A wireless data transmission system and method based on 5G communication

Abstract

The present invention relates to the field of information transmission technology, and in particular to a wireless data transmission system based on 5G communication and a method thereof. The method comprises the following steps: obtaining 5G communication data of an industrial park and performing communication demand analysis of a unit in the park to obtain communication demand data of the unit in the industrial park; performing virtual slice priority reallocation according to the communication demand data of the unit in the industrial park to obtain optimized virtual slice priority data; performing dynamic resource sharing strategy analysis according to the optimized virtual slice priority data to obtain a dynamic sharing strategy of communication resources in the industrial park; obtaining real-time 5G communication data of the industrial park and performing unit communication transmission latency evaluation to obtain unit communication transmission latency data; performing dynamic sharing timing optimization on the dynamic sharing strategy of communication resources in the industrial park based on the unit communication transmission latency data to obtain an optimized dynamic sharing strategy of communication resources in the industrial park. The present invention can improve the bandwidth utilization of the network.

Description

Technical Field

[0001] The present invention relates to the field of information transmission technology, and in particular to a wireless data transmission system and method based on 5G communication. Background Art

[0002] Traditional wireless data transmission methods face more and more challenges and limitations. As the core support of modern communication networks, the development of wireless communication technology can be traced back to early radio communications to modern 4G and 5G networks. With the continuous increase in data traffic and the diversification of network requirements, traditional wireless data transmission methods are gradually unable to meet key requirements such as high bandwidth, low latency, and high reliability, and new technological innovations are urgently needed to solve existing problems. Among the traditional wireless data transmission methods, there are mainly wireless communication technologies based on 2G (GSM), 3G (UMTS), and 4G (LTE). These technologies have effectively solved the needs of large-scale mobile communications during their application period, but with the rapid growth of network capacity and data rate requirements, they have gradually exposed several major defects. Traditional wireless communication systems are often limited by network architecture design, and the data transmission delay is high. Especially in applications such as industrial automation with extremely high real-time requirements, the delay of traditional wireless transmission methods is too long, which seriously affects the response speed and reliability of the system. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a wireless data transmission system and method based on 5G communication to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a wireless data transmission method based on 5G communication includes the following steps:

[0005] Step S1: Acquire 5G communication data of the industrial park, and perform network communication topology analysis of the industrial park based on the 5G communication data of the industrial park, thereby obtaining a network topology model of the industrial park; perform park unit communication demand analysis on the network topology model of the industrial park, thereby obtaining unit communication demand data of the industrial park;

[0006] Step S2: Designing a park unit communication mode according to the industrial park unit communication demand data, thereby obtaining park unit communication mode data; reallocating virtual slice priorities of the industrial park 5G communication data based on the park unit communication mode data, thereby obtaining optimized virtual slice priority data;

[0007] Step S3: Optimize the unit slice resource allocation according to the optimized virtual slice priority data, so as to obtain the optimized resource allocation data of the industrial park unit slice; perform dynamic resource sharing strategy analysis on the optimized resource allocation data of the industrial park unit slice according to the communication demand data of the industrial park unit, so as to obtain the dynamic sharing strategy of the industrial park communication resources, and upload it to the industrial park network communication management platform to execute the equipment communication task;

[0008] Step S4: acquiring real-time 5G communication data of the industrial park, and performing a park unit transmission performance evaluation on the real-time 5G communication data of the industrial park, thereby obtaining the park unit transmission performance data; performing a unit communication transmission delay evaluation on the park unit transmission performance data according to the industrial park unit communication demand data, thereby obtaining the unit communication transmission delay data;

[0009] Step S5: Based on the unit communication transmission delay data, the dynamic sharing timing of the industrial park communication resource dynamic sharing strategy is optimized to obtain the industrial park communication resource optimization dynamic sharing strategy, and upload it to the industrial park network communication management platform to execute the equipment communication task.

[0010] By acquiring and analyzing the 5G communication data of the industrial park, the present invention can accurately construct the topological structure model of the park network, thereby providing a scientific basis for the subsequent communication needs and mode design. This analysis process provides accurate data support for the communication needs and resource allocation of different units in the park, ensuring that the communication needs of various types of equipment can be maximized in the network design stage. By optimizing and redistributing the priority of virtual slices, it is helpful to improve the utilization efficiency of network resources, ensure that high-priority tasks can be guaranteed, and avoid waste or shortage of resources, and further improve the bandwidth and delay performance of the system. Optimizing the allocation of unit slice resources based on the optimized virtual slice priority data can not only improve the communication efficiency between the units in the industrial park, but also ensure that resource sharing between different regions and different devices is more reasonable. In this process, dynamic resource sharing strategy analysis is crucial to ensure the reasonable allocation of communication resources under different load conditions. This strategy can help the system flexibly adjust resources according to real-time needs, avoid excessive congestion or excess resources, thereby improving the transmission performance of the entire park and the response speed of the network, and is particularly suitable for industrial automation systems with extremely high real-time requirements. Based on real-time 5G communication data of industrial parks, the transmission performance evaluation of park units can accurately monitor the current communication status, timely discover network bottlenecks or potential problems, and provide data support for subsequent optimization. By evaluating the transmission latency, high-latency areas in the network can be accurately predicted and identified, providing direction for further optimizing the network structure and adjusting resource allocation. Based on these evaluation data, the optimization of dynamic sharing timing helps to reduce conflicts and delays in the resource allocation process, improve the stability and predictability of network operation, and ensure that communication tasks are completed in the shortest time. In summary, the above steps can significantly improve the bandwidth utilization of the network, reduce latency, and improve reliability through comprehensive optimization of 5G communications in industrial parks, and maintain the efficient operation of industrial systems under dynamically changing communication requirements. This flexible resource allocation and sharing strategy provides more efficient and reliable communication support for industrial parks, and is particularly suitable for industrial automation applications that require high precision and low latency. At the same time, this solution also has strong adaptability and scalability, and can cope with the rapid changes and growth of future network requirements.

[0011] Optionally, step S1 specifically includes:

[0012] Step S11: Acquire 5G communication data of the industrial park, and perform data preprocessing on the 5G communication data of the industrial park, so as to obtain 5G communication data to be analyzed;

[0013] Step S12: extracting virtual slice communication features from the 5G communication data to be analyzed, thereby obtaining virtual slice communication data;

[0014] Step S13: Perform slice communication connection frequency statistics according to the virtual slice communication data, so as to obtain high communication connection frequency slice data and low communication connection frequency slice data; perform slice operation carrier link feature extraction on the high communication connection frequency slice data, so as to obtain park network equipment link data; perform slice operation carrier link feature extraction on the low communication connection frequency slice data, so as to obtain park industrial equipment link data;

[0015] Step S14: performing a park network communication topology analysis based on the park network device link data and the park industrial device link data, thereby obtaining an industrial park network topology model;

[0016] Step S15: Performing park unit communication demand analysis on the industrial park network topology model, thereby obtaining industrial park unit communication demand data.

[0017] The present invention can effectively optimize the network resource configuration within the industrial park, improve communication efficiency, and provide support for the intelligent upgrade of the industrial park by acquiring, processing and analyzing the 5G communication data of the industrial park. Acquiring the 5G communication data of the industrial park and preprocessing it can remove noise and outliers, providing a clean and reliable data basis for subsequent analysis. On this basis, virtual slice communication feature extraction is performed to help extract valuable information from different communication needs and load scenarios, so that the slices can be more refined and personalized to meet the needs of different business scenarios. By statistically analyzing the slice communication connection frequency of the virtual slice, it is not only possible to identify which slices are high-loaded and which slices are low-loaded, but also to perform targeted optimization according to the slice characteristics of different frequencies, thereby improving the utilization of communication resources and the stability of the slices. By extracting the carrier link features of different slice data, it can help distinguish different communication needs between network equipment and industrial equipment, thereby realizing accurate identification and dynamic scheduling between park equipment, and further optimizing the synergy between industrial equipment and network equipment. Combining the connection data of the park equipment and analyzing the park network communication topology structure will help accurately grasp the communication network layout of the entire industrial park, discover potential bottlenecks and optimization points, and improve the fault tolerance and flexibility of the network. Finally, based on the analysis of the park network topology structure, the communication needs of the park units are evaluated to provide more accurate data support for the network optimization and resource allocation of the park.

[0018] Optionally, step S15 is specifically:

[0019] Step S151: dividing the network equipment intensive units according to the industrial park network topology model, thereby obtaining the park network equipment intensive unit structure data;

[0020] Step S152: Acquire the industrial park functional unit description data, and spatially associate the park network equipment dense unit structure data and the industrial park functional unit description data, thereby obtaining the industrial park functional unit structure data;

[0021] Step S153: extracting the historical 5G communication features of the industrial park 5G communication data according to the functional unit structure data of the industrial park, thereby obtaining the historical 5G communication data of the functional unit, and calculating the operating flow of the functional unit equipment based on the historical 5G communication data of the functional unit, thereby obtaining the operating flow data of the functional unit equipment;

[0022] Step S154: Calculate the functional unit equipment density of the industrial park functional unit structure data, thereby obtaining functional unit equipment density data;

[0023] Step S155: performing communication demand prediction of the functional units based on the functional unit operation flow data and the functional unit equipment density data, thereby obtaining communication demand prediction data of the functional units;

[0024] Step S156: construct a communication demand matrix based on the communication demand forecast data of the functional units, thereby obtaining the communication demand data of the industrial park units.

[0025] The present invention can effectively identify the load conditions of each network area in the park by dividing the network equipment into dense units, and provide accurate data support for subsequent resource allocation. By obtaining the description data of the park's functional units and performing spatial association, the equipment distribution can be accurately matched with the actual functions of the park, optimizing network design and operation. Extracting communication features based on historical 5G communication data and calculating equipment operation traffic can help to discover potential communication bottlenecks or imbalances in advance and ensure that network capacity matches actual needs. Through equipment density calculation, the resource concentration of each functional unit can be further understood, providing data basis for facility construction and network optimization. Combining equipment operation traffic and density data to predict communication needs can help the park plan bandwidth and equipment configuration in advance, improve network service quality, and avoid overload. By constructing a communication demand matrix, the specific needs of each functional unit can be clearly presented, providing accurate and operational data support for the park's future communication architecture design and scheduling decisions, ensuring efficient and stable operation of the network.

[0026] Optionally, step S2 specifically includes:

[0027] Step S21: Calculate the base station coverage range according to the industrial park network topology model, so as to obtain the industrial park base station coverage range data;

[0028] Step S22: classifying the functional unit structure data of the industrial park based on the coverage data of the base station of the industrial park, thereby obtaining the base station full coverage unit structure data and the base station half coverage unit structure data;

[0029] Step S23: selecting a unit base station coverage communication mode for the industrial park unit communication demand data according to the base station full coverage unit structure data, thereby obtaining the base station full coverage unit communication mode data; selecting a unit cluster communication mode for the industrial park unit communication demand data according to the base station half coverage unit structure data, thereby obtaining the base station half coverage unit communication mode data;

[0030] Step S24: performing communication mode integration on the base station full coverage unit communication mode data and the base station half coverage unit communication mode data, thereby obtaining the campus unit communication mode data;

[0031] Step S25: Re-allocate the virtual slice priority of the industrial park 5G communication data based on the park unit communication mode data to obtain optimized virtual slice priority data.

[0032] The present invention calculates the coverage range of base stations based on the network topology model of the industrial park, and can accurately determine the coverage area of ​​each base station, ensuring that the signal coverage of each functional unit in the park does not leave blind spots. This lays the foundation for the subsequent unit classification and communication mode selection, and can make the communication needs of each area more accurately matched, thereby improving the overall performance of the network. By classifying the functional units of the park through the base station coverage data, the base station full coverage and semi-coverage areas can be clearly distinguished. This classification method helps to adopt different communication modes in different areas, avoiding the waste or shortage of resources. According to the structural data of the full coverage unit and the semi-coverage unit, the communication mode selection is combined with the unit communication demand data of the park, so that the communication demand of each unit can be matched with the appropriate communication mode, and the communication load and data transmission efficiency of different areas in the park are optimized. In the full coverage unit, by selecting the appropriate base station communication mode, the base station resources can be maximized to ensure the communication quality; while in the semi-coverage unit, the selection of the cluster communication mode can make up for the lack of coverage of a single base station and ensure the stability and continuity of communication in the area. By integrating the communication modes of the full coverage unit and the semi-coverage unit, various communication needs can be comprehensively considered, and the overall communication mode suitable for the park can be uniformly formulated. This integration not only enhances the manageability of the campus network, but also maximizes network performance. Based on the communication mode data of the campus unit, virtual slice priority reallocation can achieve optimal configuration of network resources, ensuring that units with different communication needs can obtain necessary network resources at the most appropriate priority, thereby improving the overall performance and user experience of 5G communications in the industrial park. Through this dynamic adjustment of priority, not only can the resource utilization of the network be optimized, but it can also be flexibly adjusted according to actual communication needs and changes in network load, avoiding excessive waste of resources or excessive congestion, and ensuring that communication needs within the park are responded to and efficiently met in a timely manner.

[0033] Optionally, step S25 is specifically:

[0034] Step S251: extracting the functional unit 5G communication virtual slices of the industrial park 5G communication data according to the functional unit structure data of the industrial park, thereby obtaining the functional unit 5G communication virtual slice data;

[0035] Step S252: extracting virtual slice priority features according to the functional unit 5G communication virtual slice data, thereby obtaining unit 5G communication virtual slice priority data;

[0036] Step S253: performing park unit communication simulation based on the park unit communication mode data to obtain park unit communication simulation data, and classifying park unit communication loads according to the park unit communication simulation data to obtain high communication load unit data and low communication load unit data;

[0037] Step S254: performing a cell intersection operation based on the high communication load cell data and the base station full coverage cell structure data, thereby obtaining high communication priority cell data; performing a cell intersection operation based on the low communication load cell data and the base station half coverage cell structure data, thereby obtaining low communication priority cell data;

[0038] Step S255: Adjust the unit 5G communication virtual slice priority data according to the high communication priority unit data and the low communication priority unit data, so as to obtain optimized virtual slice priority data.

[0039] The present invention can ensure that the resource allocation of virtual slices accurately reflects the communication needs of different units in the park by extracting 5G communication virtual slice data from the functional unit structure data of the industrial park. The priority feature extraction of virtual slices further helps to identify the differences in the demand for network resources of different functional units, so that reasonable resource scheduling can be performed according to the priority to ensure the communication quality of high-priority units. Then, through the communication simulation based on the communication mode data of the park unit, the communication conditions of each unit inside the park can be accurately simulated, providing a basis for subsequent load classification, so that the unit data with high communication load and low communication load can be effectively identified, and resource allocation can be optimized. Through the data intersection operation of high communication load units and low communication load units, not only can the division of unit communication priorities be further refined, but also through the combination with the base station coverage structure, it can be reasonably determined which units should be given priority to more communication resource support, thereby ensuring the stable communication needs of high communication load units, while reasonably allocating the resources of low load units to avoid resource waste. By adjusting the priority of virtual slices based on these priority data, the communication load balance in the entire park can be achieved, making the 5G communication network of the entire industrial park more efficient and stable.

[0040] Optionally, step S3 specifically includes:

[0041] Step S31: extracting unit slice allocation resource features from the functional unit 5G communication virtual slice data, thereby obtaining unit slice resource allocation data;

[0042] Step S32: reallocate the unit slice resource allocation data according to the optimized virtual slice priority data, thereby obtaining the optimized resource allocation data of the unit slices of the industrial park;

[0043] Step S33: performing periodic calculation of the communication demand of the industrial park units according to the communication demand data of the industrial park units, thereby obtaining the periodic communication demand data of the industrial park units;

[0044] Step S34: Integrate the periodic resource sharing strategy of the industrial park unit slice optimization resource allocation data according to the periodic communication demand data of the park unit, so as to obtain the dynamic sharing strategy of the industrial park communication resources, and upload it to the industrial park network communication management platform to execute the equipment communication task.

[0045] The present invention can effectively improve the utilization efficiency and communication quality of communication resources in the industrial park by allocating and optimizing the resources of 5G communication virtual slices. By extracting the features of the functional unit 5G communication virtual slice data, accurate unit slice resource allocation data is obtained, which can provide basic data support for subsequent resource reallocation and optimization. This process helps to understand the data characteristics of different slices and lays a good data foundation for subsequent operations. Resource reallocation based on optimized virtual slice priority data helps to reasonably allocate resources for other applications while ensuring the priority of key applications, thereby achieving flexibility and efficient utilization of resources. Through optimized resource allocation, the relationship between the communication needs of each unit in the park can be balanced, resource waste can be avoided, and the smooth execution of various communication tasks can be ensured. Through the periodic calculation of the communication needs of the park units, the changing trend of the park communication needs in each time period can be accurately grasped, thereby providing data support for the integration of subsequent resource sharing strategies. This process helps to predict the fluctuation of resource demand in the park, and to make resource scheduling and reservation in advance to ensure that there will be no resource shortage during peak hours. Through the integration of periodic resource sharing strategies, resource sharing between different units in the park can be achieved, and the elasticity and adaptability of the overall communication system can be enhanced. After uploading the strategy to the industrial park network communication management platform, it can realize real-time dynamic resource scheduling and efficient equipment communication task execution, effectively improving the network management level and communication quality of the industrial park. At the same time, the dynamic resource sharing strategy can automatically adjust resource allocation in response to sudden demands or anomalies to ensure the stability and continuity of the communication system. This highly automated and intelligent resource management method not only improves the efficiency of resource use, but also provides more reliable and flexible communication services for enterprises in the park, promoting the overall informatization and intelligent construction of the industrial park.

[0046] Optionally, step S4 is specifically:

[0047] Step S41: acquiring real-time 5G communication data of the industrial park, and performing data preprocessing on the real-time 5G communication data of the industrial park, so as to obtain real-time 5G communication data to be processed;

[0048] Step S42: dividing the real-time 5G communication data to be processed into park unit communication data, thereby obtaining the real-time 5G communication data of the park unit;

[0049] Step S43: extracting unit 5G transmission performance characteristics from the real-time 5G communication data of the park unit, thereby obtaining unit 5G transmission performance characteristic data, and performing unit transmission performance timing analysis based on the unit 5G transmission performance characteristic data, thereby obtaining unit transmission performance timing data;

[0050] Step S44: performing timing fluctuation statistics according to the unit transmission performance timing data, thereby obtaining the unit transmission performance fluctuation timing data, and performing campus unit transmission performance evaluation based on the unit transmission performance fluctuation timing data, thereby obtaining the campus unit transmission performance data;

[0051] Step S45: evaluating the unit communication transmission delay of the industrial park unit transmission performance data according to the industrial park unit communication demand data, thereby obtaining the unit communication transmission delay data.

[0052] The present invention can grasp the 5G communication status of the industrial park in real time and ensure the integrity and validity of the data by acquiring and preprocessing data in real time. The data is divided into park unit communication data, which can clearly divide the communication behaviors of different park units and provide an accurate data basis for subsequent performance analysis. Based on the extraction of unit 5G transmission performance characteristics, the 5G communication quality of each park unit can be accurately analyzed, and transmission performance problems can be discovered in time, providing data support for optimizing and adjusting the network. The unit transmission performance timing analysis can reveal the timing variation law of communication performance, thereby providing a basis for further analyzing network fluctuations and identifying potential faults. The introduction of timing fluctuation statistics can more intuitively display the fluctuation trend of each park unit in the time dimension, thereby providing network operation and maintenance personnel with an effective tool for diagnosing network performance fluctuations. By evaluating the park unit transmission performance of the park unit transmission performance fluctuation timing data, the overall performance of each park unit can be comprehensively evaluated, the bottlenecks or weaknesses in the network can be identified, and resource allocation and transmission strategies can be optimized. Evaluating transmission latency based on the communication demand data of industrial park units can ensure that the communication needs of different application scenarios are met. Especially in industrial applications with high real-time requirements, latency evaluation helps to determine whether the requirements of production equipment or applications are met.

[0053] Optionally, step S45 is specifically:

[0054] Step S451: extracting the delay demand of the industrial park unit from the communication demand data of the industrial park unit, thereby obtaining the delay demand data of the industrial park unit;

[0055] Step S452: extracting unit delay performance characteristics according to the park unit transmission performance data, thereby obtaining the park unit delay performance data;

[0056] Step S453: Calculate the unit delay performance error of the park unit delay demand data and the park unit delay performance data, thereby obtaining the unit delay performance error data;

[0057] Step S454: performing delay error time series statistics according to the unit delay performance error data, thereby obtaining high-value delay error time series data and low-value delay error time series data;

[0058] Step S455: Calculate the unit delay error ratio based on the high-cost delay error time series data and the low-cost delay error time series data, so as to obtain the unit delay error time series proportion data;

[0059] Step S456: Evaluate the unit transmission delay according to the unit delay error timing ratio data, so as to obtain the unit communication transmission delay data.

[0060] By extracting the delay demand data of the park units, the present invention can clarify the specific requirements of each unit for delay, and ensure that the communication system can meet the transmission delay requirements of different units. By extracting the delay performance characteristics of the units, the actual transmission capacity of each unit in the park can be fully understood, thereby providing data support for subsequent delay error analysis and performance optimization. By calculating the delay performance error, it is possible to accurately identify which units have deviations in delay performance, providing direction for further optimization. By performing time series statistics on delay errors, the laws of occurrence of different types of delay errors can be revealed, which helps to determine which time periods or units have more serious delay problems, so as to take targeted optimization measures. Further analyzing the time series data of high and low delay errors, by calculating the delay error ratio, the delay error ratio can be evaluated, revealing the distribution of delay problems in the system, and helping to identify high-load time periods or performance bottleneck areas. Through comprehensive evaluation of these data, the transmission delay evaluation results of each unit in the park can be obtained, providing data support and decision-making basis for the optimization of the park communication system, thereby improving the stability and efficiency of the entire park communication system, ensuring that the communication needs of each unit are effectively met, and promoting the improvement of the production and operation efficiency of the park.

[0061] Optionally, step S5 specifically includes:

[0062] Step S51: extracting the unit high delay error timing sequence according to the unit communication transmission delay data, thereby obtaining the unit high delay error timing sequence data;

[0063] Step S52: extracting the time resource sharing strategy of the industrial park communication resource dynamic sharing strategy based on the unit high delay error timing data, thereby obtaining the unit high delay error timing resource sharing strategy;

[0064] Step S53: Adaptively optimize the unit high delay error timing resource sharing strategy according to the park unit delay demand data, so as to obtain an optimized unit high delay error timing resource sharing strategy;

[0065] Step S54: Couple the resource sharing strategy of the optimization unit high delay error timing resource sharing strategy and the industrial park communication resource dynamic sharing strategy to obtain the industrial park communication resource optimization dynamic sharing strategy, and upload it to the industrial park network communication management platform to execute the equipment communication task.

[0066] The present invention can accurately reflect the delay error occurring between units during the communication process by extracting the unit high delay error timing data, which provides accurate basic data for subsequent optimization. The acquisition of this data enables personalized optimization measures to be taken in the subsequent strategy optimization according to the delay characteristics of different units to ensure efficient use of resources. By extracting the dynamic sharing strategy of communication resources according to the unit high delay error timing data, the resources of different units can be better allocated on demand. Through the accurate analysis of the delay error, the dynamic changes of communication needs in each area can be identified, the reasonable allocation of resources can be achieved, and the utilization rate of communication resources in the park can be improved. This strategy optimization helps to reduce resource waste and avoid communication bottlenecks caused by improper resource allocation in some units. By automatically adjusting the timing of the resource sharing strategy based on the delay demand data of the park unit, the resource competition caused by the delay error can be minimized, ensuring that the communication needs of each unit in different time periods are balanced and satisfied, and avoiding excessive concentration or excessive idleness of communication resources. The implementation of the adaptive optimization strategy enables the system to respond quickly in a changing network environment and maintain a good dynamic balance. The optimized sharing strategy is coupled with the existing dynamic sharing strategy of the industrial park, and the final strategy is uploaded to the network communication management platform to execute the equipment communication task. By coupling the optimization strategy, the resources within the park can be more effectively integrated, the communication efficiency of the entire park can be improved, the communication delay can be reduced, and the stability and efficient completion of equipment tasks can be ensured. After uploading to the management platform, resources can be monitored and scheduled in real time to ensure the communication stability and efficiency of the equipment during the task execution process, thereby significantly improving the overall operation efficiency of the park.

[0067] Optionally, the present invention further provides a wireless data transmission system based on 5G communication, which is used to execute the wireless data transmission method based on 5G communication as described above, and the wireless data transmission system based on 5G communication includes:

[0068] The network topology analysis module is used to obtain the 5G communication data of the industrial park, and perform the network communication topology analysis of the industrial park based on the 5G communication data of the industrial park, so as to obtain the network topology model of the industrial park; perform the communication demand analysis of the industrial park unit on the network topology model of the industrial park, so as to obtain the communication demand data of the industrial park unit;

[0069] The priority reallocation module is used to design the communication mode of the industrial park unit according to the communication demand data of the industrial park unit, so as to obtain the communication mode data of the industrial park unit; based on the communication mode data of the industrial park unit, the virtual slice priority is reallocated for the 5G communication data of the industrial park, so as to obtain the optimized virtual slice priority data;

[0070] The slice resource allocation optimization module is used to optimize the unit slice resource allocation according to the optimized virtual slice priority data, so as to obtain the optimized resource allocation data of the industrial park unit slice; according to the industrial park unit communication demand data, the dynamic resource sharing strategy of the industrial park unit slice optimized resource allocation data is analyzed, so as to obtain the dynamic sharing strategy of the industrial park communication resources, and upload it to the industrial park network communication management platform to execute the equipment communication task;

[0071] The transmission delay evaluation module is used to obtain real-time industrial park 5G communication data, and perform park unit transmission performance evaluation on the real-time industrial park 5G communication data, so as to obtain park unit transmission performance data; perform unit communication transmission delay evaluation on the park unit transmission performance data according to the industrial park unit communication demand data, so as to obtain unit communication transmission delay data;

[0072] The dynamic sharing timing analysis module is used to optimize the dynamic sharing timing of the industrial park communication resources based on the unit communication transmission delay data, so as to obtain the optimized dynamic sharing strategy of the industrial park communication resources and upload it to the industrial park network communication management platform to execute the equipment communication tasks.

[0073] The wireless data transmission system based on 5G communication of the present invention can implement any one of the wireless data transmission methods based on 5G communication of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the wireless data transmission method based on 5G communication. The internal modules of the system cooperate with each other, thereby improving the bandwidth utilization of the network, reducing latency, and improving reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0075] Figure 1This is a schematic diagram of the steps of the wireless data transmission method based on 5G communication of the present invention;

[0076] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0077] Figure 3 Detailed step flow diagram of step S2 in the present invention;

[0078] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0079] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0080] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0081] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0082] To achieve this, please refer to Figures 1 to 3 The present invention provides a wireless data transmission method based on 5G communication, the method comprising the following steps:

[0083] Step S1: Acquire 5G communication data of the industrial park, and perform network communication topology analysis of the industrial park based on the 5G communication data of the industrial park, thereby obtaining a network topology model of the industrial park; perform park unit communication demand analysis on the network topology model of the industrial park, thereby obtaining unit communication demand data of the industrial park;

[0084] In this embodiment, a large number of 5G base stations and sensors are deployed in the industrial park to collect communication data of the industrial park in real time. These data include network traffic, latency, bandwidth usage, signal strength and other information. The communication status and device connection status of each area are monitored and recorded in real time. These data can be summarized through the network management platform. Based on these data, machine learning algorithms (such as K-means clustering analysis) are used to model the network topology structure to identify the communication flow, load distribution and communication path between each unit in the park. This process can be automated to obtain a network topology model in the park and identify key communication bottlenecks and uneven resource allocation.

[0085] Step S2: Designing a park unit communication mode according to the industrial park unit communication demand data, thereby obtaining park unit communication mode data; reallocating virtual slice priorities of the industrial park 5G communication data based on the park unit communication mode data, thereby obtaining optimized virtual slice priority data;

[0086] In this embodiment, the communication mode of the park unit is designed according to the obtained network topology and communication demand data, combined with the specific usage scenarios of different units in the industrial park (such as production line equipment, automated robots, sensors, etc.). Different units may have different bandwidth requirements, latency requirements, and data transmission frequencies. For example, some production equipment requires real-time data transmission with low latency, while other equipment only needs to report data regularly. During design, a communication mode based on quality of service (QoS) can be adopted to assign appropriate communication priorities and resource requirements to each park unit. After obtaining the communication mode of the park unit, the virtual slicing technology is used to manage the 5G network resources. Through the network management platform, the slice priority can be reallocated, and the resources can be dynamically adjusted based on the communication needs of different units. For example, the communication priority of the production line equipment is increased to ensure the stability of real-time data transmission, while the priority of sensor data is lower, thereby optimizing the network traffic of the entire park.

[0087] Step S3: Optimize the unit slice resource allocation according to the optimized virtual slice priority data, so as to obtain the optimized resource allocation data of the industrial park unit slice; perform dynamic resource sharing strategy analysis on the optimized resource allocation data of the industrial park unit slice according to the communication demand data of the industrial park unit, so as to obtain the dynamic sharing strategy of the industrial park communication resources, and upload it to the industrial park network communication management platform to execute the equipment communication task;

[0088] In this embodiment, based on the communication demand data of the park units, a resource optimization algorithm (such as a genetic algorithm or a deep learning optimization method) is used to dynamically allocate and optimize the slice resources of the industrial park 5G network. By analyzing the communication mode and resource requirements of each unit, appropriate computing and bandwidth resources can be allocated to each unit. For example, some high-priority units in the park, such as the robot system of the smart warehouse, require higher bandwidth and low latency support, while other ordinary sensors can share lower resources. In order to improve resource utilization efficiency, the resource allocation data after unit slice optimization is dynamically analyzed for sharing strategies, and a demand-based dynamic resource scheduling strategy is adopted considering the real-time changes between different units in the industrial park. For example, in a certain period of time, the communication demand of some units is low, and the idle resources can be shared with units with higher demand to optimize the overall resource utilization, and the final sharing strategy is uploaded to the network management platform of the park for execution.

[0089] Step S4: acquiring real-time 5G communication data of the industrial park, and performing a park unit transmission performance evaluation on the real-time 5G communication data of the industrial park, thereby obtaining the park unit transmission performance data; performing a unit communication transmission delay evaluation on the park unit transmission performance data according to the industrial park unit communication demand data, thereby obtaining the unit communication transmission delay data;

[0090] In this embodiment, real-time 5G communication data in the park is uploaded to the data center in real time through sensors and monitoring equipment. The data includes bandwidth utilization, signal strength, transmission delay, etc. Using these real-time data, the transmission performance of each unit in the park is evaluated through network performance evaluation tools (such as Ping, Traceroute, QoS analysis tools). For each communication unit, evaluate indicators such as the delay, throughput, and packet loss rate of its data transmission. Transmission delay evaluation is performed based on the actual communication demand data of the industrial park units. For example, if some devices require low-latency real-time response, while other devices can tolerate higher delays, unit communication transmission delay data will be generated to ensure that key applications in the park (such as production control systems) have priority low-latency guarantees, while non-critical applications (such as environmental monitoring systems) can tolerate higher delays.

[0091] Step S5: Based on the unit communication transmission delay data, the dynamic sharing timing of the industrial park communication resource dynamic sharing strategy is optimized to obtain the industrial park communication resource optimization dynamic sharing strategy, and upload it to the industrial park network communication management platform to execute the equipment communication task.

[0092] In this embodiment, through the dynamic sharing timing optimization method based on latency data analysis, the communication resources in the park can be more accurately scheduled. For example, in certain time periods, the peak demand of some devices is high, while the demand of other devices is low. The resource sharing timing is automatically adjusted to ensure that the resource needs of high-demand devices are met first. In this process, queuing theory, Markov decision process and other methods can be used to optimize the dynamic scheduling of resources. The optimized communication resource sharing strategy will be automatically generated and uploaded to the network communication management platform of the park. The platform will automatically adjust the allocation of network resources according to the new sharing strategy, provide real-time support for each device, thereby improving the communication efficiency and system stability between devices in the industrial park.

[0093] Optionally, step S1 specifically includes:

[0094] Step S11: Acquire 5G communication data of the industrial park, and perform data preprocessing on the 5G communication data of the industrial park, so as to obtain 5G communication data to be analyzed;

[0095] In this embodiment, the communication data of the industrial park is collected through 5G communication base stations, edge computing devices and sensors in the industrial park. These data may include but are not limited to traffic data, latency, bandwidth utilization, device connection status, etc. The collected data can be monitored in real time through the network management platform of the 5G network or a dedicated IoT data acquisition platform. The preprocessing steps include data cleaning (removing missing or invalid data), denoising (for example, filtering out abnormal data caused by equipment failure or external interference), and standardization (such as normalizing or standardizing the data so that the data of various indicators can be effectively compared). For example, use the pandas and NumPy libraries in Python to clean and standardize the data, remove incomplete communication session records or erroneous sensor data, and ensure the quality and availability of the data.

[0096] Step S12: extracting virtual slice communication features from the 5G communication data to be analyzed, thereby obtaining virtual slice communication data;

[0097] In this embodiment, the communication features are extracted by analyzing the obtained 5G communication data. The extraction of virtual slice communication features can be based on key indicators such as communication delay, bandwidth occupancy, network resource utilization, packet loss rate, etc. In this process, it is necessary to apply signal processing technology (such as Fourier transform) to extract the spectral features of the signal, or use machine learning methods such as cluster analysis to identify the virtual slices of the network. Assume that a data processing platform such as TensorFlow or PyTorch is used to analyze the 5G data stream using a deep learning model to identify the communication modes and characteristics of different virtual slices, and generate corresponding slice feature data, such as delay distribution, data throughput, etc.

[0098] Step S13: Perform slice communication connection frequency statistics according to the virtual slice communication data, so as to obtain high communication connection frequency slice data and low communication connection frequency slice data; perform slice operation carrier link feature extraction on the high communication connection frequency slice data, so as to obtain park network equipment link data; perform slice operation carrier link feature extraction on the low communication connection frequency slice data, so as to obtain park industrial equipment link data;

[0099] In this embodiment, slice connection frequency statistics are performed based on the obtained virtual slice communication data. This process mainly evaluates the activity of each virtual slice by analyzing indicators such as the number of virtual slice connections and session duration. When counting data, a time window method (for example, statistics by hour or day) can be used to count the connection frequency of each virtual slice in a certain period of time, and these data are divided into high-frequency and low-frequency slices. High-frequency slices usually refer to slices with a large number of connections and a long duration. The operating carrier of the slice is generally a network device such as a base station and a router, while a low-frequency slice is a slice with a small number of connections and low communication activity. The operating carrier of the slice is generally an industrial device such as a sensor and a digital machine tool. Then, for high-frequency slices, extract the link characteristics of its operating carrier (such as the number of connected base stations, the transmission delay between devices in the network topology, etc.); for low-frequency slices, analyze the connection characteristics of its industrial equipment (such as device type, workload, response time, etc.). With the help of data analysis tools, such as Matplotlib or Seaborn libraries in Python, a frequency distribution diagram can be drawn to visualize the communication characteristics of different slices.

[0100] Step S14: performing a park network communication topology analysis based on the park network device link data and the park industrial device link data, thereby obtaining an industrial park network topology model;

[0101] In this embodiment, the high-frequency and low-frequency slice data are further analyzed to extract their device connection data. The data of the high-frequency slice focuses on analyzing the communication characteristics between the campus network devices, such as the connection between switches, routers and 5G base stations. The low-frequency slice focuses on analyzing the communication with industrial equipment, such as smart sensors, robots, production line equipment, etc. By analyzing the connection relationship between devices (such as signal strength, communication delay, packet loss, etc. between devices), the network communication topology of the campus is constructed. For example, a network topology modeling tool, such as NetFlow Analyzer, can be used to visualize the connection relationship of devices in the campus, build a flow diagram and connection diagram of network traffic, and reveal potential problems such as bottlenecks and congestion points in the network. The network topology structure of the campus is modeled by analyzing the obtained network device link data and industrial device link data. The network topology can be constructed by graph theory algorithms (such as shortest path algorithm, network flow analysis, etc.) to show the connection relationship between each device, slice and network. For example, the network topology model of the campus can be generated by combining the connection frequency, transmission delay, bandwidth and other indicators of the device through Kruskal or Prim algorithm. In terms of tools, graph analysis libraries (such as NetworkX in Python) can be used to visualize and analyze network structures.

[0102] Step S15: Performing park unit communication demand analysis on the industrial park network topology model, thereby obtaining industrial park unit communication demand data.

[0103] In this embodiment, based on the obtained park network topology model, the communication needs of each unit in the park (such as production workshops, storage areas, logistics areas, etc.) are analyzed. This analysis needs to be combined with the business scenarios of different units in the park to evaluate factors such as the communication load and real-time requirements of each unit in different time periods. For example, the production workshop may have a high demand for low-latency, large-bandwidth communications, while the storage area has high requirements for stability and low bandwidth requirements. During the analysis process, traffic forecasting and demand analysis can be performed in combination with the park business development plan to ensure that the network can meet the communication needs of each unit. Commonly used analysis methods include regression analysis based on historical traffic data, load balancing algorithms, etc. At the same time, the communication load and demand of the park network can be simulated through simulation software (such as NS-3 or OMNeT++) to optimize the allocation of network resources.

[0104] Optionally, step S15 is specifically:

[0105] Step S151: dividing the network equipment intensive units according to the industrial park network topology model, thereby obtaining the park network equipment intensive unit structure data;

[0106] In this embodiment, the distribution of equipment in each area is analyzed based on the actual network topology model of the industrial park. The network topology can be obtained through the integrated data of GIS (Geographic Information System) and the network management platform. By analyzing the density of network nodes (such as switches, routers, wireless base stations, etc.), the park can be divided into multiple network equipment dense units (such as "high density areas" and "low density areas"). For example, in the production workshop and data center areas of the park, there is usually a higher density of network equipment, while in offices and public areas it is lower. Therefore, through this division, equipment deployment and network optimization can be better performed.

[0107] Step S152: Acquire the industrial park functional unit description data, and spatially associate the park network equipment dense unit structure data and the industrial park functional unit description data, thereby obtaining the industrial park functional unit structure data;

[0108] In this embodiment, the functional unit description data of the industrial park is obtained through the management center of the industrial park, which includes the functional uses of different areas, such as production workshops, storage areas, office areas, logistics areas, etc. The function, area, layout and equipment configuration of each functional unit will affect the use requirements of the network. Through spatial association algorithms, such as spatial interpolation or spatial statistical analysis, the network equipment intensive unit structure data can be combined with the functional unit description data to analyze the equipment requirements and actual usage of each area. For example, the network topology structure of the park can be associated with the regional function through the overlay analysis method to obtain the network equipment density data corresponding to each functional unit.

[0109] Step S153: extracting the historical 5G communication features of the industrial park 5G communication data according to the functional unit structure data of the industrial park, thereby obtaining the historical 5G communication data of the functional unit, and calculating the operating flow of the functional unit equipment based on the historical 5G communication data of the functional unit, thereby obtaining the operating flow data of the functional unit equipment;

[0110] In this embodiment, the communication characteristics (such as data flow, latency, bandwidth requirements, etc.) of each functional unit are extracted based on historical 5G communication data. These data can be collected through the 5G base station monitoring system or traffic management platform of the industrial park. For each functional unit (such as a production workshop, office area, etc.), its historical traffic characteristics can be analyzed based on the time series of 5G communication traffic. For example, during peak production periods, the 5G communication traffic in a production workshop may increase sharply, reflecting the intensive use of equipment in the area. By analyzing these characteristics, the equipment operation traffic can be calculated to obtain the equipment operation load in the area in different time periods.

[0111] Step S154: Calculate the functional unit equipment density of the industrial park functional unit structure data, thereby obtaining functional unit equipment density data;

[0112] In this embodiment, the functional unit equipment density calculation is to calculate the concentration of equipment by comparing the number of equipment in each functional unit in the park with the area it occupies. Assuming that the area of ​​a production workshop is 5,000 square meters and there are 100 devices (such as PLC controllers, sensors, automation equipment, etc.), the equipment density is 100 / 5,000 devices / square meter. Through this calculation, equipment density data can be generated for each functional unit, thereby evaluating the equipment load and communication requirements of each area.

[0113] Step S155: performing communication demand prediction of the functional units based on the functional unit operation flow data and the functional unit equipment density data, thereby obtaining communication demand prediction data of the functional units;

[0114] In this embodiment, based on the operation flow data and device density data of the functional unit equipment, a machine learning algorithm (such as regression analysis, time series prediction, neural network, etc.) is used to predict the future communication needs of the functional unit. For example, if historical data shows that the equipment in a production workshop will generate a large amount of data transmission demand during a specific period of each month, the future communication needs of the functional unit can be predicted based on these historical trends. In addition, factors such as equipment type, workload, and personnel activities can be combined to further improve the prediction accuracy.

[0115] Step S156: construct a communication demand matrix based on the communication demand forecast data of the functional units, thereby obtaining the communication demand data of the industrial park units.

[0116] In this embodiment, the communication demand matrix within the park is constructed by predicting the communication demand of each functional unit. For example, the communication demand of each functional unit (such as office area, production area, storage area, etc.) with other functional units in the park will be presented in the form of a matrix, and each element in the matrix represents the expected communication traffic between two functional units. Based on this communication demand matrix, the overall network load of the park can be evaluated, and the network architecture can be optimized or bandwidth planning can be performed. For example, if the communication demand between a production area and a storage area is high, more network resources, such as wireless access points, optical fiber links, etc., are deployed between the areas to ensure smooth transmission of traffic.

[0117] Optionally, step S2 specifically includes:

[0118] Step S21: Calculate the base station coverage range according to the industrial park network topology model, so as to obtain the industrial park base station coverage range data;

[0119] In this embodiment, in the network topology of the industrial park, the location and performance parameters (such as power, frequency, antenna type, etc.) of each base station in the park are first obtained through the industrial park management center. The coverage radius of each base station is calculated by using a wireless propagation model (such as the Hata model or the COST-231 model) combined with the physical characteristics of the base station and environmental data (such as terrain, building height and density, etc.). During the calculation process, the impact of different types of areas in the park (such as factory areas, storage areas, office areas, etc.) on signal propagation is considered. Based on this model, the coverage data of each base station can be obtained, including information such as signal strength and signal quality. These coverage data will serve as the basis for subsequent functional unit classification and communication mode selection.

[0120] Step S22: classifying the functional unit structure data of the industrial park based on the coverage data of the base station of the industrial park, thereby obtaining the base station full coverage unit structure data and the base station half coverage unit structure data;

[0121] In this embodiment, based on the functional unit structure data of the industrial park (including the spatial division of each functional area in the park, such as production workshops, office areas, storage areas, etc.), each area is divided into a full coverage area and a semi-coverage area using the base station coverage data. For a full coverage unit, the base station signal can cover the entire unit range; for a semi-coverage unit, the base station signal coverage partially covers the unit, and there may be a signal blind spot or a weak signal area. At this time, the signal strength and coverage density of the base stations in the park are used to determine which units belong to full coverage units and which units belong to semi-coverage units, and generate base station full coverage unit structure data and base station semi-coverage unit structure data.

[0122] Step S23: selecting a unit base station coverage communication mode for the industrial park unit communication demand data according to the base station full coverage unit structure data, thereby obtaining the base station full coverage unit communication mode data; selecting a unit cluster communication mode for the industrial park unit communication demand data according to the base station half coverage unit structure data, thereby obtaining the base station half coverage unit communication mode data;

[0123] In this embodiment, for the full coverage unit, a coverage communication mode based on a single base station is selected. Specifically, the communication requirements (such as bandwidth requirements, number of users, etc.) of each full coverage unit are evaluated, and the appropriate base station coverage mode is selected according to these requirements, such as each unit selecting a main base station for communication to ensure the quality of user communication. In the semi-coverage unit, due to insufficient base station signal coverage, a cluster-based communication mode is selected. That is, in the semi-coverage unit, multiple base stations jointly provide services for the unit, and the signal blind spots are supplemented by collaborative communication (such as coordinated multi-point transmission, CoMP technology). Finally, according to the communication demand data and coverage mode of the unit, the communication mode data of the base station full coverage unit and the communication mode data of the base station semi-coverage unit are obtained.

[0124] Step S24: performing communication mode integration on the base station full coverage unit communication mode data and the base station half coverage unit communication mode data, thereby obtaining the campus unit communication mode data;

[0125] In this embodiment, the communication mode data of the base station full coverage unit and the communication mode data of the base station half coverage unit are integrated to form the communication mode data of the park unit. During the integration process, the coverage characteristics and communication requirements between different units are considered, and a priority merging strategy is adopted. For the full coverage unit, bandwidth and resources can be allocated preferentially to ensure its communication quality; for the half coverage unit, dynamic resource allocation, time slot multiplexing and power control can be used to ensure stable communication even in areas with weak signals. Finally, these data are integrated to obtain the optimal communication mode of each unit in the park.

[0126] Step S25: Re-allocate the virtual slice priority of the industrial park 5G communication data based on the park unit communication mode data to obtain optimized virtual slice priority data.

[0127] In this embodiment, the priority of virtual slices of the 5G network is reallocated according to the communication mode data of the park unit. In the industrial park, the communication requirements of different units vary greatly (for example, the production workshop may have a higher real-time requirement, while the office area may focus on bandwidth requirements). By analyzing the communication mode data, the virtual slices are reallocated based on the priority of the user group and the service quality requirements (such as bandwidth, latency, etc.). For example, for high-priority applications in full coverage units (such as industrial automation control), more resources are allocated to ensure low latency and high bandwidth requirements; for semi-coverage units, resources are dynamically adjusted according to signal quality. Finally, by optimizing the priority and resource allocation of virtual slices, the overall performance of 5G communication in the park is improved.

[0128] Optionally, step S25 is specifically:

[0129] Step S251: extracting the functional unit 5G communication virtual slices of the industrial park 5G communication data according to the functional unit structure data of the industrial park, thereby obtaining the functional unit 5G communication virtual slice data;

[0130] In this embodiment, in the industrial park, the deployment of the 5G network is usually divided according to the functional units in the park. Assume that the industrial park contains multiple functional units, such as production lines, storage areas, offices, and research laboratories, and each functional unit may have different communication requirements and business priorities. Through data mining techniques (such as cluster analysis and principal component analysis), the 5G communication virtual slice data of each functional unit is extracted. The virtual slice data may include communication bandwidth, latency, reliability requirements, etc. For example, for the functional units of the production line, low latency and high reliability communication slices may be required, while the requirements of the storage area may focus on large-capacity data transmission.

[0131] Step S252: extracting virtual slice priority features according to the functional unit 5G communication virtual slice data, thereby obtaining unit 5G communication virtual slice priority data;

[0132] In this embodiment, after obtaining the 5G communication virtual slice data of the functional unit, it is necessary to extract features based on the communication requirements and business priorities of the virtual slice. Specifically, machine learning methods (such as decision trees or support vector machines) can be used to analyze the priority features in the virtual slice data. For example, a priority is assigned to each functional unit based on factors such as data traffic, latency requirements, and business importance. Assume that the production line requires real-time data processing and low-latency response, so its virtual slice has a higher priority; while the virtual slice in the storage area may have a lower priority, mainly large bandwidth transmission, and is less dependent on low latency. In this way, corresponding virtual slice priority data can be generated for each functional unit.

[0133] Step S253: performing park unit communication simulation based on the park unit communication mode data to obtain park unit communication simulation data, and classifying park unit communication loads according to the park unit communication simulation data to obtain high communication load unit data and low communication load unit data;

[0134] In this embodiment, according to the communication modes of different functional units in the industrial park (such as high-frequency data exchange or low-frequency data monitoring), the communication simulation of the park units is performed. Use simulation tools (such as NS3-based simulation platform) to simulate the communication performance of different units under different load conditions. Through simulation calculation, the load situation of each unit is analyzed, and the communication load data of each unit is obtained. For example, for production units with high-frequency data exchange, a higher communication load will be generated; while for storage areas with low-frequency data updates, the load is lower. Based on the simulation results, the units are divided into high-communication load units and low-communication load units, and classified. High-communication load units require more network resource support, while the resource allocation of low-communication load units can be relatively low.

[0135] Step S254: performing a cell intersection operation based on the high communication load cell data and the base station full coverage cell structure data, thereby obtaining high communication priority cell data; performing a cell intersection operation based on the low communication load cell data and the base station half coverage cell structure data, thereby obtaining low communication priority cell data;

[0136] In this embodiment, an intersection operation is performed based on the obtained high communication load unit data and the full coverage unit structure data of the base station. This operation can be implemented through a spatial data analysis method to determine which high communication load units are within the full coverage of the base station, and based on the load conditions of these units, determine that they should have a higher communication priority in the network. For example, a production workshop is located in the full coverage area of ​​the base station and has a high load, so its communication priority will be increased. An intersection operation is performed based on the low communication load unit data and the semi-coverage unit structure data of the base station to identify units with low load and located in the semi-coverage area of ​​the base station, and these units have a lower communication priority. The specific communication priority of the unit is determined by the intersection operation, and then a high priority and low priority unit data set is generated. This process ensures that when network resources are allocated, units with heavier communication loads can obtain the necessary resource support first.

[0137] Step S255: Adjust the unit 5G communication virtual slice priority data according to the high communication priority unit data and the low communication priority unit data, so as to obtain optimized virtual slice priority data.

[0138] In this embodiment, the 5G communication virtual slice priority of each functional unit is adjusted according to the obtained high communication priority and low communication priority unit data, combined with the aforementioned virtual slice priority data. The adjustment rules are as follows: For high communication priority units, first ensure the low latency and high bandwidth requirements of these units, and prioritize the allocation of more network resources to ensure that their slice priority remains at a high level; for low communication priority units, their priority can be lowered to reduce the occupation of network resources. For example, the communication slice in the storage area can lower its priority when the load is low to ensure that it does not occupy too much bandwidth in the allocation of network resources. In this way, the optimized virtual slice priority data can effectively improve the resource utilization of the 5G network in the park, reduce communication delays, and ensure that high-priority functional units are fully guaranteed.

[0139] Optionally, step S3 specifically includes:

[0140] Step S31: extracting unit slice allocation resource features from the functional unit 5G communication virtual slice data, thereby obtaining unit slice resource allocation data;

[0141] In this embodiment, a prediction model or heuristic algorithm based on machine learning is used to extract resource features for each virtual slice. This process accurately calculates the resource usage of each slice by real-time monitoring of device status, transmission requirements, and historical communication data. For example, if a slice mainly supports automated device control, low latency and high stability are required, so the corresponding resource configuration is performed based on these characteristics. The extracted resource feature data includes: CPU usage, bandwidth requirements, storage space, etc., and forms the resource allocation data of the unit slice.

[0142] Step S32: reallocate the unit slice resource allocation data according to the optimized virtual slice priority data, thereby obtaining the optimized resource allocation data of the unit slices of the industrial park;

[0143] In this embodiment, resources are optimized and reallocated based on the collected virtual slice priority data. For example, if the production lines in some industrial parks require higher real-time and more stable network connections, while other functional units have lower network requirements, resources can be adjusted according to priority. In specific implementation, algorithms (such as weighted polling algorithms or priority queue scheduling algorithms) are used to dynamically adjust the resource allocation of each functional unit. For virtual slices that control key equipment in the factory, higher priority is given to ensure that they obtain more bandwidth and lower latency, while for non-critical slices with large data transmission volumes, resources can be reallocated through load balancing. For example, when reallocating resources, priority parameters can be set, and monitoring units in industrial parks that need to process large amounts of real-time video data can obtain slices with higher bandwidth and low latency without affecting the network requirements of other units, ensuring that the communication needs of all units in the park are met.

[0144] Step S33: performing periodic calculation of the communication demand of the industrial park units according to the communication demand data of the industrial park units, thereby obtaining the periodic communication demand data of the industrial park units;

[0145] In this embodiment, the historical communication data of the industrial park is analyzed to calculate the periodic characteristics of the communication demand of each park unit. For example, by statistically analyzing the device communication time window, the communication demand data such as bandwidth and delay of each functional unit in different time periods can be obtained. These demand data will be presented in a periodic pattern. For example, some equipment requires more bandwidth during peak working hours, but the demand is lower during non-peak hours. Based on these demand characteristics, the periodic communication demand data of each park unit is calculated. These data reflect the daily fluctuation pattern of device communication, which helps to make more accurate resource scheduling. The specific implementation method can be to capture and predict the changing trend of communication demand of park units through time series data analysis algorithms, such as FFT (Fast Fourier Transform)-based periodic detection methods, or sequence prediction models based on deep learning.

[0146] Step S34: Integrate the periodic resource sharing strategy of the industrial park unit slice optimization resource allocation data according to the periodic communication demand data of the park unit, so as to obtain the dynamic sharing strategy of the industrial park communication resources, and upload it to the industrial park network communication management platform to execute the equipment communication task.

[0147] In this embodiment, in combination with periodic communication demand data, the optimized unit slice resource allocation data is adjusted and the sharing strategy is integrated through a periodic resource sharing strategy integration algorithm (such as a resource allocation algorithm based on game theory). This strategy should not only consider the needs of a single virtual slice, but also dynamically allocate resources based on the synergy of multiple slices in the park. For example, when some equipment in the park is in a low-demand period, some idle resources can be allocated to other high-demand equipment. The integrated resource sharing strategy will cover the dynamic adjustment of resources of multiple park units, cross-unit resource collaborative scheduling, and other contents. Finally, the integrated "dynamic sharing strategy for communication resources in industrial parks" will be uploaded to the network communication management platform of the park for real-time execution. The platform will dynamically adjust resource allocation according to the real-time network status and equipment task requirements to meet the communication task requirements of equipment in the industrial park.

[0148] Optionally, step S4 is specifically:

[0149] Step S41: acquiring real-time 5G communication data of the industrial park, and performing data preprocessing on the real-time 5G communication data of the industrial park, so as to obtain real-time 5G communication data to be processed;

[0150] In this embodiment, 5G communication data is collected in real time by deploying multiple 5G base stations and network equipment. Data sources include but are not limited to: transmission rate, latency, packet loss rate, signal strength, network load, etc. of the device. The collected raw data is stored in a standardized format (such as JSON or CSV). In order to ensure the accuracy and consistency of the data, the data is preprocessed, including removing noise data, filling missing values, standardization (such as normalizing all numerical data) and outlier detection. For example, if the transmission rate exceeds the maximum bandwidth or the latency exceeds a specific threshold, the data point is considered abnormal and is removed.

[0151] Step S42: dividing the real-time 5G communication data to be processed into park unit communication data, thereby obtaining the real-time 5G communication data of the park unit;

[0152] In this embodiment, according to the functional unit division of the previous industrial park, the communication data of different functional units in the real-time 5G communication data are divided to obtain the real-time 5G communication data of the park unit.

[0153] Step S43: extracting unit 5G transmission performance characteristics from the real-time 5G communication data of the park unit, thereby obtaining unit 5G transmission performance characteristic data, and performing unit transmission performance timing analysis based on the unit 5G transmission performance characteristic data, thereby obtaining unit transmission performance timing data;

[0154] In this embodiment, relevant 5G transmission performance characteristics are extracted from the real-time 5G communication data of the divided park units, such as the network rate (download rate, upload rate), delay (end-to-end delay, transmission delay, etc.), packet loss rate, and link quality indicators in each unit. The transmission performance characteristic data of each park unit is obtained by calculating the mean, maximum value, minimum value, standard deviation and other statistics of these characteristics. In addition, machine learning algorithms (such as support vector machines or decision trees) can be used based on historical data to predict future transmission performance trends and analyze possible performance bottlenecks or optimization space. Use time series analysis methods (such as autoregressive integrated moving average ARIMA model or long short-term memory neural network LSTM) to analyze the transmission performance data at each time point to obtain the unit transmission performance of each time series.

[0155] Step S44: performing timing fluctuation statistics according to the unit transmission performance timing data, thereby obtaining the unit transmission performance fluctuation timing data, and performing campus unit transmission performance evaluation based on the unit transmission performance fluctuation timing data, thereby obtaining the campus unit transmission performance data;

[0156] In this embodiment, the fluctuation trend of transmission performance is identified by performing time series analysis on the unit 5G transmission performance characteristic data. For example, a time series analysis method (such as an autoregressive integrated moving average ARIMA model or a long short-term memory neural network LSTM) is used to predict the transmission performance data at each time point to determine whether there is a significant fluctuation in the transmission performance. The standard deviation and mean value of the fluctuation can reflect the overall performance stability of the park unit. Then, based on these time series fluctuation data, further performance evaluation is performed to obtain the transmission performance level of each unit in the park (such as excellent, good, medium, and poor) and provide a detailed transmission performance report.

[0157] Step S45: evaluating the unit communication transmission delay of the industrial park unit transmission performance data according to the industrial park unit communication demand data, thereby obtaining the unit communication transmission delay data.

[0158] In this embodiment, the communication transmission delay of the unit is evaluated based on the real-time transmission performance data of each park unit and the actual communication needs of the industrial park (for example, the minimum delay requirement that needs to be supported in each unit, the data transmission frequency between devices, etc.). The actual delay data of each unit can be compared with the predetermined delay threshold (such as the required maximum transmission delay not exceeding 10ms) for analysis to determine whether it meets the communication requirements. If the delay of a unit exceeds the preset requirements, it is necessary to reduce the delay by increasing the number of base stations, optimizing the network topology, or adopting a more efficient transmission protocol. In the specific evaluation process, the delay performance score of each unit is obtained by considering factors such as the number of devices, the type of communication protocol (such as NOMA, MIMO, etc.), and the degree of interference of wireless signals.

[0159] Optionally, step S45 is specifically:

[0160] Step S451: extracting the delay demand of the industrial park unit from the communication demand data of the industrial park unit, thereby obtaining the delay demand data of the industrial park unit;

[0161] In this embodiment, the unit delay requirements contained in the industrial park unit communication demand data are extracted to obtain the park unit delay demand data. For example, some units have high delay requirements for data transmission of video surveillance systems, while other units mainly involve batch data transmission and have relatively low delay requirements. By analyzing these data, the specific delay requirements of each unit are extracted, and the delays are classified and quantified according to different application scenarios, so as to obtain accurate park unit delay demand data.

[0162] Step S452: extracting unit delay performance characteristics according to the park unit transmission performance data, thereby obtaining the park unit delay performance data;

[0163] In this embodiment, performance characteristics related to latency are extracted based on the transmission performance data of each campus unit (such as bandwidth, transmission rate, signal quality, etc.). For example, the latency characteristics of each unit can be evaluated using parameters such as network bandwidth, link load, network topology, and device hardware performance. Assuming that a campus unit uses a high-bandwidth, low-load fiber optic network, theoretically its latency should be low; while a campus unit may rely on a wireless communication network and be affected by wireless interference, resulting in higher latency. By processing these transmission performance data, the latency performance data of each unit is extracted, including statistical information such as the average, maximum, minimum, and standard deviation of the latency.

[0164] Step S453: Calculate the unit delay performance error of the park unit delay demand data and the park unit delay performance data, thereby obtaining the unit delay performance error data;

[0165] In this embodiment, the delay demand data and delay performance data of the park unit are compared to calculate the delay error. Specifically, the error calculation formula can be used: Error = Actual Delay - Demand Delay. For example, assuming that the communication demand delay of a unit is 50ms, but the actual transmission delay is 80ms, then the error is 30ms. If the error exceeds the preset tolerance range (for example, the error exceeds 20ms), it is considered that there is a problem with the communication quality of the unit. In this step, by calculating the delay error of each unit, the delay performance error data of each unit is obtained to provide a basis for subsequent steps.

[0166] Step S454: performing delay error time series statistics according to the unit delay performance error data, thereby obtaining high-value delay error time series data and low-value delay error time series data;

[0167] In this embodiment, the delay performance error data is counted and classified in chronological order, and the time series data of high delay errors and low delay errors are counted separately. For example, for each park unit, the delay error data is grouped and counted by hour, day or week, and classified into "high delay error" (for example, the delay error is greater than 30ms) and "low delay error" (for example, the delay error is less than 10ms). Then, according to the size and frequency of the delay error, the fluctuation of the park communication network performance in different time periods can be analyzed. For example, a unit may frequently have high delay errors during peak hours, while the delay error is smaller during low-load periods. Finally, the time series data of high delay errors and low delay errors are counted to provide data support for subsequent analysis.

[0168] Step S455: Calculate the unit delay error ratio based on the high-cost delay error time series data and the low-cost delay error time series data, so as to obtain the unit delay error time series proportion data;

[0169] In this embodiment, the time ratio of the high delay error and the low delay error is calculated to obtain the unit delay error timing ratio data. For example, if the high delay error of a unit occurs in 30% of the total time period, and the low delay error occurs in 60% of the total time period, the unit delay error timing ratio data is: high delay error ratio = 30%; low delay error ratio = 60%. This ratio data can help analyze the delay performance of different units in different time periods and evaluate network performance. For example, this ratio can be used to identify which time periods and units have poor communication quality, so as to optimize them.

[0170] Step S456: Evaluate the unit transmission delay according to the unit delay error timing ratio data, so as to obtain the unit communication transmission delay data.

[0171] In this embodiment, the transmission delay of the unit is evaluated based on the obtained delay error timing ratio data. For example, by calculating the weighted average of the delay error timing ratio or the evaluation method based on the quality threshold, it is determined whether the transmission delay of a park unit meets the predetermined quality requirements. If the high-amount delay error ratio of a unit is large and the ratio lasts for a long time, the transmission delay of the unit is evaluated as poor, and hardware upgrades or optimization of the communication network configuration may be required. Conversely, if the low-amount delay error ratio is high, it means that the delay of the unit is good. Through this evaluation, the communication quality of the park unit can be comprehensively evaluated based on the delay performance data, helping managers make further network optimization decisions.

[0172] Optionally, step S5 specifically includes:

[0173] Step S51: extracting the unit high delay error timing sequence according to the unit communication transmission delay data, thereby obtaining the unit high delay error timing sequence data;

[0174] In this embodiment, based on the real-time collected unit communication delay data, statistical analysis methods (such as time series analysis) are applied to extract those high delay errors that exceed the predetermined threshold. By monitoring the different nodes of unit communication, especially when the load is high, the delay fluctuation of each communication unit is evaluated. For example, if some units have a delay error of more than 100ms and the duration exceeds 5 seconds, the delay error can be used as a "high delay error time series" mark for subsequent steps. The delay data is fitted using a time series analysis algorithm (such as an autoregressive integrated moving average ARIMA model) to obtain the high delay error time series data of each unit. This time series data can reflect the impact of factors such as network load changes, traffic fluctuations, and device response speed on delay errors.

[0175] Step S52: extracting the time resource sharing strategy of the industrial park communication resource dynamic sharing strategy based on the unit high delay error timing data, thereby obtaining the unit high delay error timing resource sharing strategy;

[0176] In this embodiment, according to the unit high delay error timing data, the resource sharing strategy of the timing in the industrial park communication resource dynamic sharing strategy is extracted to obtain the unit high delay error timing resource sharing strategy.

[0177] Step S53: Adaptively optimize the unit high delay error timing resource sharing strategy according to the park unit delay demand data, so as to obtain an optimized unit high delay error timing resource sharing strategy;

[0178] In this embodiment, adaptive optimization is performed based on the latency demand data of the park units. The extracted resource sharing strategy is optimized using an adaptive optimization algorithm (such as a genetic algorithm GA or a particle swarm optimization PSO). The optimization process adjusts the resource allocation strategy according to the real-time latency demand to ensure that units with higher latency demands receive priority support, while reasonably compressing resources for other units. A lower latency strategy can be configured for devices with strict latency requirements (such as automated production line control systems) to ensure that their communication latency is stable within an acceptable range. For example, when a device requests higher real-time performance, the system can dynamically adjust its resource allocation ratio and optimize latency scheduling to reduce latency fluctuations.

[0179] Step S54: Couple the resource sharing strategy of the optimization unit high delay error timing resource sharing strategy and the industrial park communication resource dynamic sharing strategy to obtain the industrial park communication resource optimization dynamic sharing strategy, and upload it to the industrial park network communication management platform to execute the equipment communication task.

[0180] In this embodiment, after obtaining the optimized unit high delay error timing resource sharing strategy, it is necessary to couple it with the existing communication resource dynamic sharing strategy of the industrial park. The relationship between different strategies is coordinated to achieve the goal of maximizing resource utilization and minimizing delay. Specifically, by using a coupled optimization algorithm (such as the Lagrange multiplier method or a game theory model), the delay error and resource requirements of each unit are combined to generate the final optimized sharing strategy. These strategies are uploaded in real time through the network communication management platform and assigned to specific devices through the task scheduling system of the platform. For example, when a production line is under high load, its communication resource demand will increase because the equipment and sensors are sensitive to delay. The system will automatically optimize resource allocation, give priority to the delay requirements of the production line, and reduce the resource occupancy of other units. Through this coupled optimization, the overall communication resources of the industrial park can be efficiently utilized, and the communication delay between devices is kept within an appropriate range to ensure the smooth execution of tasks.

[0181] Optionally, the present invention further provides a wireless data transmission system based on 5G communication, which is used to execute the wireless data transmission method based on 5G communication as described above, and the wireless data transmission system based on 5G communication includes:

[0182] The network topology analysis module is used to obtain the 5G communication data of the industrial park, and perform the network communication topology analysis of the industrial park based on the 5G communication data of the industrial park, so as to obtain the network topology model of the industrial park; perform the communication demand analysis of the industrial park unit on the network topology model of the industrial park, so as to obtain the communication demand data of the industrial park unit;

[0183] The priority reallocation module is used to design the communication mode of the industrial park unit according to the communication demand data of the industrial park unit, so as to obtain the communication mode data of the industrial park unit; based on the communication mode data of the industrial park unit, the virtual slice priority is reallocated for the 5G communication data of the industrial park, so as to obtain the optimized virtual slice priority data;

[0184] The slice resource allocation optimization module is used to optimize the unit slice resource allocation according to the optimized virtual slice priority data, so as to obtain the optimized resource allocation data of the industrial park unit slice; according to the industrial park unit communication demand data, the dynamic resource sharing strategy of the industrial park unit slice optimized resource allocation data is analyzed, so as to obtain the dynamic sharing strategy of the industrial park communication resources, and upload it to the industrial park network communication management platform to execute the equipment communication task;

[0185] The transmission delay evaluation module is used to obtain real-time industrial park 5G communication data, and perform park unit transmission performance evaluation on the real-time industrial park 5G communication data, so as to obtain park unit transmission performance data; perform unit communication transmission delay evaluation on the park unit transmission performance data according to the industrial park unit communication demand data, so as to obtain unit communication transmission delay data;

[0186] The dynamic sharing timing analysis module is used to optimize the dynamic sharing timing of the industrial park communication resources based on the unit communication transmission delay data, so as to obtain the optimized dynamic sharing strategy of the industrial park communication resources and upload it to the industrial park network communication management platform to execute the equipment communication tasks.

[0187] The wireless data transmission system based on 5G communication of the present invention can implement any one of the wireless data transmission methods based on 5G communication of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the wireless data transmission method based on 5G communication. The internal modules of the system cooperate with each other, thereby improving the bandwidth utilization of the network, reducing latency, and improving reliability.

[0188] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0189] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A wireless data transmission method based on 5G communication, characterized in that: The following steps are involved: Step S1: Acquire 5G communication data of the industrial park, and perform network communication topology analysis of the industrial park based on the 5G communication data of the industrial park, thereby obtaining a network topology model of the industrial park; perform park unit communication demand analysis on the network topology model of the industrial park, thereby obtaining unit communication demand data of the industrial park; Step S2: Designing a park unit communication mode according to the industrial park unit communication demand data, thereby obtaining park unit communication mode data; reallocating virtual slice priorities of the industrial park 5G communication data based on the park unit communication mode data, thereby obtaining optimized virtual slice priority data; Step S3: Optimize the unit slice resource allocation according to the optimized virtual slice priority data, so as to obtain the optimized resource allocation data of the industrial park unit slice; perform dynamic resource sharing strategy analysis on the optimized resource allocation data of the industrial park unit slice according to the communication demand data of the industrial park unit, so as to obtain the dynamic sharing strategy of the industrial park communication resources, and upload it to the industrial park network communication management platform to execute the equipment communication task; Step S4: Acquire real-time industrial park 5G communication data, and perform park unit transmission performance evaluation on the real-time industrial park 5G communication data, thereby obtaining park unit transmission performance data; perform unit communication transmission delay evaluation on the park unit transmission performance data according to the industrial park unit communication demand data, thereby obtaining unit communication transmission delay data; the specific steps for obtaining unit communication transmission delay data are as follows: Step S451: extracting the delay demand of the industrial park unit from the communication demand data of the industrial park unit, thereby obtaining the delay demand data of the industrial park unit; Step S452: extracting unit delay performance characteristics according to the park unit transmission performance data, thereby obtaining the park unit delay performance data; Step S453: Calculate the unit delay performance error of the park unit delay demand data and the park unit delay performance data, thereby obtaining the unit delay performance error data; Step S454: performing delay error time series statistics according to the unit delay performance error data, thereby obtaining high-value delay error time series data and low-value delay error time series data; Step S455: Calculate the unit delay error ratio based on the high-cost delay error time series data and the low-cost delay error time series data, so as to obtain the unit delay error time series proportion data; Step S456: evaluating the unit transmission delay according to the unit delay error timing ratio data, thereby obtaining the unit communication transmission delay data; Step S5: Based on the unit communication transmission delay data, the dynamic sharing timing of the industrial park communication resource dynamic sharing strategy is optimized to obtain the industrial park communication resource optimization dynamic sharing strategy, and upload it to the industrial park network communication management platform to execute the equipment communication task.

2. The wireless data transmission method based on 5G communication according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: Acquire 5G communication data of the industrial park, and perform data preprocessing on the 5G communication data of the industrial park, so as to obtain 5G communication data to be analyzed; Step S12: extracting virtual slice communication features from the 5G communication data to be analyzed, thereby obtaining virtual slice communication data; Step S13: Perform slice communication connection frequency statistics according to the virtual slice communication data, so as to obtain high communication connection frequency slice data and low communication connection frequency slice data; perform slice operation carrier link feature extraction on the high communication connection frequency slice data, so as to obtain park network equipment link data; perform slice operation carrier link feature extraction on the low communication connection frequency slice data, so as to obtain park industrial equipment link data; Step S14: performing a park network communication topology analysis based on the park network device link data and the park industrial device link data, thereby obtaining an industrial park network topology model; Step S15: Performing park unit communication demand analysis on the industrial park network topology model, thereby obtaining industrial park unit communication demand data.

3. The wireless data transmission method based on 5G communication according to claim 2 is characterized in that: Step S15 is specifically as follows: Step S151: dividing the network equipment intensive units according to the industrial park network topology model, thereby obtaining the park network equipment intensive unit structure data; Step S152: Acquire the industrial park functional unit description data, and spatially associate the park network equipment dense unit structure data and the industrial park functional unit description data, thereby obtaining the industrial park functional unit structure data; Step S153: extracting the historical 5G communication features of the industrial park 5G communication data according to the functional unit structure data of the industrial park, thereby obtaining the historical 5G communication data of the functional unit, and calculating the operating flow of the functional unit equipment based on the historical 5G communication data of the functional unit, thereby obtaining the operating flow data of the functional unit equipment; Step S154: Calculate the functional unit equipment density of the industrial park functional unit structure data, thereby obtaining functional unit equipment density data; Step S155: performing communication demand prediction of the functional units based on the functional unit operation flow data and the functional unit equipment density data, thereby obtaining communication demand prediction data of the functional units; Step S156: construct a communication demand matrix based on the communication demand forecast data of the functional units, thereby obtaining the communication demand data of the industrial park units.

4. The wireless data transmission method based on 5G communication according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: Calculate the base station coverage range according to the industrial park network topology model, so as to obtain the industrial park base station coverage range data; Step S22: classifying the functional unit structure data of the industrial park based on the coverage data of the base station of the industrial park, thereby obtaining the base station full coverage unit structure data and the base station half coverage unit structure data; Step S23: selecting a unit base station coverage communication mode for the industrial park unit communication demand data according to the base station full coverage unit structure data, thereby obtaining the base station full coverage unit communication mode data; selecting a unit cluster communication mode for the industrial park unit communication demand data according to the base station half coverage unit structure data, thereby obtaining the base station half coverage unit communication mode data; Step S24: performing communication mode integration on the base station full coverage unit communication mode data and the base station half coverage unit communication mode data, thereby obtaining the campus unit communication mode data; Step S25: Re-allocate the virtual slice priority of the industrial park 5G communication data based on the park unit communication mode data to obtain optimized virtual slice priority data.

5. The wireless data transmission method based on 5G communication according to claim 4 is characterized in that: Step S25 is specifically as follows: Step S251: extracting the functional unit 5G communication virtual slices of the industrial park 5G communication data according to the functional unit structure data of the industrial park, thereby obtaining the functional unit 5G communication virtual slice data; Step S252: extracting virtual slice priority features according to the functional unit 5G communication virtual slice data, thereby obtaining unit 5G communication virtual slice priority data; Step S253: performing park unit communication simulation based on the park unit communication mode data to obtain park unit communication simulation data, and classifying park unit communication loads according to the park unit communication simulation data to obtain high communication load unit data and low communication load unit data; Step S254: performing a cell intersection operation based on the high communication load cell data and the base station full coverage cell structure data, thereby obtaining high communication priority cell data; performing a cell intersection operation based on the low communication load cell data and the base station half coverage cell structure data, thereby obtaining low communication priority cell data; Step S255: Adjust the unit 5G communication virtual slice priority data according to the high communication priority unit data and the low communication priority unit data, so as to obtain optimized virtual slice priority data.

6. The wireless data transmission method based on 5G communication according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: extracting unit slice allocation resource features from the functional unit 5G communication virtual slice data, thereby obtaining unit slice resource allocation data; Step S32: reallocate the unit slice resource allocation data according to the optimized virtual slice priority data, thereby obtaining the optimized resource allocation data of the unit slices of the industrial park; Step S33: performing periodic calculation of the communication demand of the industrial park units according to the communication demand data of the industrial park units, thereby obtaining the periodic communication demand data of the industrial park units; Step S34: Integrate the periodic resource sharing strategy of the industrial park unit slice optimization resource allocation data according to the periodic communication demand data of the park unit, so as to obtain the dynamic sharing strategy of the industrial park communication resources, and upload it to the industrial park network communication management platform to execute the equipment communication task.

7. The wireless data transmission method based on 5G communication according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: acquiring real-time 5G communication data of the industrial park, and performing data preprocessing on the real-time 5G communication data of the industrial park, so as to obtain real-time 5G communication data to be processed; Step S42: dividing the real-time 5G communication data to be processed into park unit communication data, thereby obtaining the real-time 5G communication data of the park unit; Step S43: extracting unit 5G transmission performance characteristics from the real-time 5G communication data of the park unit, thereby obtaining unit 5G transmission performance characteristic data, and performing unit transmission performance timing analysis based on the unit 5G transmission performance characteristic data, thereby obtaining unit transmission performance timing data; Step S44: performing timing fluctuation statistics according to the unit transmission performance timing data, thereby obtaining the unit transmission performance fluctuation timing data, and performing campus unit transmission performance evaluation based on the unit transmission performance fluctuation timing data, thereby obtaining the campus unit transmission performance data; Step S45: evaluating the unit communication transmission delay of the industrial park unit transmission performance data according to the industrial park unit communication demand data, thereby obtaining the unit communication transmission delay data.

8. The wireless data transmission method based on 5G communication according to claim 1, characterized in that: Step S5 is specifically as follows: Step S51: extracting the unit high delay error timing sequence according to the unit communication transmission delay data, thereby obtaining the unit high delay error timing sequence data; Step S52: extracting the time resource sharing strategy of the industrial park communication resource dynamic sharing strategy based on the unit high delay error timing data, thereby obtaining the unit high delay error timing resource sharing strategy; Step S53: Adaptively optimize the unit high delay error timing resource sharing strategy according to the park unit delay demand data, so as to obtain an optimized unit high delay error timing resource sharing strategy; Step S54: Couple the resource sharing strategy of the optimization unit high delay error timing resource sharing strategy and the industrial park communication resource dynamic sharing strategy to obtain the industrial park communication resource optimization dynamic sharing strategy, and upload it to the industrial park network communication management platform to execute the equipment communication task.

9. A wireless data transmission system based on 5G communication, characterized in that: Used to execute the wireless data transmission method based on 5G communication as claimed in claim 1, the wireless data transmission system based on 5G communication comprises: The network topology analysis module is used to obtain the 5G communication data of the industrial park, and perform the network communication topology analysis of the industrial park based on the 5G communication data of the industrial park, so as to obtain the network topology model of the industrial park; perform the communication demand analysis of the industrial park unit on the network topology model of the industrial park, so as to obtain the communication demand data of the industrial park unit; The priority reallocation module is used to design the communication mode of the industrial park unit according to the communication demand data of the industrial park unit, so as to obtain the communication mode data of the industrial park unit; based on the communication mode data of the industrial park unit, the virtual slice priority is reallocated for the 5G communication data of the industrial park, so as to obtain the optimized virtual slice priority data; The slice resource allocation optimization module is used to optimize the unit slice resource allocation according to the optimized virtual slice priority data, so as to obtain the optimized resource allocation data of the industrial park unit slice; according to the industrial park unit communication demand data, the dynamic resource sharing strategy of the industrial park unit slice optimized resource allocation data is analyzed, so as to obtain the dynamic sharing strategy of the industrial park communication resources, and upload it to the industrial park network communication management platform to execute the equipment communication task; The transmission delay evaluation module is used to obtain real-time industrial park 5G communication data, and perform park unit transmission performance evaluation on the real-time industrial park 5G communication data, so as to obtain park unit transmission performance data; perform unit communication transmission delay evaluation on the park unit transmission performance data according to the industrial park unit communication demand data, so as to obtain unit communication transmission delay data; The dynamic sharing timing analysis module is used to optimize the dynamic sharing timing of the industrial park communication resources based on the unit communication transmission delay data, so as to obtain the optimized dynamic sharing strategy of the industrial park communication resources and upload it to the industrial park network communication management platform to execute the equipment communication tasks.

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