Intelligent converged terminal data optimization uploading method and system under link state awareness

Abstract

This invention provides a method and system for optimizing data upload from intelligent fusion terminals under link status awareness, relating to the field of data transmission technology. The method includes: dividing and configuring multiple data transmission links for intelligent fusion terminals; collecting transmission status data for indicator prediction and evaluation; classifying and prioritizing terminal data based on factors to determine multi-level terminal data and extracting attribute feature information; constructing a dynamic data upload strategy for dynamic allocation to determine multi-level data matching links; and introducing a data retransmission mechanism to encapsulate, encode, and optimize the upload control of multi-level terminal data through multi-level data matching links. This solves the technical problem of existing technologies lacking real-time link status awareness and dynamic data scheduling mechanisms, leading to low data upload efficiency and poor transmission stability in multi-link environments for intelligent fusion terminals. It achieves multi-link adaptive scheduling and reliable transmission control, improving data upload efficiency and transmission stability.

Description

Technical Field

[0001] This invention relates to the field of data transmission technology, specifically to a method and system for optimizing data upload from intelligent fusion terminals under link status awareness. Background Technology

[0002] In intelligent converged terminal application scenarios, terminals typically integrate multiple communication interfaces and service processing capabilities, enabling simultaneous access to various heterogeneous communication links such as 5G, WiFi, and private networks. They are used to collect and process multi-source terminal data, such as service control data, environmental perception data, and multimedia data. In existing technologies, intelligent converged terminals typically employ fixed link selection or simple scheduling methods based on static rules when uploading data. This means that a certain type of data is pre-defined to be transmitted via a specific communication link; for example, video data is fixed to use a 5G link, and log data is fixed to use a WiFi link. This approach lacks the ability to perceive real-time changes in link status and cannot dynamically adjust based on link quality indicators. Furthermore, it lacks a refined adaptation mechanism to the differences in real-time performance, reliability, and bandwidth requirements of different types of data.

[0003] In real-world operating environments with multiple links coexisting, the quality of each link dynamically changes with network load, signal strength, and external interference. However, existing scheduling methods cannot detect these changes in a timely manner and update their strategies accordingly. This leads to problems such as link congestion, increased transmission latency, or higher packet loss rates during data transmission. Furthermore, due to the lack of hierarchical processing capabilities for data priority, critical control data and ordinary log data are often treated equally or simply assigned to fixed links. This can cause high-priority data to be mistakenly assigned to low-quality links, further impacting the real-time performance of service responses and the reliability of data transmission, resulting in low overall link resource utilization.

[0004] In summary, existing technologies lack real-time link status awareness and dynamic data scheduling mechanisms, resulting in low data upload efficiency and poor transmission stability for intelligent fusion terminals in multi-link environments. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for optimizing data upload of intelligent fusion terminals under link status awareness, in order to solve the technical problem that the lack of real-time link status awareness and dynamic data scheduling mechanism in the existing technology leads to low data upload efficiency and poor transmission stability of intelligent fusion terminals in multi-link environments.

[0006] In view of the above problems, this application provides a method and system for optimizing data upload of intelligent fusion terminals under link status awareness.

[0007] The first aspect of this application provides a method for optimizing data upload from an intelligent fusion terminal under link state awareness. The method includes: dividing and configuring multiple data transmission links of the intelligent fusion terminal; a communication module collecting real-time transmission state data of the multiple data transmission links; performing index prediction and evaluation on the transmission state data to obtain multi-link state level parameters; classifying and prioritizing the terminal data collected by the intelligent fusion terminal to determine multi-level terminal data and extracting attribute feature information from the multi-level terminal data; constructing a dynamic data upload strategy; dynamically allocating the multi-link state level parameters and the attribute feature information using the dynamic data upload strategy to determine multi-level data matching links; and introducing a data retransmission mechanism to encapsulate, encode, and optimize the upload of the multi-level terminal data through the multi-level data matching links.

[0008] Optionally, the transmission network topology, terminal interface capabilities, service transmission requirements, and transmission resource constraints of the intelligent converged terminal are obtained; transmission link partitioning rules are constructed, including service hierarchical isolation, transmission redundancy design, and transmission load balancing; the transmission network topology, terminal interface capabilities, service transmission requirements, and transmission resource constraints are partitioned according to the transmission link partitioning rules to obtain an initial transmission link set; the initial transmission link set is subjected to transmission testing and configuration adjustment to obtain the multi-data transmission links of the intelligent converged terminal.

[0009] Optionally, a Kalman filter is used to filter, denoise, and smooth the transmission status data to obtain usable transmission status data; time series analysis and trend prediction are performed on the usable transmission status data to obtain predicted transmission status change data; a transmission status evaluation index set is constructed, and the predicted transmission status change data is evaluated based on the transmission status evaluation index set to generate a multi-link status index matrix; the multi-link status index matrix is ​​divided into quality levels according to the link quality level system to obtain multi-link status level parameters.

[0010] Optionally, a data classification factor set is obtained, which includes data source, business type, data size, and data importance; the terminal data collected by the intelligent fusion terminal is classified according to the data classification factor set to obtain a terminal data factor parameter set; a data transmission priority coordinate system is constructed based on the data classification factor set; the terminal data factor parameter set is mapped to the data transmission priority coordinate system for priority labeling to determine multi-level terminal data.

[0011] Optionally, a multidimensional data coordinate system is defined using the information of each factor in the data classification factor set as coordinate axes. The multidimensional data coordinate system includes multidimensional coordinate axis factor content and corresponding coordinate axis content scale intervals. A data priority division standard is constructed according to the terminal data transmission requirements. The multidimensional coordinate axis factor content and corresponding coordinate axis content scale intervals are spatially prioritized according to the data priority division standard to obtain multidimensional coordinate axis spatial priority information. Based on the multidimensional coordinate axis spatial priority information, the multidimensional data coordinate system is prioritized and a data transmission priority coordinate system is constructed.

[0012] Optionally, the terminal data factor parameter set is mapped to the data transmission priority coordinate system to obtain terminal data parameter coordinate information; the center coordinate information of each label space region in the data transmission priority coordinate system is obtained; the Euclidean distance set between the terminal data parameter coordinate information and the center coordinate information of each label space region is calculated; the terminal data factor parameter set is prioritized and labeled based on the Euclidean distance set of the center coordinate information to determine multi-level terminal data.

[0013] Optionally, the data upload dynamic strategy is used to match the multi-link status level parameters with the attribute feature information to obtain an initial data matching link; the multi-link status level parameters are updated and evaluated through a real-time monitoring mechanism to obtain multi-link status level update parameters; the initial data matching link is dynamically updated based on the multi-link status level update parameters to determine multi-level data matching links.

[0014] Optionally, based on the multi-level data matching link, a data encapsulation format and data encoding rules are selected; the multi-level terminal data is encapsulated and encoded using the data encapsulation format and data encoding rules to obtain multi-level terminal encoded data blocks; a data retransmission mechanism is introduced to optimize the upload control of the multi-level terminal encoded data blocks through the multi-level data matching link.

[0015] Optionally, based on the data retransmission mechanism, data retransmission conditions and data retransmission strategies are determined, wherein the data retransmission strategies include exponential backoff, link upgrade, and priority retention; when the data retransmission conditions are triggered, the multi-level terminal encoded data blocks are optimized and uploaded through the multi-level data matching link based on the data retransmission strategies.

[0016] A second aspect of this application provides a data optimization and upload system for intelligent converged terminals under link status awareness. The system includes: a data evaluation component for classifying and configuring multiple data transmission links of the intelligent converged terminal; a communication module for real-time collection of transmission status data of the multiple data transmission links; performance evaluation of the transmission status data to obtain multi-link status level parameters; a feature acquisition component for classifying and prioritizing terminal data collected by the intelligent converged terminal, determining multi-level terminal data, and extracting attribute feature information of the multi-level terminal data; a dynamic allocation component for constructing a dynamic data upload strategy, dynamically allocating the multi-link status level parameters and the attribute feature information using the dynamic data upload strategy to determine multi-level data matching links; and a data upload component for introducing a data retransmission mechanism to encapsulate, encode, and optimize the upload control of the multi-level terminal data through the multi-level data matching links.

[0017] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0018] The method provided in this application divides and configures multiple data transmission links of a smart fusion terminal. A communication module collects real-time transmission status data of these links, performs index prediction and evaluation on the transmission status data, and obtains multi-link status level parameters. The method then classifies and prioritizes the terminal data collected by the smart fusion terminal to determine multi-level terminal data and extracts attribute feature information from this multi-level data. A dynamic data upload strategy is constructed, and this strategy is used to dynamically allocate the multi-link status level parameters and the attribute feature information to determine multi-level data matching links. A data retransmission mechanism is introduced to encapsulate, encode, and optimize the upload control of the multi-level terminal data through the multi-level data matching links. This achieves the technical effect of improving data upload efficiency and transmission stability by using link status awareness and dynamic strategy to allocate data matching links, enabling multi-link adaptive scheduling and reliable transmission control.

[0019] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0021] Figure 1 A flowchart illustrating the data optimization and uploading method for intelligent fusion terminals under link state awareness provided in this application.

[0022] Figure 2 A schematic diagram of the structure of the intelligent fusion terminal data optimization and upload system under link state awareness provided in this application.

[0023] Figure labeling: Data evaluation component 11, feature acquisition component 12, dynamic allocation component 13, data upload component 14. Detailed Implementation

[0024] This application provides a method and system for optimizing data upload in intelligent fusion terminals under link-state awareness. It addresses the technical problem of low data upload efficiency and poor transmission stability in multi-link environments caused by the lack of real-time link-state awareness and dynamic data scheduling mechanisms in existing technologies. The method achieves the technical effect of improving data upload efficiency and transmission stability by using link-state awareness and dynamic strategy allocation to match data to links, enabling adaptive scheduling and reliable transmission control across multiple links.

[0025] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0026] Example 1, as Figure 1 As shown, this application provides a method for optimizing data upload of intelligent converged terminals under link state awareness, the method comprising: The system divides and configures multiple data transmission links of the intelligent fusion terminal. The communication module collects the transmission status data of the multiple data transmission links in real time, performs index prediction and evaluation on the transmission status data, and obtains the multi-link status level parameters.

[0027] Furthermore, the process of configuring multiple data transmission links for the intelligent converged terminal includes: acquiring the transmission network topology, terminal interface capabilities, service transmission requirements, and transmission resource constraints of the intelligent converged terminal; constructing transmission link partitioning rules, which include service hierarchical isolation, transmission redundancy design, and transmission load balancing; partitioning the transmission network topology, terminal interface capabilities, service transmission requirements, and transmission resource constraints according to the transmission link partitioning rules to obtain an initial transmission link set; and performing transmission testing and configuration adjustments on the initial transmission link set to obtain the multiple data transmission links for the intelligent converged terminal.

[0028] Specifically, this involves comprehensively acquiring information related to the intelligent converged terminal, including the transmission network topology, terminal interface capabilities, service transmission requirements, and transmission resource constraints. The transmission network topology refers to the connection relationships and path information between the terminal and external networks, reflecting the network environment in which the intelligent converged terminal operates, including various network nodes and the connections between them. This is obtained by using the system network protocol stack, such as the TCP / IP stack, to acquire the routing tables, gateway information, and link types of currently available network interfaces, such as cellular networks, WiFi, Ethernet, or private network links. Terminal interface capabilities refer to the physical and protocol capability parameters of each communication interface, determining the data transmission methods and rates that the terminal can support. This is obtained by querying the terminal device's hardware specifications or through driver layer interfaces, such as the Netlink mechanism or device driver APIs, and includes parameters such as maximum bandwidth, supported communication types, transmit power range, and the number of concurrent connections per interface. Service transmission requirements clarify the data transmission requirements of different services, determined by upper-layer applications or service modules, and provided in the form of structured parameters, such as data type, expected latency, tolerable packet loss rate, and transmission cycle. Data types include video streams, control signaling, and log data. Transmission resource constraints include the current network resource status, terminal power consumption limits, and operational policy restrictions, which are obtained in real time through the network management system. The current network resource status includes remaining bandwidth and link occupancy rate.

[0029] Transmission link partitioning rules are constructed based on service type, real-time requirements, and data reliability needs. These rules include service-level isolation, transmission redundancy design, and transmission load balancing. Service-level isolation refers to allocating different types of data to logically independent or priority-different-priority links according to service importance or real-time requirements, avoiding mutual interference between different services. Transmission redundancy design involves reserving backup links or establishing multi-path transmission capabilities for critical services to improve reliability; for example, simultaneously allocating primary and backup links for control signaling. Transmission load balancing involves distributing data traffic across multiple links to achieve a balanced load across all links, preventing any single link from being overloaded and affecting transmission efficiency. For example, data can be evenly distributed across multiple links based on link bandwidth and currently used bandwidth. Transmission link partitioning rules can be generated through preset policy templates or based on a policy engine, such as rule matching. High-priority and real-time services can be isolated by using independent links or high-priority channels to avoid competing for resources with low-priority services. For critical services, both primary and backup links can be configured to form a transmission redundancy design, so as to achieve seamless switching or dual-channel protection in the event of link anomalies or performance degradation. For non-critical or large-volume services, dynamic allocation can be performed based on the real-time bandwidth and load status of each link to achieve transmission load balancing among multiple links, thereby improving the overall link resource utilization efficiency and transmission stability.

[0030] According to the established transmission link partitioning rules, the obtained transmission network topology, terminal interface capabilities, service transmission requirements, and transmission resource constraints are partitioned. Different services are allocated to different network nodes and links according to service classification. Based on transmission redundancy design requirements, appropriate backup links are selected for critical services. Based on the transmission load balancing principle, combined with terminal interface capabilities and transmission resource constraints, the amount of data to be allocated to each link is calculated, thereby obtaining the initial transmission link set. The initial transmission link set includes several logical link combinations, and each logical link clearly corresponds to a specific interface and the type of service it carries.

[0031] Based on this, transmission tests and configuration adjustments are performed on the initial transmission link set. The transmission tests include active probing and passive statistics. Active probing includes sending test data packets, measuring round-trip time (RTT), packet loss rate, and throughput. Passive statistics include collecting historical communication data to evaluate the actual performance of the links. Based on the transmission test results, configuration adjustments are performed, including modifying link priorities, adjusting load balancing ratios, and enabling or disabling redundant links. Adjusting link priorities includes, for example, increasing the weight of low-latency links in scheduling. Modifying load balancing ratios means recalculating the proportion of service traffic carried by each link. Enabling or disabling redundant links means switching to backup links or enabling parallel transmission of dual links when the performance of the primary link degrades or becomes unstable. The link configuration parameters are continuously adjusted based on the test results until the preset performance indicators are met, ultimately forming a stable multi-data transmission link for the intelligent converged terminal. The preset performance indicators are determined jointly based on business requirements and the capabilities of the converged terminal system, derived from the requirements of the service level agreement or the system design goals. Specifically, they may include: end-to-end latency threshold, upper limit of packet loss rate, minimum guaranteed throughput value, link stability indicators, and redundancy handover success rate. For example, the end-to-end latency threshold for the service is controlled to be ≤20ms, the upper limit of packet loss rate is ≤1% or ≤0.1%, the minimum guaranteed throughput value for video services is ≥30Mbps, the performance fluctuation variance is less than 0.1 during a period of continuous operation, and the redundancy handover success rate is ≥99%.

[0032] For example, the intelligent converged terminal simultaneously possesses 5G links, WiFi links, and low-speed private network links. The 5G link has a maximum bandwidth of 100Mbps and a latency of 20ms; the WiFi link has a maximum bandwidth of 50Mbps and a latency of 10ms; and the low-speed private network link has a maximum bandwidth of 5Mbps and a latency of 50ms. The terminal's current services include: high-definition video streaming (requiring 30Mbps bandwidth and <50ms latency); small control signaling data volume (<20ms latency and high reliability); and low-bandwidth log data (not latency-sensitive). After collecting interface capabilities and service requirements, transmission link partitioning rules are constructed: control signaling uses a redundant design; video streaming prioritizes 5G supplemented by WiFi load balancing; and log data is allocated to the private network link. After initial partitioning, testing revealed a high packet loss rate on the WiFi link under the current environment. The strategy is adjusted to shift the main load of the video streaming to the 5G link while retaining a small amount of WiFi traffic for offloading. This ultimately forms a stable multi-data transmission link configuration, enabling efficient collaborative transmission of different services in a multi-link environment.

[0033] By collecting multi-dimensional data such as transmission network topology, terminal interface capabilities, service transmission requirements, and transmission resource constraints, and constructing transmission link partitioning rules, intelligent converged terminals are equipped with a perceptible, schedulable, and scalable multi-link transmission foundation. This ensures that different services can be transmitted on appropriate links, avoiding link congestion and service interference, and improving the stability and efficiency of data transmission.

[0034] Furthermore, the transmission status data is evaluated and predicted to obtain multi-link status level parameters, including: using a Kalman filter to filter, denoise, and smooth the transmission status data to obtain usable transmission status data; performing time series analysis and trend prediction on the usable transmission status data to obtain predicted transmission status change data; constructing a transmission status evaluation index set; evaluating the predicted transmission status change data based on the transmission status evaluation index set to generate a multi-link status index matrix; and classifying the multi-link status index matrix into quality levels according to the link quality level system to obtain multi-link status level parameters.

[0035] Specifically, the communication module uses a built-in link monitoring system to collect real-time status data of the multiple data transmission links configured in the intelligent fusion terminal, forming transmission status data. This transmission status data consists of continuous measurement results at the link layer and network layer, including but not limited to: link bandwidth utilization, round-trip time (RTT), packet loss rate, and signal quality indicators such as RSSI and SINR. Link bandwidth utilization reflects the ratio of the currently used bandwidth to the maximum available bandwidth, obtained from network devices such as routers and switches through network management protocols such as SNMP. Round-trip time (RTT) refers to the time it takes for a data packet to travel from the sender to the receiver, obtained using specialized network testing tools such as the Ping command by sending ICMP echo request packets and measuring the round-trip time. Packet loss rate is the ratio of the number of data packets lost during transmission to the total number of data packets sent, calculated by counting the number of data packets at both the sender and receiver.

[0036] After acquiring the transmission status data, a Kalman filter is used to filter, denoise, and smooth the data, establishing a state-space model of the link status. The actual link status, such as actual delay or actual throughput, is defined as an implicit state variable, while the actual collected observation data is used as noisy observations. State transition equations and observation equations are defined. The state transition equations describe the changes in link status over time, such as the evolution process affected by network congestion or signal fluctuations. The observation equations describe the mapping relationship between the observed values ​​and the actual state, and initialize the state estimates and the error covariance matrix. Within each sampling period, the current state is estimated based on the state at the previous moment, and then updated by incorporating the actual observations. The predicted and observed values ​​are weighted and fused using Kalman gain to suppress burst noise, such as the impact of instantaneous packet loss or short-term jitter. The final output is more stable, continuous, and closer to the actual link status, providing usable transmission status data.

[0037] Time series analysis and trend prediction are performed on available transmission status data. For example, an autoregressive moving average model is used to analyze the available transmission status data, and stationarity tests, such as the ADF test, are performed to determine whether the mean and variance change over time. If the data is not stationary, first-order or multi-order differencing is used to subtract data from adjacent time points to eliminate trend components and transform it into a stationary time series. Then, the autocorrelation function (ACF) and partial autocorrelation function (PACF) are plotted based on the stationary series. The ACF reflects the overall correlation decay of the data, and the PACF determines the degree of direct lag effect, thereby determining the autoregressive order p, differencing order d, and moving average order q in the ARIMA model.

[0038] After determining the ARIMA model structure, the ARIMA model parameters are fitted using historical available transmission state data through maximum likelihood estimation or least squares method to obtain a complete ARIMA prediction model. Based on the ARIMA prediction model, the link state at several future time steps is predicted, generating transmission state prediction change data. The transmission state prediction change data includes the link performance evolution results at future times, such as the predicted bandwidth utilization rate within the future time window, the predicted RTT delay curve, the packet loss rate trend, the decreasing or increasing trend of signal quality indicators, and the link stability fluctuation range, etc.

[0039] A transmission status evaluation index set is constructed. This set is a standardized set of indicators used for unified quantitative evaluation of the quality of different links. Basic evaluation parameters are selected from two dimensions: link performance and service quality. Link performance indicators include average round-trip time (RTT), throughput, bandwidth utilization, and packet loss rate; link stability indicators include RSSI, SINR, and link availability. A normalization mechanism is then introduced, using Min-Max normalization or Z-score normalization methods to convert indicators with different dimensions into unified dimensionless values. Weight coefficients are set according to business requirements. For example, in real-time service scenarios, latency has the highest weight, such as 0.4, throughput is set to 0.3, and packet loss rate and stability are set to 0.2 and 0.1 respectively. In big data transmission scenarios, throughput weight can be increased. Weights can be preset through expert experience or dynamically calculated using the Analytic Hierarchy Process (AHP). Finally, a transmission status evaluation index set consisting of indicator items, weights, and normalization rules is formed, used to constrain the unified evaluation standard for different links. The transmission status prediction change data is evaluated by using a transmission status evaluation index set. The scores of each index are calculated and weighted summed to generate a multi-link status index matrix. Each row in the multi-link status index matrix represents a link, each column represents an evaluation index, and each matrix element represents the evaluation result of the corresponding link under a certain index.

[0040] The multi-link status index matrix is ​​classified into quality levels according to a predefined link quality level system. The link quality level system is based on a comprehensive scoring threshold, for example: a comprehensive score ≥ 0.9 is excellent, 0.8~0.9 is good, 0.7~0.8 is average, and below 0.7 is poor. The evaluation result of the multi-link status index of each link in the multi-link status index matrix is ​​compared one by one with the comprehensive scoring threshold range in the link quality level system to determine its level, thereby generating multi-link status level parameters for subsequent link scheduling and data allocation decisions.

[0041] By collecting, processing, and predicting transmission status data in real time, we can accurately analyze the operational status and quality of the transmission status data. We can not only perceive the current link status but also predict its changing trends, thereby providing a forward-looking decision-making basis for data upload strategies and improving the overall stability and reliability of transmission.

[0042] The terminal data collected by the intelligent fusion terminal is classified by factors and prioritized to determine multi-level terminal data, and the attribute feature information of the multi-level terminal data is extracted.

[0043] Furthermore, the terminal data collected by the intelligent fusion terminal is classified and prioritized to determine multi-level terminal data, including: obtaining a data classification factor set, which includes data source, business type, data size, and data importance; classifying the terminal data collected by the intelligent fusion terminal according to the data classification factor set to obtain a terminal data factor parameter set; constructing a data transmission priority coordinate system based on the data classification factor set; and mapping the terminal data factor parameter set to the data transmission priority coordinate system for priority marking to determine multi-level terminal data.

[0044] Specifically, the terminal collects various types of information in real time during operation through various business interfaces and sensing interfaces. These include application-layer business interfaces for acquiring data streams generated by the business system, device driver interfaces for reading hardware operating status and sensor data, network communication interfaces for acquiring external interactive data streams, and system log interfaces for collecting operation logs and abnormal event information. The collection process adopts a combination of periodic polling and event triggering. For example, continuous data, such as video streams and environmental monitoring data, is acquired through a timed sampling mechanism, while sudden data, such as control commands or status changes, is captured through interrupt or message subscription mechanisms. Through collection and integration, terminal data is formed, which includes, but is not limited to, the following types: business data, sensing data, multimedia data, status data, and log and event data. Among them, business data includes control commands and task execution results; sensing data includes sensor data such as temperature, humidity, location, and acceleration; multimedia data includes high-bandwidth data such as audio, video, and image streams; status data includes device operating status, link connection status, and power information; and log and event data includes system operation logs, abnormal alarm records, and operation trajectory data.

[0045] A data classification factor set is constructed based on data source, business type, data size, and data importance for classification and priority labeling. The data source refers to the place where the terminal data is generated, such as local sensors, external network input, or application processes. The business type is related to the specific task performed by the terminal, such as real-time control, monitoring and analysis, log recording, or multimedia transmission. The data size, such as byte-level, KB-level, or MB-level continuous stream, reflects the amount of resources occupied by the data during storage and transmission. Data importance is an assessment of the degree of impact of terminal data on system operation and decision-making, and is divided according to the degree of business criticality, such as high importance for core control data, medium importance for auxiliary analysis data, and low importance for background recording data.

[0046] After obtaining the data classification factor set, the terminal data collected by the intelligent fusion terminal is classified according to the data classification factor set. That is, for each collected terminal data, its data source, business type, data size, and its data importance are analyzed to obtain the terminal data factor parameter set. For example, if a terminal data is collected by a temperature sensor for production monitoring, the data size is 100 bytes, and this temperature data is crucial for controlling the temperature of the production environment, then the corresponding factor parameters for this data will be recorded in the terminal data factor parameter set.

[0047] A data transmission priority coordinate system is constructed based on a data classification factor set. This coordinate system is a multi-dimensional coordinate system, constructed by using each factor in the data classification factor set as a coordinate axis. For each data point in the terminal data factor parameter set, its position is located on the corresponding coordinate axis based on its respective factor parameters. Then, its priority is determined based on the overall situation of its position in the data transmission priority coordinate system, and priority labeling is applied. For example, in constructing the data transmission priority coordinate system, data with high importance, moderate size, and belonging to key business types is labeled as high priority, while data with low importance, large size, and belonging to non-key business types is labeled as low priority. Through hierarchical labeling, terminal data is divided into multiple levels, such as high priority, medium priority, and low priority levels, thus obtaining multi-level terminal data.

[0048] After completing the terminal data priority labeling and forming multi-level terminal data, the terminal data is first grouped according to priority labels. Then, for each level of data set, corresponding attribute feature information is extracted based on its data content and accompanying metadata, such as timestamps, data length, source identifiers, and business type labels. The attribute feature information refers to key indicators that can characterize the data transmission behavior and business characteristics of that level, including but not limited to: data generation frequency obtained by counting the number of data entries per unit time, average data size and data volume distribution obtained by statistical analysis of the data length field, latency sensitivity obtained by mapping from business type labels (e.g., control type is highly sensitive), and data fluctuation characteristics obtained by calculating variance or rate of change through time series.

[0049] For example, the intelligent fusion terminal collected the following terminal data: Data 1: Collected by a soil moisture sensor for irrigation control, the data size is 50 bytes. This soil moisture data is crucial for proper irrigation and preventing crop water shortage or waterlogging, and its importance is assessed as high. Data 2: Collected by an air temperature sensor for environmental monitoring, the data size is 40 bytes. Air temperature data has some impact on crop growth, but its importance is slightly lower than that of soil moisture data, and its importance is assessed as medium. Data 3: Image data collected by a camera inside the greenhouse for security monitoring, the data size is 1000 bytes. Image data is useful for timely detection of anomalies, but the real-time requirement is relatively low, and its importance is assessed as low. A set of data classification factors is obtained, including data source, business type, data size, and data importance.

[0050] The three terminal data sets are categorized according to their data classification factor sets, resulting in the following terminal data factor parameter sets: Data 1 factor parameters: Data source - soil moisture sensor, business type - irrigation control, data size - 50 bytes, data importance - high. Data 2 factor parameters: Data source - air temperature sensor, business type - environmental monitoring, data size - 40 bytes, data importance - medium. Data 3 factor parameters: Data source - camera, business type - security monitoring, data size - 1000 bytes, data importance - low. A data transmission priority coordinate system is constructed, with data importance as the data importance axis (high corresponds to 3, medium to 2, low to 1), data size as the resource consumption axis, and business type as the business type axis (irrigation control corresponds to 3, environmental monitoring to 2, security monitoring to 1). Data source is used as the constraint correction axis. The terminal data factor parameter sets are mapped to the data transmission priority coordinate system: Data 1 is located at (3, 50, 3). Data 2 is located at (2, 40, 2). The position of data 3 in the data transmission priority coordinate system is (1, 1000, 1). Based on the comprehensive situation of these positions in the data transmission priority coordinate system, the priority is determined. Data 1 is marked as high priority, data 2 is marked as medium priority, and data 3 is marked as low priority. This identifies the multi-level terminal data and extracts the corresponding attribute feature information.

[0051] By classifying and prioritizing terminal data, the characteristics and importance of each piece of terminal data can be clearly identified. Dividing complex and diverse terminal data into different levels helps to allocate resources rationally according to the priority of terminal data during data transmission, storage, and processing. This ensures that high-priority terminal data can be processed in a timely and prioritized manner, thereby improving the overall efficiency and reliability of data upload and meeting the real-time and accuracy requirements of different businesses.

[0052] Furthermore, based on the data classification factor set, a data transmission priority coordinate system is constructed, including: defining a multi-dimensional data coordinate system using the factor information in the data classification factor set as coordinate axes, wherein the multi-dimensional data coordinate system includes multi-dimensional coordinate axis factor content and corresponding coordinate axis content scale intervals; constructing a data priority division standard according to the terminal data transmission requirements; dividing the multi-dimensional coordinate axis factor content and corresponding coordinate axis content scale intervals into spatial priorities according to the data priority division standard to obtain multi-dimensional coordinate axis spatial priority information; and prioritizing the multi-dimensional data coordinate system based on the multi-dimensional coordinate axis spatial priority information to construct a data transmission priority coordinate system.

[0053] Specifically, based on the established set of data classification factors, each factor is used as a coordinate axis to define a multidimensional data coordinate system. The set of data classification factors includes data source, business type, data size, and data importance. Each factor is defined as an independent coordinate axis. For example, data importance corresponds to the data importance axis, representing the degree of business criticality; business type corresponds to the business type axis, reflecting the difference between real-time requirements and business attributes; data size corresponds to the resource consumption axis, reflecting bandwidth consumption; and data source serves as a constraint correction axis, distinguishing different acquisition domains or device sources. This multidimensional data coordinate system is a multidimensional feature space composed of multiple semantically meaningful coordinate axes. Each coordinate axis includes multidimensional coordinate axis factor content and a corresponding coordinate axis content scale range. Among them, the multidimensional coordinate axis factor content refers to the business attribute meaning expressed by the dimension, and the scale interval refers to the quantitative division range of the factor. For example, the importance of data can be divided into 1 to 3 levels of scale, and the data size is divided according to the actual data size range, such as 0-100 bytes, 101-500 bytes, 501 bytes and above, etc., so as to achieve a unified expression from qualitative factors to quantitative scale.

[0054] A data priority classification standard is constructed based on the terminal data transmission requirements. This standard is derived from system business strategies and service quality requirements, and is generated through a preset rule base or business strategy engine. For example, a high priority threshold is set for real-time control services, while a low priority threshold is set for log-related services, allowing for delayed transmission. The data priority classification standard defines the priority level mapping relationship corresponding to each coordinate axis scale interval; that is, different numerical intervals correspond to different priority level labels, such as high priority, medium priority, and low priority.

[0055] Based on the data priority classification standard, the factors and their scale intervals of the multi-dimensional coordinate axes are spatially prioritized. Priority segmentation rules are introduced on each coordinate axis to divide continuous or discrete scale intervals into multiple priority regions. For example, on the data importance axis, level 1 is divided into a low priority region, level 2 into a medium priority region, and level 3 into a high priority region. On the data size axis, small data intervals are divided into low resource consumption regions, and large data intervals are divided into high resource consumption regions. These are then cross-corrected by combining business type weights, thereby forming multi-dimensional coordinate axis spatial priority information with spatial semantics. This multi-dimensional coordinate axis spatial priority information assigns different priority weights to different regions in the multi-dimensional space for subsequent data mapping determination.

[0056] Based on this spatial priority information, priority labeling is performed on the multi-dimensional coordinate system. This involves assigning explicit priority labels, such as high priority, medium priority, and low priority, to different regions or grid cells within the data transmission priority coordinate system space, thereby constructing the data transmission priority coordinate system. This data transmission priority coordinate system not only possesses geometric representation capabilities but also semantic priority determination capabilities, directly completing priority classification and scheduling decisions through spatial mapping.

[0057] By transforming the originally discrete and unstructured multi-factor data classification system into a unified, intuitive, and operable data transmission priority coordinate system, the data priority determination is upgraded from a rule-based matching mode to a spatial mapping, thereby significantly improving the accuracy of data classification and the automation capability of subsequent link scheduling. This ensures that high-priority data can be transmitted in a timely and prioritized manner, thereby improving the transmission efficiency and reliability of the entire fusion terminal data upload.

[0058] Furthermore, mapping the terminal data factor parameter set to the data transmission priority coordinate system for priority labeling and determining multi-level terminal data includes: mapping the terminal data factor parameter set to the data transmission priority coordinate system to obtain terminal data parameter coordinate information; obtaining the center coordinate information of each label space region in the data transmission priority coordinate system; calculating the Euclidean distance set between the terminal data parameter coordinate information and the center coordinate information of each label space region; and determining and labeling the terminal data factor parameter set based on the Euclidean distance set of the center coordinate information to determine multi-level terminal data.

[0059] Specifically, the terminal data factor parameter set is mapped to the data transmission priority coordinate system. During the mapping process, based on the parameter values ​​in the terminal data factor parameter set, its position on the corresponding coordinate axis in the data transmission priority coordinate system is determined, thereby obtaining the specific coordinate information of the terminal data in the data transmission priority coordinate system, i.e., the terminal data parameter coordinate information. After completing the data point mapping, the center coordinate information of each label space region in the data transmission priority coordinate system is obtained. The label space region refers to different level regions divided in the data transmission priority coordinate system according to the priority classification criteria. Each label space region represents a specific priority level, such as high priority area, medium priority area, low priority area, etc. Each label space region can be regarded as a geometric partition in the data transmission priority coordinate system. The center coordinate information refers to the coordinate value of the geometric center point of each label space region in the data transmission priority coordinate system, representing the average characteristics of the region. The center coordinate information is obtained by calculating the average coordinate value of all points in the label space region.

[0060] Then, the Euclidean distance set between the terminal data parameter coordinate information and the center coordinate information of each label spatial region is calculated. The Euclidean distance refers to the straight-line distance between two points in multi-dimensional space, used to measure the proximity of a data point to the center of different priority regions. It is calculated by taking the square root of the sum of the squares of the differences in each dimension. By calculating the distance between each piece of terminal data and the center point of all priority regions, a distance set, i.e., the Euclidean distance set, is formed. This set reflects the degree of matching between the data and each priority category.

[0061] Priority determination and labeling of terminal data factor parameter sets are performed based on the Euclidean distance set of center coordinate information. That is, the label space region with the smallest distance is selected as the priority category to which the terminal data belongs. The smaller the distance, the closer the data is to that category in the feature space, thus achieving optimal matching. For example, if a terminal data has the smallest Euclidean distance to a high-priority label space region, then the terminal data is determined as high-priority data and labeled with the corresponding priority label, thus determining multi-level terminal data.

[0062] By introducing multidimensional spatial mapping and Euclidean distance calculation mechanisms, the priority classification of terminal data is upgraded from static rule matching to a dynamic optimal matching method based on spatial geometric relationships. This improves the accuracy of data classification and the adaptability to complex multidimensional business characteristics, thereby meeting the real-time and reliability requirements of data transmission in different business scenarios.

[0063] A dynamic data upload strategy is constructed, which is used to dynamically allocate the multi-link status level parameters and the attribute feature information to determine multi-level data matching links.

[0064] Furthermore, determining multi-level data matching links includes: using the dynamic data upload strategy to transmit and match the multi-link status level parameters with the attribute feature information to obtain an initial data matching link; updating and evaluating the multi-link status level parameters through a real-time monitoring mechanism to obtain multi-link status level update parameters; and dynamically updating the initial data matching link based on the multi-link status level update parameters to determine multi-level data matching links.

[0065] Specifically, a dynamic data upload strategy is constructed based on multi-link status level parameters and attribute feature information of multi-level terminal data to match data with links. First, the multi-link status level parameters are quantified into link capability vectors, for example, mapping link levels to corresponding numerical ranges, and combining this with actual indicators such as available bandwidth, latency, and packet loss rate to form a set of link capability parameters. Simultaneously, the attribute feature information of multi-level terminal data is transformed into data demand vectors; for example, high-priority data corresponds to low latency and high reliability requirements, while large data volumes correspond to high bandwidth requirements.

[0066] Then, a matching function, such as a weighted scoring function, is constructed to calculate the difference between link capacity and data requirements. For example, weights are assigned to latency matching, bandwidth satisfaction, and reliability satisfaction, and a comprehensive score is calculated. The weights can be set based on actual needs. For each type of data, the link with the best score is selected as its target transmission path, forming a dynamic mapping rule between data type and link resources, i.e., a dynamic data upload strategy. This can be updated in real time by recalculating the matching function when the link status changes. For example, the matching function can be defined as a weighted scoring function to quantify the degree of matching between link capacity and data requirements in multiple dimensions. For example, for a certain type of high-priority control data, the requirements are: latency ≤ 20ms, packet loss rate ≤ 1%, and low bandwidth requirement, 1Mbps is sufficient. First, the actual parameters of the candidate links are normalized, and the matching degree of each dimension is calculated: latency matching, reliability matching, and bandwidth satisfaction are all quantified using a unified normalization calculation method to ensure comparability between different dimensions. The latency matching degree is calculated by normalizing the ratio of the maximum allowable latency requirement of the service to the actual latency of the link, which is defined as Min(1, allowable latency / actual latency). When the actual latency is less than or equal to the allowable latency, it is 1, indicating that the requirement is fully met. When the actual latency is greater than the allowable value, it is reduced proportionally. The reliability matching degree is calculated based on the packet loss rate normalization, which is defined as 1. (Actual packet loss rate / Maximum allowable packet loss rate) is 1 when the packet loss rate is 0, and gradually decreases to 0 as the packet loss rate approaches or exceeds the threshold, which is used to reflect the degree of transmission reliability satisfaction; the bandwidth satisfaction is calculated based on the normalized ratio of the required bandwidth to the actual available bandwidth, which is defined as Min(1, actual available bandwidth / required bandwidth). It is 1 when the link bandwidth can fully meet or exceed the requirement, and decreases proportionally when it is lower than the requirement.

[0067] By employing the three normalization calculation methods described above, link metrics with different dimensions are uniformly mapped to a standard range of 0 to 1. Then, weights are assigned based on actual needs and experience for weighted summation. For example, for control data, a latency weight of 0.5, a reliability weight of 0.3, and a bandwidth weight of 0.2 are assigned. The final comprehensive score is calculated as: 0.5 × latency matching degree + 0.3 × reliability matching degree + 0.2 × bandwidth satisfaction degree. This score is calculated for all candidate links, and the link with the highest comprehensive score is selected as the target transmission path for that type of data. For different types of data, such as video streams or log data, the weight ratios can be adjusted, for example, by increasing the bandwidth weight, thereby achieving a differentiated matching strategy.

[0068] A dynamic data upload strategy is employed to match multi-link status level parameters with attribute feature information to obtain initial data matching links, i.e., the target transmission links corresponding to each type of data. After obtaining the initial data matching links, a real-time monitoring mechanism is introduced to continuously update the multi-link status level parameters. This real-time monitoring mechanism refers to the communication module continuously collecting link status data at fixed time intervals or through event triggering, and recalculating the link status level to generate updated multi-link status level parameters. For example, if a link experiences increased latency due to network congestion, the real-time monitoring mechanism will detect this change and adjust the link's status level from high to medium or low based on calculations.

[0069] Based on the updated link status level parameters, the initial data matching links are dynamically updated. This involves re-inputting the updated multi-link status level parameters into the dynamic data upload strategy, re-evaluating the original matching relationships. When a link's status is detected to have decreased or no longer meet the transmission requirements of a certain type of data, a re-matching process is triggered. For example, high-priority data is migrated from the degraded link to a higher-priority link, or a backup link is activated for replacement. Simultaneously, for links whose status has improved, some low-priority data is migrated to that link to achieve load balancing. Through dynamic updates, multi-level data matching links are determined. This dynamic update mechanism ensures that data is always transmitted on the most suitable link, adapting to constantly changing network environments.

[0070] By constructing dynamic strategies and real-time monitoring mechanisms, dynamic adaptive matching between terminal data and multi-link resources is achieved, enabling data transmission strategies to be adjusted in real time according to changes in link status. This ensures the transmission quality of high-priority data, while improving the overall link resource utilization efficiency. It ensures that data at different levels can be transmitted quickly and accurately on the most suitable link, meeting the data upload requirements of different business scenarios.

[0071] A data retransmission mechanism is introduced to encapsulate, encode, and optimize the upload control of the multi-level terminal data through the multi-level data matching link.

[0072] Furthermore, a data retransmission mechanism is introduced to encapsulate, encode, and optimize the upload control of the multi-level terminal data through the multi-level data matching link. This includes: selecting a data encapsulation format and data encoding rules according to the multi-level data matching link; encapsulating and encoding the multi-level terminal data using the data encapsulation format and data encoding rules to obtain multi-level terminal encoded data blocks; and introducing a data retransmission mechanism to optimize the upload control of the multi-level terminal encoded data blocks through the multi-level data matching link.

[0073] Specifically, based on the multi-level data matching link, data encapsulation formats and data encoding rules are selected. The data encapsulation format refers to the message format used to organize terminal data according to a unified protocol structure, such as JSON structured encapsulation, TLV format, or binary frame structure, used to achieve data field standardization and cross-link compatible transmission. The data encoding rules refer to the set of rules for compressing, correcting errors, or encrypting the encapsulated data. For example, lightweight compression algorithms and error correction coding are used. Lightweight compression algorithms, such as LZ4, are used for low-latency scenarios, while error correction coding, such as Reed-Solomon, is used for high-reliability scenarios. The multi-level data matching link includes transmission link information corresponding to different levels of data. Appropriate data encapsulation formats and data encoding rules are matched based on the transmission link information. For high-priority, low-latency data, low-overhead encapsulation formats and fast encoding methods are selected. For data with high reliability requirements, encoding methods with redundant error correction capabilities are selected to improve packet loss resistance. For large-volume data transmission, high compression ratio encoding is prioritized to reduce bandwidth consumption.

[0074] After determining the data encapsulation format and data encoding rules, these rules are used to encapsulate and encode multi-level terminal data. During the encapsulation and encoding process, for each level of data, it is organized into data packets with a specific structure according to the selected encapsulation format. The data packets contain data content, data identifiers, priority information, etc. Then, the data packets are encoded according to the selected encoding rules, converting the terminal data into encoded data suitable for transmission on the link, generating multi-level terminal encoded data blocks. The multi-level terminal encoded data blocks are transmission units that have undergone structured organization and encoding enhancement. They contain data header information, payload data, and check information. The data header information includes data type, priority identifier, timestamp, and sequence number. The check information includes CRC or redundancy check fields, used to support integrity verification and reassembly recovery during subsequent transmission.

[0075] Furthermore, a data retransmission mechanism is introduced to optimize the upload control of multi-level terminal encoded data blocks through multi-level data matching links. Under the conditions of link fluctuation, packet loss, or congestion, based on the collaborative judgment of data priority and link status, differentiated retransmission scheduling and path reselection are performed on encoded data blocks that have not been successfully transmitted or whose transmission quality has degraded. This allows high-priority data to be prioritized for rapid retransmission or multi-path redundant transmission through high-quality links, while low-priority data is retransmitted with delay or merged according to the link's idle level. This ensures the reliable delivery of critical data while reducing the overhead of repeated transmission and improving the overall link utilization efficiency and transmission stability.

[0076] By selecting appropriate encapsulation and encoding rules based on multi-level data matching links, data can better adapt to the characteristics of different links, improving data transmission efficiency and reliability. Introducing a data retransmission mechanism and optimizing upload control further ensures that terminal data is not lost or corrupted during transmission, especially for high-priority data, guaranteeing its timely and accurate arrival at the receiving end, meeting the data transmission requirements of different business scenarios.

[0077] Furthermore, a data retransmission mechanism is introduced to optimize the upload control of the multi-level terminal encoded data blocks through the multi-level data matching link. This includes: determining data retransmission conditions and data retransmission strategies according to the data retransmission mechanism, wherein the data retransmission strategies include exponential backoff, link upgrade, and priority retention; when the data retransmission conditions are triggered, optimizing the upload control of the multi-level terminal encoded data blocks through the multi-level data matching link based on the data retransmission strategies.

[0078] Specifically, based on real-time transmission feedback information and link status monitoring results of multi-level data matching links, data retransmission conditions are determined. These conditions originate from continuous monitoring data of the transmission process by the communication module and include triggering events such as acknowledgment timeout, data packet loss, retransmission count exceeding a threshold, and link quality level degradation. For example, if a coded data block does not receive an acknowledgment response within a preset time window (e.g., 0.2 seconds) or the link packet loss rate exceeds a preset threshold (e.g., 5%), a retransmission condition is triggered. Simultaneously, priority identifiers in the multi-level terminal coded data blocks, such as high priority, medium priority, or low priority, are used as triggering criteria, thus forming a dual-condition mechanism of status triggering and priority constraint.

[0079] After determining the retransmission conditions, a set of data retransmission strategies is further constructed, including exponential backoff, link upgrade, and priority preservation. The exponential backoff strategy gradually extends the retransmission interval when retransmissions fail consecutively. That is, when a data retransmission occurs, the sender does not immediately retransmit the data but waits for a random time interval, which increases exponentially with the number of retransmissions. For example, the waiting time for the first retransmission is T, the second is 2T, the third is 4T, and so on, reducing the probability of multiple senders simultaneously retransmitting data and causing further conflicts. The link upgrade strategy automatically switches to a higher-level or backup link for transmission when the quality of the currently matched link deteriorates or the number of retransmission failures exceeds a threshold, such as two times. The priority preservation strategy maintains the transmission priority of high-priority data during retransmission scheduling, prioritizing the allocation of retransmission resources for critical data blocks even in the event of congestion or resource contention.

[0080] When retransmission conditions are met, the data retransmission strategy execution process is triggered. Based on data priority, it is determined whether to employ a priority retention mechanism; high-priority encoded data blocks are immediately added to the fast retransmission queue. The current link status level is then assessed to determine whether to perform a link upgrade operation. Simultaneously, for consecutively failed terminal encoded data blocks, an exponential backoff mechanism is introduced to dynamically adjust the retransmission interval to reduce network impact. During execution, retransmission paths are scheduled in real-time through multi-level data matching links, enabling the combined execution of different strategies to achieve optimized upload control of multi-level terminal encoded data blocks.

[0081] By constructing a retransmission control mechanism that combines triggering conditions, strategy combinations, and dynamic link scheduling, adaptive error correction and dynamic recovery can be achieved in complex network fluctuation environments. This ensures reliable transmission of high-priority data while reducing overall retransmission overhead, thereby improving transmission stability and resource utilization efficiency in multi-link environments and meeting the real-time and reliability requirements of different services.

[0082] Example 2, based on the same inventive concept as the intelligent fusion terminal data optimization and uploading method under link state awareness in the aforementioned examples, such as... Figure 2 As shown, this application provides a data optimization and uploading system for intelligent converged terminals under link status awareness, wherein the intelligent converged terminal data optimization and uploading system under link status awareness includes: The data evaluation component 11 is used to divide and configure multiple data transmission links of the intelligent fusion terminal. The communication module collects the transmission status data of the multiple data transmission links in real time, performs index prediction and evaluation on the transmission status data, and obtains multi-link status level parameters. The feature acquisition component 12 is used to classify and prioritize the terminal data collected by the intelligent fusion terminal, determine multi-level terminal data, and extract the attribute feature information of the multi-level terminal data. The dynamic allocation component 13 is used to construct a dynamic data upload strategy, and uses the dynamic data upload strategy to dynamically allocate the multi-link status level parameters and the attribute feature information to determine multi-level data matching links. The data upload component 14 is used to introduce a data retransmission mechanism to encapsulate, encode, and optimize the upload control of the multi-level terminal data through the multi-level data matching links.

[0083] Furthermore, the data evaluation component 11 is also used to: acquire the transmission network topology, terminal interface capabilities, service transmission requirements, and transmission resource constraints of the intelligent converged terminal; construct transmission link partitioning rules, which include service hierarchical isolation, transmission redundancy design, and transmission load balancing; partition the transmission network topology, terminal interface capabilities, service transmission requirements, and transmission resource constraints according to the transmission link partitioning rules to obtain an initial transmission link set; and perform transmission testing and configuration adjustments on the initial transmission link set to obtain the multi-data transmission links of the intelligent converged terminal.

[0084] Furthermore, the data evaluation component 11 is also used to: filter, denoise, and smooth the transmission status data using a Kalman filter to obtain usable transmission status data; perform time series analysis and trend prediction on the usable transmission status data to obtain predicted transmission status change data; construct a transmission status evaluation index set; evaluate the predicted transmission status change data based on the transmission status evaluation index set to generate a multi-link status index matrix; and classify the multi-link status index matrix into quality levels according to the link quality level system to obtain multi-link status level parameters.

[0085] Furthermore, the feature acquisition component 12 is also used to: acquire a data classification factor set, the data classification factor set including data source, business type, data size and data importance; classify the terminal data collected by the intelligent fusion terminal according to the data classification factor set to obtain a terminal data factor parameter set; construct a data transmission priority coordinate system according to the data classification factor set; map the terminal data factor parameter set to the data transmission priority coordinate system for priority labeling, and determine multi-level terminal data.

[0086] Furthermore, the feature acquisition component 12 is also used to: define a multidimensional data coordinate system using the information of each factor in the data classification factor set as coordinate axes, wherein the multidimensional data coordinate system includes multidimensional coordinate axis factor content and corresponding coordinate axis content scale intervals; construct a data priority division standard according to the terminal data transmission requirements; perform spatial priority division on the multidimensional coordinate axis factor content and corresponding coordinate axis content scale intervals according to the data priority division standard to obtain multidimensional coordinate axis spatial priority information; and perform priority labeling on the multidimensional data coordinate system based on the multidimensional coordinate axis spatial priority information to construct a data transmission priority coordinate system.

[0087] Furthermore, the feature acquisition component 12 is also used to: map the terminal data factor parameter set to the data transmission priority coordinate system to obtain terminal data parameter coordinate information; obtain the center coordinate information of each label space region in the data transmission priority coordinate system; calculate the Euclidean distance set between the terminal data parameter coordinate information and the center coordinate information of each label space region; and determine the priority and label the terminal data factor parameter set based on the Euclidean distance set of the center coordinate information to determine multi-level terminal data.

[0088] Furthermore, the dynamic allocation component 13 is also used to: use the data upload dynamic strategy to transmit and match the multi-link status level parameters with the attribute feature information to obtain an initial data matching link; update and evaluate the multi-link status level parameters through a real-time monitoring mechanism to obtain multi-link status level update parameters; and dynamically update the initial data matching link based on the multi-link status level update parameters to determine multi-level data matching links.

[0089] Furthermore, the data upload component 14 is also used to: select a data encapsulation format and data encoding rules according to the multi-level data matching link; encapsulate and encode the multi-level terminal data using the data encapsulation format and data encoding rules to obtain multi-level terminal encoded data blocks; and introduce a data retransmission mechanism to optimize the upload control of the multi-level terminal encoded data blocks through the multi-level data matching link.

[0090] Furthermore, the data upload component 14 is also used to: determine data retransmission conditions and data retransmission strategies according to the data retransmission mechanism, wherein the data retransmission strategies include exponential backoff, link upgrade, and priority retention; when the data retransmission conditions are triggered, optimize the upload control of the multi-level terminal encoded data blocks through the multi-level data matching link based on the data retransmission strategies.

[0091] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The link status awareness-based intelligent converged terminal data optimization and uploading method and specific examples in the aforementioned embodiment one are also applicable to the link status awareness-based intelligent converged terminal data optimization and uploading system of this embodiment. Through the foregoing detailed description of the link status awareness-based intelligent converged terminal data optimization and uploading method, those skilled in the art can clearly understand the link status awareness-based intelligent converged terminal data optimization and uploading system of this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0092] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily 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 this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0093] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for optimizing data upload from an intelligent fusion terminal under link status awareness, characterized in that, The method includes: The system divides and configures multiple data transmission links of the intelligent fusion terminal, and the communication module collects the transmission status data of the multiple data transmission links in real time. The system performs index prediction and evaluation on the transmission status data to obtain the multi-link status level parameters. The terminal data collected by the intelligent fusion terminal is classified by factors and prioritized to determine multi-level terminal data, and the attribute feature information of the multi-level terminal data is extracted. A dynamic data upload strategy is constructed, and the multi-link status level parameters and attribute feature information are dynamically allocated using the dynamic data upload strategy to determine multi-level data matching links; A data retransmission mechanism is introduced to encapsulate, encode, and optimize the upload control of the multi-level terminal data through the multi-level data matching link.

2. The method for optimizing and uploading data from an intelligent fusion terminal under link status awareness as described in claim 1, characterized in that, The configuration of multiple data transmission links for intelligent converged terminals includes: Obtain the transmission network topology, terminal interface capabilities, service transmission requirements, and transmission resource constraints of the intelligent converged terminal; Construct transmission link partitioning rules, which include service hierarchical isolation, transmission redundancy design, and transmission load balancing. The transmission network topology, terminal interface capabilities, service transmission requirements, and transmission resource constraints are divided according to the transmission link division rules to obtain an initial transmission link set. The initial transmission link set is tested and its configuration is adjusted to obtain the multiple data transmission links of the intelligent fusion terminal.

3. The method for optimizing and uploading data from an intelligent fusion terminal under link status awareness as described in claim 1, characterized in that, The transmission status data is used to predict and evaluate indicators to obtain multi-link status level parameters, including: The transmission status data is filtered, denoised, and smoothed using a Kalman filter to obtain usable transmission status data. Perform time series analysis and trend prediction on the available transmission status data to obtain transmission status prediction change data; Construct a transmission status evaluation index set, evaluate the transmission status prediction change data based on the transmission status evaluation index set, and generate a multi-link status index matrix. The multi-link status index matrix is ​​divided into quality levels according to the link quality level system to obtain multi-link status level parameters.

4. The method for optimizing and uploading data from an intelligent fusion terminal under link status awareness as described in claim 1, characterized in that, The terminal data collected by the intelligent fusion terminal is classified by factors and prioritized to determine multi-level terminal data, including: Obtain a set of data classification factors, which includes data source, business type, data size, and data importance; The terminal data collected by the intelligent fusion terminal is classified according to the data classification factor set to obtain the terminal data factor parameter set; Based on the data classification factor set, construct a data transmission priority coordinate system; The terminal data factor parameter set is mapped to the data transmission priority coordinate system for priority labeling, thereby determining multi-level terminal data.

5. The method for optimizing and uploading data from an intelligent fusion terminal under link status awareness as described in claim 4, characterized in that, Based on the data classification factor set, a data transmission priority coordinate system is constructed, including: Using the information of each factor in the data classification factor set as coordinate axes, a data multidimensional coordinate axis system is defined, which includes the multidimensional coordinate axis factor content and the corresponding coordinate axis content scale interval; Based on the terminal data transmission requirements, a data priority classification standard is constructed; According to the data priority classification standard, the multidimensional coordinate axis factor content and the corresponding coordinate axis content scale interval are spatially prioritized to obtain multidimensional coordinate axis spatial priority information. Based on the multidimensional coordinate axis spatial priority information, the data multidimensional coordinate axis system is prioritized and labeled to construct a data transmission priority coordinate system.

6. The method for optimizing and uploading data from an intelligent fusion terminal under link status awareness as described in claim 4, characterized in that, The terminal data factor parameter set is mapped to the data transmission priority coordinate system for priority labeling to determine multi-level terminal data, including: Map the terminal data factor parameter set to the data transmission priority coordinate system to obtain the terminal data parameter coordinate information; Obtain the center coordinate information of each label space region in the data transmission priority coordinate system; Calculate the Euclidean distance set between the coordinate information of the terminal data parameters and the center coordinate information of each label spatial region; Based on the Euclidean distance set of the center coordinate information, the terminal data factor parameter set is prioritized and labeled to determine multi-level terminal data.

7. The method for optimizing and uploading data from an intelligent fusion terminal under link status awareness as described in claim 6, characterized in that, Determine multi-level data matching links, including: The data upload dynamic strategy is used to match the multi-link status level parameters with the attribute feature information to obtain an initial data matching link. The multi-link status level parameters are updated and evaluated through a real-time monitoring mechanism to obtain the multi-link status level update parameters. The initial data matching link is dynamically updated based on the multi-link status level update parameters to determine multi-level data matching links.

8. The method for optimizing and uploading data from an intelligent fusion terminal under link status awareness as described in claim 1, characterized in that, A data retransmission mechanism is introduced to encapsulate, encode, and optimize the upload control of the multi-level terminal data through the multi-level data matching link, including: Based on the multi-level data matching link, select the data encapsulation format and data encoding rules; The multi-level terminal data is encapsulated and encoded using the aforementioned data encapsulation format and data encoding rules to obtain multi-level terminal encoded data blocks. A data retransmission mechanism is introduced to optimize the upload control of the multi-level terminal encoded data blocks through the multi-level data matching link.

9. The method for optimizing and uploading data from an intelligent fusion terminal under link status awareness as described in claim 8, characterized in that, A data retransmission mechanism is introduced to optimize the upload control of the multi-level terminal encoded data blocks through the multi-level data matching link, including: Based on the data retransmission mechanism, the data retransmission conditions and data retransmission strategies are determined, wherein the data retransmission strategies include exponential backoff, link upgrade, and priority retention. When the data retransmission condition is triggered, the multi-level terminal encoded data block is optimized and uploaded based on the data retransmission strategy through the multi-level data matching link.

10. A data optimization and uploading system for intelligent fusion terminals under link status awareness, characterized in that, The steps for implementing the link-state-aware intelligent fusion terminal data optimization and uploading method according to any one of claims 1 to 9 include: The data evaluation component is used to divide and configure multiple data transmission links of the intelligent fusion terminal. The communication module collects the transmission status data of the multiple data transmission links in real time, performs index prediction and evaluation on the transmission status data, and obtains the multi-link status level parameters. The feature acquisition component is used to classify and prioritize the terminal data collected by the intelligent fusion terminal, determine multi-level terminal data, and extract the attribute feature information of the multi-level terminal data. A dynamic allocation component is used to construct a dynamic data upload strategy. The dynamic data upload strategy is used to dynamically allocate the multi-link status level parameters and the attribute feature information to determine multi-level data matching links. The data upload component is used to introduce a data retransmission mechanism to encapsulate, encode, and optimize the upload control of the multi-level terminal data through the multi-level data matching link.

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