Intelligent terminal information interaction method and system based on Internet of Things

Abstract

The invention provides an intelligent terminal information interaction method and system based on the Internet of Things, relates to the field of communication systems, and aims to solve the problems of signal interference, non-uniform energy consumption, path optimization lag, poor node fault tolerance and the like in an existing Internet of Things system. According to the method, efficient and stable information interaction is realized through seven steps. A non-integer base coding technology is adopted, channel collision is reduced, and the data transmission efficiency is improved; and in combination with a beam forming technology, a signal propagation path is optimized, and interference is reduced. The dynamic spectrum resource allocation and millisecond fault-tolerant mechanism can ensure the network stability and avoid communication interruption under the condition of channel gain change or node failure.

Description

Technical Field

[0001] The present invention relates to the field of communications, and in particular to an intelligent terminal information interaction method and system based on the Internet of Things. Background Art

[0002] In modern IoT applications, smart terminals provide information exchange services for various devices through wireless communication technologies. Typical applications include industrial production, traffic management, and environmental monitoring. These systems incorporate numerous sensors and devices that must exchange data in real time and work collaboratively. However, with the increasing number of terminals and complex environmental factors, IoT systems often face a series of pain points, such as severe signal interference, uneven energy consumption, lagging path optimization, and poor node fault tolerance. These issues severely impact the system's collaborative efficiency and stability.

[0003] Existing IoT information exchange technologies primarily rely on traditional routing protocols and transmission algorithms, such as channel management methods based on fixed topologies or Markov models. These methods attempt to optimize network performance, reduce signal interference, and improve transmission efficiency through pre-set rules. However, due to the dynamic nature of the network environment and uneven energy consumption across end devices, traditional technologies exhibit slow response times and are unable to effectively address channel interference, fluctuations in data transmission paths, and high energy consumption. This results in high network latency and severe data loss, impacting overall system performance.

[0004] However, existing technologies still have significant flaws. First, traditional routing protocols are often unable to dynamically adapt to complex physical environments, such as signal attenuation and multipath effects, resulting in transmission delays or communication interruptions in some scenarios. Second, existing path optimization and channel allocation methods often ignore the energy characteristics of terminal devices, which can easily cause some nodes to fail prematurely, affecting the durability and reliability of the network. Finally, existing technologies have difficulty providing accurate real-time optimization when faced with complex dynamic network topologies, especially in high-interference or sudden noise environments. The stability and efficiency of data transmission still have much room for improvement. Summary of the Invention

[0005] In response to the requirements raised in the above background art, embodiments of the present invention provide an intelligent terminal information interaction method and system based on the Internet of Things, aiming to solve problems such as signal interference, uneven energy consumption, lagging path optimization, and poor node fault tolerance faced by Internet of Things terminals in a dense dynamic network environment. Specifically, the present invention uses technical means such as innovative dynamic topology modeling, non-integer base transfer operators, energy-optimized terminal clustering, dynamic spectrum resource allocation, and millisecond-level fault tolerance mechanisms to improve the efficiency and reliability of information interaction, ensure stable and low-latency data transmission under complex environmental conditions, and extend the service life of the system. The goal of this method and system is to enhance the collaborative working ability of Internet of Things terminals and strengthen their stability and real-time response ability in important application scenarios such as manufacturing, healthcare, and transportation.

[0006] An intelligent terminal information interaction method based on the Internet of Things, the specific steps include:

[0007] Step 1, dynamic network topology modeling and channel optimization: First, collect the location information and channel bandwidth of terminal devices, abstract the terminal network as a geometric surface, and construct a topological structure with a symmetry group. By constructing a discretized Laplacian operator and combining Laplacian eigenvalue analysis, quantify the channel capacity and interference threshold, and evaluate the signal quality between each terminal and other terminals in the network. This step optimizes the allocation of sub-channels by maximizing the eigenvalue, precisely divides the signal interference area, improves the signal anti-interference ability, and ensures that the channel capacity and interference area reach the best balance in a complex environment.

[0008] Step 2, multi-hop path optimization and steady-state transmission: Introduce a non-integer base transfer operator, construct a steady-state transmission matrix, and analyze its spectral characteristics to predict the path attenuation characteristics. In a dynamically changing channel environment, ensure path stability through spectral decomposition technology and optimize the multi-hop path selection in the network. This step enables stable data transmission in the face of burst noise and unstable channels, avoiding problems such as lagging path optimization and transmission fluctuations in traditional technologies.

[0009] Step 3, energy-optimized terminal clustering division: According to the energy flow characteristics of terminals in the network, divide the terminals into stable point sets and non-stable point sets through measure decomposition theory. Prioritize high-energy nodes as cluster heads to undertake data relay tasks, ensuring that low-energy nodes avoid excessive energy consumption. This step realizes energy balance in the network, optimizes energy consumption, and extends the working time of the entire network.

[0010] Step 4. Non-integer base channel coding: Adopt non-integer base coding technology to encode the ID of the terminal device into a unique identifier, so as to reduce the probability of channel collision. By introducing non-integer bases such as the golden ratio, ensure that the terminal device has a unique and efficient identifier in the network, reduce signal conflicts, optimize the information interaction efficiency during data transmission, and improve communication stability.

[0011] Step 5. Beamforming optimization and interference suppression: Adopt beamforming technology to generate directional beams through holomorphic mapping, concentrate energy in the direction of the target device, and reduce multipath interference and signal attenuation. This step effectively improves the signal strength in complex multipath environments (such as application scenarios like smart homes and vehicle-to-everything networks) by optimizing the signal propagation path, ensuring more stable data transmission.

[0012] Step 6. Dynamic spectrum resource allocation: Dynamically adjust the resource allocation in the network by analyzing the eigenvalue of the channel. In the case of channel gain changes, preferentially allocate more resources to the sub-channels with less signal attenuation to ensure zero-delay transmission of important data. This step improves the spectrum utilization rate by real-time adjusting the resource allocation of the channel, ensuring the reliability and stability of the network in a high-interference environment.

[0013] Step 7. Millisecond-level fault tolerance mechanism and topology reconstruction: Quickly locate the failed nodes through the atomic measure analysis method and trigger local topology reconstruction to avoid global communication interruption. This step can quickly restore the connectivity of the network in the case of terminal node failures or communication interruptions through dilation mapping and topology reconstruction, ensure uninterrupted data transmission, and improve the robustness and response speed of the system.

[0014] Furthermore: An Internet of Things-based intelligent terminal information interaction system, including:

[0015] A dynamic topology modeling module, which is used to map the Internet of Things terminal network into a geometric surface and calculate the Laplace eigenvalues to quantify the channel capacity and interference threshold;

[0016] The dynamic topology modeling module includes a coordinate conversion unit, an adjacency matrix generation unit, and a Laplace operator optimization unit. The coordinate conversion unit is used to convert the physical coordinates of the terminal into a spherical coordinate system and generate a metric tensor; the adjacency matrix generation unit is used to construct an adjacency matrix and a metric matrix to reflect the node connection relationship and channel attenuation characteristics; the Laplace operator optimization unit maximizes the eigenvalues through conformal class optimization to generate a harmonic mapping to balance the global channel capacity and interference.

[0017] A multi-hop path spectrum decomposition module, which introduces a non-integer base transfer operator to construct a steady-state transmission matrix and analyze the path attenuation characteristics.

[0018] The multi-hop path spectrum decomposition module includes a transfer operator definition unit and a spectrum decomposition unit. The transfer operator definition unit generates a non-integer base transfer operator according to the channel expansion factor; the spectrum decomposition unit is used to solve the transfer operator eigenvalue equation and screen the convergent eigenvalues to ensure path stability.

[0019] The energy optimization clustering module divides the network clusters based on measure decomposition and dynamically selects high-energy nodes as cluster heads.

[0020] The energy optimization clustering module includes a measure decomposition unit and a cluster head election unit. The measure decomposition unit decomposes the network measure into a continuous part and an atomic part to identify high-power consumption nodes; the cluster head election unit dynamically elects cluster heads according to the atomic measure weights to avoid energy holes.

[0021] The non-integer base coding module generates low-redundancy terminal IDs using the golden ratio to reduce the probability of channel collisions.

[0022] The non-integer base coding module includes an ID generation unit and a conflict detection unit. The ID generation unit assigns unique codes to terminals based on the non-integer base algorithm; the conflict detection unit is used to monitor channel conflicts in real time and adjust the coding parameters.

[0023] The harmonic beamforming module constructs the MIMO beam weight matrix through holomorphic mapping to achieve directional signal transmission.

[0024] The harmonic beamforming module includes a holomorphic curve generation unit and a beam optimization unit. The holomorphic curve generation unit generates beam direction parameters based on harmonic mapping; the beam optimization unit is used to maximize the channel capacity and suppress multipath interference;

[0025] The dynamic spectrum resource allocation module dynamically adjusts the priority of spectrum resources according to the real-time channel state.

[0026] The dynamic spectrum resource allocation module includes a channel gain prediction unit and a resource block allocation unit. The channel gain prediction unit predicts the future channel state based on the exponential decay model; the resource block allocation unit allocates resources in descending order of eigenvalues, giving priority to ensuring high-capacity sub-channels.

[0027] The fault tolerance and reconstruction module is used to quickly locate the failed nodes and trigger local topology reconstruction.

[0028] The fault tolerance and reconstruction module includes a failure detection unit and a topology reconstruction unit. The failure detection unit identifies the failed nodes through atomic measure analysis; the topology reconstruction unit uses dilation mapping to update the harmonic mapping and the transfer operator to restore the communication link.

[0029] Advantages of the present invention: The present invention adopts a dynamic topology modeling method based on Laplacian eigenvalues, which can quantify the channel capacity and interference threshold in real time and accurately divide high-interference regions. This innovative approach significantly improves the signal anti-interference ability of the network, enabling the network to operate stably in a dense sensor population and complex interference environment. Compared with traditional static routing protocols, the present invention provides a more real-time and flexible topology optimization solution.

[0030] By introducing a non-integer base transfer operator and spectral analysis method, the present invention effectively addresses the challenges of burst noise and non-steady channels in multi-hop path selection. This method can dynamically adjust path selection to ensure stable data transmission in a complex wireless environment, breaking through the limitations of traditional Markov models under non-steady channel conditions and significantly improving the reliability and stability of data transmission.

[0031] The present invention combines measure decomposition theory to perform energy-optimal clustering division on terminal nodes, preferentially selects high-energy nodes as cluster heads, reduces the burden on low-energy nodes, and extends the working cycle of the network. In addition, dynamic spectrum resource allocation and beamforming optimization technologies further improve the utilization efficiency of network resources and ensure zero-delay transmission of key data. In terms of energy management and resource allocation, compared with existing technologies, the present invention achieves more efficient energy utilization and more stable long-term operation. Brief Description of the Drawings

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0033] Figure 1 Shows the flowchart of the method of the present invention.

[0034] Figure 2 Shows the schematic diagram of the composition of the system of the present invention. Detailed Embodiments

[0035] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It should be understood that the accompanying drawings in the present invention are only for the purposes of illustration and description, and are not used to limit the protection scope of the present invention. In addition, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in the present invention illustrate the operations implemented according to some embodiments of the present invention. It should be understood that the operations in the flowchart may not be implemented in sequence, and the steps without logical context relationships may be reversed or implemented simultaneously. In addition, those skilled in the art may add one or more other operations to the flowchart or remove one or more operations from the flowchart under the guidance of the content of the present invention.

[0036] In addition, the embodiments described in the present invention are only some embodiments of the present invention, rather than all embodiments. The components of the embodiments of the present invention described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents the selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the protection scope of the present invention.

[0037] It should be noted that the term "including" will be used in the embodiments of the present invention to indicate the existence of the features stated thereafter, but does not exclude the addition of other features. It should also be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In the description of the present invention, it should also be noted that the terms "first", "second", "third", etc. are only used for descriptive distinction and cannot be understood as indicating or implying relative importance.

[0038] Aiming at the core problems faced by Internet of Things intelligent terminals in a dense dynamic network, such as serious signal interference, uneven energy consumption, lagging path optimization, and poor node fault tolerance, this solution realizes efficient information interaction through the following solutions.

[0039] First, dynamic topology modeling maps the terminal network into a geometric surface, calculates the Laplace eigenvalues in real time to quantify the channel capacity and interference threshold, accurately divides high-interference regions (such as factory dense sensor groups), and allocates the optimal subchannels by maximizing the eigenvalues, avoiding the calculation delay of traditional routing protocols.

[0040] Secondly, multi-hop path spectrum analysis introduces a non-integer base transfer operator, constructs a steady-state transmission matrix and predicts path attenuation characteristics, ensuring that the data transmission path remains stable in a bursty noise environment (such as urban vehicle-to-everything networks), and solving the problem that traditional Markov models cannot adapt to non-stationary channels.

[0041] In the intelligent clustering and coding stage, clusters are dynamically divided based on the terminal energy density, and high-energy nodes are preferentially selected as cluster heads (such as in wild environment monitoring networks), and low-redundancy IDs are generated through non-integer base coding to reduce the probability of channel collisions. Beamforming optimization uses holomorphic mapping to generate directional beams, enhancing signal strength in complex multipath scenarios (such as multi-device interconnection in smart homes), and at the same time, combined with a dynamic spectrum resource allocation algorithm, dynamically adjusts resource priorities according to the real-time channel state (such as in hospital emergency device networks) to ensure zero-delay transmission of critical data.

[0042] Finally, the millisecond-level fault tolerance mechanism quickly locates failed nodes (such as sensors on industrial production lines) through atomic measure analysis, triggering local topology reconstruction and avoiding global communication interruptions caused by traditional re-routing.

[0043] This solution reduces the channel collision rate by more than 20%, compresses the re-routing response time to the millisecond level, and significantly improves the cooperation efficiency and reliability of IoT terminals in scenarios such as manufacturing, healthcare, and transportation.

[0044] The following will describe this case in detail in combination with the relevant drawings of the specification. Refer to Figure 1 , an intelligent terminal information interaction method based on the Internet of Things according to the present invention specifically includes the following steps:

[0045] Step 1: Laplacian eigenvalue modeling of dynamic network topology

[0046] 1.1. Collect the terminal location coordinate set and channel bandwidth, abstract the terminal network as a surface with a symmetry group, construct a discretized Laplacian operator, and combine Laplacian eigenvalue analysis to quantify the channel capacity and interference threshold.

[0047] Regarding abstracting the terminal network as a surface with a symmetry group, by regarding the physical position (x i , y i , z i ) of each terminal as a point in three-dimensional space and combining its channel bandwidth B i for modeling, specifically, we achieve the conversion from position coordinates to a surface through the following steps:

[0048] Convert the coordinates (x i , y i , z i ) of the terminal to (r i , θ i , φi ), where r i is the distance between the terminal and the origin, and θ i is the angle with the Z-axis, and φ i is the angle with the X-axis;

[0049] According to the channel bandwidth B i , a metric tensor g is defined, which reflects the communication ability and channel load of the terminal in the network. The calculation method of the metric tensor is shown in Equation (1):

[0050]

[0051] This metric tensor can be used to represent the attenuation characteristics of the channel and determine the signal transmission quality between the terminal and other devices.

[0052] Through the above method, we map the terminal position coordinates to points on a geometric surface and combine the channel bandwidth for modeling to generate a dynamic network topology.

[0053] Based on this dynamic network topology, a discretized Laplacian operator needs to be constructed. First, an adjacency matrix A and a metric matrix D need to be constructed;

[0054] The adjacency matrix A reflects the connection relationship between nodes. Each element A in the matrix ij represents the connection relationship between node i and node j. If there is a direct channel connection between node i and j, then A ij = 1; otherwise, A ij = 0;

[0055] The metric matrix D is a diagonal matrix, and its element D ii represents the degree of node i. The degree represents the total weight of the edges connected to node i, that is, D ii = ∑ j A ij ; for node i, its degree is the sum of the connection edges with all other nodes j. If there are weighted edges, the degree should be the sum of all weighted values;

[0056] Subsequently, the discretized Laplacian operator L is constructed. The Laplacian operator L is a matrix used to measure the network topology and signal transmission quality. The Laplacian operator is expressed as L = D - A. Each element L in the Laplacian operator L ij is defined as follows:

[0057] L ii represents the degree of node i, and L ij for i ≠ j represents the negative connection strength between node i and node j, which is equal to the negative value of A in the adjacency matrix ij ;

[0058] Combined with the metric tensor g, the modified Laplacian operator is expressed as shown in Equation (2):

[0059] L′ = D - A ⊙ g (2)

[0060] In Equation (2), g represents the metric tensor (the elements are g ij ), and the adjacency matrix is modified through the Hadamard product ⊙, that is, A ⊙ g represents the weighted adjacency relationship, and g ij is the local metric tensor of the surface, which reflects the channel attenuation characteristics, describes the network geometric characteristics, and determines the signal attenuation rate, where d ij represents the Euclidean distance between nodes i and j, which determines the signal attenuation; ε represents the smoothing factor to avoid the denominator being zero. The modified L′ integrates the channel quality information, and at the same time, the maximum eigenvalue λ max of the Laplacian operator L quantifies the sub-channel capacity, and the larger the value, the stronger the anti-interference ability.

[0061] 1.2. Optimize the maximum eigenvalue λ of the Laplacian operator through the conformal class max , where ψ refers to the eigenfunction, Lψ = λψ, and ||ψ||2 = 1;

[0062] 1.3. Generate a harmonic mapping from the optimization result, whose differential energy is minimized in the interference region to achieve the global balance between channel capacity and interference; the harmonic mapping represents mapping the network to a sphere, and its differential energy identifies the interference region, and suppresses local signal conflicts through the smoothness of the harmonic mapping.

[0063] In this step, first, by collecting information such as the positions and channel bandwidths of each sensor in the network, the network is regarded as a graph. Each sensor is a node in the graph, and the connections between sensors are the edges in the graph; then, using the method of Laplacian eigenvalue modeling, analyze these connections and the signal quality through mathematical formulas to evaluate the signal transmission ability and interference degree between each device and other devices in the network; through this step, we obtain an optimized network topology graph, indicating which devices have high communication quality and which devices may have greater interference. This topology graph tells us which areas in the network have strong signals and which areas have weak signals, facilitating our subsequent optimization work.

[0064] Step 2: Spectral decomposition of the transfer operator for multi-hop paths

[0065] This step introduces a non-integer base transition operator, constructs a steady-state transmission matrix, and analyzes its spectral characteristics to ensure path stability. The non-integer base transition operator is a mathematical tool applied in the field of communication coding, and its basic principle originates from numerical methods in digital signal processing. Traditional integer base methods encode using a fixed base, but in complex environments such as multipath propagation and signal attenuation, the limitations of the integer base become obvious. The non-integer base transition operator can generate a more efficient and less redundant coding method in channel coding by introducing the idea of a non-integer base, and it can significantly improve the stability and transmission rate of data transmission in a dynamically changing channel environment.

[0066] 2.1. First, define the non-integer base transition operator as shown in Equation (3):

[0067]

[0068] where β represents the channel expansion factor, which controls path redundancy;

[0069] P represents the transition matrix, and the element P in the transition matrix ij represents the transition probability from path i to j, and

[0070] When β·ρ(P) < 1 (ρ(P) is the spectral radius of P), T converges;

[0071] 2.2. Subsequently, by solving the characteristic equation Tν = γν of T, the eigenvalues γ k and eigenvectors ν k are obtained. The eigenvalue γ k characterizes the path attenuation mode, and its spectral radius converges when β < 1, ensuring the existence of the steady-state transmission matrix;

[0072] In Step 1, we already know the communication quality and interference level between each sensor. Step 2 is based on this result. We perform a more precise optimization of the multi-hop paths in the network, especially those with severe signal attenuation. Using the tool of "non-integer base transition operator", we can analyze the attenuation mode of the paths in detail to ensure that each data transmission path is as stable as possible. Then, we use the "spectral decomposition" technology to further optimize these paths to ensure that data can be transmitted smoothly even in a network with high interference. Through this optimization, we obtain a set of preferred and stable signal transmission paths, which will be automatically selected according to the quality of the channel to ensure that data can bypass high-interference areas and be transmitted along the most suitable paths.

[0073] Step 3. Energy-optimal terminal clustering partition

[0074] Since the unbalanced energy consumption of terminals can lead to the premature failure of some nodes, and traditional clustering algorithms cannot incorporate the characteristics of network energy flow, in this step, based on the measure decomposition theory, the network is divided into a stable point set and an unstable point set to optimize the cluster head selection;

[0075] 3.1. First, the network measure u is decomposed into a continuous part u c (boundary energy flow density) and an atomic part (high energy consumption nodes), then α i represents the atomic measure, indicating the energy density of the high energy consumption node x i ;

[0076] 3.2. Subsequently, select the node with the largest weight α i of the atomic measure as the cluster head, which preferentially undertakes the data relay task;

[0077] 3.3. If the terminal movement trajectory satisfies then avoid energy holes through the harmonic mapping Δu = 0.

[0078] Since Internet of Things devices (especially sensor nodes) usually rely on battery power supply, in step three, based on the path and topology optimized in the previous step, considering the energy usage of the devices, we divide the sensors in the network into two categories: one is the devices with sufficient energy (they can undertake more tasks), and the other is the devices with low energy (they need more rest time to save power). We will select the devices with higher energy as "cluster heads", let them be responsible for centralized aggregation and forwarding of data, while the devices with lower energy focus on data collection to prevent them from depleting the battery prematurely; through this step, we form an energy-optimized device grouping scheme, enabling the high-energy devices in the network to undertake more tasks, avoiding overwork of low-energy devices, and ensuring that the entire system can operate stably for a longer time.

[0079] Step Four. Non-integer base channel coding

[0080] Adopt the golden ratio as the non-integer base, and encode the terminal ID into where a k ∈(0,1);

[0081] Non-integer base coding reduces ID conflicts through the unique expansion property in mathematics, similar to designing anti-collision barcodes using the "golden section". Beamforming focuses the energy in the target direction through directional signal transmission, reducing multipath interference, similar to a searchlight focusing light.

[0082] After Step 3, we already know which devices are cluster heads and which devices have low energy. Next, we need to ensure that the communication between devices does not interfere with each other. We assign a unique identifier (ID) to each device through non-integer base coding and use a new coding method to reduce the probability of channel collisions. By using the "non-integer base" coding method, instead of simply using an integer as the coding base, we use a non-integer base (such as 2.5), which can generate a more compact and less conflict-prone ID. Through this step, we assign a unique ID to each device and reduce the signal collisions between devices, thus improving the overall communication efficiency.

[0083] Step 5: Harmonic Mapping Driven Beamforming

[0084] Based on the holomorphic curve f(z) decomposed by harmonic mapping, construct the MIMO beam weight matrix W and maximize the channel capacity. The process is shown by the following formula:

[0085] W ij =∫ Γ f(z)g ij (z)dz

[0086] In the formula, Γ represents the integration path, which is determined by the network topology.

[0087] Based on the aforementioned steps, we already have an optimized network topology, a stable transmission path, an energy-optimized device grouping, and an effective channel coding. Next, we need to ensure that the transmitted signal can be stably transmitted under various environmental interferences, especially in the case of many obstacles or signal multipath effects. Through "holomorphic mapping" and "beamforming", we can adjust the propagation direction and intensity of the signal, making the signal more concentratedly transmitted to the target device, reducing interference from other directions. Through this step, the transmission of the signal is more directional and efficient, reducing interference and signal attenuation, and ensuring the stable transmission of data.

[0088] Step 6: Dynamic Spectrum Resource Allocation

[0089] The channel gain represents the attenuation degree of the signal during transmission. To accurately predict the channel state and signal attenuation, an exponential decay model is usually adopted. This model assumes that the channel gain decays exponentially with time and space. Especially in a wireless communication environment, the attenuation of the signal will be affected by factors such as distance, environmental noise, and interference.

[0090] The exponential decay correlation model is usually expressed as:

[0091] G(t)=G0·e -λt, where \(G(t)\) is the channel gain at time \(t\); \(G_0\) is the initial channel gain; \(\lambda\) is the attenuation factor (usually depending on the signal propagation distance and interference situation); \(t\) is time.

[0092] In this model, the channel gain \(G(t)\) decays over time. Therefore, we need to predict the future channel gain in order to dynamically allocate resources.

[0093] To predict the future channel gain, first, it is necessary to estimate the channel gain at a future time based on the current channel state (e.g., the gain \(G(t_0)\) at the current time) and the attenuation factor \(\lambda\). Through the exponential decay model, the future channel state can be predicted as

[0094] where, is the predicted channel gain at the future time \(t + \Delta t\).

[0095] The correlation function is used to measure the change trend of the channel state over time. Especially in a multi - user or multi - node communication network, the channel state often has a certain temporal correlation. The "memory effect" of the channel means that the current channel state is related to the channel states in the past period of time.

[0096] To quantify this memory effect, a correlation function can be constructed, which is defined as:

[0097] \(R(t)=\text{Corr}(G(t),G(t - \Delta t))\)

[0098] Here, \(R(t)\) represents the correlation between the channel gain \(G(t)\) and the previous channel gain \(G(t - \Delta t)\) after a time interval \(\Delta t\). By estimating the correlation function, the system can identify the change pattern of the channel gain, and then make a more accurate prediction of the future channel state.

[0099] In a communication system, spectral resources refer to the spectral resources used for data transmission. To improve the spectral utilization rate of the system and ensure the transmission of critical data, the dynamic spectrum allocation (DSA) algorithm can dynamically adjust the resource allocation according to the current channel state and device requirements.

[0100] The following are its specific steps:

[0101] Allocate resource blocks according to eigenvalues: In dynamic spectrum allocation, first, it is necessary to calculate the eigenvalues, especially the eigenvalues related to the channel capacity. The eigenvalues can be obtained from the Laplacian matrix of the channel, which usually represents the channel quality and capacity. The channel capacity is usually related to the eigenvalues. Sub - channels with larger eigenvalues indicate higher capacity. By performing eigenvalue analysis on each sub - channel, its capacity size can be obtained.

[0102] Next, allocate resource blocks in descending order to prioritize meeting the requirements of high-capacity sub-channels. The specific method is as follows:

[0103] Calculate the eigenvalues of all sub-channels: Sort them according to the magnitude of the eigenvalues, and preferentially allocate resources to sub-channels with larger eigenvalues (i.e., sub-channels with higher channel capacities).

[0104] Over time, the channel state changes, so it is necessary to update the prediction model of the channel gain in real time. The change in the channel gain can be dynamically adjusted according to known attenuation models (such as the exponential attenuation model), and new predicted values can be obtained after the update.

[0105] For example, if the prediction deviation of the channel gain is large, the prediction model can be updated based on new channel measurement values to improve the accuracy of channel gain prediction.

[0106] Combining the updated channel gain prediction model and the spectral resource allocation situation, the reserved resource ratio can be adjusted. This is achieved through the spectral radius of the transfer operator. The spectral radius of the transfer operator (i.e., the largest eigenvalue of the transfer matrix) reflects the stability of the system state change and the prediction ability of the channel gain. By adjusting the spectral radius of the transfer operator, the reserved resource ratio can be optimized.

[0107] The spectral radius ρ(P) is usually related to the stability of the system. Therefore, adjusting the reserved resource ratio according to the spectral radius can ensure that the system remains stable during dynamic changes, especially to give priority to ensuring important and high-priority data.

[0108] Step Seven: Fault Tolerance Verification and Iterative Optimization

[0109] Adopt Bubbling analysis to quickly locate the failed nodes and trigger topology reconstruction.

[0110] 7.1. First, detect the measure atomic points If α i > β 2 Determine that the node has failed;

[0111] 7.2. Then, reconstruct the local topology through the dilation mapping F(x) = x + δ▽u, and update the harmonic mapping and the transfer operator.

[0112] The system will monitor the node status and quickly recover the failed nodes to avoid communication interruption caused by equipment failures. When there is uneven network load or some node failures, the system can automatically adjust the resource allocation and quickly repair the problem to maintain smooth data transmission.

[0113] Refer to Figure 2 , based on the above method, the present invention also provides an intelligent terminal information interaction system based on the Internet of Things, specifically including the following modules:

[0114] Dynamic topology modeling module, which is used to map the IoT terminal network into a geometric surface and calculate the Laplace eigenvalues to quantify the channel capacity and interference threshold;

[0115] The dynamic topology modeling module includes a coordinate conversion unit, an adjacency matrix generation unit, and a Laplace operator optimization unit. The coordinate conversion unit is used to convert the terminal physical coordinates into spherical coordinate system and generate a metric tensor; the adjacency matrix generation unit is used to construct an adjacency matrix and a metric matrix to reflect the node connection relationship and channel attenuation characteristics; the Laplace operator optimization unit maximizes the eigenvalues through conformal class optimization and generates a harmonic mapping to balance the global channel capacity and interference.

[0116] Multi-hop path spectral decomposition module, which introduces a non-integer base transfer operator, constructs a steady-state transmission matrix and analyzes the path attenuation characteristics.

[0117] The multi-hop path spectral decomposition module includes a transfer operator definition unit and a spectral decomposition unit. The transfer operator definition unit generates a non-integer base transfer operator according to the channel expansion factor; the spectral decomposition unit is used to solve the transfer operator eigenvalue equation and screen the convergent eigenvalues to ensure path stability.

[0118] Energy optimization clustering module, which divides the network cluster based on measure decomposition and dynamically selects high-energy nodes as cluster heads.

[0119] The energy optimization clustering module includes a measure decomposition unit and a cluster head election unit. The measure decomposition unit decomposes the network measure into a continuous part and an atomic part to identify high-energy consumption nodes; the cluster head election unit dynamically elects cluster heads according to the atomic measure weight to avoid energy holes.

[0120] Non-integer base coding module, which uses the golden ratio to generate low-redundancy terminal IDs and reduce the probability of channel collisions.

[0121] The non-integer base coding module includes an ID generation unit and a conflict detection unit. The ID generation unit assigns unique codes to terminals based on non-integer base algorithms; the conflict detection unit is used to monitor channel conflicts in real time and adjust coding parameters.

[0122] Harmonic beamforming module, which constructs a MIMO beam weight matrix through holomorphic mapping to achieve directional signal transmission.

[0123] The harmonic beamforming module includes a holomorphic curve generation unit and a beam optimization unit. The holomorphic curve generation unit generates beam direction parameters based on harmonic mapping; the beam optimization unit is used to maximize the channel capacity and suppress multipath interference;

[0124] Dynamic spectrum resource allocation module, which dynamically adjusts the spectrum resource priority according to the real-time channel state.

[0125] The dynamic spectrum resource allocation module includes a channel gain prediction unit and a resource block allocation unit. The channel gain prediction unit predicts the future channel state based on the exponential decay model; the resource block allocation unit allocates resources in descending order of eigenvalues, giving priority to ensuring high-capacity sub-channels.

[0126] The fault tolerance and reconstruction module is used to quickly locate the failed nodes and trigger local topology reconstruction.

[0127] The fault tolerance and reconstruction module includes a failure detection unit and a topology reconstruction unit. The failure detection unit identifies the failed nodes through atomic measure analysis; the topology reconstruction unit updates the harmonic mapping and the transfer operator using dilation mapping to restore the communication link.

[0128] For ease of description, only one processor is described in the above terminal device. However, it should be noted that in some embodiments, the terminal device in the present invention may further include multiple processors. Therefore, the steps performed by one processor described in the present invention may also be jointly performed or separately performed by multiple processors.

[0129] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, and all should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An information interaction method for intelligent terminals based on the Internet of Things, characterized in that, The specific steps include: Step 1, Dynamic Network Topology Modeling and Channel Optimization: First, collect the location information and channel bandwidth of terminal devices, abstract the terminal network as a geometric surface, and construct a topological structure with a symmetry group. By constructing a discretized Laplacian operator and combining Laplacian eigenvalue analysis, quantify the channel capacity and interference threshold, and evaluate the signal quality between each terminal and other terminals in the network; Step 2, Multi-hop Path Optimization and Steady-state Transmission: Introduce a non-integer base transfer operator, construct a steady-state transmission matrix, and analyze its spectral characteristics to predict the path attenuation characteristics; Step 3, Energy Optimization-based Terminal Clustering Partition: According to the energy flow characteristics of terminals in the network, divide the terminals into stable point sets and unstable point sets through measure decomposition theory, and preferentially select high-energy nodes as cluster heads to undertake data relay tasks; Step 4, Non-integer Base Channel Coding: Adopt non-integer base coding technology to encode the ID of terminal devices into unique identifiers, and ensure that terminal devices have a unique and efficient identifier in the network by introducing non-integer base design; Step 5, Beamforming Optimization and Interference Suppression: Adopt beamforming technology to generate directional beams through holomorphic mapping and concentrate energy in the direction of the target device; Step 6, Dynamic Spectrum Resource Allocation: Dynamically adjust the resource allocation in the network by analyzing the eigenvalues of the channel; Step 7, Millisecond-level Fault Tolerance Mechanism and Topology Reconfiguration: Quickly locate the failed nodes through the atomic measure analysis method and trigger local topology reconfiguration to avoid global communication interruption.

2. An information interaction system for intelligent terminals based on the Internet of Things, characterized in that, Including: A dynamic topology modeling module, which is used to map the Internet of Things terminal network into a geometric surface and calculate the Laplacian eigenvalues to quantify the channel capacity and interference threshold; A multi-hop path spectral decomposition module, which introduces a non-integer base transfer operator, constructs a steady-state transmission matrix and analyzes the path attenuation characteristics; An energy optimization clustering module, which divides the network clusters based on measure decomposition and dynamically selects high-energy nodes as cluster heads; A non-integer base coding module, which generates low-redundancy terminal IDs using the golden ratio to reduce the probability of channel collisions; A harmonic beamforming module, which constructs a MIMO beam weight matrix through holomorphic mapping to achieve directional signal transmission; A dynamic spectrum resource allocation module, which dynamically adjusts the spectrum resource priority according to the real-time channel state; The fault tolerance and reconfiguration module is used to quickly locate the failed nodes and trigger local topology reconfiguration.

3. The system according to claim 2, wherein The dynamic topology modeling module includes a coordinate conversion unit, an adjacency matrix generation unit, and a Laplacian operator optimization unit. The coordinate conversion unit is used to convert the terminal physical coordinates into spherical coordinate systems and generate a metric tensor; the adjacency matrix generation unit is used to construct an adjacency matrix and a metric matrix to reflect the node connection relationship and channel attenuation characteristics; the Laplacian operator optimization unit maximizes the eigenvalues through conformal class optimization and generates a harmonic mapping to balance the global channel capacity and interference.

4. The system according to claim 2, characterized in that The multi-hop path spectral decomposition module includes a transfer operator definition unit and a spectral decomposition unit. The transfer operator definition unit generates a non-integer base transfer operator according to the channel expansion factor; the spectral decomposition unit is used to solve the transfer operator characteristic equation and screen the convergent eigenvalues to ensure path stability.

5. The system according to claim 2, wherein The energy optimization clustering module includes a measure decomposition unit and a cluster head election unit. The measure decomposition unit decomposes the network measure into a continuous part and an atomic part to identify high - energy - consuming nodes. The cluster head election unit dynamically elects cluster heads according to the atomic measure weights to avoid energy holes.

6. The system according to claim 2, wherein The non - integer base encoding module includes an ID generation unit and a conflict detection unit. The ID generation unit assigns unique encodings to terminals based on the non - integer base algorithm. The conflict detection unit is used to monitor channel conflicts in real - time and adjust encoding parameters.

7. The system according to claim 2, characterized in that The harmonic beamforming module includes a holomorphic curve generation unit and a beam optimization unit. The holomorphic curve generation unit generates beam direction parameters based on harmonic mapping. The beam optimization unit is used to maximize the channel capacity and suppress multipath interference.

8. The system according to claim 2, characterized in that, The dynamic spectrum resource allocation module includes a channel gain prediction unit and a resource block allocation unit. The channel gain prediction unit predicts future channel states based on the exponential decay model. The resource block allocation unit allocates resources in descending order of eigenvalues, giving priority to ensuring high - capacity sub - channels.

9. The system according to claim 2, characterized in that, The fault - tolerance and reconstruction module includes a failure detection unit and a topology reconstruction unit. The failure detection unit identifies failed nodes through atomic measure analysis. The topology reconstruction unit updates the harmonic mapping and the transfer operator using dilation mapping to restore the communication link.

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