A digital twin method and system for narrowband internet of things wireless communication optimization

Abstract

The application discloses a kind of narrowband internet of things wireless communication optimization digital twin method and system, it is related to wireless communication optimization technical field, method includes the digital twin environment corresponding to target industrial scene is constructed, for simulating wireless signal propagation characteristic;NB-IoT network is deployed in digital twin environment, and channel data is obtained by running wireless communication simulation;According to channel data, the communication performance index of terminal is calculated, and the terminal to be optimized is identified;For the terminal to be optimized, select relay terminal in digital twin environment and determine the gain of amplify and forward, simulate the process of cooperative communication;Re-run simulation to verify optimization effect, if it does not meet the requirement, then adjust parameter and repeat optimization, until the communication performance of all terminals meets the standard.The application migrates the optimization process of traditional dependence on site traversal test to virtual space, without large-scale artificial road test in live area such as transformer substation, shorten optimization period, reduce cost.

Description

Technical Field

[0001] This invention relates to the field of wireless communication optimization technology, and in particular to a digital twin method and system for optimizing narrowband Internet of Things (IoT) wireless communication. Background Technology

[0002] With the deepening of the digital transformation of the power grid, scenarios such as smart substations and distribution network automation are placing higher demands on the communication reliability of IoT terminals. Narrowband Internet of Things (NB-IoT), with its advantages of low power consumption, wide coverage, and massive connectivity, has become an important access technology for the power Internet of Things.

[0003] In various power grid environments, NB-IoT terminals are typically deployed in substations, switch rooms, and other environments with strong electromagnetic interference. Due to factors such as obstruction by large power equipment and multipath reflections, the communication link quality between terminal devices and network devices varies significantly. Some remote or obstructed terminals exhibit high bit error rates (BER), leading to unreliable data reporting. Traditional optimization methods rely on on-site drive testing and empirical adjustments, requiring substantial investment of manpower and resources for comprehensive testing. Furthermore, they only address exposed problems after network construction, making it difficult to achieve low-cost, high-efficiency communication performance improvements in high-reliability scenarios like power grids. Summary of the Invention

[0004] To address the problem that existing wireless communication optimization technologies rely on on-site drive testing and empirical adjustments, making it difficult to achieve low-cost, high-efficiency communication performance improvements in high-reliability scenarios such as power grids, this invention provides a digital twin method and system for narrowband IoT wireless communication optimization. This method completely migrates the traditional optimization process, which relies on on-site traversal testing, to a virtual space, eliminating the need for large-scale manual drive testing in energized areas such as substations. This shortens the optimization cycle and reduces optimization costs. The specific technical solution is as follows: This application provides a digital twin method for optimizing narrowband IoT wireless communication, including: Construct a digital twin environment corresponding to the target environment, wherein the digital twin environment is used to simulate the propagation characteristics of wireless signals in the target environment; An NB-IoT network is deployed in the digital twin environment, the NB-IoT network including at least one network device and multiple terminal devices; wireless communication simulation is run in the NB-IoT network to obtain channel data of the communication link between the terminal devices and the network device; Based on the channel data, calculate the communication performance index of each terminal device and identify terminals whose communication performance is lower than a preset threshold that need to be optimized. For each terminal to be optimized, optimization operations are performed in the digital twin environment: at least one other terminal device is selected as a relay terminal for the terminal to be optimized, and the amplification forwarding gain that the relay terminal should use when forwarding signals is determined according to the channel quality between the terminal to be optimized and the relay terminal, and the collaborative communication process of the terminal to be optimized forwarding data to the network device through the relay terminal is simulated. In the digital twin environment, the wireless communication simulation including the optimization operation is rerun. If the communication performance of all terminals to be optimized is improved to above the preset threshold, the current optimization strategy is output; otherwise, the selection conditions of the relay terminal or the calculation parameters of the amplification and forwarding gain in the optimization operation are adjusted, and the optimization operation and re-simulation steps are repeated until the requirements are met.

[0005] Preferably, the construction of the digital twin environment corresponding to the target environment includes: A three-dimensional model of the equipment and obstacles in the industrial scene is constructed using geometric modeling tools, and their electromagnetic material properties are defined. Based on the aforementioned 3D model, electromagnetic simulation tools are used to generate data on the propagation path, reflection, diffraction, and transmission of wireless signals in the industrial setting using ray tracing technology. The output data of the electromagnetic simulation tool is aggregated using data processing tools to form a channel model for wireless communication simulation.

[0006] Preferably, the number of reflections and diffractions set in the ray tracing technology are determined based on the balance between simulation accuracy and computational efficiency.

[0007] Preferably, the channel data includes at least one of channel impulse response, path loss, and signal propagation path; the communication performance includes at least one of bit error rate, signal-to-interference-plus-noise ratio, and effective throughput.

[0008] Preferably, calculating the communication performance indicators of each terminal device based on the channel data includes: The channel data is randomly sampled multiple times using the Monte Carlo simulation method to generate a statistical distribution of communication performance indicators; the statistical distribution includes a cumulative distribution function and / or a probability density function.

[0009] Preferably, at least one other terminal device is selected as a relay terminal for the terminal to be optimized, specifically including: Calculate the signal-to-noise ratio of the link between each other terminal device and the network device; One or more other terminal devices with the highest signal-to-noise ratio are identified as the relay terminal.

[0010] Preferably, the amplification and forwarding gain is determined by the following formula: in, The transmitted signal energy of the terminal to be optimized. The channel attenuation from the terminal to be optimized to the relay terminal, The noise power of the link from the terminal to be optimized to the relay terminal.

[0011] Preferably, determining the amplification and forwarding gain to be used when the relay terminal forwards the signal further includes: The noise power of the received signal of the relay terminal is estimated, and the amplification and forwarding gain is adjusted according to the noise power estimation result so that the output power of the relay terminal does not exceed its maximum transmit power limit.

[0012] Preferably, after identifying terminals whose communication performance is below a preset threshold, the method further includes: In the digital twin environment, root cause analysis is performed on the terminal to be optimized to identify physical factors that lead to performance degradation. These physical factors include at least one of obstacle occlusion, multipath interference, and co-channel interference. The optimization operation adjusts the selection parameters of the relay terminal or the calculation parameters for amplifying the forwarding gain based on the results of the root cause analysis.

[0013] This application also provides a digital twin system optimized for narrowband IoT wireless communication, comprising: An environment construction module is used to construct a digital twin environment corresponding to the target environment, wherein the digital twin environment is used to simulate the propagation characteristics of wireless signals in the target environment; A simulation module is used to deploy an NB-IoT network in the digital twin environment, the NB-IoT network including at least one network device and multiple terminal devices; to run a wireless communication simulation in the NB-IoT network and acquire channel data of the communication link between the terminal devices and the network devices; The diagnostic module is used to calculate the communication performance index of each terminal device based on the channel data, and identify terminals whose communication performance is lower than a preset threshold that need to be optimized. An optimization module is used to perform optimization operations in the digital twin environment for each terminal to be optimized: selecting at least one other terminal device as a relay terminal for the terminal to be optimized, determining the amplification forwarding gain that the relay terminal should use when forwarding signals based on the channel quality between the terminal to be optimized and the relay terminal, and simulating the cooperative communication process of the terminal to be optimized forwarding data to the network device through the relay terminal. The iteration module is used to rerun the wireless communication simulation, including the optimization operation, in the digital twin environment. If the communication performance of all terminals to be optimized is improved to above the preset threshold, the current optimization strategy is output; otherwise, the selection conditions of the relay terminal or the calculation parameters of the amplification and forwarding gain in the optimization operation are adjusted, and the optimization operation and re-simulation steps are repeated until the requirements are met.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a digital twin method for optimizing narrowband IoT wireless communication in industrial scenarios. By constructing a digital twin environment corresponding to the target environment, it completely migrates the traditional optimization process, which relies on on-site traversal testing, to a virtual space. This eliminates the need for large-scale manual drive testing in energized areas such as substations, avoiding the risks associated with power outages and significantly shortening the optimization cycle and cost. Furthermore, this invention runs the simulation, diagnosis, optimization, and verification processes within the digital twin environment. The optimization strategy can dynamically adjust relay selection and gain parameters based on iterative simulation results until performance requirements are met. The final optimized configuration can directly guide terminal deployment and parameter tuning in the physical network, achieving synergistic linkage between virtual optimization and physical deployment. This provides a standardized and reproducible technical path for large-scale deployment in power grids, demonstrating promising application prospects and significant promotional value. Attached Figure Description

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0016] Figure 1 A flowchart of a digital twin method for optimizing narrowband Internet of Things (IoT) wireless communication is provided in an embodiment of the present invention.

[0017] Figure 2 This is a simplified flowchart illustrating the implementation phase of wireless NB-IoT in a target industrial environment, as provided in this embodiment of the invention.

[0018] Figure 3 This is a schematic diagram showing the bit error rate distribution of each terminal device in the NB-IoT network before the implementation of the collaborative communication scheme provided in this embodiment of the invention.

[0019] Figure 4 A comparative diagram showing the improvement in bit error rate of 14 terminal devices to be optimized after the collaborative communication optimization scheme provided in this embodiment of the invention.

[0020] Figure 5This is a schematic diagram comparing the signal-to-interference-plus-noise ratio of a single NB-IoT terminal on a direct transmission link and after relay forwarding, as provided in an embodiment of the present invention.

[0021] Figure 6 This is a schematic diagram of the cumulative effective throughput distribution function of the first group of terminals to be optimized before and after optimization, provided for an embodiment of the present invention.

[0022] Figure 7 This is a schematic diagram of the cumulative effective throughput distribution function of the second group of terminals to be optimized before and after optimization, provided in an embodiment of the present invention.

[0023] Figure 8 This is a schematic diagram of a digital twin system optimized for narrowband Internet of Things wireless communication, provided as an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] It should be understood that, when used in this specification, the terms “comprising” and “including” indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0026] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0027] It should also be further understood that the term "and / or" as used in this specification refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes such combinations.

[0028] Please refer to the following examples. Figures 1 to 8 .

[0029] This application provides a digital twin method for optimizing narrowband IoT wireless communication, including: Step S1: Construct a digital twin environment corresponding to the target environment, wherein the digital twin environment is used to simulate the propagation characteristics of wireless signals in the target environment; Specifically, the construction of the digital twin environment corresponding to the target environment includes: S11. Use geometric modeling tools to construct three-dimensional models of equipment and obstacles in the industrial scene, and define their electromagnetic material properties; Based on the actual layout of the target scene, a 3D geometric model is constructed using geometric modeling tools. For critical industrial equipment, such as transformers, switchgear, and large pipelines, geometric modeling is performed, depicting their shape, dimensions, and surface details, as these features significantly affect the reflection and diffraction of radio waves. For macroscopic architectural structures such as factory buildings, partitions, and columns, a 3D model repository can be used for rapid construction, and the electromagnetic material properties of each object can be defined, such as the relative permittivity and conductivity of concrete, metal, and glass.

[0030] S12. Using electromagnetic simulation tools based on the three-dimensional model, generate data on the propagation path, reflection, diffraction, and transmission of wireless signals in the industrial scene through ray tracing technology; Import the integrated 3D geometric model into a professional electromagnetic simulation tool. Set up a ray tracing propagation model within the tool. Run the ray tracing simulation at a specified operating frequency (900MHz in this example) to generate data on the propagation path, reflection, diffraction, and transmission of the wireless signal in an industrial environment. This data reflects the signal's behavior in complex environments, including multipath effects and shadow fading.

[0031] S13. Use data processing tools to aggregate the simulation data output by the wireless scenario simulation software to form a channel model for wireless communication simulation.

[0032] Simulation data is aggregated using data processing tools to form a channel model for subsequent communication simulations. This model includes information such as channel impulse response, path loss, and angle of arrival between each pair of transmit and receive locations.

[0033] Step S2: Deploy an NB-IoT network in the digital twin environment, the NB-IoT network including at least one network device and multiple terminal devices; run a wireless communication simulation in the NB-IoT network to obtain channel data of the communication link between the terminal devices and the network devices; In practice, within the constructed digital twin environment, an NB-IoT network model conforming to the 3GP PRELEASE 14 standard is deployed. The network includes a central network device (base station) and multiple terminal devices. Based on the zoning plan of the target scenario, 25 NB-IoT terminals are deployed at the corresponding locations of the actual devices, forming a spatial distribution pattern that highly matches the actual physical deployment.

[0034] In this embodiment, the parameters of the NB-IoT network are set according to Table 1, with an operating bandwidth of 200kHz, an operating frequency of 900MHz, a terminal transmit power of 23dBm, and a noise floor set to be higher than -3dB (i.e., greater than -3dBm) to simulate typical background electromagnetic interference levels in the target environment. By simulating a complete uplink and downlink communication process, channel data of the communication link between each terminal device and the network device is obtained, including channel impulse response, path loss, signal propagation path, etc.

[0035] Table 1 - Parameters and System Assumptions Step S3: Calculate the communication performance index of each terminal device based on the channel data, and identify terminals whose communication performance is lower than a preset threshold that need to be optimized. Specifically, the channel data includes at least one of channel impulse response, path loss, and signal propagation path; the communication performance includes at least one of bit error rate, signal-to-interference-plus-noise ratio, and effective throughput.

[0036] In this embodiment, the performance metrics include bit error rate (BER), signal-to-interference-plus-noise ratio (SINR), and effective throughput. To overcome the randomness of a single simulation, a Monte Carlo simulation method is used to randomly sample the channel data 500 times, generating statistical distributions for each performance metric, such as the cumulative distribution function (CDF) and probability density function.

[0037] By analyzing the statistical distribution of various performance indicators, a quantitative representation of network performance can be achieved. For example... Figure 3 The image shows the BER distribution of each terminal device before optimization. Some terminal devices have significantly higher BERs than the average, indicating poor performance. By optimizing these devices with BERs exceeding a preset threshold (e.g., 10), optimization can be achieved. -2 Terminals marked as requiring optimization were identified. In this embodiment, a total of 14 terminals requiring optimization were identified (numbered 12-25).

[0038] Based on ray tracing technology, multi-dimensional channel data such as channel impulse response and path loss are generated. Combined with Monte Carlo simulation to generate statistical distribution, the communication performance of each terminal can be quantitatively evaluated, and terminals with indicators such as BER and SINR below the threshold can be automatically identified for optimization.

[0039] Step S4: For each terminal to be optimized, perform optimization operations in the digital twin environment: select at least one other terminal device as a relay terminal for the terminal to be optimized, and determine the amplification forwarding gain that the relay terminal should use when forwarding signals based on the channel quality between the terminal to be optimized and the relay terminal, and simulate the cooperative communication process of the terminal to be optimized forwarding data to the network device through the relay terminal. For the 14 terminals identified in step S3 that require optimization, a virtual amplification and forwarding collaborative communication scheme is deployed in the digital twin environment without modifying any physical devices. The specific optimization operations are as follows: S41. Selecting a relay terminal: In this embodiment, at least one other terminal device is selected as a relay terminal for the terminal to be optimized, specifically including: Calculate the signal-to-noise ratio of the link between each other terminal device and the network device; One or more other terminal devices with the highest signal-to-noise ratio are identified as the relay terminal.

[0040] For each terminal to be optimized (called the source terminal), one or more other terminals are selected as relay terminals (NAT). The relay selection follows these criteria: the signal-to-noise ratio (SNR) of the link between each other terminal and the base station is calculated, and the one or more terminals with the highest SNR are identified as relay terminals. For example... Figure 5 As shown, the SINR of the NAT-base station link is significantly higher than that of the direct transmission link from the source terminal to the base station, ensuring that the relay link itself has optimal quality.

[0041] S42. Determine the amplification and forwarding gain: After receiving the signal from the source terminal, the relay terminal needs to amplify the signal before forwarding it. To prevent noise from being excessively amplified, the amplification gain needs to be normalized according to the channel conditions. In this embodiment, the amplification and forwarding gain is determined by the following formula: in, The transmitted signal energy of the terminal to be optimized. The channel attenuation from the terminal to be optimized to the relay terminal, The noise power of the link from the terminal to be optimized to the relay terminal.

[0042] The above formula ensures that the power of the relay signal is reasonably controlled, avoiding the introduction of additional noise. In actual calculations, and It can be extracted from the channel data obtained in step S2.

[0043] S43. Simulated Cooperative Communication Process: Simulate two-stage cooperation in a digital twin environment. First, in the coordination phase, the terminal to be optimized sends data to the selected relay terminal; subsequently, in the cooperation phase, the relay terminal processes the received noisy signal according to gain. The signal is amplified and forwarded to network devices. Network devices receive two independent copies of the signal from the direct path and the relay path, and can employ techniques such as maximum ratio combining to improve demodulation reliability.

[0044] By selecting the nearest terminal with the best signal-to-noise ratio to the base station as a relay, and adaptively adjusting the amplification and forwarding gain based on the industrial background noise model (>-3dB), the equivalent link quality of weak terminals is significantly improved.

[0045] Step S5: Rerun the wireless communication simulation including the optimization operation in the digital twin environment. If the communication performance of all terminals to be optimized is improved to above the preset threshold, the current optimization strategy is output; otherwise, adjust the selection conditions of the relay terminal or the calculation parameters of the amplification and forwarding gain in the optimization operation, and repeat the optimization operation and re-simulation steps until the requirements are met.

[0046] The optimization scheme implemented in step S4 was applied to the 14 terminals to be optimized in the digital twin model, and the wireless communication simulation was run again to verify the optimization effect.

[0047] Simulation results are as follows Figure 4 , Figure 5 , Figure 6 , Figure 7 As shown. Figure 4 The results show the improvement in BER for 14 terminals before and after optimization. The BER of all target terminals decreased significantly, with terminal 25 showing an improvement of more than 17%. Figure 5 The SINR of a single terminal direct transmission link was compared with that of an equivalent link after relay forwarding, confirming that relay forwarding significantly improves signal quality. Figure 6 and Figure 7 The effective throughput CDF curves of the first group of terminals to be optimized (12-18) and the second group of terminals to be optimized (19-25) are shown before and after optimization. The curves shift to the right as a whole, indicating that the system bottleneck has been effectively alleviated.

[0048] If the communication performance of all terminals to be optimized is improved to a preset threshold (e.g., BER is below 10) -2 If the current optimization strategy (including relay selection results, amplification gain, and other parameters) is determined as the final solution and output, then this optimization strategy can be used to guide the deployment adjustment or parameter configuration of terminals in the physical network.

[0049] If some terminals still fail to meet the performance requirements, adjust the parameters in the optimization process. For example, change the relay selection criteria, such as adjusting from the highest SNR to the second highest SNR, considering load balancing, fine-tuning the noise estimate in the amplification gain calculation, or reselecting other relay terminals. Then repeat steps S4 and S5 until the requirements are met. The entire process forms a closed-loop feedback and iterative optimization system, such as... Figure 2 The loop arrow in the image is shown.

[0050] This embodiment achieves low-cost, high-efficiency, and iterative optimization of industrial NB-IoT networks in a virtual environment through the above steps, ultimately obtaining a reliable configuration scheme with improved performance that can be directly applied to actual physical networks.

[0051] This invention provides a digital twin method for optimizing narrowband IoT wireless communication in industrial scenarios. By constructing a digital twin environment corresponding to the target environment, it completely migrates the traditional optimization process, which relies on on-site traversal testing, to a virtual space. This eliminates the need for large-scale manual drive testing in energized areas such as substations, avoiding the risks associated with power outages and significantly shortening the optimization cycle and cost. Furthermore, this invention runs the simulation, diagnosis, optimization, and verification processes within the digital twin environment. The optimization strategy can dynamically adjust relay selection and gain parameters based on iterative simulation results until performance requirements are met. The final optimized configuration can directly guide terminal deployment and parameter tuning in the physical network, achieving synergistic linkage between virtual optimization and physical deployment. This provides a standardized and reproducible technical path for large-scale deployment in power grids, demonstrating promising application prospects and significant promotional value.

[0052] In other embodiments, the difference lies in the selection method of the relay terminal in step S41. In addition to considering the signal-to-noise ratio between the terminal device and the network device, the remaining energy or load status of the relay terminal is also introduced as an auxiliary selection factor to balance network energy consumption and extend the overall network lifespan. Specifically, terminals with high signal-to-noise ratio and sufficient remaining energy are preferentially selected as relay terminals to avoid critical relay terminals from failing prematurely due to excessive energy consumption.

[0053] Specifically, in a preferred embodiment of this application, the number of reflections and diffractions set in the ray tracing technology are determined based on the balance requirements between simulation accuracy and computational efficiency.

[0054] First, an electromagnetic environment pre-analysis is conducted on the target industrial scenario. Taking a smart substation as an example, the scenario contains a large number of metal electrical equipment, such as transformers, circuit breakers, busbars, and GIS pipelines. These devices have strong radio wave reflection characteristics; at the same time, significant diffraction effects occur at narrow gaps between devices and at device edges. Based on the scenario characteristics, it is preliminarily determined that reflection is the dominant mechanism for signal propagation, while diffraction is a secondary but not negligible mechanism.

[0055] In digital twin construction tools, the number of reflections and diffractions are key parameters affecting the computational cost of ray tracing. Each additional reflection or diffraction increases the number of rays exponentially, leading to a dramatic increase in computation time. Therefore, a trade-off model needs to be established: It must be able to capture the main energy path to the receiving point, i.e., the path loss calculation error is less than 3dB, and be able to reproduce typical multipath delay distributions.

[0056] The runtime of a single full-scene simulation should be kept within an acceptable range (e.g., within 30 minutes) to support subsequent iterative optimization processes.

[0057] The optimal number of reflections and diffractions was determined using a parameter scanning method: starting from 0, the number of reflections was gradually increased to 12, and the change in path loss calculation and simulation time were recorded after each simulation. It was assumed that when the number of reflections increased from 8 to 10, the change in path loss calculation was less than 0.5 dB, and the accuracy improvement was no longer significant; however, when the number of reflections exceeded 10, the simulation time increased exponentially. At this point, 10 reflections were determined as the accuracy-efficiency balance point. With the number of reflections fixed at 10, the number of diffractions was scanned from 0 to 5. It was assumed that the results showed that increasing the number of diffractions from 2 to 3 could capture the additional multipath components generated by diffraction at the device edge; however, after the number of diffractions exceeded 3, the energy attenuation of the newly added path exceeded -40 dB, and its contribution to the received signal was negligible. At this point, 3 diffractions were determined.

[0058] In the electromagnetic simulation software, the ray tracing parameters were set to 10 reflections and 3 diffractions. After configuring the parameters, the simulation was run and data such as path loss and channel impulse response were output. The simulation results were compared with actual measurement points at 3-5 key locations in the scenario to ensure that the path loss error was within 3dB and the multipath delay structure was basically consistent.

[0059] In subsequent iterative optimization processes, if the simulation results for specific areas such as severely occluded blind spots deviate significantly from the expected values, the number of reflections can be increased to 12 or the number of diffractions to 4 for that area, and the simulation can be repeated. After optimization is completed, the global parameters can be restored.

[0060] Specifically, in a preferred embodiment of this application, determining the amplification and forwarding gain to be used when the relay terminal forwards the signal further includes: The noise power of the received signal of the relay terminal is estimated, and the amplification and forwarding gain is adjusted according to the noise power estimation result so that the output power of the relay terminal does not exceed its maximum transmit power limit.

[0061] The difference in this embodiment lies in the method of determining the amplification and forwarding gain in step S4. In this embodiment, the amplification and forwarding gain is adaptively adjusted based on the industrial background noise model. An industrial background noise model is preset in the digital twin environment, such as setting the noise floor to be higher than -3dB, and the noise power at the relay terminal is estimated in real time during each simulation. If the estimated noise power is high, the amplification gain is appropriately reduced to avoid excessive noise amplification; at the same time, it is ensured that the output power of the relay terminal does not exceed its maximum transmit power limit. Through this adaptive adjustment, the optimized scheme has stronger robustness to dynamically changing industrial electromagnetic environments.

[0062] Specifically, in a preferred embodiment of this application, after identifying the terminal to be optimized whose performance is below a preset threshold, the method further includes: In the digital twin environment, root cause analysis is performed on the terminal to be optimized to identify physical factors that lead to performance degradation. These physical factors include at least one of obstacle occlusion, multipath interference, and co-channel interference. The optimization operation adjusts the selection parameters of the relay terminal or the calculation parameters for amplifying the forwarding gain based on the results of the root cause analysis.

[0063] In a preferred embodiment of this application, after step S3 identifies terminals whose communication performance is below a preset threshold, it further includes root cause analysis and optimization adjustments based on the analysis results. The specific implementation process is as follows: In a digital twin environment, for each terminal marked as to be optimized, multi-dimensional information such as channel impulse response (CIR), signal propagation path data, path loss and SINR decomposition, and spatial location relationship are extracted from the channel data obtained in step S2.

[0064] The system has a built-in root cause analysis engine that automatically identifies physical factors contributing to performance degradation based on preset rules. (1) Obstacle occlusion determination Judgment rule: Check whether there is a direct path between the terminal and the base station. If there is no direct path in the ray tracing results, and the sum of the signal energy of all reflection / diffraction paths is lower than a preset threshold, for example, more than 20dB higher than the free space loss, then it is judged as an obstacle occlusion.

[0065] In practice, in the 3D model, a virtual ray is emitted from the location of the network device (base station) to the location of the terminal device. If the ray intersects with any obstacle defined as being made of metal or concrete during its propagation, and the intersection point is located between the starting point and the ending point of the ray, it is marked as an obstruction.

[0066] (2) Multipath interference determination Judgment rule: Analyze the channel impulse response. If there are two or more multipath components with similar amplitudes (amplitude difference less than 6dB) and a delay difference less than one OFDM symbol period, then it is judged as multipath interference.

[0067] In practice, all multipath components with amplitudes higher than the main path amplitude by 10 dB are extracted from the CIR, and their delay spread is calculated. If the RMS delay spread exceeds the cyclic prefix length (approximately 4.7 μs for NB-IoT) and the amplitude ratio of the main path to the secondary path is less than 3 dB, then the multipath interference is considered severe. Simultaneously, ray tracing technology is used to locate the specific object generating strong reflections, such as the metal wall behind it or the surface of large equipment.

[0068] (3) Determination of co-channel interference Judgment rule: Analyze the SINR decomposition data. If the interference power (I) accounts for more than 70% of the total interference plus noise power (I+N) and the interference power has obvious directional characteristics, it is judged as co-frequency interference.

[0069] In practice, within a digital twin environment, neighboring terminal devices using the same frequency resources as the terminal to be optimized are identified. Ray tracing is performed on these potential interference sources to calculate the interference power of their signals reaching the terminal to be optimized. If the contribution of a particular interference source exceeds 50% of the total interference power, the source and its propagation path are located, such as through wall reflections into the receiver.

[0070] Based on the judgment results, the system automatically adjusts the optimization operation parameters in step S4: For root cause type obstacle obstruction, the system prioritizes selecting a terminal located on the other side of the obstruction with a direct path to the base station as a relay; the amplification and forwarding gain is appropriately increased (+2~3dB) to compensate for obstruction loss; for root cause type multipath interference, the system selects a terminal located outside the interference reflection path as a relay terminal to avoid the relay terminal itself being affected by the same multipath; the amplification and forwarding gain is calculated using a formula and no additional adjustment is made; for root cause type co-channel interference, the system selects a terminal spatially isolated from the interference source as a relay; if this cannot be avoided, the system may consider adjusting the time-frequency resource allocation of the relay to avoid interference frequencies.

[0071] After completing one round of optimization in step S5, the root cause analysis is rerun to verify whether the optimization has eliminated or mitigated the original physical degradation factors: for occlusion-type terminals, verify whether an effective diffraction / reflection path has been established through relays; for multipath-type terminals, verify whether strong multipath components have been eliminated in the CIR of the relay link; for interference-type terminals, verify whether the proportion of interference power in the optimized SINR has decreased.

[0072] If the root cause is not eliminated, further adjust and optimize the parameters or select other relays until the root cause is effectively alleviated.

[0073] In this preferred embodiment, by conducting root cause analysis, differentiated strategies are adopted for different physical factors such as obstruction, multipath, and interference, providing clear directions for improvement in physical network deployment, such as adjusting device positions and adding reflectors.

[0074] This application also provides a digital twin system optimized for narrowband IoT wireless communication, including: An environment construction module is used to construct a digital twin environment corresponding to the target environment, wherein the digital twin environment is used to simulate the propagation characteristics of wireless signals in the target environment; A simulation module is used to deploy an NB-IoT network in the digital twin environment, the NB-IoT network including at least one network device and multiple terminal devices; to run a wireless communication simulation in the NB-IoT network and acquire channel data of the communication link between the terminal devices and the network devices; The diagnostic module is used to calculate the communication performance index of each terminal device based on the channel data, and identify terminals whose communication performance is lower than a preset threshold that need to be optimized. An optimization module is used to perform optimization operations in the digital twin environment for each terminal to be optimized: selecting at least one other terminal device as a relay terminal for the terminal to be optimized, determining the amplification forwarding gain that the relay terminal should use when forwarding signals based on the channel quality between the terminal to be optimized and the relay terminal, and simulating the cooperative communication process of the terminal to be optimized forwarding data to the network device through the relay terminal. The iteration module is used to rerun the wireless communication simulation, including the optimization operation, in the digital twin environment. If the communication performance of all terminals to be optimized is improved to above the preset threshold, the current optimization strategy is output; otherwise, the selection conditions of the relay terminal or the calculation parameters of the amplification and forwarding gain in the optimization operation are adjusted, and the optimization operation and re-simulation steps are repeated until the requirements are met.

[0075] The functional explanations of each module in this embodiment are the same as those of a digital twin method for optimizing narrowband IoT wireless communication, and the technical effects are the same, so they will not be repeated here.

[0076] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0077] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0078] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0079] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the specification of the present invention.

Claims

1. A digital twin method for optimizing narrowband IoT wireless communication, characterized in that, include: Construct a digital twin environment corresponding to the target environment, wherein the digital twin environment is used to simulate the propagation characteristics of wireless signals in the target environment; An NB-IoT network is deployed in the digital twin environment, the NB-IoT network including at least one network device and multiple terminal devices; wireless communication simulation is run in the NB-IoT network to obtain channel data of the communication link between the terminal devices and the network device; Based on the channel data, calculate the communication performance index of each terminal device and identify terminals whose communication performance is lower than a preset threshold that need to be optimized. For each terminal to be optimized, optimization operations are performed in the digital twin environment: at least one other terminal device is selected as a relay terminal for the terminal to be optimized, and the amplification forwarding gain that the relay terminal should use when forwarding signals is determined according to the channel quality between the terminal to be optimized and the relay terminal, and the cooperative communication process of the terminal to be optimized forwarding data to the network device through the relay terminal is simulated. In the digital twin environment, the wireless communication simulation including the optimization operation is rerun. If the communication performance of all terminals to be optimized is improved to above the preset threshold, the current optimization strategy is output. Otherwise, adjust the selection conditions of the relay terminal or the calculation parameters of the amplified forwarding gain in the optimization operation, and repeat the optimization operation and re-simulation steps until the requirements are met.

2. The digital twin method for optimizing narrowband IoT wireless communication according to claim 1, characterized in that, The construction of the digital twin environment corresponding to the target environment includes: A three-dimensional model of the equipment and obstacles in the industrial scene is constructed using geometric modeling tools, and their electromagnetic material properties are defined. Based on the aforementioned 3D model, electromagnetic simulation tools are used to generate data on the propagation path, reflection, diffraction, and transmission of wireless signals in the industrial setting using ray tracing technology. The output data of the electromagnetic simulation tool is aggregated using data processing tools to form a channel model for wireless communication simulation.

3. The digital twin method for optimizing narrowband IoT wireless communication according to claim 2, characterized in that, The number of reflections and diffractions set in the ray tracing technology are determined based on the balance between simulation accuracy and computational efficiency.

4. The digital twin method for optimizing narrowband IoT wireless communication according to claim 1, characterized in that, The channel data includes at least one of channel impulse response, path loss, and signal propagation path; the communication performance includes at least one of bit error rate, signal-to-interference-plus-noise ratio, and effective throughput.

5. The digital twin method for optimizing narrowband IoT wireless communication according to claim 4, characterized in that, Based on the channel data, the communication performance indicators of each terminal device are calculated as follows: The channel data is randomly sampled multiple times using the Monte Carlo simulation method to generate a statistical distribution of communication performance indicators; the statistical distribution includes a cumulative distribution function and / or a probability density function.

6. The digital twin method for optimizing narrowband IoT wireless communication according to claim 1, characterized in that, Selecting at least one other terminal device as a relay terminal for the terminal to be optimized specifically includes: Calculate the signal-to-noise ratio of the link between each other terminal device and the network device; One or more other terminal devices with the highest signal-to-noise ratio are identified as the relay terminal.

7. The digital twin method for optimizing narrowband IoT wireless communication according to claim 1, characterized in that, The amplification and forwarding gain is determined by the following formula: in, The transmitted signal energy of the terminal to be optimized. The channel attenuation from the terminal to be optimized to the relay terminal, The noise power of the link from the terminal to be optimized to the relay terminal.

8. The digital twin method for optimizing narrowband IoT wireless communication according to claim 1, characterized in that, The determination of the amplification and forwarding gain to be used when forwarding signals by the relay terminal also includes: The noise power of the received signal of the relay terminal is estimated, and the amplification and forwarding gain is adjusted according to the noise power estimation result so that the output power of the relay terminal does not exceed its maximum transmit power limit.

9. The digital twin method for optimizing narrowband IoT wireless communication according to claim 1, characterized in that, After identifying terminals whose communication performance is below a preset threshold, the process further includes: In the digital twin environment, root cause analysis is performed on the terminal to be optimized to identify physical factors that lead to performance degradation. These physical factors include at least one of obstacle occlusion, multipath interference, and co-channel interference. The optimization operation adjusts the selection parameters of the relay terminal or the calculation parameters for amplifying the forwarding gain based on the results of the root cause analysis.

10. A digital twin system optimized for narrowband Internet of Things (IoT) wireless communication, characterized in that, include: An environment construction module is used to construct a digital twin environment corresponding to the target environment, wherein the digital twin environment is used to simulate the propagation characteristics of wireless signals in the target environment; The simulation module is used to deploy an NB-IoT network in the digital twin environment, the NB-IoT network including at least one network device and multiple terminal devices; to run a wireless communication simulation in the NB-IoT network and acquire channel data of the communication link between the terminal devices and the network devices; The diagnostic module is used to calculate the communication performance index of each terminal device based on the channel data, and identify terminals whose communication performance is lower than a preset threshold that need to be optimized. An optimization module is used to perform optimization operations in the digital twin environment for each terminal to be optimized: selecting at least one other terminal device as a relay terminal for the terminal to be optimized, determining the amplification forwarding gain that the relay terminal should use when forwarding signals based on the channel quality between the terminal to be optimized and the relay terminal, and simulating the cooperative communication process of the terminal to be optimized forwarding data to the network device through the relay terminal. An iterative module is used to rerun the wireless communication simulation, including the optimization operation, in the digital twin environment. If the communication performance of all terminals to be optimized is improved to above the preset threshold, the current optimization strategy is output. Otherwise, adjust the selection conditions of the relay terminal or the calculation parameters of the amplified forwarding gain in the optimization operation, and repeat the optimization operation and re-simulation steps until the requirements are met.

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