according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: Calculate the channel frequency load of the interactive logical virtual channels between multiple devices to obtain frequency load data of each virtual channel; Step S62: Optimizing inter-channel delay based on the virtual channel delay parameter of the device and each virtual channel frequency load data, thereby obtaining virtual channel delay optimization data; Step S63: dynamically reconfigure the channel of the intelligent network device node based on the virtual channel delay optimization data to generate a virtual channel reconfiguration strategy; Step S64: Perform deep signal optimization mining based on dynamic frequency optimization strategy and virtual channel reconfiguration strategy to build a smart home signal interconnection optimization model.

9. A signal interconnection optimization system for smart home network devices, characterized in that: The method for performing the signal interconnection optimization method of the smart home network device according to claim 1 comprises: A topology dependency module is used to identify all smart network device nodes in the home area and obtain the real-time signal status of all smart home network devices; perform inter-device topology dependency mining on the smart network device nodes and construct a network device topology dependency graph; A signal interference module, used to perform inter-device signal interference analysis on a network device topology dependency graph based on the real-time signal status, thereby obtaining signal interference characteristics within the area; A signal transmission frequency module, used to calculate the signal transmission frequency of the intelligent network device node to obtain the signal transmission frequency of the network device; Dynamic frequency coordination module, used to dynamically coordinate and optimize the signal transmission frequency of network devices based on the signal interference characteristics in the area, and build a dynamic frequency optimization strategy; A virtual channel identification module is used to identify the logical virtual channels of the signal transmission frequencies of network devices and extract the interactive logical virtual channels between multiple devices; The signal interconnection optimization module is used to dynamically reconfigure the interactive logical virtual channels between multiple devices to generate a virtual channel reconfiguration strategy; a smart home signal interconnection optimization model is constructed based on the dynamic frequency optimization strategy and the virtual channel reconfiguration strategy.

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