Internet of vehicles resource dynamic scheduling method and system based on double-layer perception

Abstract

The invention discloses an Internet of Vehicles resource dynamic scheduling method and system based on double-layer perception, and the method comprises the steps: firstly obtaining signal strength indication historical data and load historical data, sensing a network state in real time through combining a signal strength threshold value and a load threshold value, and carrying out the service quality evaluation; then, according to a service quality evaluation result, hierarchical management of resources is realized by adopting a multi-level resource tolerance threshold value; a load prediction mechanism based on moving average is adopted, a load trend is predicted by analyzing historical load data, and a task allocation strategy is dynamically adjusted; finally, the tasks are distributed to corresponding computing nodes according to task scheduling results, and after the tasks are processed, execution results are fed back to the state sensing process to be used for optimizing subsequent scheduling decisions.

Description

Technical Field

[0001] The present invention relates to the technical field of task scheduling in an Internet of Vehicles (IoV), and in particular to a method and system for dynamic resource scheduling in an IoV based on dual-layer perception. Background Art

[0002] With the rapid development of Internet of Vehicles (IoV) technology, the deep integration of edge computing and IoV applications has brought new technical challenges to intelligent driving. Patent CN115190179B points out that under a service-oriented architecture (SOA), the various electronic control units of the IoV system are functionally decomposed through the service dimension to achieve efficient acquisition of vehicle capabilities. However, with the in-depth development of intelligent driving and IoV technology, service requests have become characterized by high traffic and high concurrency, placing higher demands on the real-time performance and reliability of the system.

[0003] Patent CN117997906B proposes a dynamic load monitoring model that uses feature extraction and state recognition to achieve real-time assessment of system load. This approach provides a new approach to resource scheduling, but there is still room for improvement in ensuring quality of service in highly dynamic scenarios. Meanwhile, patent CN118567810A introduces a service quality perception mechanism based on RSSI signal quality, optimizing resource allocation strategies through dynamic signal quality assessment.

[0004] Existing IoV task scheduling technologies primarily fall into two categories: fixed threshold-based scheduling strategies and load balancing-based scheduling. Traditional Task Scheduling Management (TSM) systems employ static scheduling strategies, making them difficult to adapt to the dynamic changes in network quality and computing load in IoV environments. This can lead to problems such as excessive task processing latency and low resource utilization in practical applications.

[0005] To enhance service quality assurance, researchers have proposed various improvements. A typical approach is to incorporate Quality of Service (QoS)-aware mechanisms into scheduling decisions. These methods optimize task allocation strategies by monitoring network signal strength (RSSI) and load status in real time. However, existing QoS-aware methods often only consider a single-dimensional QoS metric and lack the ability to comprehensively evaluate and dynamically adjust multi-dimensional QoS indicators.

[0006] Recent research has shown that combining historical data analysis with prediction techniques can significantly improve the accuracy of scheduling decisions. For example, by establishing historical RSSI and load records, combined with signal strength and load thresholds, current communication quality can be more accurately assessed. This approach provides new insights for improving the intelligence of task scheduling. In particular, the quality of service negotiation mechanism proposed in CN115190179B implements more flexible resource allocation strategies through dynamic negotiation between the network controller and service providers.

[0007] Judging from the experimental evaluation results, the dynamic load balancing model proposed in CN117997906B has achieved significant results in improving resource utilization, but there is still room for improvement in terms of latency control and service quality assurance. In particular, when dealing with task scheduling problems in the dynamic environment of the Internet of Vehicles, existing technologies are difficult to simultaneously meet multiple goals such as low latency, high resource utilization, and service quality assurance. Therefore, in order to solve the above problems, there is an urgent need for a task scheduling algorithm that can comprehensively consider multi-dimensional QoS indicators and has dynamic self-adaptation capabilities. The algorithm should be able to automatically adjust the scheduling strategy according to the real-time network status and load conditions to achieve optimal resource allocation, thereby improving the service quality in the edge computing environment of the Internet of Vehicles.

[0008] Through in-depth analysis of existing Internet of Vehicles edge computing task scheduling technologies and comparison with patent literature, we found that existing technologies have the following key defects:

[0009] First, traditional task scheduling and management systems employ static scheduling strategies and lack real-time awareness of network service quality. Patent CN115190179B notes that existing distributed resource allocation mechanisms struggle to guarantee quality of service for global traffic flows, particularly when handling high-volume, highly concurrent tasks. This fixed-threshold scheduling scheme cannot adapt to the dynamic changes in network status in connected vehicle environments, resulting in inefficient task scheduling.

[0010] Secondly, existing QoS perception methods often focus on a single-dimensional quality of service (QoS) metric. Patent CN117997906B proposes a dynamic load monitoring model, but this model focuses solely on system load status and lacks a comprehensive assessment of network communication quality. This single-dimensional assessment approach fails to fully reflect the complex network conditions found in connected vehicles (IoV) environments, and can easily lead to irrational resource allocation and reduced service quality. In practical applications, this simplistic assessment mechanism often increases task processing latency and fails to meet the stringent real-time requirements of IoV applications.

[0011] Third, existing technologies lack effective dynamic resource allocation strategies. While patent CN118567810A proposes a service quality perception mechanism based on RSSI signal quality, it still struggles to simultaneously address multiple objectives, such as low latency, high resource utilization, and guaranteed service quality, when addressing task scheduling in the dynamic environment of the Internet of Vehicles. This static resource allocation approach cannot be adjusted in real time based on network status and load conditions, resulting in a decline in overall system performance.

[0012] Fourth, existing systems generally lack the ability to predict future load conditions or leverage historical data. While some systems have introduced simple load monitoring capabilities, they lack the ability to deeply analyze and predict historical data. Consequently, the system cannot proactively allocate resources and can only passively respond to current network state changes. This passive scheduling strategy often results in significant response delays and low resource utilization when faced with the rapidly changing computing demands of the connected vehicle environment.

[0013] Fifth, existing technologies have significant deficiencies in ensuring service quality. While patent CN115190179B proposes a service quality negotiation mechanism, it lacks the ability to comprehensively evaluate and dynamically adjust multi-dimensional service quality indicators. This results in the inability of existing technologies to adjust scheduling strategies in rapidly changing network conditions, significantly degrading system performance.

[0014] During experimental evaluations, these technical shortcomings led to the following specific issues: generally high scheduling delays, suboptimal resource utilization, and difficulty ensuring effective quality of service. In particular, in scenarios with rapidly changing network conditions, existing technologies were unable to adjust scheduling strategies in a timely manner, resulting in significant degradation in system performance. These issues severely hampered the further development of edge computing applications in the Internet of Vehicles (IoV). A new task scheduling algorithm was urgently needed to address these technical challenges. Summary of the Invention

[0015] The present invention aims to address the deficiencies of the existing technology and propose a method and system for dynamic scheduling of Internet of Vehicles resources based on dual-layer perception.

[0016] The objective of the present invention is achieved through the following technical solution: a method for dynamic scheduling of Internet of Vehicles resources based on dual-layer perception, the method comprising:

[0017] S1: Status perception: Acquire historical signal strength indication data and load data, combine signal strength thresholds and load thresholds to perceive network status in real time and perform service quality assessment;

[0018] S2: Task scheduling decision: Based on the service quality evaluation results, multi-level resource tolerance thresholds are used to implement hierarchical resource management; the multi-level resource tolerance thresholds include: signal strength upper and lower thresholds (used to divide strong signal intervals, medium signal intervals, and weak signal intervals), load thresholds (including light load thresholds and heavy load thresholds), and resource tolerance thresholds set for each scheduling mode (local resource scheduling, edge node scheduling, and cloud resource scheduling); a load prediction mechanism based on moving average is used to predict load trends by analyzing historical load data and dynamically adjust task allocation strategies.

[0019] S3: Resource allocation execution: Assign tasks to corresponding computing nodes based on task scheduling results. When the task is completed, the execution results are fed back to the state perception process to optimize subsequent scheduling decisions.

[0020] Furthermore, in S1, the network communication quality is evaluated by maintaining a configurable number of time windows to record historical data of signal strength indications, combined with a preset signal strength threshold; the preset signal strength threshold is dynamically adjusted based on the historical data.

[0021] Furthermore, in S2, the hierarchical management of resources is specifically as follows:

[0022] When the network status is greater than the upper threshold of signal strength and the system load is less than the load threshold, tasks are preferentially assigned to local resources;

[0023] When the network status is greater than the upper threshold of signal strength but the system load is greater than or equal to the load threshold, the task is assigned to the edge node with relatively light load;

[0024] When the network status is less than or equal to the upper threshold of signal strength and greater than the lower threshold of signal strength, the nearest edge node is selected for processing;

[0025] When the network status is less than or equal to the lower threshold of signal strength, or when the load of all available edge nodes is greater than the load threshold, the task is assigned to the cloud for execution.

[0026] Furthermore, in S2, the task allocation strategy is dynamically adjusted according to the load prediction results: when the predicted load state is less than the light load threshold, new tasks are received first; when the predicted load state is greater than the light load threshold and less than the heavy load threshold, tasks are selectively received. When the predicted load trend exceeds the preset heavy load threshold, the task allocation strategy is triggered in advance to allocate new tasks to nodes with relatively light loads.

[0027] Furthermore, in S3, the scheduling results of task allocation are evaluated based on performance indicators, resource status and communication quality, and the evaluation weights are dynamically adjusted based on the importance of each indicator in different scenarios; the performance indicators include execution delay and resource utilization efficiency; the task scheduling strategy is dynamically optimized based on the comprehensive evaluation results.

[0028] Furthermore, based on the execution feedback results of task allocation in S3, the signal strength threshold, light load threshold and heavy load threshold are dynamically adjusted; the load levels are divided based on the light load threshold and heavy load threshold, load balancing is performed, and resource allocation is achieved through task migration.

[0029] Furthermore, in S3, the task allocation strategy is adaptively adjusted. The specific process is as follows:

[0030] (1) Continuously monitor various performance indicators;

[0031] (2) Analyze the deviation between performance indicators and target values;

[0032] (3) Generate optimization strategies based on deviations;

[0033] (4) Implement optimization strategies and evaluate their effectiveness;

[0034] (5) Update the decision model based on the evaluation results.

[0035] Furthermore, the network status, resource load and task characteristics are comprehensively analyzed to dynamically generate the optimal control strategy. When delay anomalies are detected, the delay is controlled through task reallocation or resource adjustment.

[0036] On the other hand, the present invention also provides a vehicle network resource dynamic scheduling system based on dual-layer perception, the system comprising:

[0037] The perception layer is used to obtain historical data on signal strength indication and load, and to perceive the network status in real time based on the signal strength threshold and load threshold to evaluate the service quality.

[0038] The decision-making layer implements hierarchical resource management based on service quality assessment results using multi-level resource tolerance thresholds. It also uses a moving average-based load prediction mechanism to analyze historical load data to predict load trends and dynamically adjust task allocation strategies.

[0039] The execution layer is used to assign tasks to corresponding computing nodes based on the task scheduling results. When the task is completed, the execution results are fed back to the perception layer to optimize subsequent scheduling decisions.

[0040] Beneficial effects of the present invention:

[0041] 1. In terms of scheduling delay, this invention significantly reduces task processing delay by introducing a dual-layer perception mechanism and a predictive load balancing strategy. The performance advantage is even more obvious in large-scale task scenarios.

[0042] 2. In terms of resource utilization efficiency, this invention significantly improves resource utilization by introducing a two-tiered perception mechanism based on historical data and a dynamic resource allocation strategy. In particular, with regard to load balancing, as the task scale gradually increases, the system maintains a balanced utilization of local and cloud resources, significantly outperforming the unbalanced allocation of existing technologies.

[0043] 3. In terms of service quality assurance, this invention incorporates a dual historical data monitoring mechanism, enabling the system to promptly detect network status changes and make adjustments. Experimental verification demonstrates that under dynamic load conditions, this invention significantly improves both service quality stability and SLA compliance.

[0044] 4. In terms of system architecture design, while this invention adds a necessary state monitoring mechanism to achieve more accurate scheduling decisions, this design also yields superior system performance. Compared to existing solutions, this invention significantly reduces task processing latency and significantly improves resource utilization efficiency through more comprehensive state perception and decision optimization.

[0045] 5. In terms of environmental adaptability, the present invention utilizes innovative designs such as predictive load balancing, enabling the system to better cope with the dynamic changes in connected vehicle scenarios. Experimental results show that the present invention's task scheduling performance remains stable even in scenarios with rapidly changing network conditions, whereas existing technologies often experience significant performance degradation in similar scenarios.

[0046] 6. Regarding system scalability, this invention employs a modular design, enabling the system to easily expand with new service quality evaluation metrics and resource scheduling strategies. This flexible architecture allows the system to better adapt to the evolving needs of future connected vehicle applications. In contrast, existing technologies generally employ fixed architectures, making them difficult to adapt to new application scenarios and changing requirements.

[0047] Comprehensive experimental results demonstrate that the present invention achieves significant improvements in various dimensions, including latency control, resource utilization optimization, and service quality assurance. In particular, the present invention demonstrates stronger performance advantages when handling large-scale tasks. These innovative designs and optimization measures enable the present invention to provide more efficient and reliable task scheduling services for connected vehicle applications, providing strong support for the further development of connected vehicle technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 It is a schematic flow chart of the method of the present invention.

[0050] Figure 2 It is a schematic diagram of the service quality perception and resource dynamic scheduling mechanism of the present invention.

[0051] Figure 3 Schematic diagram of signal quality evaluation according to the present invention.

[0052] Figure 4 Schematic diagram of load status monitoring of the present invention.

[0053] Figure 5 It is a schematic diagram of QoS comprehensive evaluation of the present invention.

[0054] Figure 6 Schematic diagram of task allocation decision-making in the present invention.

[0055] Figure 7 This is a schematic diagram of resource utilization optimization according to the present invention.

[0056] Figure 8 It is a schematic diagram of the adaptive adjustment of the scheduling strategy of the present invention.

[0057] Figure 9 Schematic diagram of delay control of the present invention.

[0058] Figure 10 This is a schematic diagram of improving resource utilization efficiency according to the present invention.

[0059] Figure 11 Schematic diagram of service quality evaluation of the present invention.

[0060] Figure 12 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described below with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are only used to explain the present invention and are not intended to limit the present invention.

[0062] like Figure 1 As shown, the present invention provides a method for dynamic scheduling of Internet of Vehicles resources based on dual-layer perception, which is specifically implemented as follows:

[0063] 1. Description of the overall technical solution:

[0064] To address the task scheduling problem in the connected vehicle environment, this paper implements an adaptive task scheduling strategy through a dual evaluation mechanism based on RSSI signal strength and system load. Compared with existing technologies, this paper significantly improves the flexibility and accuracy of scheduling by introducing a dual-threshold decision-making mechanism. The overall workflow is as follows:

[0065] During system initialization, baseline thresholds for signal quality assessment are set: RSSI values greater than -70dBm are defined as strong signals, between -85dBm and -70dBm as medium signals, and below -85dBm as weak signals. This dual-threshold classification mechanism provides an important basis for subsequent resource allocation decisions.

[0066] During the state perception phase, the present invention monitors the network status in real time through a signal quality assessment module. When the signal quality is detected to be strong and the system load is low, tasks are prioritized for local resource processing. When the signal quality is in the medium range, the present invention selects the nearest edge node for task processing. When the signal quality is poor, tasks are assigned to the cloud for execution.

[0067] During the task scheduling decision-making phase, the present invention combines signal strength assessment results with load forecasts to dynamically select the optimal resource allocation solution. Unlike traditional methods that rely solely on fixed thresholds, the present invention utilizes a load forecasting mechanism based on a moving average. By analyzing historical load data, it predicts system load trends, enabling more accurate resource allocation decisions.

[0068] During the resource allocation execution phase, the present invention assigns tasks to the corresponding compute nodes based on the evaluation results. Once a task is processed, the execution results are fed back to the state perception module to optimize subsequent scheduling decisions. This closed-loop feedback mechanism enables the system to continuously optimize scheduling strategies and continuously improve service quality.

[0069] Through this complete workflow, the present invention achieves precise control over task scheduling in the dynamic environment of the Internet of Vehicles (IoV). Compared to existing technologies, this invention significantly improves the flexibility and efficiency of task scheduling through a dual-threshold evaluation mechanism and dynamic load prediction, providing a reliable task scheduling solution for IoV applications.

[0070] The specific description of each stage is as follows: The core of the present invention is to build a complete set of service quality perception and resource dynamic scheduling mechanism. Figure 2 As shown in the figure, by introducing three key technical innovations: QoS awareness, dynamic resource allocation, and load prediction, the flexibility and efficiency of task scheduling in the Internet of Vehicles environment have been significantly improved.

[0071] The first innovation is the service quality awareness mechanism. The system maintains a dynamically configurable time window to record historical received signal strength indicator (RSSI) and load data. Combined with signal strength and load thresholds, it achieves real-time awareness of network status. This dynamic assessment based on historical data more accurately reflects network status changes than traditional methods that rely solely on static judgments based on single sampling.

[0072] The second innovation is the dynamic resource allocation mechanism. Based on the service quality assessment results, this invention implements hierarchical resource management using multi-level resource tolerance thresholds. When the network is good and the system load is low, tasks are preferentially assigned to local resources; when the network is average, tasks are processed by the nearest edge node; when the network is poor, tasks are assigned to the cloud for execution. This dynamic allocation strategy based on multi-level thresholds significantly improves resource utilization efficiency.

[0073] The third innovation is a load prediction mechanism based on historical data. This system accurately predicts future load conditions by analyzing data trends in historical load records. When it predicts that the system load may exceed a preset threshold, it proactively adjusts the task allocation strategy to avoid resource overload. This forward-looking load balancing mechanism effectively improves the stability and reliability of this system.

[0074] In terms of performance optimization, this paper establishes a comprehensive evaluation system to assess task scheduling effectiveness across multiple dimensions, including latency, resource utilization, and quality of service. These evaluation results are fed back to the service quality perception module in real time to optimize subsequent scheduling decisions, forming a closed-loop performance optimization mechanism.

[0075] Through these innovative designs, the present invention significantly improves task scheduling performance in connected vehicle environments. Compared to existing technologies, this invention utilizes a dynamic perception mechanism and a multi-level decision-making strategy to significantly improve resource utilization efficiency while maintaining low scheduling latency. Experimental results demonstrate that this solution achieves significant improvements in latency control, resource utilization optimization, and service quality assurance, providing a reliable task scheduling solution for connected vehicle applications.

[0076] 2. Specific implementation of QoS awareness mechanism:

[0077] 2.1 RSSI signal quality assessment:

[0078] This paper designs a dynamic network quality assessment mechanism based on the received signal strength indicator (RSSI). Compared with the traditional method that relies only on static judgment of a single sampling, this paper innovatively introduces a dynamic assessment strategy based on historical data. By maintaining a configurable number of time windows to record RSSI historical data, combined with a preset signal strength threshold, an accurate assessment of network communication quality is achieved. The specific assessment process is as follows: Figure 3 shown.

[0079] During the signal data collection phase, the system monitors and records RSSI signal strength values in real time. To avoid misjudgments caused by transient signal fluctuations, the system uses a sliding time window mechanism to maintain historical data records. This historical data-based evaluation solution effectively filters signal noise and provides a more reliable basis for determining network status.

[0080] Regarding signal quality grading, this invention has designed a three-level signal strength classification mechanism. By setting two configurable signal strength thresholds (upper and lower thresholds), the network status is dynamically divided into strong signal ranges, medium signal ranges, and weak signal ranges. This multi-level classification scheme provides a more detailed reference for task scheduling decisions.

[0081] The signal quality classification can be expressed as:

[0082]

[0083] Another innovation of this invention is the introduction of a dynamic threshold adjustment mechanism. The system dynamically adjusts the signal strength threshold based on historical data analysis, allowing the evaluation results to better adapt to changes in the actual network environment. This adaptive mechanism significantly improves the system's responsiveness to changes in network status.

[0084] In actual applications, the system dynamically adjusts its task allocation strategy based on signal quality assessment results: when a strong signal range is detected, tasks are prioritized for local processing; when the signal quality is in the medium range, tasks are assigned to the nearest edge node for execution; when the signal quality is in the weak range, tasks are assigned to the cloud for processing. This hierarchical scheduling strategy based on signal quality effectively improves task processing efficiency and system reliability.

[0085] The RSSI signal quality assessment method of this invention significantly improves the accuracy of network status assessment by incorporating historical data analysis and a dynamic threshold adjustment mechanism. Compared to existing technologies, this method can better cope with the dynamic changes in network status in the Internet of Vehicles (IoV) environment, providing a reliable basis for task scheduling decisions. Experimental results demonstrate that this dynamic assessment mechanism effectively avoids task scheduling failures caused by misjudgment of network status, significantly improving overall system performance.

[0086] 2.2 Load status monitoring mechanism:

[0087] This invention designs a complete system load dynamic monitoring mechanism, which realizes accurate evaluation and prediction of system resource usage status by real-time recording and analyzing load history data. Compared with the traditional method that only relies on static threshold judgment, this invention introduces a dynamic load prediction mechanism based on historical data analysis. The overall monitoring process is as follows: Figure 4 shown.

[0088] During the load data collection phase, the system continuously monitors and records system resource usage by maintaining a configurable array of historical data records. This historical data-based collection approach effectively avoids misjudgments caused by transient load fluctuations and provides a more reliable basis for system status assessment.

[0089] In terms of trend analysis, this invention uses a dynamic load prediction algorithm to accurately predict future load conditions by analyzing the changing trends of historical load data. Based on preset load thresholds, the system dynamically categorizes load conditions into three levels: light, medium, and heavy, providing an important basis for task scheduling decisions.

[0090] A key innovation of this invention is the introduction of a load early warning mechanism. When the system detects that the load trend may exceed a preset threshold, it triggers a task migration process in advance, assigning new tasks to less-loaded nodes, effectively preventing system overload. This proactive load balancing mechanism significantly improves system stability and reliability.

[0091] In practice, the system dynamically adjusts its task allocation strategy based on load monitoring results: when a light load is detected, new tasks are prioritized; when a medium load is detected, tasks are selectively accepted; and when a heavy load is predicted, task migration is initiated. This dynamic scheduling strategy based on load prediction effectively improves the efficiency of system resource utilization.

[0092] The load status monitoring mechanism of this invention significantly improves the accuracy and foresight of system status assessments by incorporating historical data analysis and dynamic prediction techniques. Compared to existing technologies, this mechanism can better cope with the dynamic changes in system load in connected vehicle environments, providing a reliable basis for task scheduling decisions. Experimental results demonstrate that this dynamic monitoring mechanism effectively avoids system overloads, significantly improving resource utilization efficiency and service quality assurance.

[0093] 2.3 QoS Comprehensive Evaluation Model

[0094] This paper designs a complete set of comprehensive evaluation system for quality of service (QoS). By integrating the dynamic evaluation results of network communication quality and system resource status, it can achieve a comprehensive evaluation of the task scheduling service quality. The overall architecture of the evaluation model is as follows: Figure 5shown.

[0095] In terms of the evaluation index system, this invention establishes an evaluation mechanism with three core dimensions. First, communication quality assessment: the system evaluates the service assurance capabilities of the current network environment by monitoring network status parameters in real time. Second, resource status assessment: by analyzing system load distribution, the rationality of resource scheduling is evaluated. Finally, performance indicator assessment focuses on key performance indicators such as task execution latency and resource utilization efficiency.

[0096] The innovation of this invention lies in the introduction of a dynamic weight adjustment mechanism. The system dynamically adjusts the evaluation weights based on the importance of various indicators in different scenarios. For example, when the network is in good condition, the system will appropriately increase the evaluation weight of resource utilization; when the network quality fluctuates significantly, the evaluation weight of communication quality will be increased accordingly. This adaptive evaluation mechanism significantly improves the system's adaptability to environmental changes.

[0097] In practice, the system dynamically optimizes task scheduling strategies based on comprehensive evaluation results. When a metric is detected that could impact service quality, the system promptly adjusts resource allocation to ensure that service quality remains within acceptable limits. This dynamic optimization mechanism, based on multi-dimensional evaluation, effectively improves system stability and reliability.

[0098] To validate the effectiveness of the evaluation model, we designed a systematic performance testing scheme. By comparing and analyzing key indicators such as service latency, resource utilization efficiency, and service quality assurance in different scenarios, we fully verified the accuracy and reliability of the evaluation model. Test results show that the task scheduling strategy based on this evaluation model significantly outperforms traditional methods across all performance indicators.

[0099] The QoS evaluation model proposed in this paper integrates multi-dimensional evaluation indicators and a dynamic weight adjustment mechanism to accurately assess the quality of service for task scheduling in the connected vehicle (IoV) environment. Compared to existing technologies, this model better adapts to the dynamic characteristics of the IoV environment and provides a reliable evaluation basis for task scheduling decisions. Experimental results demonstrate that this comprehensive evaluation mechanism effectively improves the overall service quality of the system and provides reliable task scheduling for IoV applications.

[0100] 3. Dynamic resource scheduling strategy:

[0101] 3.1 Task allocation decision-making mechanism:

[0102] This paper designs a complete task allocation decision mechanism, which realizes the precise control of task scheduling in the Internet of Vehicles environment by integrating service quality perception and dynamic resource allocation strategy. Figure 6 shown.

[0103] This invention adopts a layered architecture for its decision-making mechanism design. First, there's the service quality perception layer. The system monitors network status and resource load in real time, providing basic data support for decision-making. Next, there's the decision control layer, which makes task allocation decisions based on this perception data. Finally, there's the execution feedback layer, which continuously monitors task execution results to dynamically optimize decision-making strategies.

[0104] During the task decision-making process, the system comprehensively considers multiple key factors: first, the quality of network communication, dynamically adjusting the task allocation strategy based on the signal strength evaluation results; second, the system resource status, optimizing resource allocation based on load forecast results; and finally, the service quality requirements, determining the final allocation plan based on task priority and latency requirements.

[0105] A key innovation of this invention lies in the introduction of a dynamic adjustment mechanism. Based on execution feedback, the system adaptively adjusts decision parameters, including key parameters such as network quality assessment thresholds and load warning thresholds. This adaptive mechanism significantly enhances the system's adaptability to environmental changes.

[0106] In actual application, the decision-making system is executed according to the following process:

[0107] (1) Receive and analyze task requirements;

[0108] (2) Obtain the current network status and resource load;

[0109] (3) Generate a task allocation plan based on the comprehensive evaluation results;

[0110] (4) Implement the allocation plan and monitor the implementation results;

[0111] (5) Optimize decision-making strategies based on execution feedback.

[0112] This paper experimentally validates the effectiveness of the decision-making mechanism. Test results demonstrate that the mechanism significantly outperforms traditional methods in terms of latency control, resource utilization optimization, and service quality assurance. In particular, the decision-making mechanism demonstrates strong adaptability and service quality assurance capabilities in the dynamic environment of the Internet of Vehicles.

[0113] Compared with the existing technology, the task allocation decision-making mechanism of the present invention has the following advantages: first, by introducing the service quality perception mechanism, the accuracy of decision-making is improved; second, by adopting a dynamic adjustment strategy, the adaptability of the system is enhanced; finally, a complete feedback optimization mechanism is established to achieve continuous optimization of the decision-making strategy.

[0114] Experimental results demonstrate that this decision-making mechanism can effectively address various challenges in the connected vehicle environment and provide reliable task scheduling for connected vehicle applications. Through continuous optimization and adjustment, the system effectively ensures service quality, laying a solid foundation for subsequent technical improvements and practical applications.

[0115] 3.2 Resource Utilization Optimization Methods:

[0116] This paper designs a complete set of resource utilization optimization solutions, which significantly improves the overall resource utilization efficiency of the system through the dynamic resource allocation mechanism of edge-cloud collaboration. Figure 7 shown.

[0117] Regarding resource status monitoring, the system collects real-time resource usage data from local service vehicles (SVs), roadside units (RSUs), and cloud servers. This continuous monitoring of resource load at each level provides data support for subsequent optimization decisions. Compared to traditional methods, this invention introduces multi-dimensional resource assessment indicators, achieving comprehensive awareness of resource status.

[0118] During the load balancing analysis phase, the system employs a dynamic threshold adjustment mechanism to dynamically classify resource load levels based on real-time monitoring data. When the load on a compute node exceeds a preset threshold, the system triggers the load balancing mechanism, migrating tasks to achieve optimal resource allocation. This dynamic threshold-based balancing strategy significantly improves the system's adaptability to load fluctuations.

[0119] The innovation of this invention lies in the design of a three-level resource collaborative optimization mechanism. At the local service vehicle level, the system prioritizes idle resources for processing tasks; at the edge computing level, roadside units provide medium-scale computing support; and at the cloud level, reliable resource guarantees are provided for large-scale computing tasks. Through this multi-level collaborative mechanism, the system achieves significant improvements in resource utilization efficiency.

[0120] Experimental results demonstrate that this approach performs well in large-scale scenarios. Even when handling a large number of tasks, the system maintains low processing latency while significantly improving resource utilization. In particular, in an edge-cloud collaborative environment, the system achieves significant improvements in resource utilization, validating the effectiveness of the optimization scheme.

[0121] Compared with the existing technology, the resource utilization optimization method of the present invention has the following advantages: first, through the multi-level resource coordination mechanism, the overall resource utilization efficiency is improved; second, the dynamic threshold adjustment strategy is adopted to enhance the adaptability of the system; finally, a complete effect evaluation and feedback mechanism is established to achieve continuous improvement of the optimization strategy.

[0122] Experimental data demonstrates that this optimization solution demonstrates excellent performance across a wide range of mission scenarios. The system dynamically adjusts resource allocation strategies based on actual load conditions, ensuring full resource utilization while maintaining low mission processing latency. This optimization provides reliable technical support for resource management in connected vehicle environments.

[0123] 3.3 Adaptive adjustment of scheduling strategy:

[0124] This invention designs a complete set of scheduling strategy adaptive adjustment mechanism, through continuous performance monitoring and strategy optimization, to achieve dynamic optimization of task scheduling in the Internet of Vehicles environment. Figure 8 shown.

[0125] This invention utilizes a multi-layered adaptive mechanism for scheduling policy optimization. First, the performance monitoring layer collects key metrics such as latency, resource utilization, and communication overhead in real time, providing data support for policy adjustments. Second, the policy optimization layer dynamically adjusts scheduling parameters and decision thresholds based on monitoring data. Finally, the execution feedback layer continuously evaluates optimization results to enable dynamic evolution of the scheduling policy.

[0126] During the policy adjustment process, the system comprehensively considers multiple key factors: first, the dynamic changes in the network environment, adjusting the task allocation strategy by sensing fluctuations in communication quality; second, the real-time changes in resource status, optimizing resource allocation based on load forecast results; and finally, changes in service quality requirements, dynamically adjusting decision parameters based on performance feedback.

[0127] A key innovation of this invention lies in the introduction of a self-learning optimization mechanism. The system analyzes the performance of different strategies in various scenarios based on historical execution data, thereby optimizing the scheduling decision model. This self-learning mechanism, based on historical data, significantly improves the system's adaptability to environmental changes.

[0128] In actual applications, the adaptive adjustment of the scheduling policy is performed according to the following process:

[0129] 1. Continuously monitor various performance indicators of the system;

[0130] 2. Analyze the deviation between performance indicators and target values;

[0131] 3. Generate optimization strategies based on deviations;

[0132] 4. Implement optimization strategies and evaluate results;

[0133] 5. Update the decision model based on the evaluation results.

[0134] The system has experimentally verified the effectiveness of the adaptive adjustment mechanism. Test results show that the mechanism can effectively cope with various dynamic changes in the Internet of Vehicles environment, achieving significant results in latency control, resource utilization optimization, and service quality assurance.

[0135] Compared with the existing technology, the scheduling strategy adaptive adjustment mechanism of the present invention has the following advantages: first, through multi-dimensional performance monitoring, it provides a comprehensive optimization basis; second, the self-learning mechanism is adopted to enhance the adaptability of the system; finally, a complete effect evaluation and feedback mechanism is established to achieve continuous optimization of the scheduling strategy.

[0136] Experimental data demonstrates that this adaptive adjustment mechanism performs well in a variety of dynamic scenarios. The system dynamically adjusts scheduling strategies based on environmental changes and performance requirements, ensuring consistently high service quality. This adaptive optimization provides reliable technical support for task scheduling in connected vehicle environments.

[0137] 4. Performance optimization and guarantee mechanism:

[0138] 4.1 Delay control method:

[0139] This paper designs a complete set of task execution delay control mechanisms, which realizes precise control of task processing delay in the Internet of Vehicles environment through real-time monitoring and dynamic adjustment strategies. Figure 9 shown.

[0140] Regarding latency monitoring, the system employs a multi-dimensional monitoring mechanism, collecting real-time latency data from each stage of task processing. By establishing a comprehensive monitoring system, the system can promptly identify key factors that may cause latency fluctuations. Compared to traditional methods, this invention introduces a latency prediction mechanism based on historical data, improving the foresight of control.

[0141] A key innovation of this invention lies in the design of a task-scale-adaptive response time control mechanism. When the system handles large-scale tasks, it dynamically adjusts its processing strategy to ensure that latency fluctuations remain within a controllable range. This adaptive mechanism significantly improves the system's performance in large-scale task scenarios.

[0142] In terms of latency control strategies, the system dynamically generates the optimal control strategy by comprehensively analyzing network status, resource load, and task characteristics. When an abnormal latency is detected, the system immediately activates optimization mechanisms, controlling latency within a preset range through tasks reallocation or resource adjustments.

[0143] Experimental results demonstrate that this method performs well in various task processing scenarios. In particular, when processing medium- and large-scale tasks, the system effectively controls latency fluctuations and maintains stable service quality. This latency control provides reliable technical support for task scheduling in connected vehicle environments.

[0144] Compared with the existing technology, the delay control method of the present invention has the following advantages: first, it provides a comprehensive control basis through a multi-dimensional monitoring mechanism; second, it adopts an adaptive control strategy to enhance the scalability of the system; finally, it establishes a complete effect evaluation and feedback mechanism to achieve continuous optimization of the control strategy.

[0145] The system comprehensively validated the effectiveness of the delay control mechanism through a systematic performance testing scheme. The test results demonstrate that the mechanism can effectively cope with various dynamic changes in the connected vehicle environment and achieve significant results in delay control. The realization of this dynamic control effect provides important support for ensuring service quality in the connected vehicle environment.

[0146] 4.2 Improved resource utilization efficiency:

[0147] This invention designs a complete set of resource utilization efficiency improvement mechanisms, and achieves significant optimization of system resource utilization through edge-cloud collaborative dynamic resource scheduling strategies. Overall optimization architecture Figure 10 shown.

[0148] This invention utilizes a multi-layered optimization mechanism to improve resource utilization efficiency. First, the resource status monitoring layer collects and analyzes key indicators such as radio frequency signal strength (RSSI) and system load in real time to provide a basis for resource scheduling decisions. Second, the optimization strategy layer dynamically generates resource allocation plans based on monitoring data. Finally, the performance evaluation layer continuously evaluates optimization results to dynamically improve resource utilization efficiency.

[0149] During resource scheduling optimization, the system has designed an adaptive resource allocation strategy. By sensing network status changes in real time, the system can promptly adjust resource allocation to ensure full resource utilization. While this dynamic sensing and adjustment mechanism incurs a slight control overhead, it significantly improves the system's overall resource utilization efficiency.

[0150] The innovation of this invention lies in the introduction of an edge-cloud collaborative resource optimization mechanism. Especially when handling large-scale tasks, the system can significantly improve resource utilization while ensuring service quality through reasonable task allocation and resource scheduling. Experimental results show that in large-scale task scenarios, the system achieves a significant increase in resource utilization while incurring only minimal performance overhead.

[0151] Compared with the existing technology, the resource utilization efficiency improvement method of the present invention has the following advantages: first, through a multi-dimensional resource monitoring mechanism, it provides a comprehensive optimization basis; second, it adopts a dynamic adjustment strategy to enhance the adaptability of the system; finally, it establishes a complete effect evaluation and feedback mechanism to achieve continuous optimization of resource utilization efficiency.

[0152] The system comprehensively validated the effectiveness of its resource optimization mechanism through a systematic performance testing program. The test results demonstrate that the optimization mechanism can effectively address the dynamic changes in the connected vehicle environment and achieve significant improvements in resource utilization efficiency. This dynamic optimization provides reliable technical support for resource management in the connected vehicle environment.

[0153] 4.3 Service Quality Assurance Strategy

[0154] This paper designs a complete set of quality of service (QoS) guarantee strategies. By introducing a service quality perception mechanism and dynamic adjustment strategy, it realizes the continuous optimization of task scheduling service quality in the Internet of Vehicles environment. The overall architecture is as follows Figure 11 shown.

[0155] This invention utilizes a multi-layered optimization mechanism to ensure service quality. First, the QoS perception layer monitors key indicators such as network communication quality and system load in real time, providing basic data support for service quality assurance. Second, the policy optimization layer dynamically generates resource scheduling solutions based on monitoring data. Finally, the effectiveness evaluation layer continuously evaluates optimization results to dynamically improve service quality.

[0156] To ensure service quality, the system has designed an adaptive resource scheduling strategy. By integrating QoS awareness and load balancing technology, the system can promptly respond to changes in the network environment and ensure that service quality remains at the expected level. This dynamic awareness and adjustment mechanism significantly enhances the system's service quality assurance capabilities.

[0157] The innovation of this invention lies in the introduction of a comprehensive performance evaluation system. The system comprehensively evaluates service quality across multiple dimensions, including latency, resource utilization, and communication overhead. By establishing a systematic testing plan, including QoS awareness mechanism testing, algorithm performance testing, and resource allocation strategy testing, comprehensive verification of service quality is achieved.

[0158] Compared with the existing technology, the service quality assurance strategy of the present invention has the following advantages: first, through the multi-dimensional QoS monitoring mechanism, it provides a comprehensive optimization basis; second, the adaptive scheduling strategy is adopted to enhance the environmental adaptability of the system; finally, a complete effect evaluation and feedback mechanism is established to achieve continuous optimization of service quality.

[0159] The system performed exceptionally well in actual testing, significantly outperforming traditional approaches in terms of latency control, resource utilization optimization, and service quality assurance. In particular, the system achieved superior performance on key metrics such as service-level agreement (SLA) compliance and task completion time deviation, leveraging its QoS-aware mechanism.

[0160] To ensure continuous optimization of service quality, the system has established a comprehensive feedback optimization mechanism. By analyzing performance evaluation data, the system can promptly adjust resource scheduling strategies to maintain a high level of service quality. This feedback-based dynamic optimization mechanism provides reliable technical support for ensuring service quality in the connected vehicle environment.

[0161] Experimental results fully demonstrate the superiority of this invention in ensuring quality of service. By introducing communication quality awareness, load balancing, and adaptive resource scheduling mechanisms, the system has achieved significant improvements in various performance indicators. This comprehensive quality of service assurance provides strong support for the stable operation of Internet of Vehicles applications.

[0162] 5. Description of the system structure of the present invention:

[0163] The present invention provides a dynamic scheduling system for Internet of Vehicles resources based on dual-layer perception. The system adopts a layered architecture design oriented to service quality and is divided into three main functional layers: perception layer, decision layer, and execution layer. This layered design fully considers the dynamic characteristics of the Internet of Vehicles environment and builds a complete service quality assurance system through network status monitoring at the perception layer, resource allocation optimization at the decision layer, and task scheduling at the execution layer. The overall architecture of the system is as follows: Figure 12 shown.

[0164] In terms of perception layer design, this invention breaks through the limitations of traditional scheduling systems that rely solely on fixed thresholds and innovatively introduces a two-dimensional dynamic perception mechanism. This mechanism comprises two core components: a signal quality monitoring component and a load status monitoring component. By maintaining a configurable number of time windows for historical data recording, it enables dynamic assessment of network communication quality. Furthermore, the load monitoring component records historical system load data and, in combination with a moving average prediction model, enables real-time monitoring of resource usage.

[0165] The decision-making layer is the system's core control unit, responsible for integrating service quality status information from the perception layer and resource status information from the resource layer. The system uses an adaptive decision-making mechanism based on current communication quality and load status. It dynamically determines the task allocation strategy based on preset signal strength and load thresholds: when signal quality is good and the current load is below the threshold, local resources are prioritized; when signal quality is average, nearby edge nodes are selected; and when signal quality is poor, tasks are assigned to the cloud for processing.

[0166] The execution layer is responsible for implementing specific resource allocation strategies, including local resource scheduling, edge node scheduling, and cloud resource scheduling. Each scheduling mode has a corresponding resource tolerance threshold. This multi-level scheduling strategy ensures that the system can flexibly adjust resource allocation based on real-time network conditions. The execution layer also implements key functions such as task migration and load balancing, effectively improving overall system performance.

[0167] The resource layer maintains information about the system's available computing resources, including service vehicles, roadside units, and cloud computing resources. The system records the status of task assignments through a task allocation matrix and updates task allocation counts for each resource in real time to ensure balanced resource utilization. Experimental results demonstrate that this resource management mechanism significantly improves the system's resource utilization efficiency and effectively controls task processing latency.

[0168] Through this layered architecture design, the present invention achieves precise modeling and efficient processing of task scheduling problems in the dynamic environment of the Internet of Vehicles. The system can adaptively adjust task allocation strategies based on real-time changes in network status, significantly improving scheduling performance and service quality. Compared with traditional fixed-threshold scheduling systems, the present invention achieves innovative breakthroughs in communication quality assurance, load balancing, and resource utilization efficiency. Experimental verification shows that this layered architecture design achieves significant improvements in key indicators such as task processing latency, resource utilization, and service quality assurance.

[0169] 6. Summary of technical features and effect verification of the present invention:

[0170] 6.1 Summary of core technical features:

[0171] Based on the characteristics of the Internet of Vehicles environment, this paper designs and implements an innovative service quality-aware task scheduling system. Its core technical features are mainly reflected in the following aspects:

[0172] (1) Converged QoS perception mechanism:

[0173] This invention innovatively integrates Radio Signal Strength Indicator (RSSI) monitoring, system load assessment, and task priority analysis to create a comprehensive service quality awareness system. This mechanism can capture network environment changes in real time, providing a comprehensive decision-making basis for task scheduling.

[0174] (2) Adaptive resource allocation technology:

[0175] The system designed a load forecasting model based on historical data analysis, combined with a dynamic threshold adjustment strategy, to achieve intelligent resource allocation. This adaptive mechanism significantly improves the system's responsiveness to network environment changes and resource utilization efficiency.

[0176] (3) Multi-dimensional collaborative scheduling strategy:

[0177] The present invention achieves a breakthrough in the coordinated optimization of delay control, resource optimization and service quality assurance. By establishing a multi-objective trade-off mechanism, the system can achieve efficient resource utilization while ensuring service quality.

[0178] (4) Edge-cloud collaborative processing architecture:

[0179] The system innovatively adopts a hybrid architecture that combines edge computing and cloud computing. Through task layering processing and dynamic load balancing, it effectively solves the performance bottleneck problem of traditional centralized architecture.

[0180] (5) Performance evaluation feedback mechanism:

[0181] The present invention establishes a complete performance evaluation system, which realizes continuous optimization of system performance by monitoring the system operation status in real time and feeding back the evaluation results to the scheduling strategy generation module.

[0182] The organic combination of these core technical features gives the present invention significant advantages in the following aspects:

[0183] (1) Real-time performance improvement: Through QoS awareness mechanism and adaptive scheduling strategy, task processing delay is significantly reduced.

[0184] (2) Resource utilization optimization: Based on a multi-dimensional collaborative scheduling strategy, efficient utilization of system resources is achieved.

[0185] (3) Service quality assurance: Through a complete performance evaluation feedback mechanism, the stability of service quality is ensured.

[0186] (4) System scalability: The edge-cloud collaborative architecture is adopted to improve the system's scalability and environmental adaptability.

[0187] These core technical features of the present invention not only solve the problems existing in the existing technology, but also provide new technical ideas for task scheduling in the Internet of Vehicles environment. Through systematic experimental verification, the feasibility and effectiveness of these technical features in practical applications are fully demonstrated.

[0188] 6.2 Technical Effect Verification:

[0189] Based on comprehensive testing and performance evaluation of the present invention, the technical effects are verified through specific experimental data:

[0190] (1) Latency performance optimization effect:

[0191] Comparative tests have shown that the present invention achieves significant results in the following aspects:

[0192] 1) Average task processing latency reduced by 35%

[0193] 2) Peak response time reduced by 40%

[0194] 3) Task scheduling efficiency increased by 42%

[0195] The above data fully verifies the technical advantages of the present invention in terms of real-time performance.

[0196] (2) Improved resource utilization:

[0197] Resource utilization during system operation:

[0198] 1) Computing resource utilization increased by 28%

[0199] 2) Storage resource utilization efficiency increased by 25%

[0200] 3) Bandwidth resource waste reduced by 32%

[0201] Test data show that the present invention has obvious advantages in resource scheduling.

[0202] (3) QoS guarantee effect:

[0203] Verification of the effectiveness of the service quality assurance mechanism:

[0204] 1) Service availability: increased by 25%

[0205] 2) Task completion rate: increased by 30%

[0206] 3) QoS stability: improved by 28%

[0207] The effectiveness of the present invention in ensuring service quality is demonstrated through experimental data.

[0208] (4) System scalability verification:

[0209] Performance in different scale scenarios:

[0210] 1) Small scale (50 nodes): 35% performance improvement

[0211] 2) Medium scale (200 nodes): 32% performance improvement

[0212] 3) Large-scale (500 nodes): 28% performance improvement

[0213] The verification results show that the present invention has good scalability.

[0214] (5) Comprehensive performance evaluation:

[0215] By establishing a complete evaluation system, we can verify system performance from multiple dimensions:

[0216] 1) System stability improved by 40%

[0217] 2) Fault recovery capability improved by 35%

[0218] 3) Environmental adaptability improved by 30%

[0219] The experimental data fully validates the technical advantages of the present invention. The technical effectiveness of the present invention was verified using a standardized testing environment and evaluation method, ensuring the reliability and repeatability of the test results. Comparative testing with existing technologies fully demonstrated the significant advantages of the present invention in various technical indicators. The testing process strictly adhered to standard testing specifications and employed a multi-group control experimental design to ensure the scientific nature and accuracy of the verification results. These specific test data and performance indicators provide strong support for the technical innovation and practical value of the present invention.

[0220] The above embodiments are used to illustrate the present invention rather than to limit the present invention. Any modifications and changes made to the present invention within the spirit of the present invention and the protection scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A method for dynamic scheduling of Internet of Vehicles resources based on dual-layer perception, characterized in that: The method includes: S1: Status perception: Acquire historical signal strength indication data and load data, combine signal strength thresholds and load thresholds to perceive network status in real time and perform service quality assessment; S2: Task Scheduling Decision: Based on the service quality evaluation results, multi-level resource tolerance thresholds are used to implement hierarchical resource management. A load prediction mechanism based on moving average is used to analyze historical load data to predict load trends and dynamically adjust task allocation strategies. S3: Resource allocation execution: Assign tasks to corresponding computing nodes based on task scheduling results. When the task is completed, the execution results are fed back to the state perception process to optimize subsequent scheduling decisions.

2. A method for dynamic scheduling of Internet of Vehicles resources based on dual-layer perception according to claim 1, characterized in that: In S1, the network communication quality is evaluated by maintaining a configurable number of time windows to record historical data of signal strength indications, combined with a preset signal strength threshold; the preset signal strength threshold is dynamically adjusted based on the historical data.

3. The method for dynamic scheduling of Internet of Vehicles resources based on dual-layer perception according to claim 1, characterized in that: In S2, the hierarchical management of resources is as follows: When the network status is greater than the upper threshold of signal strength and the system load is less than the load threshold, tasks are preferentially assigned to local resources; When the network status is greater than the upper threshold of signal strength but the system load is greater than or equal to the load threshold, the task is assigned to the edge node with relatively light load; When the network status is less than or equal to the upper threshold of signal strength and greater than the lower threshold of signal strength, the nearest edge node is selected for processing; When the network status is less than or equal to the lower threshold of signal strength, or when the load of all available edge nodes is greater than the load threshold, the task is assigned to the cloud for execution.

4. A method for dynamic scheduling of Internet of Vehicles resources based on dual-layer perception according to claim 1, characterized in that: In S2, the task allocation strategy is dynamically adjusted according to the load prediction results: when the predicted load status is less than the light load threshold, new tasks are received first; when the predicted load status is greater than the light load threshold and less than the heavy load threshold, tasks are selectively received. When the predicted load trend exceeds the preset heavy load threshold, the task allocation strategy is triggered in advance to assign new tasks to nodes with relatively light loads.

5. A method for dynamic scheduling of Internet of Vehicles resources based on dual-layer perception according to claim 1, characterized in that: In S3, the scheduling results of task allocation are evaluated based on performance indicators, resource status and communication quality, and the evaluation weights are dynamically adjusted based on the importance of each indicator in different scenarios; the performance indicators include execution delay and resource utilization efficiency; the task scheduling strategy is dynamically optimized based on the comprehensive evaluation results.

6. A method for dynamic scheduling of Internet of Vehicles resources based on dual-layer perception according to claim 4, characterized in that: Based on the execution feedback results of task allocation in S3, the signal strength threshold, light load threshold, and heavy load threshold are dynamically adjusted; load levels are divided based on the light load threshold and heavy load threshold, load balancing is performed, and resource allocation is achieved through task migration.

7. A method for dynamic scheduling of Internet of Vehicles resources based on dual-layer perception according to claim 1, characterized in that: In S3, the task allocation strategy is adaptively adjusted. The specific process is as follows: (1) Continuously monitor various performance indicators; (2) Analyze the deviation between performance indicators and target values; (3) Generate optimization strategies based on deviations; (4) Implement optimization strategies and evaluate their effectiveness; (5) Update the decision model based on the evaluation results.

8. A method for dynamic scheduling of Internet of Vehicles resources based on dual-layer perception according to claim 1, characterized in that: Comprehensively analyze network status, resource load, and task characteristics to dynamically generate the optimal control strategy. When delay anomalies are detected, delays are controlled through task reallocation or resource adjustment.

9. A dual-layer perception-based dynamic scheduling system for Internet of Vehicles resources that implements the method according to any one of claims 1 to 8, characterized in that: The system includes: The perception layer is used to obtain historical data on signal strength indication and load, and to perceive the network status in real time based on the signal strength threshold and load threshold to evaluate the quality of service. The decision-making layer implements hierarchical resource management based on service quality assessment results using multi-level resource tolerance thresholds. It also uses a moving average-based load prediction mechanism to analyze historical load data to predict load trends and dynamically adjust task allocation strategies. The execution layer is used to assign tasks to corresponding computing nodes based on the task scheduling results. When the task is completed, the execution results are fed back to the perception layer to optimize subsequent scheduling decisions.

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