Communication equipment harness path optimization system based on 5G base station distribution data

Abstract

The present invention relates to the field of network planning technology, and specifically to a communication equipment harness path optimization system based on 5G base station distribution data. The system comprises receiving base station distribution data and signal frequency characteristics, establishing a dielectric constant gradient analysis model, calculating the optimal dielectric constant distribution of the harness insulation layer, and generating an impedance distribution matrix; calculating the electromagnetic field strength distribution of each layer based on the impedance distribution matrix, obtaining a structural parameter set, establishing a comprehensive transmission loss prediction model, calculating the weight coefficient of each transmission path in combination with the base station distribution data, and obtaining a dynamic path priority and a power allocation scheme; updating a network topology analysis unit based on the base station distribution data, simulating preset base station signal changes, obtaining the robustness of the dynamic path priority and the power allocation scheme under different network conditions, and outputting simulated transmission performance data; thereby improving the system's adaptability to sudden network changes and significantly reducing signal degradation caused by path interference and load concentration.

Description

Technical Field

[0001] The present invention specifically relates to the technical field of network planning, and is a communication equipment harness path optimization system based on 5G base station distribution data. Background Art

[0002] With the rapid development of 5G mobile communication technology, the density and complexity of base station networks are gradually increasing, and the wiring harness layout and path planning of communication equipment are facing huge challenges.

[0003] Existing technologies typically use fixed layouts for communication equipment wiring harnesses, failing to fully consider the actual distribution characteristics of 5G base stations, signal coverage, and dynamic changes in network load. This approach leads to redundant wiring harnesses, inefficient signal transmission, and increased complexity in equipment installation and maintenance. In high-density base station deployments, wiring harness crossover and signal interference become particularly prominent, severely impacting the overall performance of the communication network.

[0004] Furthermore, current network planning tools primarily focus on optimizing base station locations and coverage planning, but lack effective technical means for optimizing the routing of wiring harnesses within communications equipment. Existing wiring harness design methods are unable to respond to changes in base station distribution in real time, nor can they adapt to actual signal strength and transmission requirements, resulting in irrational resource allocation and low system efficiency.

[0005] To this end, a communication equipment harness path optimization system based on 5G base station distribution data is proposed. Summary of the Invention

[0006] The present invention aims to provide a communication equipment harness path optimization system based on 5G base station distribution data, receive base station distribution data and signal frequency characteristics, establish a dielectric constant gradient analysis model, calculate the optimal dielectric constant distribution of the harness insulation layer, and generate an impedance distribution matrix; calculate the electromagnetic field strength distribution of each layer based on the impedance distribution matrix, obtain a structural parameter set, establish a comprehensive transmission loss prediction model, calculate the weight coefficient of each transmission path in combination with the base station distribution data, obtain dynamic path priority and power allocation scheme; update the network topology analysis unit based on the base station distribution data, simulate the preset base station signal changes, obtain the robustness of the dynamic path priority and power allocation scheme under different network conditions, and output simulated transmission performance data; obtain the transmission performance data of the harness in actual operation in real time, compare it with the simulated transmission performance data, and generate a correction instruction when the error exceeds a preset threshold. Thereby improving the system's adaptability to sudden network changes and significantly reducing the signal degradation caused by path interference and load concentration.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The communication equipment harness path optimization system based on 5G base station distribution data includes:

[0009] The dielectric gradient analysis module receives base station distribution data and signal frequency characteristics, establishes a dielectric constant gradient analysis model, calculates the optimal dielectric constant distribution of the wiring harness insulation layer, and generates an impedance distribution matrix;

[0010] The geometric structure optimization module calculates the electromagnetic field intensity distribution of each layer based on the impedance distribution matrix and obtains the structural parameter set;

[0011] The intelligent path optimization module establishes a comprehensive transmission loss prediction model based on the structural parameter set, calculates the weight coefficient of each transmission path based on base station distribution data, and obtains dynamic path priority and power allocation schemes. It also updates the network topology analysis unit based on base station distribution data, simulates preset base station signal changes, obtains the robustness of dynamic path priority and power allocation schemes under different network conditions, and outputs simulated transmission performance data.

[0012] The full-link self-correction module obtains the transmission performance data of the wiring harness in actual operation in real time, compares it with the simulated transmission performance data, and generates correction instructions when the error between the two exceeds the preset threshold.

[0013] Preferably, the dielectric constant gradient analysis model includes a dielectric constant calculation layer, a gradient analysis layer and an impedance matrix generation layer;

[0014] The dielectric constant calculation layer processes the frequency characteristics and environmental dielectric parameters in the base station distribution data to obtain the initial dielectric constant of each harness insulation layer;

[0015] The gradient analysis layer performs spatial gradient calculation on the initial dielectric constant to obtain an optimal dielectric constant distribution;

[0016] The impedance matrix generation layer calculates characteristic impedance based on the optimal dielectric constant distribution and generates an impedance distribution matrix.

[0017] Preferably, the electric field intensity distribution data and the magnetic field intensity distribution data of the conductor layer, the insulation layer, the shielding layer and the outer sheath layer in the wiring harness are calculated based on the impedance distribution matrix;

[0018] The electric field intensity distribution data and the magnetic field intensity distribution data are used as constraints, and the particle swarm optimization algorithm is used to perform global optimization on the range of helical angle, pitch ratio and interlayer spacing.

[0019] By iteratively optimizing the particle position and velocity, a structural parameter set including the inner helix angle parameter, outer helix angle parameter, pitch ratio parameter, layer spacing parameter and shielding effectiveness function is output.

[0020] Preferably, the comprehensive transmission loss prediction model includes a loss factor analysis layer, a path attenuation calculation layer, a weight coefficient generation layer and a priority sorting layer;

[0021] The loss factor analysis layer calculates the conductor loss, dielectric loss and radiation loss based on the helix angle, pitch ratio and layer spacing in the structural parameter set;

[0022] The path attenuation calculation layer calculates the signal attenuation degree of each transmission path by combining the signal transmission distance and environmental factors in the base station distribution data;

[0023] The weight coefficient generation layer uses the hierarchical analysis method to assign weights to conductor loss, dielectric loss, radiation loss and signal attenuation, and calculates the comprehensive weight coefficient of each transmission path;

[0024] The priority sorting layer uses a priority sorting algorithm to determine the dynamic path priority based on the comprehensive weight coefficient, and uses a power control algorithm to generate the corresponding power allocation plan.

[0025] Preferably, the network topology analysis unit uses a graph theory algorithm to establish a connection relationship between base station nodes, and constructs a network topology structure with the location coordinates in the base station distribution data as nodes and the signal coverage range as edge weights;

[0026] The network topology analysis unit includes:

[0027] The signal change simulation subunit simulates different network states, including base station signal strength changes, frequency drift, and load fluctuations. The robustness evaluation subunit applies dynamic path priority and power allocation schemes to different network states to evaluate the robustness of the schemes. The performance data generation subunit generates simulated transmission performance data including transmission delay, bit error rate, and signal strength based on the robustness evaluation results.

[0028] Preferably, the generation and application of the correction instructions include: a performance monitoring unit, which collects the transmission delay, bit error rate and signal strength during the operation of the wiring harness in real time to form actual transmission performance data; an error analysis unit, which uses statistical analysis methods to calculate the root mean square error and relative error between the actual transmission performance data and the simulated transmission performance data; a threshold judgment unit, which compares the calculated error with a preset threshold and triggers a correction mechanism when the error exceeds the threshold; an instruction generation unit, which uses feedback control theory to generate a correction instruction containing the parameter adjustment direction and adjustment amplitude based on the error analysis result, and feeds the correction instruction back to the medium gradient analysis module and the geometric structure optimization module for parameter update.

[0029] Preferably, the intelligent path optimization module further includes:

[0030] The multipath transmission coordination unit receives the dynamic path priority and power allocation scheme output by the priority sorting layer, optimizes the signal allocation ratio of each transmission path, calculates the signal allocation coefficient of each path based on the comprehensive weight coefficient, and uses the maximum ratio combining technology to achieve coordinated transmission of multipath signals;

[0031] The dynamic load balancing unit monitors network load changes in base station distribution data in real time, uses a load prediction algorithm to analyze the congestion status of each transmission path, and dynamically adjusts the load distribution of each path using a traffic distribution algorithm. When it detects that the path load exceeds a preset threshold, it triggers a load redistribution mechanism, updates the dynamic path priority, and regenerates the power allocation plan;

[0032] The multipath transmission coordination unit and the dynamic load balancing unit work together to feed back the optimized path allocation result to the network topology analysis unit for robustness verification.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. The present invention uses a dielectric gradient analysis module and a geometric structure optimization module to model the dielectric constant distribution of the wiring harness in the communication equipment and globally optimize the structural geometric parameters. The present invention introduces a dielectric constant gradient model constructed based on 5G base station distribution data and frequency characteristics to generate an impedance distribution matrix. Furthermore, a particle swarm optimization algorithm is used to perform multi-objective optimization on structural parameters such as helix angle, pitch ratio and interlayer spacing under electromagnetic constraints, and output a set of structural parameters. This not only improves the transmission adaptability of the wiring harness to high-frequency signals, but also effectively reduces radiation loss and crosstalk problems, thereby improving the overall transmission performance and anti-interference capability.

[0035] 2. This invention integrates structural parameter sets, base station distribution data, and environmental dynamics to establish a comprehensive transmission loss prediction model, dynamically outputting path priority and power allocation schemes. By implementing a loss factor analysis layer, a path attenuation calculation layer, and a weight ranking layer, this invention calculates conductor, dielectric, and radiation losses, assigns weights based on the actual transmission distance and network environment, and evaluates the overall transmission performance of each path. A network topology analysis unit simulates the signal states of different base stations (such as frequency drift and load fluctuations) to perform dynamic priority sorting and power control. This not only improves the system's adaptability to sudden network changes, but also significantly reduces signal degradation caused by path interference and load concentration.

[0036] 3. The present invention realizes the comparison and dynamic correction between actual operation data and preset simulation data. The present invention collects performance indicators such as transmission delay, bit error rate and signal strength in real time during the operation of the wiring harness, and performs root mean square error and relative error analysis with the simulation data. Once the error exceeds the threshold, the instruction generation unit generates a correction instruction based on feedback control theory to dynamically adjust the dielectric constant gradient model and structural optimization parameters. While ensuring the communication performance of the system, it realizes continuous self-learning and optimization of parameters, enhancing the system's long-term adaptability to factors such as environmental disturbances and aging effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A schematic diagram of the structure of a communication equipment harness path optimization system based on 5G base station distribution data provided by the present invention;

[0038] Figure 2 A schematic diagram of the process flow of the communication equipment harness path optimization system based on 5G base station distribution data provided by the present invention;

[0039] Figure 3 This is a schematic diagram of the dielectric constant gradient analysis model structure provided by the present invention. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] Example 1:

[0042] The present invention provides a communication equipment harness path optimization system based on 5G base station distribution data, which includes four modules: a dielectric gradient analysis module, a geometric structure optimization module, an intelligent path optimization module, and a full-link self-correction module. Figure 1 ; For the specific content of each module, refer to Figure 2 , the specific technical solutions are as follows:

[0043] The dielectric gradient analysis module receives base station distribution data and signal frequency characteristics and performs preprocessing, including denoising, cleaning, and standardization. Based on the preprocessed base station distribution data and signal frequency characteristics, it establishes a dielectric constant gradient analysis model, calculates the optimal dielectric constant distribution of the wiring harness insulation layer, and generates an impedance distribution matrix.

[0044] The dielectric constant gradient analysis model includes a dielectric constant calculation layer, a gradient analysis layer and an impedance matrix generation layer. Figure 3 ;

[0045] The dielectric constant calculation layer processes the frequency characteristics and environmental dielectric parameters in the base station distribution data to obtain the initial dielectric constant of each harness insulation layer;

[0046] The gradient analysis layer performs spatial gradient calculation on the initial dielectric constant to obtain an optimal dielectric constant distribution;

[0047] The impedance matrix generation layer calculates characteristic impedance based on the optimal dielectric constant distribution and generates an impedance distribution matrix.

[0048] In this embodiment, the dielectric constant calculation layer, gradient analysis layer, and impedance matrix generation layer work together to efficiently process frequency characteristics and environmental parameters, optimize dielectric constant distribution, and enhance signal transmission stability and impedance matching accuracy. This improves wiring harness design efficiency and reduces the risk of signal attenuation and interference.

[0049] The geometric structure optimization module calculates the electromagnetic field intensity distribution of each layer based on the impedance distribution matrix and obtains the structural parameter set;

[0050] The electric field intensity distribution data and magnetic field intensity distribution data of the conductor layer, insulation layer, shielding layer and outer sheath layer in the wiring harness are calculated based on the impedance distribution matrix;

[0051] The electric field intensity distribution data and the magnetic field intensity distribution data are used as constraints, and the particle swarm optimization algorithm is used to perform global optimization on the range of helical angle, pitch ratio and interlayer spacing.

[0052] By iteratively optimizing the particle position and velocity, a structural parameter set including the inner helix angle parameter, outer helix angle parameter, pitch ratio parameter, layer spacing parameter and shielding effectiveness function is output.

[0053] In this embodiment, a structural parameter set including the inner layer helix angle, outer layer helix angle, pitch ratio, layer spacing and shielding effectiveness is output through iterative optimization to improve the electromagnetic compatibility and shielding effectiveness of the wiring harness and reduce signal interference and energy loss.

[0054] A machine learning prediction unit is included, wherein the machine learning prediction unit includes:

[0055] The historical data collection subunit collects historical operating data such as base station distribution data, network load changes, and signal transmission performance, and establishes a time series database;

[0056] The deep learning training sub-unit uses a long short-term memory network to extract features and learn patterns from historical data, building network status prediction models and performance trend prediction models;

[0057] The intelligent prediction subunit, based on the trained prediction model, predicts base station load distribution, signal attenuation trends, and network topology changes in the future period and generates prediction result data;

[0058] The prediction feedback sub-unit inputs the prediction result data into the intelligent path optimization module in advance to achieve forward-looking adjustment of path optimization, and performs online learning optimization on the prediction model based on actual operation results.

[0059] Through machine learning prediction capabilities, network status change trends can be predicted in advance, enabling proactive optimization and adjustment rather than passive response, improving the foresight and accuracy of path optimization.

[0060] The intelligent path optimization module establishes a comprehensive transmission loss prediction model based on the structural parameter set, calculates the weight coefficient of each transmission path based on base station distribution data, and obtains dynamic path priority and power allocation schemes. It also updates the network topology analysis unit based on base station distribution data, simulates preset base station signal changes, obtains the robustness of dynamic path priority and power allocation schemes under different network conditions, and outputs simulated transmission performance data.

[0061] The comprehensive transmission loss prediction model includes a loss factor analysis layer, a path attenuation calculation layer, a weight coefficient generation layer and a priority sorting layer;

[0062] The loss factor analysis layer calculates the conductor loss, dielectric loss and radiation loss based on the helix angle, pitch ratio and layer spacing in the structural parameter set;

[0063] The path attenuation calculation layer calculates the signal attenuation degree of each transmission path by combining the signal transmission distance and environmental factors in the base station distribution data;

[0064] The weight coefficient generation layer uses the hierarchical analysis method to assign weights to conductor loss, dielectric loss, radiation loss and signal attenuation, and calculates the comprehensive weight coefficient of each transmission path;

[0065] The priority sorting layer uses a priority sorting algorithm to determine the dynamic path priority based on the comprehensive weight coefficient, and uses a power control algorithm to generate the corresponding power allocation plan.

[0066] In this embodiment, the loss factor analysis layer, path attenuation calculation layer, weight coefficient generation layer, and priority sorting layer work together to calculate conductor loss, dielectric loss, radiation loss, and signal attenuation. A hierarchy analysis method and a priority sorting algorithm are then used to optimize path selection and power allocation, improving signal transmission efficiency and network robustness. This reduces transmission loss and enhances system adaptability.

[0067] The network topology analysis unit uses a graph theory algorithm to establish the connection relationship between base station nodes, using the location coordinates in the base station distribution data as nodes and the signal coverage range as edge weights to construct a network topology structure;

[0068] The network topology analysis unit includes:

[0069] The signal change simulation subunit simulates different network states, including base station signal strength changes, frequency drift, and load fluctuations. The robustness evaluation subunit applies dynamic path priority and power allocation schemes to different network states to evaluate the robustness of the schemes. The performance data generation subunit generates simulated transmission performance data including transmission delay, bit error rate, and signal strength based on the robustness evaluation results.

[0070] In this embodiment, each subunit simulates base station signal strength variations, frequency drift, and load fluctuations to evaluate the robustness of dynamic path priority and power allocation schemes, generating performance data including transmission delay, bit error rate, and signal strength. This improves the adaptability of network topology analysis, optimizes signal transmission efficiency, and enhances robustness in complex network environments.

[0071] The intelligent path optimization module also includes:

[0072] The multipath transmission coordination unit receives the dynamic path priority and power allocation scheme output by the priority sorting layer, optimizes the signal allocation ratio of each transmission path, calculates the signal allocation coefficient of each path based on the comprehensive weight coefficient, and uses the maximum ratio combining technology to achieve coordinated transmission of multipath signals;

[0073] The dynamic load balancing unit monitors network load changes in base station distribution data in real time, uses a load prediction algorithm to analyze the congestion status of each transmission path, and dynamically adjusts the load distribution of each path using a traffic distribution algorithm. When it detects that the path load exceeds a preset threshold, it triggers a load redistribution mechanism, updates the dynamic path priority, and regenerates the power allocation plan;

[0074] The multipath transmission coordination unit and the dynamic load balancing unit work together to feed back the optimized path allocation result to the network topology analysis unit for robustness verification.

[0075] In this embodiment, the intelligent path optimization module provided by the present invention improves network transmission efficiency and stability through the collaborative work of a multipath transmission coordination unit and a dynamic load balancing unit. The multipath transmission coordination unit optimizes signal distribution ratios based on dynamic path priorities and comprehensive weight coefficients to achieve coordinated multipath signal transmission. The dynamic load balancing unit monitors network load in real time, dynamically adjusts path loads, and triggers a reallocation mechanism to update priority and power allocation schemes.

[0076] The full-link self-correction module obtains the transmission performance data of the wiring harness in actual operation in real time, compares it with the simulated transmission performance data, and generates correction instructions when the error between the two exceeds the preset threshold.

[0077] The generation and application of the correction instructions include: a performance monitoring unit, which collects the transmission delay, bit error rate and signal strength of the wiring harness in real time to form actual transmission performance data; an error analysis unit, which uses statistical analysis methods to calculate the root mean square error and relative error between the actual transmission performance data and the simulated transmission performance data; a threshold judgment unit, which compares the calculated error with a preset threshold and triggers a correction mechanism when the error exceeds the threshold; and an instruction generation unit, which uses feedback control theory based on the error analysis results to generate correction instructions including the parameter adjustment direction and adjustment amplitude, and feeds the correction instructions back to the medium gradient analysis module and the geometric structure optimization module for parameter update.

[0078] In this embodiment, the present invention significantly improves the real-time performance and accuracy of parameter adjustment, reduces transmission errors, and enhances the robustness of wiring harness performance in complex environments.

[0079] The full-link self-correction module further includes an environment adaptive adjustment unit, which includes:

[0080] The environmental parameter monitoring subunit monitors the temperature, humidity, electromagnetic field strength and mechanical vibration parameters of the wiring harness operating environment in real time to form an environmental parameter data set;

[0081] The environmental impact assessment subunit establishes a correlation model between environmental parameters and wiring harness performance, analyzing the impact of temperature and humidity changes on dielectric constant, the interference of the electromagnetic environment on signal transmission, and the impact of mechanical vibration on the geometric structure of the wiring harness;

[0082] The adaptive compensation subunit uses fuzzy control algorithms to generate environmental compensation strategies based on environmental impact assessment results, including dynamic adjustment of dielectric constant, adaptive compensation of signal power, and structural parameter fine-tuning schemes;

[0083] The real-time adjustment execution sub-unit converts the environmental compensation strategy into specific control instructions, and works together with the correction instructions to achieve real-time environmental adaptive adjustment of the medium gradient analysis module and the geometric structure optimization module.

[0084] Through the environmental adaptive adjustment function, it responds to the impact of environmental changes on the performance of the wiring harness in real time and automatically performs compensation adjustments to ensure that the optimal transmission performance can be maintained under different environmental conditions, thereby improving the environmental adaptability and stability of the system.

[0085] It also includes a multi-band collaborative optimization module, which includes:

[0086] The frequency band identification unit automatically identifies and classifies low-frequency (Sub-6GHz), mid-frequency (6-24GHz), and high-frequency (24-100GHz) signals in 5G communications, and extracts the signal characteristic parameters of each frequency band;

[0087] Cross-band interference analysis unit: establishes a multi-band electromagnetic interference model, analyzes the mutual influence and interference pattern of signals of different frequency bands in the wiring harness, and calculates the crosstalk coefficient between frequency bands;

[0088] The collaborative optimization algorithm unit uses a multi-objective genetic algorithm to jointly optimize the wiring harness structural parameters of each frequency band, with the goal of minimizing cross-band interference and maximizing the transmission efficiency of each frequency band, and generates a multi-band collaborative optimization solution;

[0089] The frequency band resource allocation unit, based on a collaborative optimization solution, performs unified scheduling of power allocation, time slot allocation, and path selection for different frequency bands, achieving efficient utilization of multi-band resources.

[0090] Through multi-band collaborative optimization, multiple frequency band signals of 5G communication are processed simultaneously, which reduces interference between frequency bands, improves spectrum utilization efficiency, and achieves higher data transmission rates and more stable communication quality.

[0091] The communication equipment harness path optimization system based on 5G base station distribution data provided by the present invention significantly improves harness design and signal transmission performance through the collaborative work of multiple modules. The dielectric gradient analysis module optimizes the dielectric constant distribution and impedance matching to reduce signal attenuation and interference; the geometric structure optimization module generates the optimal structural parameter set through the particle swarm optimization algorithm to improve electromagnetic compatibility and shielding effectiveness; the machine learning prediction module uses the long short-term memory network to predict the network status and forward-looking path optimization; the intelligent path optimization module optimizes path selection and power distribution through comprehensive transmission loss prediction and dynamic load balancing, thereby improving transmission efficiency and network robustness; the network topology analysis unit uses a graph theory algorithm to evaluate the robustness of the scheme and generate performance data; the full-link self-correction module compares the actual and simulated performance in real time, generates correction instructions, and enhances the system adaptability; the environmental adaptive adjustment unit dynamically compensates for environmental influences through a fuzzy control algorithm to ensure stable performance in complex environments; the multi-band collaborative optimization module reduces cross-band interference and improves spectrum utilization and transmission rate. The present invention improves harness design efficiency, transmission stability and environmental adaptability, and reduces bit error rate and energy loss.

[0092] Example 2:

[0093] This embodiment takes the dense deployment of 5G networks in a certain urban area as an example to illustrate the implementation process of the technical solution of the communication equipment harness path optimization system based on 5G base station distribution data.

[0094] In actual deployment, the dielectric gradient analysis module first receives the location coordinates, signal coverage radius, and operating frequency distribution of base stations in the area. The dielectric constant calculation layer then categorizes the environmental dielectric parameters within the area, including high-density building environments in commercial areas, medium-density environments in residential areas, and electromagnetic interference environments in industrial areas, each corresponding to a different dielectric constant baseline value.

[0095] The gradient analysis layer calculates the optimal dielectric constant for each cable bundle insulation layer in different environmental zones based on the base station density distribution. In high-density commercial areas, the dielectric constant is set to a lower value to reduce signal attenuation; in industrial areas, the dielectric constant is set to a higher value to enhance interference resistance. Based on the optimized dielectric constant distribution, the impedance matrix generation layer generates a four-dimensional impedance distribution matrix encompassing the conductor layer, insulation layer, shielding layer, and outer jacket layer, ensuring precise impedance matching between each layer.

[0096] Based on the generated impedance distribution matrix, the geometry optimization module calculates the electromagnetic field strength distribution of each layer of the wiring harness. In the high-frequency signal transmission path in the commercial area, the electric field strength distribution of the conductor layer is concentrated in the center, while the magnetic field strength of the shielding layer reaches its peak at the edge.

[0097] Using these electromagnetic field distribution data as constraints, the particle swarm optimization algorithm performs a global optimization of the helix angle, pitch ratio, and interlayer spacing. The algorithm sets a swarm size of 50 particles and 200 iterations. During the optimization process, the inner helix angle was adjusted within the range of 15-45 degrees, the outer helix angle within the range of 30-60 degrees, the pitch ratio within the range of 1.2-2.8, and the interlayer spacing within the range of 0.5-2.0 mm.

[0098] The comprehensive transmission loss prediction model established by the intelligent path optimization module plays a key role in this scenario. The loss factor analysis layer calculates conductor loss, dielectric loss, and radiation loss based on optimized structural parameters.

[0099] The path attenuation calculation layer combines the actual base station distribution data to calculate the attenuation of each transmission path. It calculates the signal attenuation of short-distance paths in commercial areas, medium-distance paths in residential areas, and long-distance paths in industrial areas.

[0100] The weight coefficient generation layer uses an empirical method to assign weights to each loss factor: conductor loss weight is 0.35, dielectric loss weight is 0.28, radiation loss weight is 0.22, and signal attenuation weight is 0.15. Based on the weights, the comprehensive weight coefficient of each transmission path is calculated.

[0101] The priority sorting layer determines the dynamic path priority based on the comprehensive weight coefficient, divides the base station connection path into three levels: high priority, medium priority, and low priority, and formulates a corresponding power allocation plan for each level.

[0102] The multipath transmission coordination unit receives the priority sorting results and fine-tunes the signal allocation ratio for each path. In high-density commercial areas, maximum ratio combining (MRC) technology is used to coordinate the transmission of signals from multiple high-priority paths. The signal allocation coefficient is dynamically adjusted based on real-time signal quality. The dynamic load balancing unit monitors network load changes in real time. When it detects that the load on a path exceeds a threshold, it triggers a load redistribution mechanism, shifting some traffic to a lighter-loaded backup path.

[0103] The network topology analysis unit uses graph theory algorithms to establish the node connection relationship of base stations, and constructs a network topology graph with the GPS coordinates of base stations as nodes and the signal coverage radius as edges.

[0104] The signal variation simulation subunit simulates different network conditions: base station signal strength fluctuates between -60dBm and -90dBm, frequency drift ranges from ±50kHz, and load fluctuations vary between 20% and 95%. The robustness evaluation subunit applies dynamic path prioritization and power allocation schemes to these simulated conditions, evaluating the scheme's stability under different conditions.

[0105] The performance data generation subunit generates simulation data including transmission delay, bit error rate and signal strength based on the robustness evaluation results.

[0106] The performance monitoring unit collects real-time transmission performance data of the cable harness during system operation. During the 30-day continuous monitoring period, transmission delay, bit error rate, and signal strength data were collected;

[0107] The error analysis unit uses statistical analysis methods to calculate the difference between actual data and simulated data;

[0108] The threshold judgment unit compares the calculated error with preset thresholds: delay error <10%, bit error rate error <15%, and signal strength error <5%. When the bit error rate error exceeds the threshold, the correction mechanism is triggered.

[0109] The instruction generation unit generates correction instructions based on feedback control theory: adjusting the dielectric constant parameters of the commercial area in the dielectric constant gradient model; optimizing the pitch ratio in the geometric structure parameters; updating the path weight distribution and increasing the power allocation ratio of the medium priority path.

[0110] After the correction instructions were fed back to the corresponding modules, the system re-optimized its parameters. After three iterations of correction, the bit error rate was reduced to meet the preset threshold requirements, and system performance was significantly improved.

[0111] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A communication equipment harness path optimization system based on 5G base station distribution data, characterized in that: include: The dielectric gradient analysis module receives base station distribution data and signal frequency characteristics, establishes a dielectric constant gradient analysis model, calculates the optimal dielectric constant distribution of the wiring harness insulation layer, and generates an impedance distribution matrix; The dielectric constant gradient analysis model includes a dielectric constant calculation layer, a gradient analysis layer and an impedance matrix generation layer; The dielectric constant calculation layer processes the frequency characteristics and environmental dielectric parameters in the base station distribution data to obtain the initial dielectric constant of each harness insulation layer; The gradient analysis layer performs spatial gradient calculation on the initial dielectric constant to obtain an optimal dielectric constant distribution; The impedance matrix generation layer calculates characteristic impedance based on the optimal dielectric constant distribution to generate an impedance distribution matrix; A geometric structure optimization module calculates the electromagnetic field intensity distribution of each layer based on the impedance distribution matrix to obtain a set of structural parameters; the set of structural parameters includes inner layer helical angle parameters, outer layer helical angle parameters, pitch ratio parameters, layer spacing parameters and shielding effectiveness function; The intelligent path optimization module establishes a comprehensive transmission loss prediction model based on the structural parameter set, calculates the weight coefficient of each transmission path based on base station distribution data, and obtains dynamic path priority and power allocation schemes. It also updates the network topology analysis unit based on base station distribution data, simulates preset base station signal changes, obtains the robustness of dynamic path priority and power allocation schemes under different network conditions, and outputs simulated transmission performance data. The comprehensive transmission loss prediction model includes a loss factor analysis layer, a path attenuation calculation layer, a weight coefficient generation layer and a priority sorting layer; The loss factor analysis layer calculates the conductor loss, dielectric loss and radiation loss based on the helix angle, pitch ratio and layer spacing in the structural parameter set; The path attenuation calculation layer combines the signal transmission distance and environmental factors in the base station distribution data to calculate the signal attenuation degree of each transmission path; The weight coefficient generation layer uses the hierarchical analysis method to assign weights to conductor loss, dielectric loss, radiation loss and signal attenuation, and calculates the comprehensive weight coefficient of each transmission path; The priority sorting layer uses a priority sorting algorithm to determine the dynamic path priority based on the comprehensive weight coefficient, and uses a power control algorithm to generate the corresponding power allocation plan; The full-link self-correction module obtains the transmission performance data of the wiring harness in actual operation in real time, compares it with the simulated transmission performance data, and generates correction instructions when the error between the two exceeds the preset threshold.

2. A communication equipment harness path optimization system based on 5G base station distribution data according to claim 1, characterized in that: The electric field intensity distribution data and magnetic field intensity distribution data of the conductor layer, insulation layer, shielding layer and outer sheath layer in the wiring harness are calculated based on the impedance distribution matrix; The electric field intensity distribution data and the magnetic field intensity distribution data are used as constraints, and the particle swarm optimization algorithm is used to perform global optimization on the range of helical angle, pitch ratio and interlayer spacing. By iteratively optimizing the particle position and velocity, a structural parameter set including the inner helix angle parameter, outer helix angle parameter, pitch ratio parameter, layer spacing parameter and shielding effectiveness function is output.

3. The communication equipment harness path optimization system based on 5G base station distribution data according to claim 1, characterized in that: The network topology analysis unit uses a graph theory algorithm to establish the connection relationship between base station nodes, takes the location coordinates in the base station distribution data as nodes, and uses the signal coverage range as the edge weight to construct a network topology structure; The network topology analysis unit includes: The signal change simulation subunit simulates different network conditions, including base station signal strength changes, frequency drift, and load fluctuations; The robustness evaluation subunit applies dynamic path priority and power allocation schemes to different network states to evaluate the robustness of the schemes. The performance data generation subunit generates simulated transmission performance data including transmission delay, bit error rate and signal strength based on the robustness evaluation results.

4. The communication equipment harness path optimization system based on 5G base station distribution data according to claim 1, characterized in that: The generation and application of the correction instructions include: The performance monitoring unit collects the transmission delay, bit error rate and signal strength of the wiring harness in real time to form the actual transmission performance data; the error analysis unit uses statistical analysis methods to calculate the root mean square error and relative error between the actual transmission performance data and the simulated transmission performance data; the threshold judgment unit compares the calculated error with the preset threshold and triggers the correction mechanism when the error exceeds the threshold; the instruction generation unit uses feedback control theory to generate correction instructions containing parameter adjustment direction and adjustment amplitude based on the error analysis results, and feeds back the correction instructions to the medium gradient analysis module and geometric structure optimization module for parameter update.

5. The communication equipment harness path optimization system based on 5G base station distribution data according to claim 1, characterized in that: The intelligent path optimization module also includes: The multipath transmission coordination unit receives the dynamic path priority and power allocation scheme output by the priority sorting layer, optimizes the signal allocation ratio of each transmission path, calculates the signal allocation coefficient of each path based on the comprehensive weight coefficient, and uses the maximum ratio combining technology to achieve coordinated transmission of multipath signals; The dynamic load balancing unit monitors network load changes in base station distribution data in real time, uses a load prediction algorithm to analyze the congestion status of each transmission path, and dynamically adjusts the load distribution of each path using a traffic distribution algorithm. When it detects that the path load exceeds a preset threshold, it triggers a load redistribution mechanism, updates the dynamic path priority, and regenerates the power allocation plan; The multipath transmission coordination unit and the dynamic load balancing unit work together to feed back the optimized path allocation result to the network topology analysis unit for robustness verification.

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