Vehicle-mounted spectrum resource allocation method and device, equipment and storage medium

Abstract

The invention provides a vehicle-mounted spectrum resource allocation method and device, equipment and a storage medium, and the method comprises the steps: obtaining the vehicle driving scene information and the spectrum state data of a target frequency band in a vehicle, carrying out the feature extraction of the spectrum state data, obtaining the to-be-analyzed spectrum feature information, carrying out the channel occupation prediction of the to-be-analyzed spectrum feature information, and carrying out the channel occupation prediction. Obtaining predicted channel idle resources, matching the priority of the current communication equipment according to the vehicle driving scene information, and allocating channel resources to the communication equipment based on the priority of the current communication equipment and the predicted channel idle resources; the vehicle driving scene information and the spectrum state data are acquired, the channel idle resources are predicted in combination with the deep learning model, and the priority of the communication equipment is dynamically matched to perform resource allocation, so that the problem that a dynamic scene cannot be adapted is effectively solved, the spectrum fluctuation characteristics in a complex driving scene can be dynamically captured in real time, and the user experience is improved. The channel resource utilization efficiency is improved, and the service quality of key communication equipment is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vehicle communication control, in particular to a vehicle-mounted spectrum resource allocation method and device, equipment and storage medium. BACKGROUND

[0002] In the development process of intelligent networked vehicles, reasonable spectrum resource allocation is the core driving force to ensure the real-time and reliability of vehicle-mounted communication. As a key technology to support multi-modal device cooperative communication, its implementation level in the process of realizing various necessary vehicle services, such as low-delay transmission of instructions, multi-screen interaction without lag, directly constitutes the basis for efficient operation of intelligent driving systems and the guarantee of safety environment. In the specific implementation process, technologies such as Bluetooth and Wi-Fi coexistence scheduling, roadside unit cooperation, and key control signal compensation all take efficient multiplexing of public frequency bands as the form of practical application. Therefore, the effective distribution of communication channel resource allocation in the public frequency band is related to the stability and efficient cooperation of the whole vehicle driving function.

[0003] In the specific implementation process of the above-mentioned public frequency band communication architecture, the basis level coexistence communication is usually realized by channel isolation, independent decision and optimization of communication hardware layout. However, the spectrum allocation dominated by static strategy cannot capture the signal fluctuation in the driving process due to the dependence on historical data modeling, resulting in communication delay in some driving scenarios. On the other hand, when using independent decision mechanism, periodic strategy calibration is often needed, and the conflict between decisions will lead to low channel utilization rate. Moreover, the current technical architecture cannot reconcile the mutual exclusion demand between accuracy and real-time, resulting in low comprehensive utilization rate of spectrum resources, which restricts the development of communication efficiency improvement in the vehicle driving process. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide a vehicle-mounted spectrum resource allocation method, device, equipment and storage medium to solve the above technical problems.

[0005] The present application provides a vehicle-mounted spectrum resource allocation method, which comprises: acquiring vehicle driving scene information and spectrum state data of a target frequency band in a vehicle; performing feature extraction on the spectrum state data to obtain to-be-analyzed spectrum feature information, and performing channel occupation prediction on the to-be-analyzed spectrum feature information to obtain predicted channel idle resources; matching the current communication device priority according to the vehicle driving scene information, and performing channel resource allocation on the communication device based on the current communication device priority and the predicted channel idle resources.

[0006] In an embodiment of the present application, the feature extraction is performed on the spectrum state data to obtain the spectrum feature information to be analyzed, including: extracting periodic interference fluctuation features in the time dimension and multi-device signal distribution features in the space dimension from the spectrum state data respectively; generating a channel interference mode evolution graph according to the periodic interference fluctuation features and the multi-device signal distribution features, and determining the channel interference mode evolution graph as the spectrum feature information to be analyzed.

[0007] In an embodiment of the present application, the channel occupation prediction is performed on the spectrum feature information to be analyzed to obtain the predicted channel idle resources, including: inputting the channel interference mode evolution graph into a preset deep learning model to extract channel occupation rule information, to obtain the channel occupation rule information, the preset deep learning model being a deep neural network model for analyzing the channel interference mode evolution graph and extracting spectrum data mode and trend; performing prediction based on the channel occupation rule information according to preset time window information to obtain channel idle resource information in a preset time window, and determining the channel idle resource information in the preset time window as the predicted channel idle resources.

[0008] In an embodiment of the present application, the current communication device priority is matched according to the vehicle driving scene information, including: determining a current driving scene type according to the vehicle driving scene information, and determining corresponding communication device priority information in a preset communication device priority rule library based on the current driving scene type, and determining the corresponding communication device priority information as the current communication device priority, the preset communication device priority rule library predefining priority rules for communication device channel allocation in vehicles under multiple different driving scene types.

[0009] In an embodiment of the present application, the channel resource allocation is performed on the communication devices based on the current communication device priority and the predicted channel idle resources, including: determining a key communication device in the communication devices according to the current communication device priority, and performing exclusive channel resource allocation on the key communication device based on the predicted channel idle resources; performing channel resource allocation on remaining communication devices based on the predicted channel idle resources and the allocated exclusive channel resources, the remaining communication devices being the communication devices other than the key communication device; when an emergency communication event is detected, reallocating full-band channel resources to the key communication device, and suspending the data transmission right of the remaining communication devices.

[0010] In one embodiment of this application, channel resource allocation for the remaining communication devices based on predicted channel idle resources and allocated exclusive channel resources includes: determining the remaining channel idle resources based on the predicted channel idle resources and allocated exclusive channel resources; determining high idle probability channel resources in the remaining channel idle resources based on channel idle probability distribution information in the predicted channel idle resources; and constructing a secure frequency hopping set based on the high idle probability channel resources, so as to allocate channel resources for the remaining communication devices according to the secure frequency hopping set.

[0011] In one embodiment of this application, after allocating channel resources to the communication device based on the current communication device priority and predicted channel idle resources, the vehicle spectrum resource allocation method further includes: obtaining channel resource allocation results; generating scheduling instructions based on the channel resource allocation results to enable the vehicle to perform channel resource allocation according to the scheduling instructions, and monitoring the occurrence status of communication interference events; if a communication interference event occurs, recording the communication interference event as an interference event log for storage, so as to optimize the channel allocation decision information based on the interference event log, wherein the channel allocation decision information includes the configuration information of a preset deep learning model and priority rules in a preset communication device priority rule base.

[0012] This application embodiment also provides a vehicle-mounted spectrum resource allocation device, which includes: an allocation information acquisition module for acquiring vehicle driving scenario information and spectrum status data of target frequency bands in the vehicle; an idle resource prediction module for extracting features from the spectrum status data to obtain spectrum feature information to be analyzed, and predicting channel occupancy based on the spectrum feature information to obtain predicted channel idle resources; and a channel resource allocation module for matching the current communication device priority according to the vehicle driving scenario information, and allocating channel resources to the communication device based on the current communication device priority and the predicted channel idle resources.

[0013] This application also provides an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the vehicle spectrum resource allocation method as described in any of the above embodiments.

[0014] This application also provides a computer-readable storage medium storing computer-readable instructions, which, when executed by a computer's processor, cause the computer to perform the vehicle spectrum resource allocation method as described in any of the above embodiments.

[0015] The beneficial effects of this application are as follows: This application provides a method, apparatus, device, and storage medium for allocating vehicle-mounted spectrum resources. By acquiring vehicle driving scenario information and spectrum status data of the target frequency band in the vehicle, feature extraction is performed on the spectrum status data to obtain spectrum feature information to be analyzed. Channel occupancy is predicted based on the spectrum feature information to obtain predicted channel idle resources. The current communication device priority is matched according to the vehicle driving scenario information, and channel resources are allocated to the communication device based on the current communication device priority and the predicted channel idle resources. This application effectively solves the problem that static allocation strategies cannot adapt to dynamic scenarios by acquiring vehicle driving scenario information and spectrum status data, combining deep learning models to predict channel idle resources, and dynamically matching communication device priorities for resource allocation. It has the advantages of real-time dynamic capture of spectrum fluctuation characteristics under complex driving scenarios, improving channel resource utilization efficiency, and ensuring the service quality of critical communication equipment.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings: Figure 1 This is a schematic diagram illustrating an exemplary system architecture as shown in an exemplary embodiment of this application; Figure 2 This is a flowchart illustrating an exemplary embodiment of the present application of a method for allocating vehicle spectrum resources; Figure 3 This is an exemplary diagram illustrating a specific example of spectrum allocation when Wi-Fi data has high priority, as shown in an exemplary embodiment of this application. Figure 4 This is an exemplary embodiment of the present application illustrating a specific example of a spectrum allocation when Bluetooth data has a high priority; Figure 5 This is a schematic diagram of an exemplary embodiment of the present application illustrating a vehicle-mounted spectrum resource allocation device; Figure 6 This is a schematic diagram of the structure of a computer system for an electronic device, as illustrated in an exemplary embodiment of this application. Detailed Implementation

[0018] The embodiments of this application will be described below with reference to the accompanying drawings and specific examples. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be understood that the preferred embodiments are only for illustrating this application and are not intended to limit the scope of protection of this application.

[0019] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the illustrations only show the components related to this application and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0020] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the present application. However, it will be apparent to those skilled in the art that embodiments of the present application may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the present application.

[0021] The term "and / or" used in this application describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the related objects before and after it are in an "or" relationship.

[0022] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating an exemplary system architecture as shown in an exemplary embodiment of this application.

[0023] Reference Figure 1As shown, the system architecture may include a computer device 110 and a vehicle 120. The computer device 110 acquires vehicle driving scenario information and spectrum status data of the target frequency band within the vehicle 120, extracts features from the spectrum status data to obtain spectrum feature information to be analyzed, predicts channel occupancy based on the spectrum feature information to obtain predicted channel idle resources, matches the current communication device priority according to the vehicle driving scenario information, and allocates channel resources to the communication devices based on the current communication device priority and the predicted channel idle resources. The computer device 110 refers to a computing power support terminal device used to carry the program implementation environment for executing the vehicle spectrum resource allocation method, including but not limited to microcomputers, embedded computers, microcontrollers, electronic control units, and vehicle computing systems; the vehicle 120 includes at least a vehicle infotainment system, electronic control unit, radio frequency sensors, and communication devices including but not limited to Bluetooth and Wi-Fi.

[0024] Indicatively, computer device 110 acquires vehicle driving scenario information and spectrum status data of the target frequency band in vehicle 120, extracts features from the spectrum status data to obtain spectrum feature information to be analyzed, and predicts channel occupancy based on the spectrum feature information to obtain predicted channel idle resources. It then matches the current communication device priority according to the vehicle driving scenario information and allocates channel resources to the communication device based on the current communication device priority and the predicted channel idle resources. This application, by acquiring vehicle driving scenario information and spectrum status data, combining a deep learning model to predict channel idle resources, and dynamically matching communication device priorities for resource allocation, effectively solves the problem that static allocation strategies cannot adapt to dynamic scenarios. It has the advantages of real-time dynamic capture of spectrum fluctuation characteristics under complex driving scenarios, improving channel resource utilization efficiency, and ensuring the service quality of critical communication equipment.

[0025] Figure 2 This is a flowchart illustrating an exemplary embodiment of the present application of a method for allocating vehicle-mounted spectrum resources, which can be implemented in... Figure 1 It can be executed in the implementation environment described above, but it can also be implemented in other implementation environments. No specific limitations are imposed on the aforementioned implementation environments here. (See also...) Figure 2 As shown in the flowchart, the method for allocating vehicle spectrum resources includes at least steps S210 to S230, which are described in detail below: In step S210, vehicle driving scenario information and spectrum status data of the target frequency band in the vehicle are acquired.

[0026] In one embodiment of this application, vehicle driving scenario information reflects data on the current environment type of the vehicle and the driving status of the vehicle. Specifically, it can be determined by comprehensively judging and using information such as vehicle-mounted cameras, radar, navigation systems, and vehicle gear positions, and is used to identify scenario types such as emergency obstacle avoidance, highway cruising, urban congestion, parking and leisure, and outdoor off-roading. The target frequency band refers to the signal frequency band to be allocated. In this embodiment of the application, the target frequency band refers to the 2.4GHz frequency band. The spectrum status data refers to the real-time monitoring spectrum data such as signal strength, noise level, and channel occupancy rate within the target frequency band. In the implementation of this application, it can be periodically collected by deploying an on-board edge computing unit in the vehicle environment. This on-board edge computing unit integrates radio frequency sensors and can set priorities for different devices according to the vehicle's communication needs.

[0027] In step S220, feature extraction is performed on the spectrum status data to obtain the spectrum feature information to be analyzed, and channel occupancy prediction is performed on the spectrum feature information to obtain the predicted channel idle resources.

[0028] In one embodiment of this application, periodic interference fluctuation features in the time dimension and multi-device signal distribution features in the spatial dimension are extracted from the spectrum state data. The periodic interference fluctuation features refer to the regular interference change patterns exhibited by the spectrum state in a time series. In some embodiments, this is specifically implemented using a time series decomposition algorithm combined with Fourier transform, which can identify the impact of periodic signal interference sources on channel occupancy. The multi-device signal distribution features refer to the signal strength and frequency point occupancy of different communication devices in the target frequency band at the same time. In some embodiments, this is specifically implemented using a spatial clustering algorithm combined with a spectrum energy density distribution map, used to characterize the signal superposition and competition relationship during concurrent communication of multiple devices. The specific extraction process for the periodic interference fluctuation features and multi-device signal distribution features can be automatically extracted separately based on iterative learning using a convolutional neural network in the implementation of this application. In some other embodiments, feature extraction can also be performed based on independent algorithm models. Furthermore, there is no specific limitation on the specific form of feature extraction for the spectrum state data.

[0029] In one embodiment of this application, a channel interference pattern evolution map is generated based on periodic interference fluctuation characteristics and multi-device signal distribution characteristics, and this map is identified as the spectral feature information to be analyzed. The channel interference pattern evolution map refers to a comprehensive interference model that integrates periodic fluctuations in the time dimension and device distribution in the spatial dimension. A map generation algorithm can be used to model the correlation between time-series features and spatial distribution features, forming a visual representation of dynamic interference patterns and providing a multi-dimensional analytical basis for subsequent channel occupancy prediction.

[0030] Specifically, after the spectrum status data is collected in real time by vehicle-mounted sensors, it can first be divided into continuous time segments using a sliding window mechanism in the time dimension. Periodic interference fluctuation characteristics are extracted through spectrum energy change rate calculation and periodic detection algorithms. At the same time, in the spatial dimension, the device occupancy density of each frequency point in different areas is statistically analyzed based on device identification and signal source location data to generate multi-device signal distribution characteristics. Then, the two types of features are spatiotemporally correlated and modeled using a spectrum generation algorithm to form a three-dimensional spectrum structure containing timestamps, interference intensity, and device distribution density. This spectrum can dynamically reflect the evolution of channel interference patterns in the spatiotemporal dimension, providing a high-precision analytical basis for subsequent channel occupancy prediction.

[0031] This application, through the fusion of temporal and spatial features, can accurately identify interference hotspots in channel occupancy patterns caused by both periodic external interference and concurrent communication of multiple devices, thereby improving the reliability of channel idle resource prediction. It can effectively solve the problem of low channel utilization caused by static spectrum allocation strategies ignoring the spatiotemporal dimension of interference evolution. By extracting periodic interference fluctuation features and multi-device signal distribution features from real-time spectrum status data, a dynamic channel interference pattern evolution map is generated to achieve refined modeling of channel occupancy status, providing accurate data support for subsequent resource allocation decisions and reducing the probability of multi-device communication conflicts.

[0032] In one embodiment of this application, the channel interference pattern evolution map is input into a preset deep learning model to extract channel occupancy pattern information, thereby obtaining channel occupancy pattern information. The preset deep learning model is a deep neural network model used to analyze the channel interference pattern evolution map and extract spectral data patterns and trends. In some feasible environments, it can be implemented using a hybrid architecture combining convolutional neural networks and long short-term memory networks. By extracting pattern and trend information from the spectral data, channel occupancy pattern information is captured. This channel occupancy pattern information specifically refers to the spectral occupancy feature sequence output from the deep learning model, which can be further determined by the hidden state vector in the time series prediction model and is used to describe the occupancy probability distribution of the channel at different time points.

[0033] In one embodiment of this application, channel occupancy pattern information is used to predict channel idle resource information within a preset time window based on preset time window information. The channel idle resource information within the preset time window is then determined as the predicted channel idle resource. The preset time window information refers to a pre-set time period range, such as 5ms to 100ms based on vehicle speed or communication requirements, which can limit the prediction range of channel idle resources.

[0034] This application uses a deep learning model to extract time-dimensional interference features with periodic interference fluctuations as key information and spatial-dimensional interference features with multi-device signal distribution characteristics as key information from the channel interference pattern evolution map in real time. Channel occupancy pattern information is extracted separately, which can accurately capture the channel occupancy pattern shift caused by changes in driving scenarios. Furthermore, by combining a preset time window with a prediction model, channel state inference in the continuous time domain is achieved, reducing decision conflicts and improving prediction efficiency. In this way, the distribution of channel idle resources within the future time window can be dynamically predicted, solving the problem that static strategies cannot adapt to real-time signal fluctuations.

[0035] By using deep learning models to jointly analyze channel occupancy patterns under time-dimensional interference characteristics (with periodic interference fluctuations as key information) and spatial-dimensional interference characteristics (with multi-device signal distribution characteristics as key information), the accuracy of channel occupancy prediction is improved, and communication delays caused by prediction deviations are reduced. Furthermore, by combining a rolling prediction mechanism with a preset time window, the real-time resource allocation needs of critical communication equipment can be effectively supported, and spectrum resource utilization can be improved.

[0036] In step S230, the priority of the current communication device is matched according to the vehicle driving scenario information, and channel resources are allocated to the communication device based on the priority of the current communication device and the predicted channel idle resources.

[0037] In one embodiment of this application, the current driving scenario type is determined based on vehicle driving scenario information, and the corresponding communication device priority information is determined in a preset communication device priority rule base based on the current driving scenario type. The corresponding communication device priority information is then determined as the current communication device priority. The preset communication device priority rule base refers to a database that stores the mapping relationship between different driving scenarios and communication device priorities. This database predefines multiple priority rules for the allocation of communication device channels in the vehicle under different driving scenario types, and adopts a relational database or key-value pair storage structure. The communication device priority refers to the priority order of devices acquiring channel resources under different scenarios. For example, in an emergency braking scenario, the communication priority of the braking control system and the collision warning system is set to the highest.

[0038] The aforementioned current driving scenario type refers to the vehicle operating status category classified according to driving environment data, such as emergency obstacle avoidance, highway cruising, urban congestion, parking and leisure, outdoor off-roading, etc. In the feasible environment of this application, classification and judgment can be made by means of pattern recognition algorithms or decision tree models.

[0039] This application establishes a scenario-aware priority rule base, which can automatically switch the optimal priority configuration according to the real-time operating environment of the vehicle, effectively solving the problem of rigid resource allocation caused by static strategies, realizing dynamic scenario-adaptive priority management of communication devices, and ensuring that communication devices related to key driving functions obtain matching channel resource guarantees in different scenarios.

[0040] In one embodiment of this application, channel resource allocation for communication devices based on current communication device priority and predicted channel idle resources includes: identifying key communication devices among the communication devices according to current communication device priority, and allocating exclusive channel resources to the key communication devices based on predicted channel idle resources. During vehicle operation, the highest priority communication device is first determined according to current communication device priority; for example, in a highway scenario, the autonomous driving control unit is set to the highest priority. Based on the channel idle probability distribution output by a deep learning model, exclusive channel resources are reserved for the highest priority device to prevent its communication link from being interfered with by lower priority devices.

[0041] Channel resources are allocated to the remaining communication devices based on predicted channel idle resources and already allocated exclusive channel resources. These remaining communication devices are those excluding critical communication devices. A safe frequency hopping set is dynamically constructed based on the idle probability of the remaining channel resources, and resources are allocated to non-critical devices such as in-vehicle entertainment systems using a frequency hopping method.

[0042] When an emergency communication event is detected, all frequency band channel resources are reallocated to critical communication equipment, and the data transmission permissions of the remaining communication equipment are suspended. An emergency communication event refers to a sudden, high-priority communication request that requires immediate occupancy of all channel resources. This can be determined based on anomaly detection of onboard sensor data or external emergency signal reception mechanisms, and resource reallocation is triggered by real-time monitoring of vehicle safety status or receiving roadside unit alarm information. Specifically, when the collision warning system detects an emergency, it immediately terminates the communication permissions of non-critical equipment and switches all frequency band resources to the autonomous driving control unit and emergency braking system, ensuring zero-delay transmission of critical commands.

[0043] In one specific embodiment of this application, the resource allocation process includes allocation for Bluetooth devices and allocation for Wi-Fi devices. Specifically, during Wi-Fi resource allocation, orthogonal frequency division multiple access (OFDM) technology is used to divide the target frequency band, such as the 2.4 GHz band, into multiple subcarriers. A reinforcement learning decision model is used to allocate exclusive sub-channels to high-priority tasks or corresponding devices based on their priorities, ensuring the communication quality of high-priority tasks or corresponding devices. The remaining sub-channels are shared with Bluetooth devices, improving the utilization of spectrum resources. The specific allocation process can be found in [reference needed]. Figure 3 , Figure 3This is an exemplary embodiment of the present application illustrating a specific example of spectrum allocation when Wi-Fi data has high priority. Please refer to the diagram. Figure 3 As shown, when Wi-Fi data has high priority, exclusive channel resources are allocated to the highest priority Wi-Fi data and its corresponding communication devices. The remaining channel resources are then allocated to Bluetooth communication devices or other non-critical communication devices. For example, in a vehicle collision warning scenario, the system automatically identifies an emergency and prioritizes the allocation of 2.4GHz frequency band resources to Wi-Fi data and its corresponding communication devices in V2X communication based on device priority. This ensures uninterrupted emergency communication between vehicles and other vehicles and infrastructure. Simultaneously, data transmission from non-critical Bluetooth devices is suspended to avoid interfering with emergency communication. V2X (Vehicle-to-Everything) is a communication technology that enables vehicles to interact with their surroundings. Also known as vehicle-to-everything communication or vehicular wireless communication technology, it is a core technology of intelligent transportation systems. Through wireless communication, vehicles can share information in real time, provide early warnings of traffic risks, and improve driving safety, efficiency, and comfort.

[0044] On the other hand, during Bluetooth resource allocation, adaptive frequency hopping technology and reinforcement learning models are combined to dynamically adjust the frequency hopping mode of Bluetooth devices based on spectrum sensing results, avoiding channels occupied by Wi-Fi data and its corresponding communication devices. Specifically, when high interference is detected, a silent window mechanism is triggered, suspending non-critical data transmission from Bluetooth devices and prioritizing the transmission of critical data. The silent window mechanism involves sending a transmission permission freeze command to non-critical devices after an emergency communication event occurs, with the freeze lasting until the emergency event is resolved. After the emergency event is resolved, device transmission permissions are gradually restored based on the latest spectrum prediction results. For a detailed allocation process, please refer to [reference needed]. Figure 4 , Figure 4 This is an exemplary embodiment of the present application illustrating a specific example of a Bluetooth data priority allocation spectrum. Please refer to [the example diagram]. Figure 4 As shown, when Bluetooth data has a high priority, channel resources are allocated to the highest priority Bluetooth data and its corresponding communication devices using adaptive frequency hopping technology and reinforcement learning models, avoiding channels occupied by Wi-Fi data and its corresponding communication devices. For example, when rear passengers watch 4K videos via Wi-Fi projection, the Bluetooth steering wheel control signal avoids the channels occupied by the video stream through a dynamic frequency hopping mechanism, ensuring that the driver's control commands to the vehicle can be transmitted in a timely and accurate manner, achieving zero-latency command response and improving driving safety and user experience.

[0045] Traditional static allocation strategies cannot quickly release channel resources occupied by non-critical equipment in emergency situations, resulting in delays in the transmission of safety-related commands. This application achieves millisecond-level response speeds in emergency events while ensuring resource utilization in normal scenarios through dynamic priority matching and full-band resource reallocation mechanisms. It can prioritize the communication quality of safety-critical equipment in complex driving scenarios, reduce the probability of channel conflicts through predictive resource reservation, and quickly release all spectrum resources for critical equipment use in sudden emergency events, effectively solving the communication delay and low resource utilization problems caused by traditional static allocation strategies.

[0046] In one embodiment of this application, the above-mentioned channel resource allocation for the remaining communication devices based on the predicted channel idle resources and the allocated exclusive channel resources includes: determining the remaining channel idle resources based on the predicted channel idle resources and the allocated exclusive channel resources; determining high idle probability channel resources in the remaining channel idle resources based on the channel idle probability distribution information in the predicted channel idle resources; and constructing a secure frequency hopping set based on the high idle probability channel resources, so as to allocate channel resources for the remaining communication devices according to the secure frequency hopping set.

[0047] The remaining channel idle resources refer to the spectrum resources that are not occupied after the critical communication equipment has completed its exclusive allocation. This is calculated by subtracting the total idle resources from the allocated resources and is used to provide a basis for dynamic allocation to secondary equipment. The aforementioned channel idle probability distribution information refers to channel availability probability data generated based on a time series prediction model. In the embodiments of this application, the stability of different channels is quantified after performing state transition probability modeling on historical occupancy data using a Hidden Markov Model.

[0048] The aforementioned high idle probability channel resources refer to the set of channels with an idle probability higher than a preset threshold. By pre-setting the probability threshold for filtering, communication links with strong anti-interference capabilities can be constructed. The secure frequency hopping set refers to a dynamically allocated resource pool composed of high idle probability channels, used to provide low-collision frequency hopping paths for secondary devices.

[0049] Specifically, after the exclusive allocation of key communication equipment is completed, the remaining channel idle resources are obtained by subtracting the allocated exclusive channel resources from the predicted channel idle resources. Based on the channel idle probability distribution output by the deep learning model, the remaining channel idle resources are sorted according to the channel idle probability and high reliability channels are selected. Then, a dynamically updated secure frequency hopping set is constructed, and the remaining communication equipment switches to the channel in the set according to the preset frequency hopping sequence during transmission.

[0050] This application establishes a channel stability assessment system through probabilistic prediction, which makes the frequency hopping decision of the remaining communication equipment more forward-looking and reduces communication interruptions caused by channel mutations. Secondly, it achieves fast switching through a pre-generated safe frequency hopping set, which significantly reduces communication latency and effectively solves the problem of frequent interference caused by random selection during the channel allocation process of the remaining communication equipment. By guiding the construction of a safe frequency hopping set through probabilistic prediction, the remaining communication equipment can still maintain stable communication quality under limited remaining resources.

[0051] In one embodiment of this application, the application further includes obtaining channel resource allocation results, generating scheduling instructions based on the channel resource allocation results, so that the vehicle executes channel resource allocation according to the scheduling instructions, and monitoring the occurrence status of communication interference events. Here, the channel resource allocation result refers to the usage status information of the allocated channel resources; the scheduling instructions refer to a set of instructions controlling the vehicle's communication equipment to perform channel switching or occupancy operations, encapsulated into control frames based on the CAN bus protocol for transmission, ensuring that each communication node in the vehicle synchronously executes the allocation strategy; the aforementioned communication interference events refer to signal conflicts or bit error rate exceeding the standard detected on the allocated channels, specifically determined by real-time monitoring of the bit error rate indicators of each channel, which can identify failure scenarios of the allocation strategy.

[0052] If a communication interference event occurs, the communication interference event will be recorded as an interference event log and stored. The channel allocation decision information will be optimized based on the interference event log. The channel allocation decision information includes the configuration information of the preset deep learning model and the priority rules in the preset communication device priority rule base.

[0053] The process of recording communication interference events as interference event logs involves recording the characteristic data of communication interference events in a time series format and then storing it in an onboard storage module using a structured database. The aforementioned channel allocation decision information optimization refers to adjusting the parameters of the deep learning model or priority rules based on historical interference event data. Specifically, this involves converting channel resource allocation results into scheduling instructions containing channel number, occupied time period, and device identifier. These instructions are then distributed to each communication terminal via the vehicle's internal communication network for configuration execution. During communication, the signal quality of the allocated channels is continuously monitored. When an abnormal decrease in signal strength or a bit error rate exceeding a preset threshold is detected, a communication interference event alarm is triggered. Each time a communication interference event occurs, the system automatically records key parameters such as the event time, channel number, and interference type, forming a timestamped structured interference event log. The interference event logs stored in the vehicle's storage module are then periodically uploaded to a cloud server. The cloud server aggregates data from multiple interference event logs and optimizes the channel allocation decision information based on a preset global optimization model. In some feasible environments, this includes extracting the characteristics of frequently interfering channels and corresponding allocation strategy parameters, then performing backpropagation optimization on the convolutional kernel weights of the deep learning model. Simultaneously, the matching coefficients for different driving scenarios in the communication device priority rule base are adjusted to form a closed-loop optimization mechanism, resulting in optimized channel allocation decision information. This optimized channel allocation decision information is then distributed to the vehicle and iteratively updated by the vehicle's edge computing unit.

[0054] In some feasible environments, when the vehicle-mounted edge computing unit performs channel resource allocation according to scheduling instructions, it can further implement a low-latency scheduling strategy with a scheduling delay of less than 10ms to ensure real-time communication.

[0055] This application achieves adaptive iteration of channel allocation strategies by establishing a closed loop of allocation result verification, interference event monitoring, and log data-driven decision optimization. Compared with traditional fixed-period calibration methods, it can perform targeted optimization for actual interference events, avoiding the waste of computational resources caused by global parameter adjustments. At the same time, through dual optimization of priority rules and model parameters, it effectively improves the robustness of channel allocation strategies. On the other hand, this application can continuously monitor the actual communication quality after channel resource allocation, promptly detect and record interference events that occur during strategy execution, and dynamically optimize the channel allocation decision model and rule base based on real-world scenario data. This solves the problem that static allocation strategies cannot adapt to dynamic communication environments. By continuously eliminating existing interference patterns, it gradually improves channel resource utilization efficiency and avoids the recurrence of similar interference events.

[0056] This application provides a method, apparatus, device, and storage medium for allocating vehicle-mounted spectrum resources. It acquires vehicle driving scenario information and spectrum status data of the target frequency band within the vehicle, extracts features from the spectrum status data to obtain spectrum feature information to be analyzed, predicts channel occupancy based on the spectrum feature information to obtain predicted channel idle resources, matches the current communication device priority according to the vehicle driving scenario information, and allocates channel resources to the communication device based on the current communication device priority and the predicted channel idle resources. This application, by acquiring vehicle driving scenario information and spectrum status data, combining a deep learning model to predict channel idle resources, and dynamically matching communication device priorities for resource allocation, effectively solves the problem that static allocation strategies cannot adapt to dynamic scenarios. It has the advantages of real-time dynamic capture of spectrum fluctuation characteristics under complex driving scenarios, improving channel resource utilization efficiency, and ensuring the service quality of critical communication equipment.

[0057] The following describes an apparatus embodiment of this application, which can be used to execute the vehicle-mounted spectrum resource allocation method in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the vehicle-mounted spectrum resource allocation method described above.

[0058] Figure 5 This is a schematic diagram illustrating an in-vehicle spectrum resource allocation device according to an exemplary embodiment of this application. This device can be applied to… Figure 2 The method implementation process shown can be based on the device Figure 1 The implementation environment shown can be applied to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the device is applicable.

[0059] like Figure 5 As shown, the exemplary vehicle-mounted spectrum resource allocation device includes: an allocation information acquisition module 501, an idle resource prediction module 502, and a channel resource allocation module 503.

[0060] The allocation information acquisition module 501 is used to acquire vehicle driving scenario information and spectrum status data of target frequency bands in the vehicle; the idle resource prediction module 502 is used to extract features from the spectrum status data to obtain spectrum feature information to be analyzed, and to predict channel occupancy based on the spectrum feature information to obtain predicted channel idle resources; the channel resource allocation module 503 is used to match the current communication device priority according to the vehicle driving scenario information, and to allocate channel resources to the communication device based on the current communication device priority and the predicted channel idle resources.

[0061] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the vehicle spectrum resource allocation method provided in the above embodiments.

[0062] Figure 6 This is a schematic diagram illustrating the structure of a computer system for an electronic device, as shown in an exemplary embodiment of this application. It should be noted that... Figure 6 The computer system 600 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0063] like Figure 6 As shown, the computer system 600 includes a Central Processing Unit (CPU) 601, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on programs stored in Read-Only Memory (ROM) 602 or programs loaded from storage into Random Access Memory (RAM) 603. The RAM 603 also stores various programs and data required for system operation. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus. An I / O interface 605 is also connected to the bus 604, where the I / O interface 605 refers to an input / output interface.

[0064] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section performs communication processing via a network such as the Internet. A drive is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0065] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs various functions defined in the system of this application.

[0066] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0067] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0068] In the corresponding figures of the above embodiments, connecting lines can represent the connection relationship between various components, indicating more constitutive signal paths and / or one or more ends of some lines having arrows to indicate the main information flow direction. Connecting lines serve as an identifier and are not a limitation on the scheme itself, but rather, using these lines in conjunction with one or more exemplary embodiments helps to more easily connect circuits or logic units. Any signal represented (determined by design requirements or preferences) can actually include one or more signals that can be transmitted in any direction and can be implemented in any suitable type of signal scheme.

[0069] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the unit itself.

[0070] Another aspect of this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method as described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0071] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the vehicle spectrum resource allocation method as described in any of the above embodiments.

[0072] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0073] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0074] This application can be used in a wide range of general-purpose or special-purpose computing system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0075] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0076] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for allocating vehicle-mounted spectrum resources, characterized in that, The method for allocating vehicle-mounted spectrum resources includes: Acquire vehicle driving scenario information and spectrum status data of target frequency bands in the vehicle; Feature extraction is performed on the spectrum status data to obtain spectrum feature information to be analyzed, and channel occupancy prediction is performed on the spectrum feature information to be analyzed to obtain predicted channel idle resources; The current communication device priority is matched based on the vehicle driving scenario information, and channel resources are allocated to the communication device based on the current communication device priority and the predicted channel idle resources.

2. The vehicle-mounted spectrum resource allocation method according to claim 1, characterized in that, Feature extraction is performed on the spectral state data to obtain the spectral feature information to be analyzed, including: The periodic interference fluctuation characteristics in the time dimension and the multi-device signal distribution characteristics in the spatial dimension are extracted from the spectrum state data, respectively. Based on the periodic interference fluctuation characteristics and the multi-device signal distribution characteristics, a channel interference mode evolution map is generated, and the channel interference mode evolution map is determined as the spectral feature information to be analyzed.

3. The vehicle-mounted spectrum resource allocation method according to claim 2, characterized in that, Channel occupancy prediction is performed on the spectrum feature information to be analyzed to obtain the predicted channel idle resources, including: The channel interference pattern evolution map is input into a preset deep learning model to extract channel occupancy pattern information, thereby obtaining channel occupancy pattern information. The preset deep learning model is a deep neural network model used to analyze the channel interference pattern evolution map and extract spectral data patterns and trends. Based on the channel occupancy pattern information, prediction is made according to the preset time window information to obtain the channel idle resource information within the preset time window, and the channel idle resource information within the preset time window is determined as the predicted channel idle resource.

4. The vehicle-mounted spectrum resource allocation method according to claim 1, characterized in that, The priority of the current communication device is matched based on the vehicle driving scenario information, including: The current driving scenario type is determined based on the vehicle driving scenario information, and the corresponding communication device priority information is determined in the preset communication device priority rule library based on the current driving scenario type. The corresponding communication device priority information is determined as the current communication device priority. The preset communication device priority rule library predefines priority rules for the allocation of communication device channels in vehicles under multiple different driving scenario types.

5. The method for allocating vehicle-mounted spectrum resources according to claim 1, characterized in that, The channel resource allocation for communication devices based on the current communication device priority and predicted channel idle resources includes: Based on the current communication device priority, key communication devices are identified among the communication devices, and exclusive channel resources are allocated to the key communication devices based on the predicted channel idle resources; Channel resources are allocated to the remaining communication devices based on the predicted channel idle resources and the allocated exclusive channel resources. The remaining communication devices are the communication devices other than the critical communication devices. When an emergency communication event is detected, all frequency band channel resources are reallocated to critical communication equipment, and the data transmission permissions of the remaining communication equipment are suspended.

6. The vehicle-mounted spectrum resource allocation method according to claim 5, characterized in that, The allocation of channel resources to the remaining communication equipment based on the predicted channel idle resources and the already allocated exclusive channel resources includes: The remaining channel idle resources are determined based on the predicted channel idle resources and the allocated exclusive channel resources; Based on the channel idle probability distribution information in the predicted channel idle resources, high idle probability channel resources are determined in the remaining channel idle resources, and a safe frequency hopping set is constructed based on the high idle probability channel resources, so as to allocate channel resources to the remaining communication devices according to the safe frequency hopping set.

7. The method for allocating vehicle-mounted spectrum resources according to any one of claims 1-6, characterized in that, After allocating channel resources to the communication equipment based on the current communication equipment priority and predicted channel idle resources, the vehicle-mounted spectrum resource allocation method further includes: Obtain the channel resource allocation results; Based on the channel resource allocation results, a scheduling instruction is generated so that the vehicle can perform channel resource allocation according to the scheduling instruction and monitor the occurrence status of communication interference events. If a communication interference event occurs, the communication interference event is recorded as an interference event log and stored. The channel allocation decision information is optimized based on the interference event log. The channel allocation decision information includes the configuration information of a preset deep learning model and the priority rules in a preset communication device priority rule base.

8. A vehicle-mounted spectrum resource allocation device, characterized in that, The vehicle-mounted spectrum resource allocation device includes: The allocation information acquisition module is used to acquire vehicle driving scenario information and spectrum status data of the target frequency band in the vehicle; The idle resource prediction module is used to extract features from the spectrum status data to obtain spectrum feature information to be analyzed, and to predict channel occupancy based on the spectrum feature information to obtain predicted channel idle resources. The channel resource allocation module is used to match the priority of the current communication device according to the vehicle driving scenario information, and to allocate channel resources to the communication device based on the priority of the current communication device and the predicted channel idle resources.

9. An electronic device, characterized in that, It includes a processor, a memory, and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute a computer program stored in the memory to implement the vehicle spectrum resource allocation method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that enables the computer to execute the vehicle spectrum resource allocation method as described in any one of claims 1-7.

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