Intelligent safety equipment data transmission method and system based on 5G + private network fusion

Abstract

The invention provides an intelligent safety equipment data transmission method and system based on 5G + private network fusion. Belongs to the technical field of wireless communication and intelligent safety equipment. The method comprises the following steps: preprocessing original data collected by intelligent safety equipment; performing real-time monitoring and feature extraction on the initial multipath channel signal data by using a radio frequency front end of the 5G terminal to obtain antenna channel feature data; on the basis of the extracted antenna channel characteristic data, an antenna initial beam directional diagram is calculated through a phased-array antenna beam forming algorithm; performing spatial filter interference suppression processing on the initial beam directional diagram to obtain antenna optimization beam form data; adjusting the beam form data through a digital beam forming technology to obtain a final antenna beam configuration parameter; and through the high-speed transmission capability of the 5G + private network, the data transmission rate of the intelligent safety equipment is remarkably improved by combining the intelligent bandwidth aggregation and time slot division technology.

Description

TECHNICAL FIELD

[0001] The application provides an intelligent safety device data transmission method and system based on 5G+ private network fusion, and belongs to the technical fields of wireless communication and intelligent safety devices. BACKGROUND

[0002] With the advancement of smart city construction, intelligent safety devices are increasingly widely applied in industrial production, construction, public safety and other fields. However, traditional wireless communication technologies have limitations in terms of data transmission speed, reliability and coverage range, and are difficult to meet the high requirements of intelligent safety devices for real-time data transmission in complex environments. As a new type of wireless communication technology, 5G+ private network has the characteristics of high speed, low latency and large number of connections, and provides a new solution for data transmission of intelligent safety devices. SUMMARY

[0003] The application provides an intelligent safety device data transmission method and system based on 5G+ private network fusion, and belongs to the technical fields of wireless communication and intelligent safety devices. The intelligent safety device data transmission method based on 5G+ private network fusion provided by the application comprises the following steps: S1, preprocessing the original data collected by the intelligent safety device; using the radio frequency front end of the 5G terminal, real-time monitoring and feature extraction are performed on the initial multipath channel signal data to obtain antenna channel feature data; S2, based on the extracted antenna channel feature data, the initial beam pattern of the antenna is calculated through the phased array antenna beamforming algorithm; and the initial beam pattern is subjected to spatial filter interference suppression processing to obtain antenna optimized beam shape data; the beam shape data is adjusted through digital beamforming technology to obtain the final antenna beam configuration parameters; S3, according to the distribution of the intelligent safety device and the deployment position of the 5G base station, network topology node analysis is performed on the antenna beam configuration parameters to obtain the connection relationship diagram between the intelligent safety device and the 5G base station, i.e. the on-body 5G node distribution diagram; in view of the dynamic change of the network topology of the intelligent safety device in the moving process, an on-demand routing protocol is adopted to calculate the initial routing topology structure in real time; the initial routing topology structure is subjected to redundant link optimization processing to obtain a 5G hierarchical mesh routing diagram; S4, based on the 5G hierarchical mesh routing diagram, real-time state detection is performed on the dual-frequency channel resources of the 5G private network to obtain a channel resource state table; according to the channel resource state table, an intelligent bandwidth aggregation algorithm is adopted to perform dynamic bandwidth allocation and aggregation processing on multiple available channels to obtain a 5G private network bandwidth allocation scheme; according to the bandwidth allocation scheme, fine time slot division and resource scheduling processing are performed on the dual-frequency data stream of the 5G private network to obtain a 5G resource scheduling strategy; S5, according to the 5G resource scheduling strategy, the data to be transmitted of the intelligent safety device is processed by distributed network coding, and according to the data transmission condition after coding, an intelligent retransmission control mechanism is adopted to retransmit the data packets that are not successfully transmitted in time, and a 5G reliable transmission control scheme is obtained.

[0004] The intelligent safety device data transmission system based on 5G+ private network fusion provided by the application comprises: One or more processors; Memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of the above.

[0005] The application has the following advantages: through the high-speed transmission capability of 5G+ private network, combined with intelligent bandwidth aggregation and time slot division technology, the data transmission rate of intelligent safety devices is significantly improved; distributed network coding and intelligent retransmission control mechanism are adopted to effectively deal with network fluctuations and interruption problems in complex environments and ensure reliable data transmission; through network topology optimization and routing calculation technology, dynamic allocation and efficient use of 5G private network resources are realized, and the overall performance and stability of the network are improved; for the network switching problem of intelligent safety devices during movement, an improved on-demand routing protocol and redundant link optimization technology are adopted to ensure the continuity and stability of data transmission during movement of the devices. BRIEF DESCRIPTION OF DRAWINGS

[0006] Figure 1 The method steps of the application are described. DETAILED DESCRIPTION

[0007] The preferred embodiments of the application are described below in conjunction with the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to illustrate and explain the application, and are not used to limit the application; One embodiment of the application is shown in the figure, and the intelligent safety device data transmission method based on 5G+ private network fusion comprises: Figure 1 The method comprises the following steps: S1, the original data (including video, audio, sensor data, etc.) collected by the intelligent safety device (such as intelligent safety helmet, intelligent safety belt, etc.) is preprocessed; the radio frequency front end of the 5G terminal is used to monitor and extract the initial multipath channel signal data in real time, and the antenna channel feature data is obtained; the feature data includes channel fading characteristics, Doppler shift, etc. S2, based on the extracted antenna channel feature data, the initial beam pattern of the antenna is calculated through the phased array antenna beamforming algorithm; the pattern can accurately point to the target receiving device, improve the signal gain; and the initial beam pattern is subjected to spatial filter interference suppression processing to eliminate multipath effect and interference signals to obtain antenna optimized beam shape data; through digital beamforming technology, the optimized beam shape data is adjusted to obtain the final antenna beam configuration parameters; S3, according to the distribution of intelligent safety devices and the deployment position of 5G base stations, the network topology node analysis is performed on the antenna beam configuration parameters to obtain the connection relationship diagram between the intelligent safety devices and the 5G base stations, that is, the on-body 5G node distribution diagram; in view of the dynamic change of network topology of the intelligent safety devices in the moving process, an improved on-demand routing protocol is adopted to calculate the initial routing topology structure in real time; the protocol can dynamically adjust the routing path according to the network condition and device mobility to ensure the continuity and reliability of data transmission; the initial routing topology structure is subjected to redundant link optimization processing to eliminate single point failure and bottleneck link to obtain a 5G hierarchical mesh routing diagram; the routing diagram has higher fault tolerance and robustness and can cope with network fluctuations and interruptions in complex environments; S4, based on the 5G hierarchical mesh routing diagram, the real-time state detection is performed on the dual-frequency channel resources of the 5G private network to obtain a channel resource state table; the table records the bandwidth utilization, signal quality and other key indicators of each channel to provide a basis for subsequent bandwidth aggregation; according to the channel resource state table, an intelligent bandwidth aggregation algorithm is adopted to perform dynamic bandwidth allocation and aggregation processing on multiple available channels to obtain a 5G private network bandwidth allocation scheme; the scheme can fully utilize the channel resources to improve the data transmission rate and reliability; according to the bandwidth allocation scheme, fine time slot division and resource scheduling processing are performed on the dual-frequency data stream of the 5G private network to obtain a 5G resource scheduling strategy; the strategy can ensure that each intelligent safety device obtains sufficient bandwidth and time slot resources when transmitting data to avoid data conflict and loss; S5, according to the 5G resource scheduling strategy, the data to be transmitted by the intelligent safety device is subjected to distributed network coding processing; through coding and redundant transmission of data at the network layer, the fault tolerance and reliability of data transmission are improved; according to the data transmission condition after coding, an intelligent retransmission control mechanism is adopted to perform timely retransmission processing on the data packets that are not successfully transmitted; at the same time, combined with the high reliability and low latency characteristics of the 5G private network, efficient and reliable transmission of the data of the intelligent safety device is realized; a 5G reliable transmission control scheme is obtained to ensure stable transmission of data in complex environments.

[0008] The working principle and effect of the above technical scheme are that: through real-time monitoring and channel feature extraction of the radio frequency front end of the 5G terminal, accurate basis can be provided for subsequent beamforming; and the antenna beam direction can be effectively optimized, the signal gain is improved, the signal interference and multipath effect are reduced, thereby improving the quality and stability of data transmission; By adopting the improved on-demand routing protocol, the routing path can be dynamically adjusted during device movement, ensuring the continuity and reliability of data transmission; the optimized redundant link design enables the network to maintain efficient operation when facing single point failure or link bottleneck, ensuring uninterrupted data transmission; Through real-time state monitoring of the dual-frequency channel of the 5G private network, and intelligent bandwidth aggregation and resource allocation, the channel utilization rate can be maximized; not only the transmission rate can be improved, but also the device can obtain sufficient bandwidth when transmitting data, avoiding congestion and packet loss phenomenon; The distributed network coding technology can improve the fault tolerance of data, and combined with the intelligent retransmission control mechanism, the data packets that are not successfully transmitted can be retransmitted in time, further improving the reliability of transmission; at the same time, the high reliability and low latency characteristics of the 5G private network are utilized to ensure stable data transmission of intelligent safety devices in extreme environments; Through multi-level network topology optimization and redundant link design, the fault tolerance and robustness of the entire system can be effectively improved; whether it is network fluctuation or device movement, the system can adapt and automatically adjust to ensure stable data transmission in complex environments.

[0009] In an embodiment of the present application, the S1 comprises: S11, preprocessing the original data collected by the intelligent safety device (intelligent safety helmet, intelligent safety belt, editable mobile terminal, mobile camera and audio and video recorder, etc.), selecting a data compression algorithm according to the data type and characteristics to compress the preprocessed original data, for example, using H.264 or H.265 encoding compression for video data, and using AAC encoding compression for audio data, to reduce the data volume and improve the transmission efficiency; and encoding optimization is performed on the compressed data, and a more efficient encoding method such as binary encoding, Huffman encoding, etc. is used to further improve the reliability and speed of data transmission; S12, using the radio frequency front end of the 5G terminal to build a real-time channel signal monitoring system to continuously and real-timely monitor the initial multipath channel signal data; S13, extracting key features from the real-time monitored channel signal data, the key features including channel fading characteristics (such as Rayleigh fading, Rician fading, etc.), Doppler shift and delay spread; S14, utilize machine learning algorithm, carry out modeling analysis to the extracted channel feature, establish the correlation model between channel feature and signal transmission quality, provide more accurate input for subsequent beamforming algorithm.

[0010] The working principle and effect of the above technical solution are: by real-time monitoring and extracting channel features, and optimizing antenna pointing through beamforming, the signal quality can be effectively improved, the interference can be reduced, and the stability of data transmission can be improved; By adopting a dynamic routing protocol, the routing path is adjusted according to the movement of the device and the network condition, thereby ensuring the continuity and stability of data transmission; at the same time, the design of the redundant link can eliminate potential single-point failure and link bottleneck, and ensure the efficient operation of the system; By real-time monitoring of the dual-frequency channel state of the 5G private network, intelligent bandwidth aggregation is performed to improve channel utilization and ensure that the device can obtain sufficient bandwidth resources when transmitting data, avoiding congestion and packet loss; Through the distributed network coding and intelligent retransmission control mechanism, the fault tolerance of data can be improved even in extreme environments, ensuring reliable transmission of data packets, and the low-latency characteristics of 5G are utilized to ensure stable data transmission; Adopting multi-level network topology optimization enhances the adaptability of the system to network fluctuations and device movement, improves the robustness of the entire system, and ensures stable data transmission in complex environments.

[0011] In one embodiment of the present application, the S2 comprises: S21, based on the extracted antenna channel feature data, a phased array antenna beamforming algorithm is used to calculate the initial beam pattern of the antenna; the initial beam pattern is evaluated to analyze its coverage, gain, sidelobe level and other indicators to ensure that the pattern can meet the basic signal transmission requirements; S22, analyze the interference sources of the environment where the intelligent security device is located, identify the existing interference sources, including multipath effect and external electromagnetic interference; and according to the characteristics of the interference sources, design a special spatial filter, and use an adaptive filtering algorithm to adjust the filter parameters in real time; S23, apply the preset spatial filter to the initial beam pattern for interference suppression processing to eliminate multipath effect and external interference signals, and verify the interference suppression effect of the spatial filter through actual test or simulation; S24, further optimize the beam pattern after spatial filtering, adjust the beam shape parameters using particle swarm optimization algorithm, obtain better signal coverage and gain, adopt digital beamforming technology, finely adjust the optimized beam shape data, dynamically point and shape control the beam through digital signal processing algorithm, determine the final antenna beam configuration parameters according to the optimized beam shape data, the antenna beam configuration parameters include beam pointing angle, beam width and gain.

[0012] The working principle and effect of the above technical scheme are as follows: the initial beam pattern calculated by the phased array antenna beamforming algorithm is evaluated in terms of coverage range, gain and sidelobe level, etc., to ensure that the beam can meet the basic signal transmission requirements; the further optimized beam pattern can accurately cover the target area, improving the signal transmission quality. The scheme analyzes the interference sources (such as multipath effect and external electromagnetic interference) in the environment, dynamically adjusts the filter parameters using an adaptive filtering algorithm, effectively suppressing the interference signals; this interference suppression method can effectively reduce signal attenuation and interference, improving the reliability of data transmission. A special spatial filter is designed and applied to effectively eliminate multipath effect and external electromagnetic interference signals; the interference suppression effect of the filter is verified through actual testing or simulation, ensuring the stability of the system in complex environments. The particle swarm optimization algorithm is used to adjust the beam shape parameters, further improving the signal coverage range and gain; combined with digital beamforming technology, the beam shape data is finely adjusted to ensure dynamic pointing and shape control of the beam, providing more accurate signal transmission. After optimizing the beam shape, the system can determine the final antenna beam configuration parameters, including beam pointing angle, beam width and gain; this precise beam configuration improves the transmission quality of the signal and provides a more stable communication environment for intelligent security devices.

[0013] In one embodiment of the present application, the S3 comprises: S31, according to the distribution of intelligent security devices and the deployment position of 5G base stations, a network topology node information collection system is built to collect real-time data of each node, including position information, connection state and signal strength; S32, integrate and process the collected node information to establish an initial connection relationship diagram between intelligent security devices and 5G base stations; for the dynamic changes of network topology of intelligent security devices during movement, the network topology monitoring system is used to monitor the movement trajectory of nodes and the change of connection relationship in real time. S33, model and analyze the dynamic changes of network topology using graph theory, establish a network topology dynamic change model to provide accurate network state information for subsequent routing calculation; according to the network topology dynamic change model, construct an improved on-demand routing protocol, which can dynamically adjust routing discovery, routing maintenance and routing selection mechanisms according to network conditions and device mobility; use software-defined network (SDN) technology to realize flexible deployment and dynamic adjustment of the improved on-demand routing protocol, and improve the adaptability and scalability of the routing protocol; S34, use the improved on-demand routing protocol to calculate the initial routing topology structure in real time, which reflects the real-time connection relationship and possible routing path between intelligent security devices and 5G base stations; optimize the initial routing topology structure, select the optimal routing path by using the minimum spanning tree algorithm, and reduce the delay and packet loss rate of data transmission; S35, analyze the redundant link of the optimized routing topology structure, identify and eliminate single point failure and bottleneck link, and generate 5G hierarchical mesh routing graph according to the redundant link optimization result, which has the characteristics of multi-level and multi-path, and can cope with network fluctuations and interruptions in complex environments to ensure the continuity and reliability of data transmission.

[0014] The working principle and effect of the above technical scheme are as follows: by constructing a network topology node information collection system, real-time collection of connection state, position information and signal strength data between intelligent security devices and 5G base stations provides accurate information basis for subsequent network topology analysis and optimization; This scheme can monitor the dynamic changes of network topology in real time, especially during device movement, by tracking the changes of node trajectory and connection relationship in real time, which helps to identify the changes of network state in time, thereby ensuring the stability of network and the efficiency of data transmission; The network topology dynamic change model is established by using graph theory to model and analyze the dynamic changes of network topology, which provides accurate network state information for routing calculation; at the same time, through the improved on-demand routing protocol, the routing mechanism can be dynamically adjusted according to the network conditions and device mobility, the data transmission path is optimized, and the adaptability and scalability of the network are improved; The initial routing topology is optimized by the minimum spanning tree algorithm to select the optimal routing path, which effectively reduces the delay and packet loss rate of data transmission, ensures the efficiency and low delay of data transmission, and provides stable path for the optimized routing topology to cope with various network environments; Through the analysis of redundant link, the single point failure and bottleneck link are eliminated, thereby enhancing the fault tolerance of the system; the finally generated 5G hierarchical mesh routing graph has the characteristics of multi-level and multi-path, which effectively copes with network fluctuations and interruptions in complex environments, and ensures the continuity and reliability of data transmission.

[0015] In one embodiment of the present application, the S4 comprises: S41, based on the 5G hierarchical mesh routing graph, a channel resource state real-time monitoring system is built, which can monitor the state information of the dual-frequency channel resources of the 5G private network in real time, including bandwidth utilization, signal quality, interference level, etc.; a distributed monitoring architecture is adopted, and the monitoring tasks are distributed to multiple monitoring nodes; S42, through the channel resource state real-time monitoring system, the state data of each channel is collected, stored and analyzed at a certain time interval; and the collected channel resource state data is preprocessed; S43, according to the channel resource state data, based on the intelligent bandwidth aggregation algorithm according to the related indexes of each channel, the related indexes include bandwidth utilization and signal quality; dynamically allocate and aggregate the bandwidth of multiple available channels; use machine learning algorithm to optimize the intelligent bandwidth aggregation algorithm, so that it can automatically adjust the bandwidth allocation strategy according to the change of network status, and improve the utilization rate of bandwidth resources; S44, according to the calculation result of the intelligent bandwidth aggregation algorithm, a 5G private network bandwidth allocation scheme is generated, which specifies the bandwidth resources that can be used by each intelligent security device in different time periods; according to the bandwidth allocation scheme, the dual-frequency data stream of the 5G private network is finely time-slotted, the time axis is divided into multiple time slots, and each time slot is allocated with corresponding bandwidth resources; S45, formulate resource scheduling strategy, dynamically schedule time slots and bandwidth resources according to data transmission priority and data size of intelligent security devices.

[0016] The working principle and effect of the above technical scheme are: through the channel resource state real-time monitoring system built based on the 5G hierarchical mesh routing graph, the key information such as bandwidth utilization, signal quality, interference level of the dual-frequency channel of the 5G private network can be accurately monitored; this system adopts a distributed monitoring architecture, which can distribute monitoring tasks to multiple monitoring nodes, improving the coverage and real-time performance of data collection; The channel resource state data is collected by the real-time monitoring system, stored and preprocessed at a time interval, ensuring the accuracy and integrity of the data, and providing high-quality raw data support for subsequent analysis; Based on the collected channel state data, the intelligent bandwidth aggregation algorithm can dynamically allocate and aggregate the bandwidth of multiple available channels according to the bandwidth utilization and signal quality indexes; the machine learning algorithm is used to optimize the algorithm, so that it can automatically adjust the bandwidth allocation strategy according to the change of network status, thereby improving the utilization rate of bandwidth resources and avoiding the waste or excessive load of bandwidth; The generated bandwidth allocation scheme can accurately specify the bandwidth resources used by each intelligent security device in different time periods, while combining a time slot division strategy to divide the time axis into multiple time slots and allocate corresponding bandwidth resources to each time slot; this refined bandwidth allocation method helps to improve the utilization efficiency of overall network resources and the collaboration ability between devices; According to the data transmission priority and data size of the intelligent security device, a dynamic resource scheduling strategy is formulated; this enables the system to flexibly schedule time slots and bandwidth resources, ensuring that high-priority data transmission can be prioritized, while optimizing the bandwidth usage of low-priority devices, effectively improving overall network performance and data transmission quality.

[0017] In one embodiment of the present application, the S41 comprises: The connection relationship between different levels and the distribution of nodes within each level is determined; according to the structure of the routing graph and the business requirements, a clear function and role is defined for each node; for example, some nodes are defined as core nodes, responsible for handling a large amount of data aggregation and forwarding, requiring high processing power and reliability; some nodes are defined as edge nodes, mainly communicating directly with intelligent security devices, requiring good access ability and adaptability; at the same time, corresponding performance indicators and monitoring priorities are formulated for nodes with different roles, such as core nodes focusing on data forwarding delay and throughput, edge nodes focusing on access success rate and signal strength, etc. A hierarchical architecture mode is adopted to divide the system into data collection layer, data transmission layer, data processing layer and application layer; among them, the data collection layer is responsible for collecting channel resource state data from each monitoring node; the data transmission layer ensures that data can be reliably and efficiently transmitted between the collection nodes and the processing center; the data processing layer stores, analyzes and processes the collected data; the application layer presents the processing results to the user in an intuitive way and provides corresponding control interface; and further subdivides the function modules in each layer; the data collection layer includes channel parameter collection module, device state collection module, etc., which are responsible for collecting parameters such as bandwidth utilization, signal quality, interference level of the channel and the state information of intelligent security devices and monitoring nodes; the data transmission layer includes data encapsulation module, transmission protocol module, etc., which realize reliable encapsulation and efficient transmission of data; the data processing layer includes data storage module, data analysis module, algorithm optimization module, etc., which are used to store data, analyze channel resource state change trend and optimize intelligent bandwidth aggregation algorithm; the application layer includes monitoring interface module, alarm module, strategy adjustment module, etc., which provide a friendly operation interface and real-time monitoring information for the user, and alarm in time when abnormal situation occurs, while supporting users to adjust the monitoring strategy according to actual situation; According to the coverage range of the 5G private network and the distribution of intelligent security devices, the coverage range of the distributed monitoring nodes is planned; by using the geographic information system (GIS) technology and combining the signal coverage model of the 5G base station, the effective monitoring area of each monitoring node is determined to ensure that the channel resource status in the entire 5G private network area can be effectively monitored; The position of the monitoring node is optimized by using the genetic algorithm; considering factors such as topography, building distribution, signal propagation characteristics, etc., the best deployment position of each monitoring node is determined to minimize the monitoring blind area, maximize the monitoring accuracy, and reduce the deployment cost; according to the scale of the 5G private network and the change of business demand, the number of distributed monitoring nodes is dynamically determined; in the initial deployment stage, according to the network planning and predicted traffic, a basic number of nodes is determined; with the development of the network and the increase of the number of intelligent security devices, the change of the channel resource status is monitored in real time, and when the monitoring accuracy of some areas is insufficient or monitoring blind areas appear, monitoring nodes are added in time; when the traffic of some areas decreases or monitoring nodes are redundant, the monitoring nodes are appropriately reduced to realize dynamic adjustment of the number of nodes and efficient use of resources; according to the function, performance and current load of the distributed monitoring nodes, a monitoring task allocation strategy is formulated; by using the load balancing algorithm, different monitoring tasks are evenly distributed to each monitoring node to avoid the situation that some nodes are overloaded while some nodes are idle; at the same time, considering the priority and real-time requirements of the monitoring task, important monitoring tasks and high real-time requirement monitoring tasks are preferentially allocated to nodes with good performance for processing.

[0018] The working principle and effect of the above technical solution are: by defining the connection relationship between different levels and defining the function and role of each node (such as core node, edge node, etc.), the use of network resources can be optimized to ensure efficient operation of the network; the high processing capacity of the core node and the good access capacity of the edge node can effectively improve the efficiency of data transmission and the stability of the network; The hierarchical architecture mode (data acquisition layer, data transmission layer, data processing layer and application layer) is adopted to divide the complex monitoring system into more manageable and optimized modules; each level is subdivided according to its function, which not only improves the reliability of data transmission, but also ensures the efficiency and flexibility of the monitoring system; in particular, the application layer provides a friendly operation interface and real-time monitoring information for users, improving the convenience and response speed of operation; The layout of the monitoring nodes is planned by using the GIS technology and the signal coverage model to ensure that the state of the channel resources can be effectively monitored in the entire 5G private network range; this accurate coverage range planning not only improves the monitoring capability of the system, but also effectively reduces the monitoring blind area, ensuring comprehensive monitoring of the network status; The genetic algorithm is used for optimizing the selection of monitoring node positions to minimize the monitoring blind area, improve monitoring accuracy and reduce deployment cost; this optimization strategy can flexibly adjust the node layout according to actual terrain, building distribution and other factors, to ensure the comprehensiveness and accuracy of network coverage; with the change of network size and equipment demand, the number of monitoring nodes is dynamically adjusted to ensure efficient use of resources and avoid redundancy and overload; Based on the load balancing algorithm and task priority management, the system can reasonably allocate monitoring tasks to avoid the overload or idleness of some nodes; through the dynamic task allocation strategy, the stability and real-time response capability of the monitoring system are improved, and the priority processing of important tasks is ensured, effectively guaranteeing the monitoring tasks with high real-time requirements; The optimization and dynamic adjustment capability of the overall scheme enables the status of channel resources to be continuously and accurately monitored, especially when facing network state fluctuations, the system can adjust the strategy according to real-time data, improving the use efficiency of bandwidth resources, while ensuring the stability and efficiency of intelligent security devices.

[0019] In an embodiment of the present application, the S44 comprises: The output results of the intelligent bandwidth aggregation algorithm are analyzed to determine their data structure; the results usually contain information in multiple dimensions, such as the estimated bandwidth capacity of each available channel in different time periods, signal quality fluctuation range, interference level trend, etc.; detailed analysis of these data structures determines the meaning and data type of each field, providing accurate data basis for subsequent generation of bandwidth allocation scheme; key performance indicators that have important influence on bandwidth allocation are selected from the algorithm output results; in addition to bandwidth utilization and signal quality, channel stability (measured by the variance of signal quality over a period of time), interference persistence (statistical frequency and duration of interference occurrence) and other factors need to be considered; a dynamic weight allocation mechanism is designed for different key performance indicators; according to the changes of business demand and network environment of 5G private network, the weights of each indicator are adjusted in real time; for example, when the intelligent security device is performing real-time video monitoring and other services that require high bandwidth and signal quality, the weights of bandwidth utilization and signal quality are increased; in periods of large network interference, the weight of the interference level indicator is appropriately increased to ensure that the bandwidth allocation can adapt to the changing network conditions; Detailed modeling of the business needs of various intelligent security devices in the 5G private network; analyze the data transmission characteristics of different devices at different time periods, such as data size, transmission frequency, real-time requirements, etc.; for example, intrusion detection devices may need to transmit a large amount of alarm data in a short time, with extremely high real-time requirements; while environmental monitoring devices may transmit relatively small environmental parameter data at a lower frequency; according to these characteristics, develop individualized bandwidth demand models for each intelligent security device; combined with the business importance and urgency of intelligent security devices, develop a bandwidth allocation strategy based on business priority; divide the business into high, medium and low priority, high priority business (such as emergency alarm, real-time monitoring, etc.) has priority in bandwidth allocation, to ensure that it can obtain sufficient bandwidth resources to meet the real-time transmission requirements; medium priority business (such as routine data collection, device status reporting, etc.) is allocated after meeting the high priority business requirements; low priority business (such as historical data backup, non-real-time analysis, etc.) uses the remaining bandwidth resources; at the same time, consider the bandwidth elastic allocation between different priority businesses, when the high priority business demand decreases, dynamically allocate the excess bandwidth to medium and low priority businesses, to improve the overall utilization of bandwidth resources; Based on the dual-frequency channel characteristics of the 5G private network, develop a multi-channel collaborative bandwidth allocation scheme; through performance evaluation and comparison of different channels, reasonably allocate the bandwidth requirements of intelligent security devices to multiple channels to realize the complementary advantages of channel resources; for example, allocate high real-time requirement business to channels with good signal quality and low interference level; disperse large data volume business to multiple channels with large bandwidth capacity to improve data transmission efficiency and reliability; at the same time, adopt channel switching mechanism, when the performance of a certain channel decreases or fails, it can timely switch the related business to other available channels to ensure the continuity of the business; based on the dynamic time slot allocation algorithm, according to the bandwidth allocation scheme and the business requirements of intelligent security devices, real-time allocate corresponding bandwidth resources to each time slot; this algorithm should be able to dynamically adjust the allocation of time slots according to the real-time changes of business, for example, when a certain intelligent security device suddenly generates a large amount of data transmission demand, it can timely allocate more time slot resources to it; when the business demand decreases, timely release the excess time slot resources to improve the utilization rate of time slot resources; at the same time, consider the overlapping and staggered allocation between time slots to further improve the flexibility and efficiency of data transmission; The mapping relationship between time slots and 5G private network dual-frequency channels is established, and the channel used by each time slot is determined; according to the performance characteristics and service requirements of the channel, the time slots are allocated to different channels; for example, time slots with high real-time requirements are allocated to channels with good signal quality and low interference level; time slots with large data volume are dispersed to multiple channels to improve the parallelism and efficiency of data transmission; at the same time, the dynamic changes of the channel are considered, and when the performance of the channel changes, the mapping relationship between the time slot and the channel is adjusted in time to ensure the stability and reliability of data transmission.

[0020] The working principle and effect of the above technical solution are: by analyzing the output results of the intelligent bandwidth aggregation algorithm and screening out the key performance indicators, the bandwidth can be allocated specifically; this not only ensures that each intelligent security device can obtain bandwidth matching its demand, but also adjusts the weight in real time to adapt to changes in the network environment, ensuring that the needs of different business types are prioritized and optimizing the overall utilization of bandwidth; Through the multi-channel cooperative bandwidth allocation scheme, bandwidth resources can be dynamically allocated according to the performance characteristics of different channels to ensure that real-time requirements of services are fully guaranteed; for example, when the channel quality is poor, the related business can quickly switch to other high-quality channels, avoiding the problem of business interruption caused by single channel failure, and improving the reliability and fault tolerance of the network; This scheme allocates bandwidth according to the priority of the business, and after meeting the bandwidth requirements of high-priority businesses, the remaining resources can be shared by low-priority businesses; by dynamically adjusting the bandwidth elastic allocation mechanism, the use efficiency of resources can be fully improved, and the bandwidth allocation can be dynamically adjusted according to the actual demand to optimize the overall utilization of resources; Using the dynamic time slot allocation algorithm, bandwidth resources can be efficiently allocated for each time slot according to the real-time changes of the business; when the demand of a device suddenly increases, the system can quickly respond and adjust the time slot resources, thereby improving the transmission efficiency and flexibility; in addition, the reasonable mapping of time slots and channels can optimize the data transmission path according to network changes and reduce network congestion; By modeling the data transmission characteristics of different intelligent security devices in detail, a bandwidth demand model can be tailored for each device; for example, the bandwidth requirements of intrusion detection devices and environmental monitoring devices are quite different, and the scheme can allocate different bandwidth resources according to these characteristics to ensure that the business requirements of different devices are fully met; Based on real-time changes in the network environment and changes in bandwidth demand, this scheme can adjust various parameters in real time to maintain efficient use of bandwidth resources; for example, during periods of high interference, the system will adjust the weight of the interference level indicator to ensure that bandwidth allocation can maintain the stability of network performance in a changing environment.

[0021] In one embodiment of the present application, the S45 comprises: The time slot resource state of the 5G private network dual-frequency data stream is acquired in real time, the time slot resource state comprises the occupation of the time slot, the remaining bandwidth and the time delay, the time slot resource monitoring module is set in the system, the use data of the time slot resource is collected and analyzed regularly, the time slot allocation algorithm based on the priority is designed according to the data transmission priority of the intelligent safety device, the high-priority device has the priority in the time slot allocation and can obtain the available time slot resource preferentially, meanwhile, the continuity and fragmentation of the time slot are considered, the continuous time slot is allocated to the device as much as possible, the generation of the time slot fragmentation is reduced, and the utilization rate of the time slot resource is improved. Through the dynamic time slot adjustment mechanism, the allocation of the time slot is adjusted in real time according to the change of the network environment and the device demand, when the data transmission task of a certain device is completed in advance or delayed, the time slot resource occupied by the device is released or reallocated in time, when the network is congested or the channel quality is reduced, the bandwidth allocation of the time slot is adjusted, and the data transmission of the key device is preferentially guaranteed, based on the time slot conflict resolution strategy, the weighted fair queue algorithm is adopted, the time slot use probability of each device is allocated according to the priority and the data size of the device, when the conflict occurs, the allocation of the time slot is determined according to the probability size, and the fair allocation of the time slot resource is ensured. The bandwidth resource state of each channel in the 5G private network is evaluated in real time, including the available bandwidth, the bandwidth utilization rate, the bandwidth fluctuation and the like, through the dynamic evaluation of the bandwidth resource, the distribution and use of the bandwidth in the network are acquired, the basic data for the dynamic bandwidth scheduling is provided, based on the bandwidth elastic adjustment mechanism, the allocation of the bandwidth is dynamically adjusted according to the change of the network load and the device demand, when the network load is light, the bandwidth allocation of the device can be appropriately increased, and the data transmission rate is improved, when the network load is heavy, the bandwidth allocation of the non-key device is reduced, and the normal operation of the key device is guaranteed, meanwhile, the bandwidth reservation mechanism is considered, and a certain bandwidth resource is reserved for the burst service to cope with the emergency.

[0022] The working principle and effect of the above technical scheme are as follows: through the real-time acquisition of the time slot resource state of the 5G private network dual-frequency data stream and the combination of the priority allocation algorithm of the intelligent safety device, the accurate time slot resource allocation can be realized, the high-priority device is preferentially allocated the time slot, and the data transmission of the key device is preferentially guaranteed, so that the use efficiency of the time slot resource is improved. Considering the continuity and fragmentation of the time slot, the scheme tries to allocate the continuous time slot resource to the device, which can significantly reduce the generation of the time slot fragmentation and improve the overall utilization rate of the time slot resource, and the more efficient resource allocation will help to improve the overall performance of the network. Through the dynamic time slot adjustment mechanism, the time slot allocation can be flexibly adjusted in real time according to the network environment changes, equipment demand changes and equipment task completion conditions; this not only ensures that the network automatically adjusts in congestion to protect key equipment, but also avoids resource waste caused by time slot conflicts; Real-time evaluation of the bandwidth status of each channel can dynamically adjust bandwidth allocation according to changes in network load and equipment demand; when the network load is light, the bandwidth allocation of the equipment can be increased to enhance the data transmission rate; when the network load is heavy, the bandwidth allocation of non-critical equipment can be reduced to ensure stable operation of critical equipment; in addition, reserved bandwidth resources can meet the demand of bursty traffic, improving the network emergency response capability; Through the weighted fair queue algorithm, when time slot resources conflict, time slots can be allocated fairly according to the priority and data size of the equipment; this strategy can effectively avoid unfair resource allocation and ensure that all equipment obtains the corresponding time slot usage opportunity according to the priority, improving the fairness and stability of the entire system; Dynamic evaluation of bandwidth and time slot status and flexible allocation adjustment enhance the adaptability of the network under different loads; even in the case of channel quality degradation or network congestion, the system can maintain efficient operation, avoiding service interruption or performance degradation caused by uneven resource allocation.

[0023] One embodiment of the present application, the S5, comprises: S51, the data to be transmitted by the intelligent safety equipment is processed by block, large data blocks are divided into multiple small data blocks, a distributed network coding algorithm is used to code the divided data, and coded data packets are generated; S52, according to the data transmission situation after coding, the transmission state of the data packet is monitored in real time based on the intelligent retransmission control mechanism, the data packet that is not successfully transmitted is retransmitted in time; and an adaptive retransmission strategy is used, the retransmission number and retransmission interval are dynamically adjusted according to the network condition and the importance of the data packet; S53, during the implementation of the scheduling strategy, a transmission effect evaluation system is built, real-time operation data of the power system is continuously obtained, including data transmission rate, packet loss rate, delay and other indicators; the implementation effect of the scheduling strategy is evaluated based on real-time operation data, data analysis methods and evaluation index system are used to quantitatively evaluate the pros and cons of the scheduling strategy; S54, according to the evaluation result, a dynamic correction system is built to dynamically adjust and optimize the scheduling strategy, and a 5G reliable transmission control scheme is obtained.

[0024] The working principle and effect of the above technical solution are that: by performing block processing on large data blocks and adopting a distributed network coding algorithm, the anti-interference capability of data packets can be effectively enhanced, and even if part of the data packets are lost, they can be recovered through other coded data blocks, improving the reliability of data transmission; By adopting an intelligent retransmission control mechanism, the transmission state of data packets is monitored in real time, and the data packets that are not successfully transmitted can be detected in time and retransmitted; at the same time, combined with an adaptive retransmission strategy, the retransmission times and retransmission intervals are dynamically adjusted according to the network conditions and the importance of the data packets, so that unnecessary retransmission is avoided, and timely transmission of important data packets is ensured. During the implementation of the scheduling strategy, a transmission effect evaluation system is built to continuously obtain real-time operation data of the power system (such as data transmission rate, packet loss rate, delay, etc.), so that the implementation effect of the scheduling strategy can be evaluated based on real-time data, and the transmission strategy can be optimized in time according to the evaluation results; this can ensure the efficiency and stability of the network in actual operation; According to the evaluation results, the system continuously adjusts and optimizes the scheduling strategy, so as to ensure that the transmission control scheme of the 5G network can continuously adapt to the changing network environment and equipment requirements; this dynamic optimization mechanism ensures the efficiency and reliability of the network in long-term operation; Through quantitative evaluation of the transmission effect, the advantages and disadvantages of the existing scheduling strategy can be objectively judged, and data support is provided for optimization; at the same time, the strategy optimization can also ensure the fair and reasonable allocation of network resources, and improve the overall resource utilization rate; The adaptive retransmission strategy and dynamic correction mechanism can flexibly respond to complex network environment changes such as bandwidth fluctuation and signal attenuation, effectively ensuring efficient and reliable transmission of data in different situations.

[0025] In an embodiment of the present application, the S51 comprises: S511, comprehensively analyzing the characteristics of the data to be transmitted by the intelligent safety device, including data type (such as real-time monitoring data, control instruction data, historical record data, etc.), data size, data sensitivity (such as key safety alarm data, general state data, etc.), and time sensitivity of data (such as emergency control instructions, regular monitoring data, etc.); by establishing a data characteristic evaluation model, historical data samples are analyzed by using a machine learning algorithm to determine the characteristic parameters of different types of data; S512, based on the data characteristics evaluation results, combined with the current network bandwidth, storage resources and processing capacity of intelligent security equipment, using dynamic programming algorithm to determine the appropriate data block threshold; the threshold not only considers the influence of data block size on transmission efficiency, but also takes into account the calculation complexity of data block in the process of encoding and decoding; for example, for real-time control instruction data, a smaller block threshold is used to ensure fast transmission and timely processing; while for general historical record data, the block threshold can be appropriately increased to improve transmission efficiency; according to the determined block threshold, detailed data block rules are generated; the rules should clearly define the division boundary of data block, the identification information of data block (such as data block serial number, data type, etc.) and the association between data blocks; at the same time, considering the possible out-of-order situation in the transmission process, a unique identifier and sequence information are added to each data block to correctly reorganize the data at the receiving end; S513, select distributed network coding algorithm; for example, in the case of complex network topology and widely distributed nodes, select random linear network coding algorithm with strong fault tolerance and adaptability; for the selected distributed network coding algorithm, optimization processing is carried out; for example, for critical security alarm data, increase the coding redundancy to improve the reliable transmission of data; for general state data, appropriately reduce the coding redundancy to save network bandwidth; according to the optimized network coding algorithm, configure appropriate coding parameters, such as the size of coding matrix and the generation method of coding coefficient; S514, according to the generated block rule and the configured coding parameters, the coded data is processed; in the coding process, parallel computing technology is used to fully utilize the multi-core processor resources of intelligent security equipment and improve the coding efficiency; at the same time, the coding process is monitored in real time, and the coding progress and state are recorded to deal with the abnormal situation in time; the coded data is encapsulated into data packets, and necessary header information is added to each data packet, including data packet identification, coding information and check information.

[0026] The working principle and effect of the above technical scheme are: by comprehensively analyzing the characteristics of the data to be transmitted (such as type, size, sensitivity and time sensitivity), appropriate data block rules can be developed according to the needs of different data; this dynamic block strategy based on data characteristics and network resources can not only improve the efficiency of data transmission, but also ensure the integrity and accuracy of data in the transmission process; Dynamic programming algorithm is used to determine the block threshold, which fully considers the limitations of network bandwidth, storage resources and device processing capacity; this method makes the use of network resources more reasonable, avoids the waste of resources or transmission delay caused by excessive block or large block, and improves the overall performance of the network; In the process of using the distributed network coding algorithm, different types of data are optimized; for example, the coding redundancy of critical security alarm data is increased to improve its transmission reliability, while the redundancy of general state data is reduced to save bandwidth; in this way, not only the reliable transmission of important data can be ensured, but also the use efficiency of network bandwidth can be improved; For the calculation complexity in data block and coding process, parallel computing technology is adopted to fully utilize the multi-core processor resources of the device; through parallel computing, the speed and efficiency of data coding can be significantly improved, and the data transmission bottleneck caused by coding delay can be reduced; In the process of data packet division and transmission, each data packet is assigned a unique identifier and sequence information to ensure that the data can be recombined in the correct order; even if the data packets are out of order during transmission, the receiving end can accurately recombine the data through these identifiers and sequence information, improving the reliability of data transmission; Real-time monitoring is implemented in the coding process, which not only records the coding progress, but also timely discovers and handles abnormal situations; this real-time feedback mechanism ensures the smoothness of the data processing process and provides necessary protection for emergency response; By optimizing the network coding algorithm and configuring appropriate coding parameters, the system can adapt to different network conditions in complex network topology; no matter how the network topology changes, the system can ensure the reliability and efficiency of data transmission through the optimized coding scheme.

[0027] An embodiment of the present application, the S514, includes: Before starting the coding process, the data blocks divided according to the block rule are cached in the memory buffer area of the intelligent security device; according to the size of the data block and the memory capacity of the intelligent security device, the cache space is planned to ensure that enough data blocks can be accommodated, reducing the delay caused by frequent reading of data from the storage device; preloading technology is used to load the data blocks that may be coded soon into the cache, further shortening the data reading time; The multi-core processor resources of the intelligent security device are comprehensively evaluated, including the computing power of each core, the current load, etc.; according to the complexity of the coding task and the number of data blocks, the coding task is reasonably distributed to multiple cores; for example, for data blocks with large data volume and high coding complexity, they are assigned to cores with strong computing power; for data blocks with small data volume and relatively simple coding, they can be assigned to cores with light load; and a dynamic task allocation strategy is adopted to adjust the task allocation in real time according to the real-time load of each core during coding, ensuring that the computing resources of each core are fully utilized; According to the operating system and hardware architecture of the intelligent safety device, a parallel computing framework is selected, such as OpenMP, MPI, etc.; for example, for a multi-core processor with a shared memory architecture, OpenMP is selected, which can realize parallel computing through simple compilation instructions, and the development difficulty is relatively low; for a distributed memory architecture or a scenario that requires a more complex communication mechanism, MPI is selected; according to the association relationship between the data blocks and the requirements of the encoding algorithm, the data blocks are grouped; for example, for data blocks with strong time correlation, they are grouped in the same group, so that the time characteristics of the data can be better preserved during the encoding process; at the same time, the encoding order of the data blocks is determined, and the encoding of data blocks with high real-time and reliability requirements, such as critical safety alarm data, is given priority; in the determination of the encoding order, the size and encoding complexity of the data blocks can also be considered, and the data blocks with lower encoding complexity and smaller data size are processed first to improve the overall encoding efficiency; on each parallel computing core, the distributed network encoding algorithm is optimized, and the data blocks allocated are encoded and processed; for the random linear network encoding algorithm, each core independently generates an encoding matrix and encoding coefficients, and linearly combines the data blocks; during the encoding process, the advantages of parallel computing are fully utilized, and each core simultaneously performs encoding calculation, greatly shortening the encoding time; at the same time, in order to ensure the correctness and consistency of the encoding, a synchronization mechanism is adopted, and after the end of each encoding stage, each core synchronizes the data and exchanges the intermediate results and state information generated during the encoding process; During the encoding process, the encoding redundancy is dynamically adjusted according to the real-time characteristics of the data and the network status; for critical safety alarm data being transmitted, if packet loss or interference is detected in the network, the encoding redundancy is increased in real time to improve the reliable transmission of the data; for example, by increasing the number of rows of the encoding matrix, more encoding data blocks are generated, so that the receiving end can correctly decode even if some data blocks are lost; for general state data, if the network status is good, the encoding redundancy is appropriately reduced to save network bandwidth; the process of dynamically adjusting the encoding redundancy is realized by monitoring the network indicators (such as packet loss rate, delay, etc.) and data characteristic parameters in real time; based on the real-time encoding progress monitoring system, the encoding progress information of each data block is obtained in real time by inserting monitoring points in the encoding algorithm; through the state monitoring indicators and thresholds, the encoding state is monitored in real time, and when the encoding state is abnormal, a warning information is sent in time; for example, if the computing load of a core is continuously too high, it may indicate that the encoding task allocation of the core is unreasonable or there is a computing bottleneck, and the warning information can remind the operator to adjust the task allocation or optimize the encoding algorithm in time; When an abnormal situation occurs during the encoding process, such as a core failure, network interruption, etc., the system can automatically take appropriate measures; for example, for core failure, through the task migration mechanism, the encoding task on the failed core is migrated to other normal cores for continuous execution; for network interruption, the encoding process is suspended, and after the network is restored, the encoding task is restarted according to the encoding state information before the interruption, ensuring the continuity of the encoding process and the integrity of the data; at the same time, the abnormal situation is recorded in detail for subsequent fault analysis and system optimization; the encoded data is encapsulated into data packets, and necessary header information is generated for each data packet; the header information includes data packet identification, which is used to uniquely identify each data packet; encoding information, such as the encoding algorithm used, encoding parameters, etc., so that the receiving end can correctly decode; check information, generate check code using appropriate check algorithm (such as CRC check, MD5 check, etc.), used to detect whether the data packet has errors in the transmission process; according to the association relationship and encoding order between the data blocks, the encoded data blocks are encapsulated into data packets in a certain format; during the encapsulation process, ensure the correct order of the data packets, so that the receiving end can reorganize the data in the correct order; at the same time, considering the possibility of out-of-order situation of data packets in the transmission process, sequence information such as data packet number, timestamp, etc. is added to each data packet, so that the receiving end can sort and reorganize the data packets according to the sequence information.

[0028] The working principle and effect of the above technical solution are: by pre-caching data blocks, reasonably planning cache space, and applying preloading technology, the delay of reading data can be effectively reduced; dynamic allocation of multi-core processor resources to encoding tasks not only fully utilizes the computing power of each core, but also reasonably allocates resources according to task complexity and data volume, avoiding resource waste or excessive load problems; Dynamic adjustment of task allocation, timely adjustment of core tasks according to real-time load, avoids excessive concentration or excessive dispersion, improves computing efficiency; dynamic adjustment strategy based on data block characteristics and encoding complexity can prioritize processing of critical data or simpler data blocks, thereby improving the overall efficiency of the system; Dynamic adjustment of encoding redundancy, combined with real-time network conditions and data characteristics, can effectively deal with network fluctuations, packet loss, etc., ensuring reliable transmission of critical data; in the case of unstable network, by increasing redundant data blocks, the receiving end can correctly decode even if some data blocks are lost, thereby improving the fault tolerance and stability of the system; Using real-time monitoring and threshold warning system, the system can timely detect abnormalities in the encoding process and prevent performance degradation due to system bottlenecks or uneven load; when an abnormality occurs, the system can automatically take measures such as task migration or suspension and restart, ensuring the continuity of the encoding process and the integrity of the data; In the data encapsulation process, by adding coding information, check information and sequence information, not only the integrity and correctness of data are ensured, but also the order of data packet in network transmission is ensured to be correct, so that data recovery and recombination of the receiving end are facilitated; the system designs a powerful exception handling mechanism, can automatically perform task migration, network recovery and other operations, effectively avoids the interruption of coding process caused by faults or exceptions, and improves the reliability of the system.

[0029] One embodiment of the present application is a 5G+ private network fusion-based intelligent safety device data transmission system, comprising: One or more processors; Memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of the above.

[0030] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application; thus, if these modifications and variations of the present application belong to the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A method for transmitting data of intelligent safety equipment based on 5G+ private network fusion, characterized in that, The method comprises: S1, preprocessing the original data collected by the intelligent safety device; using the radio frequency front end of the 5G terminal, real-time monitoring and feature extraction are performed on the initial multipath channel signal data to obtain antenna channel feature data; S2, based on the extracted antenna channel feature data, the initial beam pattern of the antenna is calculated by the phased array antenna beamforming algorithm; and the initial beam pattern is subjected to spatial filter interference suppression processing to obtain antenna optimized beam shape data; the beam shape data is adjusted by digital beamforming technology to obtain the final antenna beam configuration parameters; S3, according to the distribution of the intelligent safety device and the deployment position of the 5G base station, the network topology node analysis is performed on the antenna beam configuration parameters to obtain the connection relationship diagram between the intelligent safety device and the 5G base station, that is, the on-body 5G node distribution diagram; in view of the dynamic change of the network topology of the intelligent safety device in the moving process, the on-demand routing protocol is adopted to calculate the initial routing topology structure in real time; the initial routing topology structure is subjected to redundant link optimization processing to obtain the 5G hierarchical mesh routing diagram; S4, based on the 5G hierarchical mesh routing diagram, the real-time state detection is performed on the dual-frequency channel resources of the 5G private network to obtain a channel resource state table; according to the channel resource state table, the intelligent bandwidth aggregation algorithm is adopted to perform dynamic bandwidth allocation and aggregation processing on multiple available channels to obtain a 5G private network bandwidth allocation scheme; according to the bandwidth allocation scheme, fine time slot division and resource scheduling processing are performed on the dual-frequency data stream of the 5G private network to obtain a 5G resource scheduling strategy; S5, according to the 5G resource scheduling strategy, the data to be transmitted by the intelligent safety device is subjected to distributed network coding processing; according to the data transmission condition after coding, the intelligent retransmission control mechanism is adopted to perform timely retransmission processing on the data packets that are not successfully transmitted; and a 5G reliable transmission control scheme is obtained.

2. The intelligent safety device data transmission method based on 5G+ private network fusion of claim 1, characterized in that, The S1 comprises: S11, preprocessing the original data collected by the intelligent safety device, selecting a data compression algorithm according to the data type and characteristics to compress the preprocessed original data, and encoding and optimizing the compressed data; S12, using the radio frequency front end of the 5G terminal, a real-time channel signal monitoring system is built to continuously and real-timely monitor the initial multipath channel signal data; S13, extracting key features from the real-time monitored channel signal data, the key features including channel fading characteristics, Doppler shift and delay spread; S14, using a machine learning algorithm, modeling and analyzing the extracted channel features to establish a correlation model between the channel features and the signal transmission quality. 3.The method of claim 1, wherein, The S2 comprises: S21, based on the extracted antenna channel feature data, the initial beam pattern of the antenna is calculated by the phased array antenna beamforming algorithm; S22, analyzing the interference sources of the environment where the intelligent safety device is located to identify the existing interference sources, the interference sources including multipath effect and external electromagnetic interference; using an adaptive filtering algorithm, the filter parameters are adjusted in real time; S23, apply the preset spatial filter to the initial beam pattern, perform interference suppression processing, eliminate multipath effect and external interference signals, and verify the interference suppression effect of the spatial filter through actual test or simulation; S24, further optimize the beam pattern after spatial filtering, adjust the beam shape parameters by using a particle swarm optimization algorithm, obtain better signal coverage and gain, and adjust the optimized beam shape data by using a digital beamforming technology, perform dynamic pointing and shape control of the beam by using a digital signal processing algorithm, and determine the final antenna beam configuration parameters according to the optimized beam shape data.

4. The intelligent safety device data transmission method based on 5G+ private network fusion of claim 1, characterized in that, The S3 comprises: S31, according to the distribution of the intelligent safety equipment and the deployment position of the 5G base station, a network topology node information collection system is built, and the data of each node is collected in real time, the data of each node includes position information, connection state and signal strength; S32, the collected node information is integrated and processed, and an initial connection relationship diagram between the intelligent safety equipment and the 5G base station is established; for the dynamic change of the network topology of the intelligent safety equipment in the moving process, the network topology monitoring system is used to monitor the moving track of the node and the change of the connection relationship in real time; S33, the dynamic change of the network topology is modeled and analyzed by using graph theory, a network topology dynamic change model is established, and an improved on-demand routing protocol is constructed according to the network topology dynamic change model; S34, the initial routing topology structure is calculated in real time by using the improved on-demand routing protocol, the initial routing topology structure is optimized, and the optimal routing path is selected by using the minimum spanning tree algorithm; S35, the redundant link of the optimized routing topology structure is analyzed, single point failure and bottleneck link are identified and eliminated, and a 5G hierarchical mesh routing diagram is generated according to the redundant link optimization result.

5. The intelligent safety device data transmission method based on 5G+ private network fusion of claim 1, characterized in that, The S4 comprises: S41, based on the 5G hierarchical mesh routing diagram, a channel resource state real-time monitoring system is built, and a distributed monitoring architecture is used to distribute the monitoring tasks to multiple monitoring nodes; S42, the state data of each channel is collected by the channel resource state real-time monitoring system, stored and analyzed at a certain time interval, and the collected channel resource state data is preprocessed; S43, according to the channel resource state data, the bandwidth of multiple available channels is dynamically allocated and aggregated based on an intelligent bandwidth aggregation algorithm, and the related indexes of each channel include bandwidth utilization and signal quality; S44, according to the calculation result of the intelligent bandwidth aggregation algorithm, a 5G private network bandwidth allocation scheme is generated, the dual-frequency data flow of the 5G private network is finely time-slotted according to the bandwidth allocation scheme, the time axis is divided into multiple time slots, and each time slot is allocated with corresponding bandwidth resources; S45, a resource scheduling strategy is formulated, and the time slots and bandwidth resources are dynamically scheduled according to the data transmission priority and data size of the intelligent safety equipment.

6. The intelligent safety device data transmission method based on 5G+ private network fusion of claim 5, characterized in that, The S41 comprises: The connection relationship between different levels and the distribution of nodes in each level is determined; according to the structure of the routing map and the business requirements, the functions and roles of each node are defined; The system is divided into data acquisition layer, data transmission layer, data processing layer and application layer by adopting hierarchical architecture mode; and the functional modules are further subdivided in each layer; According to the coverage range of 5G private network and the distribution of intelligent security devices, the coverage range of distributed monitoring nodes is planned; by using geographic information system (GIS) technology, combined with the signal coverage model of 5G base station, the effective monitoring area of each monitoring node is determined; The position of the monitoring node is optimized by using genetic algorithm; according to the size of 5G private network and the change of business requirements, the number of distributed monitoring nodes is dynamically determined; according to the function, performance and current load of the distributed monitoring node, the monitoring task allocation strategy is formulated; by using load balancing algorithm, different monitoring tasks are evenly distributed to each monitoring node.

7. The intelligent safety device data transmission method based on 5G+ private network fusion of claim 1, characterized in that, The S5 comprises: S51, the data to be transmitted by the intelligent security device is processed by block, the large data block is divided into multiple small data blocks, the distributed network coding algorithm is used to code the data after block, and the coded data packet is generated; S52, according to the data transmission situation after coding, the transmission state of data packet is monitored in real time based on intelligent retransmission control mechanism, the data packet that is not successfully transmitted is retransmitted in time; and adaptive retransmission strategy is adopted, the retransmission times and retransmission interval are dynamically adjusted according to the network condition and the importance of data packet; S53, during the implementation of the scheduling strategy, a transmission effect evaluation system is built, real-time operation data of power system is continuously obtained, and the implementation effect of the scheduling strategy is evaluated based on the real-time operation data; S54, according to the evaluation result, a dynamic correction system is built, the scheduling strategy is dynamically adjusted and optimized, and the 5G reliable transmission control scheme is obtained.

8. The intelligent safety device data transmission method based on 5G+ private network fusion of claim 7, characterized in that, The S51 comprises: S511, the data to be transmitted by the intelligent security device is comprehensively analyzed, the data characteristic evaluation model is established, the historical data samples are analyzed by using machine learning algorithm, and the characteristic parameters of different types of data are determined; S512, based on the data characteristic evaluation result, combined with the current network bandwidth, storage resource and processing capacity of the intelligent security device, the data block threshold is determined by using dynamic programming algorithm; according to the determined block threshold, the data block rule is generated; S513, the distributed network coding algorithm is selected; for example, in the case that the network topology structure is relatively complex and the nodes are widely distributed, the random linear network coding algorithm with strong fault tolerance and adaptability is selected; the selected distributed network coding algorithm is optimized; according to the optimized network coding algorithm, the coding parameters are configured; S514, the data after block is coded according to the generated block rule and configured coding parameters; the coded data is encapsulated into data packet, and necessary header information is added to each data packet.

9. The intelligent safety device data transmission method based on 5G+ private network fusion of claim 8, characterized in that, The S514 comprises: Before starting the encoding process, the data blocks divided according to the block division rule are cached in the memory buffer of the intelligent security device; the cache space is planned according to the size of the data blocks and the memory capacity of the intelligent security device; The multi-core processor resources of the intelligent security device are comprehensively evaluated, the encoding tasks are reasonably allocated to multiple cores according to the complexity of the encoding tasks and the number of data blocks, and a dynamic task allocation strategy is adopted to timely adjust the task allocation according to the real-time load of each core during the encoding process; According to the operating system and hardware architecture of the intelligent security device, a parallel computing framework is selected, the data blocks are grouped according to the association relationship between the data blocks and the requirements of the encoding algorithm, and the allocated data blocks are encoded on each parallel computing core according to the optimized distributed network encoding algorithm; During the encoding process, the encoding redundancy is dynamically adjusted according to the real-time characteristics of the data and the network status; based on the real-time encoding progress monitoring system, the encoding progress information of each data block is obtained in real time by inserting monitoring points in the encoding algorithm; the encoding state is monitored in real time through state monitoring indicators and threshold values, and when the encoding state is abnormal, early warning information is sent in time; When abnormal conditions occur during the encoding process, the system can automatically take corresponding processing measures; the encoded data is encapsulated into data packets, necessary header information is generated for each data packet, and the encoded data blocks are encapsulated into data packets in a certain format according to the association relationship between the data blocks and the encoding order.

10. An intelligent security device data transmission system based on 5G+ private network fusion, comprising: one or more processors; a memory for storing one or more programs; wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 9.

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