Device data processing method and related device based on USB interface adapter

Abstract

The present application relates to the field of data processing technology, and discloses a device data processing method based on a USB interface adapter and a related device. The method includes: performing device discovery through a USB Type-C interface adapter to obtain multiple target non-Hongmeng OS devices; creating a first device data communication soft bus framework; performing route planning and allocation to obtain a target transmission data set and a route status monitoring data set; calculating the data transmission performance feature vector and the route status monitoring feature vector, and performing vector splicing to obtain a performance status fusion feature vector; optimizing and solving the device data communication strategy through a two-layer programming model and a non-dominated sorting genetic algorithm to obtain a target device data communication optimization strategy and perform device data communication topology optimization to obtain a second device data communication soft bus framework. The present application uses a USB interface adapter to achieve compatibility with non-Hongmeng OS devices and improve the accuracy of device data processing.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a device data processing method based on a USB interface adapter and related devices. Background Art

[0002] In today's digital and networked era, USB interface adapters, as a bridge connecting various devices, play a vital role in the field of data communication. With the rapid development of Internet of Things technology and the popularization of smart devices, traditional device data processing methods are facing severe challenges. These challenges are mainly reflected in the increasing demand for efficient, secure and stable data communication between devices, while existing data processing methods often fail to meet these complex and changing communication needs. Especially in an environment where multiple devices work together, how to achieve efficient data transmission and precise routing control has become an urgent problem that needs to be solved.

[0003] With the widespread use of non-Hongmeng OS devices, data interaction between devices has become more frequent, but due to the lack of a unified standard data communication protocol, the interoperability between devices is poor and the data communication efficiency is low. In addition, the existing device discovery and connection management methods cannot effectively cope with the dynamically changing network environment of the device, which makes it difficult to ensure the stability and reliability of the data transmission process. These problems seriously restrict the performance of the smart device network and reduce the user experience. In addition, due to the complexity of the data transmission environment, traditional data processing methods are insufficient in handling multi-path data transmission, route optimization, and data transmission performance analysis. The lack of effective data monitoring and analysis means makes it difficult to monitor and evaluate the data transmission process in real time, resulting in the inability to promptly discover and handle problems in data transmission, thereby affecting the efficiency and stability of the entire data communication system. Summary of the invention

[0004] The present application provides a device data processing method and related apparatus based on a USB interface adapter, which is used to achieve compatibility with non-Hongmeng OS devices and improve the accuracy of device data processing by using a USB interface adapter.

[0005] In a first aspect, the present application provides a device data processing method based on a USB interface adapter, the device data processing method based on a USB interface adapter comprising:

[0006] Perform device discovery on the preset device WIFI communication environment through the USB Type-C interface adapter to obtain multiple initial non-Hongmeng OS devices, and perform device authentication and communication protocol configuration on the multiple initial non-Hongmeng OS devices to obtain multiple target non-Hongmeng OS devices;

[0007] Perform connection management on the multiple target non-Hongmeng OS devices, obtain connection status information of each target non-Hongmeng OS device, and create a first device data communication soft bus framework for the multiple target non-Hongmeng OS devices according to the connection status information;

[0008] According to the first device data communication soft bus framework, respectively plan and allocate routes for the multiple target non-Hongmeng OS devices, determine the optimal non-overlapping route for each target non-Hongmeng OS device, and perform data transmission and route transmission status monitoring for the multiple target non-Hongmeng OS devices according to the optimal non-overlapping route to obtain a target transmission data set and a route status monitoring data set;

[0009] The data transmission performance characteristics of the target transmission data set are calculated by using a Gaussian model to obtain a data transmission performance characteristic set, and the routing state monitoring data set is decomposed and characterized by using a complementary set empirical mode decomposition model to obtain a routing state monitoring characteristic set;

[0010] Performing feature mapping and vector conversion on the data transmission performance feature set and the routing state monitoring feature set to obtain a data transmission performance feature vector and a routing state monitoring feature vector, and performing vector concatenation on the data transmission performance feature vector and the routing state monitoring feature vector to obtain a performance state fusion feature vector;

[0011] The data transmission performance characteristic vector, the routing status monitoring characteristic vector and the performance status fusion characteristic vector are input into a preset two-layer programming model and a non-dominated sorting genetic algorithm to optimize and solve the device data communication strategy to obtain a target device data communication optimization strategy, and based on the target device data communication optimization strategy, the device data communication topology of the first device data communication soft bus framework is optimized to obtain a second device data communication soft bus framework.

[0012] In a second aspect, the present application provides a device data processing apparatus based on a USB interface adapter, the device data processing apparatus based on a USB interface adapter comprising:

[0013] A configuration module is used to perform device discovery on a preset device WIFI communication environment through a USB Type-C interface adapter to obtain multiple initial non-Hongmeng OS devices, and perform device authentication and communication protocol configuration on the multiple initial non-Hongmeng OS devices to obtain multiple target non-Hongmeng OS devices;

[0014] A creation module is used to perform connection management on the multiple target non-Hongmeng OS devices, obtain connection status information of each target non-Hongmeng OS device, and create a first device data communication soft bus framework for the multiple target non-Hongmeng OS devices according to the connection status information;

[0015] an allocation module, configured to respectively plan and allocate routes for the multiple target non-Hongmeng OS devices according to the first device data communication soft bus framework, determine the optimal non-overlapping route for each target non-Hongmeng OS device, and perform data transmission and route transmission status monitoring for the multiple target non-Hongmeng OS devices according to the optimal non-overlapping route, to obtain a target transmission data set and a route status monitoring data set;

[0016] A calculation module, configured to calculate the data transmission performance characteristics of the target transmission data set through a Gaussian model to obtain a data transmission performance characteristic set, and perform routing state monitoring decomposition and feature mapping on the routing state monitoring data set through a complementary set empirical mode decomposition model to obtain a routing state monitoring characteristic set;

[0017] a conversion module, configured to perform feature mapping and vector conversion on the data transmission performance feature set and the routing state monitoring feature set to obtain a data transmission performance feature vector and a routing state monitoring feature vector, and to perform vector concatenation on the data transmission performance feature vector and the routing state monitoring feature vector to obtain a performance state fusion feature vector;

[0018] The optimization module is used to input the data transmission performance characteristic vector, the routing status monitoring characteristic vector and the performance status fusion characteristic vector into a preset two-layer programming model and a non-dominated sorting genetic algorithm to optimize and solve the device data communication strategy, obtain the target device data communication optimization strategy, and based on the target device data communication optimization strategy, perform device data communication topology optimization on the first device data communication soft bus framework to obtain the second device data communication soft bus framework.

[0019] A third aspect of the present application provides a device data processing device based on a USB interface adapter, comprising: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the device data processing device based on the USB interface adapter executes the above-mentioned device data processing method based on the USB interface adapter.

[0020] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned device data processing method based on a USB interface adapter.

[0021] In the technical solution provided by the present application, the preset device WIFI communication environment can be quickly scanned through the USB Type-C interface adapter, and the surrounding non-Hongmeng OS devices can be accurately discovered. At the same time, the use of advanced device authentication technology ensures the communication security between devices and improves the efficiency and reliability of device authentication. Effectively manage connections, monitor the connection status of each target device in real time, and create a flexible device data communication soft bus framework based on this information. This not only ensures the stability of data communication, but also provides a high degree of adaptability and scalability for data transmission. Through advanced routing planning algorithms, the optimal non-overlapping route can be determined for each target non-Hongmeng OS device, significantly improving the efficiency of data transmission. At the same time, by monitoring the transmission status of the route, the data transmission strategy can be adjusted in real time, the data transmission process can be further optimized, and efficient and stable data transmission can be ensured. Using the Gaussian model and the complementary set empirical mode decomposition model, the data transmission performance feature set and the routing status monitoring feature set can be accurately calculated. Such analysis and monitoring can provide strong support for data communication and ensure the stability and reliability of data transmission. It can not only effectively transform and splice data transmission performance characteristics and routing status monitoring characteristics into vectors, but also optimize and solve them through a two-layer programming model and a non-dominated sorting genetic algorithm, thereby generating a data communication optimization strategy for the target device. This optimization solution mechanism greatly improves the intelligence level of the data processing method, can adaptively optimize the data communication strategy, improve the overall performance of data communication, and use a USB interface adapter to achieve compatibility with non-Hongmeng OS devices and improve the accuracy of device data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0023] Figure 1 This is a schematic diagram of an embodiment of a device data processing method based on a USB interface adapter in an embodiment of the present application;

[0024] Figure 2 This is a schematic diagram of an embodiment of a device data processing apparatus based on a USB interface adapter in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The embodiment of the present application provides a device data processing method based on a USB interface adapter and a related device. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than the content illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 , an embodiment of a device data processing method based on a USB interface adapter in an embodiment of the present application includes:

[0027] Step S101: perform device discovery on a preset device WIFI communication environment through a USB Type-C interface adapter to obtain multiple initial non-Hongmeng OS devices, and perform device authentication and communication protocol configuration on the multiple initial non-Hongmeng OS devices to obtain multiple target non-Hongmeng OS devices;

[0028] It is understandable that the execution subject of the present application can be a device data processing device based on a USB interface adapter, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0029] Specifically, the USB Type-C interface adapter is started and the device communication parameters are initialized. During the initialization process, the USB Type-C interface adapter is configured to support high-speed data transmission and device discovery modes, and communication parameters are set to adapt to different WIFI communication environments. The WIFI environment is scanned through the USB Type-C interface adapter to capture all available WIFI signals in the environment, and those signals belonging to non-Hongmeng OS devices are identified through preset standards. Device authentication is performed on non-Hongmeng OS devices to ensure data transmission security and device communication effectiveness. During the authentication process, each device must pass a series of security checks, including identity authentication and permission granting, to ensure that only legitimate devices can participate in subsequent data communications. The initial devices are screened according to the device authentication results, and qualified devices are selected as target non-Hongmeng OS devices. The screening process is not only based on the success or failure of the authentication, but may also involve factors such as the device's communication capability, data processing capability, and compatibility with the existing network environment to ensure that the selected target device is most suitable for efficient data communication. After the screening is completed, the communication protocols of multiple target non-Hongmeng OS devices are configured separately, including WIFI communication standards, data encryption methods, and transmission protocols, so as to optimize the data transmission efficiency and stability between devices.

[0030] Step S102: perform connection management on multiple target non-Hongmeng OS devices, obtain connection status information of each target non-Hongmeng OS device, and create a first device data communication soft bus framework for the multiple target non-Hongmeng OS devices according to the connection status information;

[0031] Specifically, connection management and connection status analysis are performed on multiple target non-Hongmeng OS devices. The connection status information of each target device is monitored and analyzed, and the communication capability and stability of each device are evaluated. By continuously observing the target non-Hongmeng OS devices, information about the device connection status is obtained in real time, including key parameters such as signal strength, connection disconnection frequency, and data transmission rate. The device connection stability index of each target non-Hongmeng OS device is calculated according to the connection status information. By comprehensively considering multiple factors such as signal strength, connection duration, and data transmission stability, an algorithm model is used to quantitatively evaluate the connection stability of each device. The device connection stability index reflects the reliability of the device during the communication process and provides a key indicator for the creation of the subsequent data communication soft bus framework. Based on the device connection stability index, the initial device data communication soft bus framework of multiple target non-Hongmeng OS devices is created. The framework optimizes the data transmission path and communication strategy based on the connection stability index of each device to ensure efficiency and stability during the data transmission process. The construction of the initial device data communication soft bus framework takes into account the communication protocol compatibility, data transmission requirements, and network environment differences between different devices, and provides a flexible and stable communication platform for data exchange between devices. The initial device data communication soft bus framework is scored for adaptability and constraint conditions are set. The adaptability score evaluates the performance of the soft bus framework by simulating communication performance under different network conditions and data loads, while the constraint setting involves requirements such as data transmission security, communication protocol compatibility, and bandwidth usage, ultimately forming the first device data communication soft bus framework that can meet the data communication needs of multiple target non-Hongmeng OS devices.

[0032] Step S103: perform route planning and allocation for multiple target non-Hongmeng OS devices respectively according to the first device data communication soft bus framework, determine the optimal non-overlapping route for each target non-Hongmeng OS device, and perform data transmission and route transmission status monitoring for multiple target non-Hongmeng OS devices according to the optimal non-overlapping route to obtain a target transmission data set and a route status monitoring data set;

[0033] Specifically, a multipath benefit evaluation is performed on the data communication soft bus framework of the first device to evaluate the performance and benefits of different data transmission paths. During the evaluation process, multipath benefit evaluation data are generated, which reflect the performance of each path in terms of data transmission speed, delay, stability, etc. The path disjointness measurement is performed on the data communication soft bus framework of the first device to obtain path disjointness measurement data to help identify and optimize the data transmission path to avoid potential data congestion and conflict. Based on the multipath benefit evaluation data and the path disjointness measurement data, the routing stability factor is calculated, and the stability and efficiency of data transmission are guaranteed by comprehensively considering the benefits and disjointness characteristics of the paths. The routing stability factor, as a key indicator, provides a decision basis for subsequent routing planning and allocation. The multipath load balancing index is calculated based on the routing stability factor to optimize the distribution of data flows between various paths to ensure the effective use of network resources and the high efficiency of data transmission. Routing planning and allocation are performed for multiple target non-Hongmeng OS devices based on the multipath load balancing index, and the optimal non-overlapping route for each device is determined by an algorithm. Data transmission and routing transmission status monitoring are performed on the target device based on the optimal non-overlapping path to obtain an initial transmission data set and an initial status monitoring data set. In order to extract valuable information from the initial data set and reduce the complexity of data processing, the kernel principal component analysis algorithm is used to reduce the dimension of the initial transmission data set and the initial status monitoring data set, extract key features, remove redundant information, and finally obtain the refined target transmission data set and routing status monitoring data set.

[0034] Step S104: Calculate the data transmission performance characteristics of the target transmission data set through the Gaussian model to obtain a data transmission performance characteristic set, and perform routing state monitoring decomposition and feature mapping on the routing state monitoring data set through the complementary set empirical mode decomposition model to obtain a routing state monitoring characteristic set;

[0035] Specifically, the data transmission performance characteristics of the target transmission data set are calculated by using the Gaussian model. Based on its probability distribution characteristics, the Gaussian model can calculate the distribution and fluctuation of various performance indicators in the data transmission process, thereby forming a first transmission performance feature set, which covers multiple dimensions such as transmission speed, delay, and packet loss rate. The first transmission performance feature set is subjected to feature correlation analysis to reveal the relationship between different performance indicators and help identify which features have the most significant impact on data transmission performance. The analysis is based on statistical principles, and by calculating the correlation coefficient between features, it is determined which features have a high correlation and should be retained in the performance feature set. Based on the analysis results, the features that have the greatest impact on transmission performance are screened out to form a second transmission performance feature set. Feature standardization is performed to convert feature values ​​into values ​​with unified standards, and the feature values ​​are adjusted so that they conform to the standard distribution, thereby eliminating the impact of different dimensions and magnitudes, making the performance analysis results more objective and reliable. The routing state monitoring data set is decomposed by the complementary set empirical mode decomposition model to understand the internal mechanism of routing state changes at a deeper level. Empirical mode decomposition is an analysis method suitable for nonlinear and non-stationary signals. It can decompose complex data sets into several simple mode functions, each of which reflects a basic feature or trend of the data set. By performing feature mapping on the decomposition results, the routing status monitoring feature set is finally obtained. Through the mapping process, the feature set converts various changes in routing status into a set of quantitative indicators, which is convenient for subsequent monitoring and analysis.

[0036] Step S105: performing feature mapping and vector conversion on the data transmission performance feature set and the routing state monitoring feature set to obtain a data transmission performance feature vector and a routing state monitoring feature vector, and performing vector concatenation on the data transmission performance feature vector and the routing state monitoring feature vector to obtain a performance state fusion feature vector;

[0037] Specifically, through feature mapping and vector conversion, the data transmission performance feature set and the routing state monitoring feature set are converted into data transmission performance feature vectors and routing state monitoring feature vectors. Feature weights are calculated for the data transmission performance feature set and the routing state monitoring feature set respectively to identify and emphasize those features that have the greatest impact on system performance. Through weight calculation, the first feature weight data and the second feature weight data are obtained, which correspond to the features of data transmission performance and routing state monitoring respectively. Differentiation processing is performed based on the importance of the features to ensure the accuracy of system analysis and optimization. Based on the feature weight data, the data transmission performance feature vector and the routing state monitoring feature vector are vector weighted to generate weighted transmission performance feature vectors and weighted state monitoring feature vectors. The weighted processing further enhances the accuracy and representativeness of the feature vectors in expressing the performance of data transmission and routing state monitoring. The weighted transmission performance feature vector and the weighted state monitoring feature vector are vector spliced ​​to form a performance state fusion feature vector. The fusion feature vector integrates the key information of data transmission and routing monitoring and provides a comprehensive performance view.

[0038] Step S106: input the data transmission performance characteristic vector, the routing status monitoring characteristic vector and the performance status fusion characteristic vector into a preset two-layer programming model and a non-dominated sorting genetic algorithm to optimize and solve the device data communication strategy, obtain the target device data communication optimization strategy, and optimize the device data communication topology of the first device data communication soft bus framework based on the target device data communication optimization strategy to obtain the second device data communication soft bus framework.

[0039] Specifically, the data transmission performance feature vector, the routing status monitoring feature vector, and the performance status fusion feature vector are input into the preset two-layer programming model. The two-layer programming model consists of two levels, in which the first-layer programming model focuses on preliminary policy decisions, including multiple threshold recurrent networks and an attention mechanism network, aiming to analyze feature vectors from different dimensions and identify key factors in data transmission and routing status. These threshold recurrent networks perform cyclic analysis on feature vectors by setting specific threshold values ​​to determine which features exceed the threshold value and need special attention. The attention mechanism network further enhances the model's ability to identify important features, ensuring that the model can focus on the features that have the greatest impact on system performance. The second-layer programming model uses a decision tree network to make policy decisions based on the output of the first-layer programming model. By analyzing the key features provided by the first layer and the results of the threshold recurrent network, a specific device data communication optimization strategy is formulated. The design of the two-layer programming model not only takes into account the complexity of data communication, but also ensures the efficiency and accuracy of the decision-making process. After obtaining the initial device data communication optimization strategy through the two-layer programming model, the strategy is further optimized and solved through the non-dominated sorting genetic algorithm. The non-dominated sorting genetic algorithm is particularly effective in multi-objective optimization problems. It can iteratively find the optimal or near-optimal solution by simulating natural selection and genetic mechanisms, find a balance between multiple optimization objectives, such as minimizing latency and maximizing data transmission efficiency, and finally determine the target device data communication optimization strategy. According to the target optimization strategy, the first device data communication soft bus framework is topologically optimized to achieve the best efficiency of device data communication. The topological optimization process takes into account factors such as data communication path, network resource allocation and load balancing to ensure that under the new framework, data communication can be carried out with the optimal path and the highest efficiency, and finally a more efficient and optimized second device data communication soft bus framework is obtained.

[0040] In the embodiment of the present application, through the USB Type-C interface adapter, the preset device WIFI communication environment can be quickly scanned, and the surrounding non-Hongmeng OS devices can be accurately discovered. At the same time, the use of advanced device authentication technology ensures the communication security between devices and improves the efficiency and reliability of device authentication. Effectively manage connections, monitor the connection status of each target device in real time, and create a flexible device data communication soft bus framework based on this information. This not only ensures the stability of data communication, but also provides a high degree of adaptability and scalability for data transmission. Through advanced routing planning algorithms, the optimal non-overlapping route can be determined for each target non-Hongmeng OS device, significantly improving the efficiency of data transmission. At the same time, by monitoring the transmission status of the route, the data transmission strategy can be adjusted in real time, the data transmission process can be further optimized, and efficient and stable data transmission can be ensured. Using the Gaussian model and the complementary set empirical mode decomposition model, the data transmission performance feature set and the routing status monitoring feature set can be accurately calculated. Such analysis and monitoring can provide strong support for data communication and ensure the stability and reliability of data transmission. It can not only effectively transform and splice data transmission performance characteristics and routing status monitoring characteristics into vectors, but also optimize and solve them through a two-layer programming model and a non-dominated sorting genetic algorithm, thereby generating a data communication optimization strategy for the target device. This optimization solution mechanism greatly improves the intelligence level of the data processing method, can adaptively optimize the data communication strategy, improve the overall performance of data communication, and use a USB interface adapter to achieve compatibility with non-Hongmeng OS devices and improve the accuracy of device data processing.

[0041] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0042] (1) Start the USB Type-C interface adapter and initialize the device communication parameters;

[0043] (2) Scan the Wi-Fi environment through the USB Type-C interface adapter to obtain the device’s Wi-Fi communication environment;

[0044] (3) Perform device discovery on the preset device WIFI communication environment to obtain multiple initial non-Hongmeng OS devices;

[0045] (4) Perform device authentication on multiple initial non-Hongmeng OS devices to obtain device authentication results for each initial non-Hongmeng OS device;

[0046] (5) Screen multiple initial non-Hongmeng OS devices according to the device authentication results to obtain multiple target non-Hongmeng OS devices, and configure communication protocols for the multiple target non-Hongmeng OS devices respectively.

[0047] Specifically, the USB Type-C interface adapter is started and the device communication parameters are initialized to ensure that the adapter can correctly identify and communicate with the target device in subsequent operations. Initialization includes setting the adapter's working mode, communication frequency range, encryption method, and other necessary communication parameters, so that the adapter can work efficiently in a specific WIFI environment while ensuring the security of data transmission. Scan the WIFI environment through the USB Type-C interface adapter. The adapter collects information such as the SSID, MAC address, and signal strength of surrounding devices by scanning WIFI signals. This information helps the system identify and evaluate the surrounding device communication environment. Perform device discovery on the preset device WIFI communication environment to identify non-Hongmeng OS devices. The device discovery process depends on the information obtained from the previous scan, and the signals of non-Hongmeng OS devices are filtered out through a specific algorithm. For example, non-Hongmeng OS devices are identified based on the device's MAC address or specific service information broadcast by the device. Perform device authentication on multiple initial non-Hongmeng OS devices. Each initial non-Hongmeng OS device needs to pass a series of security authentications before establishing a connection with the adapter, including password verification, digital certificates, or other forms of authentication mechanisms. After successful device authentication, each device will receive a device authentication result. Based on the device authentication results, multiple initial non-Hongmeng OS devices are screened and those devices that have successfully authenticated are selected as target devices. The communication protocol is configured for the screened target devices, including setting the WIFI standard suitable for device communication, data encryption protocol, and other necessary communication parameters to ensure that all selected target devices can exchange data in a secure and efficient network environment. For example, for devices that need to transmit large amounts of data, a highly efficient communication protocol is configured to reduce data transmission time and improve overall communication efficiency.

[0048] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0049] (1) Perform connection management and connection status analysis on multiple target non-Hongmeng OS devices to obtain connection status information of each target non-Hongmeng OS device;

[0050] (2) Calculate the device connection stability index of each target non-Hongmeng OS device based on the connection status information;

[0051] (3) Create an initial device data communication soft bus framework for multiple target non-Hongmeng OS devices based on the device connection stability index;

[0052] (4) Performing adaptability scoring and constraint setting on the initial device data communication soft bus framework, and obtaining the first device data communication soft bus framework of multiple target non-Hongmeng OS devices.

[0053] Specifically, the connection status of each target non-Hongmeng OS device is managed and analyzed to ensure that the connection quality and stability information of each device can be captured in real time. For example, by monitoring the device's signal strength, connection frequency, and data transmission rate and other indicators, detailed information about the current connection status of the device can be obtained. According to the connection status information, the device connection stability index of each target non-Hongmeng OS device is calculated separately. The stability index is obtained by comprehensively considering multiple factors such as the duration of the device connection, the range of signal strength changes, and the frequency of data transmission interruptions. The calculation process may use mathematical models such as weighted average to ensure that the stability index can accurately reflect the true stability of the device connection. For example, a device that is often disconnected due to unstable signals will have a lower stability index, while a device with stable signal strength and long connection duration will have a higher stability index. Based on the device connection stability index, create an initial device data communication soft bus framework for multiple target non-Hongmeng OS devices. The framework optimizes the communication path and resource allocation based on the stability index of the device to ensure the efficiency and stability of data communication. For example, for devices with a higher stability index, they are selected as the main nodes for data transmission, while devices with a lower stability index are assigned to secondary roles or perform data transmission during off-peak hours to reduce the impact on the stability of the entire communication network. The initial device data communication soft bus framework is scored for adaptability and constraint conditions are set to further optimize the performance and adaptability of the framework. The adaptability score evaluates the performance of the soft bus framework under various conditions by simulating different communication environments and data traffic conditions, ensuring that the framework can maintain efficient and stable communication capabilities in different usage scenarios. At the same time, the setting of constraints is intended to ensure security, compatibility, and resource efficiency during data communication. For example, security constraints ensure that all data transmission is encrypted, compatibility constraints ensure that the framework can support the communication needs of different operating systems and devices, and resource efficiency constraints are intended to optimize the use of network bandwidth and device power to avoid unnecessary waste of resources.

[0054] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0055] (1) performing a multipath benefit evaluation on the first device data communication soft bus framework to obtain multipath benefit evaluation data, and performing a path disjointness measurement on the first device data communication soft bus framework to obtain path disjointness measurement data;

[0056] (2) calculating a routing stability factor of the data communication soft bus framework of the first device according to the multipath benefit evaluation data and the path disjointness metric data;

[0057] (3) calculating a multipath load balancing index of the data communication soft bus framework of the first device according to the routing stability factor;

[0058] (4) Plan and allocate routes for multiple target non-Hongmeng OS devices based on the multipath load balancing index, and determine the optimal non-overlapping route for each target non-Hongmeng OS device;

[0059] (5) Perform data transmission and route transmission status monitoring on multiple target non-Hongmeng OS devices according to the optimal non-overlapping path to obtain an initial transmission data set and an initial status monitoring data set;

[0060] (6) The kernel principal component analysis algorithm is used to perform data dimension reduction on the initial transmission data set and the initial status monitoring data set to obtain the target transmission data set and the routing status monitoring data set.

[0061] Specifically, a multipath benefit evaluation is performed on the first device data communication soft bus framework, and the performance of each data transmission path in the framework is analyzed and compared, including key indicators such as bandwidth utilization, transmission delay, and packet loss rate of the path. A path disjointness measurement is performed on the first device data communication soft bus framework to determine the independence between each path during data transmission and reduce the possibility of intersection and conflict. The data of the path disjointness measurement provides important information about path redundancy, which helps to improve the reliability and fault tolerance of data communication. For example, the path disjointness measurement can ensure that even when a path fails, the system can maintain stable communication through other independent paths. Based on the multipath benefit evaluation data and the path disjointness measurement data, the routing stability factor of the first device data communication soft bus framework is calculated to measure the stability of the entire framework under different communication environments. For example, a high routing stability factor means that the paths within the framework are both efficient and relatively independent, and can maintain stable data transmission in a changing network environment. A multipath load balancing index is calculated based on the routing stability factor, and this index is used to evaluate the balanced distribution of data transmission load within the framework. A high multipath load balancing index indicates that the framework can effectively distribute the data transmission load among multiple paths, avoiding single point overload, thereby improving the overall communication efficiency and stability. Based on the multipath load balancing index, routing planning and allocation are performed for multiple target non-Hongmeng OS devices to ensure that the data transmission of each device adopts the optimal non-overlapping path. The routing planning takes into account the efficiency, independence and load balancing of the path, and provides the best data transmission solution for each device. For example, for devices that require high real-time data transmission, the system will give priority to allocating paths with high efficiency and good stability to ensure that data can be transmitted quickly and accurately. Data transmission and routing transmission status monitoring are performed on multiple target non-Hongmeng OS devices according to the optimal non-overlapping path to obtain the initial transmission data set and the initial status monitoring data set. The data set is reduced in dimension by the kernel principal component analysis algorithm, and the key features that affect the data transmission and routing status are effectively extracted to form the target transmission data set and the routing status monitoring data set. This not only reduces the complexity of the data, but also improves the efficiency of analysis and monitoring. For example, the key features extracted by the kernel principal component analysis can quickly identify factors that may affect the stability of data transmission, and then take corresponding optimization measures.

[0062] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0063] (1) Calculating data transmission performance characteristics of the target transmission data set using a Gaussian model to obtain a first transmission performance characteristic set;

[0064] (2) performing a feature correlation analysis on the first transmission performance feature set to obtain a feature correlation analysis result, and performing feature screening on the first transmission performance feature set according to the feature correlation analysis result to obtain a second transmission performance feature set;

[0065] (3) performing feature standardization processing on the second transmission performance feature set to obtain a data transmission performance feature set;

[0066] (4) The routing state monitoring data set is decomposed by using the complementary set empirical mode decomposition model to obtain multiple routing state monitoring decomposition results;

[0067] (5) Perform feature mapping on multiple routing status monitoring decomposition results to obtain a routing status monitoring feature set.

[0068] Specifically, the data transmission performance characteristics of the target transmission data set are calculated by using the Gaussian model. As a statistical analysis tool, the Gaussian model can accurately describe the normal distribution of data transmission characteristics. By analyzing indicators such as delay, packet loss rate, and throughput during the data transmission process, a comprehensive first transmission performance characteristic set is obtained. For example, the Gaussian model can help understand the performance fluctuations of data transmission in different time periods, thereby identifying possible performance bottlenecks. The first transmission performance characteristic set is subjected to feature correlation analysis. The correlation between different features is analyzed by statistical methods to clarify which features have a more significant impact on data transmission performance, and features with low correlation or little impact on performance are removed, thereby obtaining a more refined second transmission performance characteristic set. The second transmission performance characteristic set is subjected to feature standardization to eliminate the impact of different dimensions and ranges on performance analysis. Feature standardization adjusts the feature values ​​so that they are distributed on the same dimension, so that different performance indicators can be compared fairly. The complementary set empirical mode decomposition (CEEMD) model is used to decompose the routing state monitoring data set to obtain the intrinsic dynamics of routing state changes. The CEEMD model can effectively decompose complex time series data into several simple modal functions, each of which reveals an aspect of the change in routing status. For example, by applying the CEEMD model, the main factors affecting the network routing status can be decomposed, such as periodic network congestion, occasional routing failures, etc. Feature mapping is performed on multiple routing status monitoring decomposition results to obtain a routing status monitoring feature set. By converting the decomposition results into quantitative feature values, an operational data foundation is provided for monitoring and analyzing routing status.

[0069] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0070] (1) Performing feature mapping and vector conversion on the data transmission performance feature set and the routing status monitoring feature set to obtain a data transmission performance feature vector and a routing status monitoring feature vector;

[0071] (2) performing feature weight calculation on the data transmission performance feature set to obtain first feature weight data, and performing feature weight calculation on the routing status monitoring feature set to obtain second feature weight data;

[0072] (3) performing vector weighting on the data transmission performance feature vector based on the first feature weight data to obtain a weighted transmission performance feature vector, and performing vector weighting on the routing state monitoring feature vector based on the second feature weight data to obtain a weighted state monitoring feature vector;

[0073] (4) Concatenate the weighted transmission performance feature vector and the weighted status monitoring feature vector to obtain a performance status fusion feature vector.

[0074] Specifically, feature mapping and vector conversion are performed on the data transmission performance feature set and the routing state monitoring feature set, and these features are quantified so that they can be effectively processed in the mathematical model. For example, the data transmission performance feature set may include transmission delay, throughput, packet loss rate, etc., while the routing state monitoring feature set may include the number of route changes, routing failure frequency, etc. Through feature mapping, these features are converted into numerical data transmission performance feature vectors and routing state monitoring feature vectors. Through statistical analysis methods, such as regression analysis based on historical data, feature weights are calculated for these two sets of feature sets to determine the relative importance of each feature in the overall performance evaluation. The first feature weight data and the second feature weight data respectively reflect the weights of the data transmission performance feature and the routing state monitoring feature in the overall evaluation. For example, if the analysis finds that the impact of transmission delay on user experience is greater than that of throughput, the feature weight of transmission delay will be set higher. Based on the feature weight data, the data transmission performance feature vector and the routing state monitoring feature vector are vector-weighted to obtain a weighted transmission performance feature vector and a weighted state monitoring feature vector. Through weighted adjustment, the focus on features that have a greater impact on system performance is enhanced, while the impact of minor features is reduced. The weighted transmission performance feature vector and the weighted status monitoring feature vector are concatenated to form a performance status fusion feature vector. The fusion feature vector provides a comprehensive evaluation basis for the optimization of data communication strategies, integrating all key information about data transmission performance and routing status.

[0075] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0076] (1) Inputting the data transmission performance feature vector, the routing status monitoring feature vector, and the performance status fusion feature vector into a preset two-layer planning model, the two-layer planning model includes a first-layer planning model and a second-layer planning model, the first-layer planning model includes: a first threshold recurrent network, a second threshold recurrent network, a third threshold recurrent network, and an attention mechanism network, and the second-layer planning model includes: a decision tree network;

[0077] (2) Through the two-layer planning model, the optimization strategy decision is made on the data transmission performance feature vector, the routing status monitoring feature vector, and the performance status fusion feature vector to obtain the initial device data communication optimization strategy;

[0078] (3) The initial device data communication optimization strategy is optimized and solved by a non-dominated sorting genetic algorithm to obtain the target device data communication optimization strategy;

[0079] (4) Based on the target device data communication optimization strategy, the device data communication topology of the first device data communication soft bus framework is optimized to obtain the second device data communication soft bus framework.

[0080] Specifically, the data transmission performance feature vector, the routing status monitoring feature vector, and the performance status fusion feature vector are input into the preset two-layer planning model. The first-layer planning model consists of a first threshold recurrent network, a second threshold recurrent network, a third threshold recurrent network, and an attention mechanism network. The threshold recurrent network processes data communication features at different levels, and identifies features that exceed the normal range by setting different threshold values, thereby preliminarily screening out key factors that may affect data communication performance. For example, the first threshold recurrent network may focus on capturing data delay anomalies, the second threshold recurrent network may focus on detecting data packet loss rate, and the third threshold recurrent network may focus on signal strength fluctuations. The attention mechanism network further analyzes these key factors and determines which features have the most significant impact on system performance by learning the correlation between different features. The second-layer planning model uses a decision tree network to make more detailed optimization strategy decisions based on the output of the first-layer model. The decision tree network constructs a series of decision nodes to evaluate different data communication schemes, thereby formulating a data communication optimization strategy that meets both performance requirements and resource constraints. For example, a decision tree network can help select the best data transmission path while ensuring that the path will not affect the communication quality due to overload. The initial device data communication optimization strategy is further optimized by the non-dominated sorting genetic algorithm. The non-dominated sorting genetic algorithm is an optimization algorithm based on the principles of natural selection and genetics. It simulates the mechanisms of selection, crossover and mutation in the biological evolution process to find the optimal solution to the problem. The algorithm can find the best balance between multiple goals, such as minimizing energy consumption and delay while ensuring the data transmission rate. Based on the target device data communication optimization strategy, the first device data communication soft bus framework is topologically optimized. Adjust the data communication path, reconfigure the connection between communication nodes, and optimize the distribution of data flows to form a more efficient and stable second device data communication soft bus framework.

[0081] The above describes the device data processing method based on the USB interface adapter in the embodiment of the present application. The following describes the device data processing apparatus based on the USB interface adapter in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the device data processing device based on the USB interface adapter includes:

[0082] Configuration module 201, used to perform device discovery on a preset device WIFI communication environment through a USB Type-C interface adapter to obtain multiple initial non-Hongmeng OS devices, and perform device authentication and communication protocol configuration on the multiple initial non-Hongmeng OS devices to obtain multiple target non-Hongmeng OS devices;

[0083] A creation module 202 is used to manage the connection of multiple target non-Hongmeng OS devices, obtain the connection status information of each target non-Hongmeng OS device, and create a first device data communication soft bus framework for the multiple target non-Hongmeng OS devices according to the connection status information;

[0084] An allocation module 203 is used to plan and allocate routes for multiple target non-Hongmeng OS devices respectively according to the first device data communication soft bus framework, determine the optimal non-overlapping route for each target non-Hongmeng OS device, and perform data transmission and route transmission status monitoring for multiple target non-Hongmeng OS devices according to the optimal non-overlapping route to obtain a target transmission data set and a route status monitoring data set;

[0085] The calculation module 204 is used to calculate the data transmission performance characteristics of the target transmission data set through the Gaussian model to obtain a data transmission performance characteristic set, and perform routing state monitoring decomposition and feature mapping on the routing state monitoring data set through the complementary set empirical mode decomposition model to obtain a routing state monitoring characteristic set;

[0086] The conversion module 205 is used to perform feature mapping and vector conversion on the data transmission performance feature set and the routing state monitoring feature set to obtain a data transmission performance feature vector and a routing state monitoring feature vector, and perform vector concatenation on the data transmission performance feature vector and the routing state monitoring feature vector to obtain a performance state fusion feature vector;

[0087] The optimization module 206 is used to input the data transmission performance characteristic vector, the routing status monitoring characteristic vector and the performance status fusion characteristic vector into a preset two-layer programming model and a non-dominated sorting genetic algorithm to optimize and solve the device data communication strategy, obtain the target device data communication optimization strategy, and optimize the device data communication topology of the first device data communication soft bus framework based on the target device data communication optimization strategy to obtain the second device data communication soft bus framework.

[0088] Through the collaboration of the above components, the USB Type-C interface adapter can quickly scan the preset device WIFI communication environment and accurately discover the surrounding non-Hongmeng OS devices. At the same time, the use of advanced device authentication technology ensures the communication security between devices and improves the efficiency and reliability of device authentication. Effectively manage connections, monitor the connection status of each target device in real time, and create a flexible device data communication soft bus framework based on this information. This not only ensures the stability of data communication, but also provides high adaptability and scalability for data transmission. Through advanced routing planning algorithms, the optimal non-overlapping route can be determined for each target non-Hongmeng OS device, significantly improving the efficiency of data transmission. At the same time, by monitoring the transmission status of the route, the data transmission strategy can be adjusted in real time, the data transmission process can be further optimized, and efficient and stable data transmission can be ensured. Using the Gaussian model and the complementary set empirical mode decomposition model, the data transmission performance feature set and the routing status monitoring feature set can be accurately calculated. Such analysis and monitoring can provide strong support for data communication and ensure the stability and reliability of data transmission. It can not only effectively transform and splice data transmission performance characteristics and routing status monitoring characteristics into vectors, but also optimize and solve them through a two-layer programming model and a non-dominated sorting genetic algorithm, thereby generating a data communication optimization strategy for the target device. This optimization solution mechanism greatly improves the intelligence level of the data processing method, can adaptively optimize the data communication strategy, improve the overall performance of data communication, and use a USB interface adapter to achieve compatibility with non-Hongmeng OS devices and improve the accuracy of device data processing.

[0089] The present application also provides a device data processing device based on a USB interface adapter, the device data processing device based on a USB interface adapter includes a memory and a processor, the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the steps of the device data processing method based on the USB interface adapter in the above-mentioned embodiments.

[0090] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the device data processing method based on the USB interface adapter.

[0091] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0092] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0093] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A device data processing method based on a USB interface adapter, characterized in that: The device data processing method based on the USB interface adapter comprises: Perform device discovery on the preset device WIFI communication environment through the USB Type-C interface adapter to obtain multiple initial non-Hongmeng OS devices, and perform device authentication and communication protocol configuration on the multiple initial non-Hongmeng OS devices to obtain multiple target non-Hongmeng OS devices; Perform connection management on the multiple target non-Hongmeng OS devices, obtain connection status information of each target non-Hongmeng OS device, and create a first device data communication soft bus framework for the multiple target non-Hongmeng OS devices according to the connection status information; According to the first device data communication soft bus framework, respectively plan and allocate routes for the multiple target non-Hongmeng OS devices, determine the optimal non-overlapping route for each target non-Hongmeng OS device, and perform data transmission and route transmission status monitoring for the multiple target non-Hongmeng OS devices according to the optimal non-overlapping route to obtain a target transmission data set and a route status monitoring data set; The data transmission performance characteristics of the target transmission data set are calculated by using a Gaussian model to obtain a data transmission performance characteristic set, and the routing state monitoring data set is decomposed and characterized by using a complementary set empirical mode decomposition model to obtain a routing state monitoring characteristic set; Performing feature mapping and vector conversion on the data transmission performance feature set and the routing state monitoring feature set to obtain a data transmission performance feature vector and a routing state monitoring feature vector, and performing vector concatenation on the data transmission performance feature vector and the routing state monitoring feature vector to obtain a performance state fusion feature vector; The data transmission performance characteristic vector, the routing status monitoring characteristic vector and the performance status fusion characteristic vector are input into a preset two-layer programming model and a non-dominated sorting genetic algorithm to optimize and solve the device data communication strategy, and a target device data communication optimization strategy is obtained. Based on the target device data communication optimization strategy, the device data communication topology of the first device data communication soft bus framework is optimized to obtain a second device data communication soft bus framework; specifically comprising: inputting the data transmission performance characteristic vector, the routing status monitoring characteristic vector and the performance status fusion characteristic vector into a preset two-layer programming model, the two-layer programming model includes a first-layer planning model and a second-layer planning model, and the first-layer planning model includes : a first threshold recurrent network, a second threshold recurrent network, a third threshold recurrent network and an attention mechanism network, wherein the second-layer planning model includes: a decision tree network; the data transmission performance feature vector, the routing status monitoring feature vector and the performance status fusion feature vector are optimized through the two-layer planning model to obtain an initial device data communication optimization strategy; the initial device data communication optimization strategy is optimized and solved through the non-dominated sorting genetic algorithm to obtain a target device data communication optimization strategy; the first device data communication soft bus framework is optimized for device data communication topology based on the target device data communication optimization strategy to obtain a second device data communication soft bus framework.

2. The device data processing method based on the USB interface adapter according to claim 1, characterized in that: The device discovery is performed on the preset device WIFI communication environment through the USB Type-C interface adapter to obtain multiple initial non-Hongmeng OS devices, and the device authentication and communication protocol configuration are performed on the multiple initial non-Hongmeng OS devices to obtain multiple target non-Hongmeng OS devices, including: Start the USB Type-C interface adapter and initialize the device communication parameters; Scan the WIFI environment through the USB Type-C interface adapter to obtain the device WIFI communication environment; Perform device discovery on the preset device WIFI communication environment to obtain multiple initial non-Hongmeng OS devices; Perform device authentication on multiple initial non-Hongmeng OS devices to obtain device authentication results for each initial non-Hongmeng OS device; According to the device authentication results, the multiple initial non-Hongmeng OS devices are screened to obtain multiple target non-Hongmeng OS devices, and the communication protocols of the multiple target non-Hongmeng OS devices are configured respectively.

3. The device data processing method based on the USB interface adapter according to claim 1, characterized in that: The method of performing connection management on the multiple target non-Hongmeng OS devices, obtaining connection status information of each target non-Hongmeng OS device, and creating a first device data communication soft bus framework for the multiple target non-Hongmeng OS devices according to the connection status information includes: Perform connection management and connection status analysis on the multiple target non-Hongmeng OS devices to obtain connection status information of each target non-Hongmeng OS device; Calculate the device connection stability index of each target non-Hongmeng OS device according to the connection status information; Creating an initial device data communication soft bus framework for the multiple target non-Hongmeng OS devices according to the device connection stability index; Perform adaptability scoring and constraint setting on the initial device data communication soft bus framework to obtain the first device data communication soft bus framework of the multiple target non-Hongmeng OS devices.

4. The device data processing method based on the USB interface adapter according to claim 1, characterized in that: The method of performing route planning and allocation for the multiple target non-Hongmeng OS devices respectively according to the first device data communication soft bus framework, determining the optimal non-overlapping route for each target non-Hongmeng OS device, and performing data transmission and route transmission status monitoring for the multiple target non-Hongmeng OS devices according to the optimal non-overlapping route, and obtaining a target transmission data set and a route status monitoring data set, including: Performing a multipath benefit evaluation on the first device data communication soft bus framework to obtain multipath benefit evaluation data, and performing a path non-intersection measurement on the first device data communication soft bus framework to obtain path non-intersection measurement data; Calculating a routing stability factor of the first device data communication soft bus framework according to the multipath benefit evaluation data and the path disjointness metric data; Calculate a multipath load balancing index of the first device data communication soft bus framework according to the routing stability factor; Performing routing planning and allocation for the multiple target non-Hongmeng OS devices according to the multipath load balancing index, and determining an optimal non-overlapping route for each target non-Hongmeng OS device; Perform data transmission and route transmission status monitoring on the multiple target non-Hongmeng OS devices according to the optimal non-overlapping path to obtain an initial transmission data set and an initial status monitoring data set; The initial transmission data set and the initial status monitoring data set are respectively processed by data dimension reduction using a kernel principal component analysis algorithm to obtain a target transmission data set and a routing status monitoring data set.

5. The device data processing method based on the USB interface adapter according to claim 1, characterized in that: The data transmission performance characteristic calculation is performed on the target transmission data set by using a Gaussian model to obtain a data transmission performance characteristic set, and the routing state monitoring data set is subjected to routing state monitoring decomposition and feature mapping by using a complementary set empirical mode decomposition model to obtain a routing state monitoring characteristic set, including: Calculating data transmission performance characteristics of the target transmission data set by using a Gaussian model to obtain a first transmission performance characteristic set; Performing a feature correlation analysis on the first transmission performance feature set to obtain a feature correlation analysis result, and performing feature screening on the first transmission performance feature set according to the feature correlation analysis result to obtain a second transmission performance feature set; performing feature standardization processing on the second transmission performance feature set to obtain a data transmission performance feature set; Performing routing state monitoring decomposition on the routing state monitoring data set through a complementary set empirical mode decomposition model to obtain a plurality of routing state monitoring decomposition results; Feature mapping is performed on the multiple routing status monitoring decomposition results to obtain a routing status monitoring feature set.

6. The device data processing method based on the USB interface adapter according to claim 5, characterized in that: The performing feature mapping and vector conversion on the data transmission performance feature set and the routing state monitoring feature set to obtain a data transmission performance feature vector and a routing state monitoring feature vector, and performing vector concatenation on the data transmission performance feature vector and the routing state monitoring feature vector to obtain a performance state fusion feature vector, includes: Performing feature mapping and vector conversion on the data transmission performance feature set and the routing state monitoring feature set to obtain a data transmission performance feature vector and a routing state monitoring feature vector; Performing feature weight calculation on the data transmission performance feature set to obtain first feature weight data, and performing feature weight calculation on the routing state monitoring feature set to obtain second feature weight data; Performing vector weighting on the data transmission performance feature vector based on the first feature weight data to obtain a weighted transmission performance feature vector, and performing vector weighting on the routing state monitoring feature vector based on the second feature weight data to obtain a weighted state monitoring feature vector; The weighted transmission performance feature vector and the weighted state monitoring feature vector are concatenated to obtain a performance state fusion feature vector.

7. A device data processing device based on a USB interface adapter, characterized in that: The device data processing device based on the USB interface adapter comprises: A configuration module is used to perform device discovery on a preset device WIFI communication environment through a USB Type-C interface adapter to obtain multiple initial non-Hongmeng OS devices, and perform device authentication and communication protocol configuration on the multiple initial non-Hongmeng OS devices to obtain multiple target non-Hongmeng OS devices; A creation module is used to perform connection management on the multiple target non-Hongmeng OS devices, obtain connection status information of each target non-Hongmeng OS device, and create a first device data communication soft bus framework for the multiple target non-Hongmeng OS devices according to the connection status information; an allocation module, configured to respectively plan and allocate routes for the multiple target non-Hongmeng OS devices according to the first device data communication soft bus framework, determine the optimal non-overlapping route for each target non-Hongmeng OS device, and perform data transmission and route transmission status monitoring for the multiple target non-Hongmeng OS devices according to the optimal non-overlapping route, to obtain a target transmission data set and a route status monitoring data set; A calculation module, configured to calculate the data transmission performance characteristics of the target transmission data set through a Gaussian model to obtain a data transmission performance characteristic set, and perform routing state monitoring decomposition and feature mapping on the routing state monitoring data set through a complementary set empirical mode decomposition model to obtain a routing state monitoring characteristic set; a conversion module, configured to perform feature mapping and vector conversion on the data transmission performance feature set and the routing state monitoring feature set to obtain a data transmission performance feature vector and a routing state monitoring feature vector, and to perform vector concatenation on the data transmission performance feature vector and the routing state monitoring feature vector to obtain a performance state fusion feature vector; The optimization module is used to input the data transmission performance characteristic vector, the routing state monitoring characteristic vector and the performance state fusion characteristic vector into a preset two-layer programming model and a non-dominated sorting genetic algorithm to optimize and solve the device data communication strategy, obtain the target device data communication optimization strategy, and optimize the device data communication topology of the first device data communication soft bus framework based on the target device data communication optimization strategy to obtain the second device data communication soft bus framework; specifically includes: inputting the data transmission performance characteristic vector, the routing state monitoring characteristic vector and the performance state fusion characteristic vector into a preset two-layer programming model, the two-layer programming model includes a first-layer programming model and a second-layer programming model, the first-layer programming The model includes: a first threshold recurrent network, a second threshold recurrent network, a third threshold recurrent network and an attention mechanism network, and the second-layer planning model includes: a decision tree network; the data transmission performance feature vector, the routing status monitoring feature vector and the performance status fusion feature vector are optimized by the two-layer planning model to obtain an initial device data communication optimization strategy; the initial device data communication optimization strategy is optimized and solved by the non-dominated sorting genetic algorithm to obtain a target device data communication optimization strategy; the first device data communication soft bus framework is optimized for device data communication topology based on the target device data communication optimization strategy to obtain a second device data communication soft bus framework.

8. A device data processing device based on a USB interface adapter, characterized in that: The device data processing device based on the USB interface adapter comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the device data processing device based on the USB interface adapter to execute the device data processing method based on the USB interface adapter according to any one of claims 1-6.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the device data processing method based on the USB interface adapter as described in any one of claims 1 to 6 is implemented.

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