Lightweight terminal-based vehicle-mounted network switching and load balancing method and device

Abstract

This application provides a method and apparatus for vehicular network handover and load balancing based on a lightweight terminal. It achieves accurate quality assessment through multi-dimensional index collection and normalization processing. A handover mechanism is constructed, combining fuzzy inference and utility scoring to establish a reliable decision-making strategy. Load balancing optimization is introduced, ensuring continuous transmission improvement through multi-path management and weight allocation. This method effectively addresses the shortcomings of traditional technologies in quality assessment, decision-making, and load balancing, providing technical support for vehicular network management.

Description

Technical Field

[0001] This application relates to the field of data processing, specifically to a method and apparatus for vehicle network switching and load balancing based on a lightweight terminal. Background Technology

[0002] Existing in-vehicle network handover methods have significant shortcomings. Traditional systems perform poorly in network scanning and quality assessment, failing to accurately perceive network resources and thus affecting handover effectiveness.

[0003] Furthermore, existing technologies suffer from bottlenecks in decision-making and load allocation. Most systems lack robust fuzzy inference mechanisms and polling scheduling strategies, resulting in suboptimal network switching accuracy.

[0004] Existing systems have technical shortcomings in multipath management. The lack of in-depth analysis of bandwidth and latency makes it difficult to achieve efficient load balancing through weighted allocation, thus impacting transmission performance. Solving these problems is crucial for improving the management capabilities of vehicular networks. Summary of the Invention

[0005] To address the problems in the existing technology, this application provides a method and apparatus for vehicle network switching and load balancing based on a lightweight terminal, which can effectively solve the shortcomings of traditional technologies in quality assessment, decision-making and load balancing, and provide technical support for vehicle network management.

[0006] To solve at least one of the above problems, this application provides the following technical solution:

[0007] Firstly, this application provides a method for vehicle network handover and load balancing based on a lightweight terminal, including:

[0008] Based on the multi-type wireless network interfaces configured in the vehicle terminal, a candidate network list is obtained by periodic scanning. Multi-dimensional quality indicators are collected for each candidate network in the candidate network list to obtain a network quality indicator set. The network quality indicator set includes signal quality indicators, network load indicators, transmission latency indicators, and available bandwidth indicators. The network quality indicator set is written into the network quality database.

[0009] The network quality index set of each candidate network is read from the network quality database and normalized to obtain a normalized index set. The normalized index set is mapped by a fuzzy membership function to obtain a membership vector. Fuzzy inference is performed on the membership vector based on a preset fuzzy inference rule base to obtain a comprehensive utility score. The difference between the comprehensive utility score and the current service network utility score is calculated to obtain a utility gain. The utility gain is compared with a preset handover hysteresis threshold to obtain a handover decision result.

[0010] Based on the handover decision result, access authentication and bearer establishment are performed on the target network that meets the handover conditions to obtain the target network connection. For multi-network concurrent connection scenarios, multi-path transmission sub-streams are established based on each network interface. The available bandwidth and transmission delay of each sub-stream associated network are obtained from the network quality database to calculate the traffic allocation weight. Based on the traffic allocation weight, weighted round-robin scheduling is performed on the data packets to complete the load balancing allocation.

[0011] Furthermore, it also includes: performing periodic scanning based on the cellular network interface, vehicle-to-everything (V2X) direct communication interface, and wireless local area network (WLAN) interface configured on the vehicle terminal; adaptively adjusting the scanning period according to the vehicle's moving speed to obtain scanning configuration parameters; the scanning configuration parameters control each network interface to perform network discovery according to the corresponding period and write the discovered available network information into a candidate network list; the candidate network list includes the network identifier, network type, access point identifier, signal strength indicator, and scanning timestamp of each candidate network.

[0012] For each candidate network in the candidate network list, signal quality indicators are collected by calling the underlying driver interface of the network interface, network load indicators are collected by parsing the system information of the network broadcast, transmission delay indicators are obtained by sending probe messages to a preset network probe server and measuring round-trip delay and packet loss rate, and available bandwidth indicators are obtained by sending back-to-back message pairs and estimating the bottleneck link bandwidth based on the message interval of the receiving end. The signal quality indicators, network load indicators, transmission delay indicators and available bandwidth indicators are assembled into a network quality indicator set.

[0013] Furthermore, it also includes: the signal quality indicators include received signal strength indication, signal-to-interference-plus-noise ratio and reference signal received power; the network load indicators include cell resource block utilization and channel utilization; the transmission delay indicators include round-trip delay and packet loss rate; and the available bandwidth indicators are calculated by a bandwidth estimation algorithm to obtain the bottleneck link bandwidth estimate.

[0014] The signal quality index, network load index, transmission delay index, and available bandwidth index are validated to remove abnormal data that exceeds the preset valid range, and the valid index data is obtained. The valid index data is then indexed and organized according to the network identifier and the collection timestamp and written into the network quality database.

[0015] Furthermore, it also includes: reading the network quality index set of each candidate network from the network quality database; applying a piecewise linear normalization method to the signal quality index and mapping it to the zero-to-one interval according to the preset excellent threshold, qualified threshold and unusable threshold to obtain the signal quality normalized value; applying reverse normalization processing to the network load index and transmission delay index to obtain the load normalized value and delay normalized value; performing relative normalization processing on the available bandwidth index with the maximum available bandwidth value in each candidate network as the benchmark to obtain the bandwidth normalized value; and assembling the signal quality normalized value, the load normalized value, the delay normalized value and the bandwidth normalized value into a normalized index set.

[0016] A fuzzy membership function is defined for each quality dimension. The fuzzy membership function maps the normalized values ​​of each dimension in the normalized index set to four fuzzy levels of membership: poor, average, good, and excellent. The membership degrees of each quality dimension are then assembled into a membership vector.

[0017] Furthermore, it also includes: traversing each fuzzy rule based on a preset fuzzy inference rule base and calculating the activation intensity of each rule, wherein the activation intensity is the minimum value of the membership degree corresponding to each fuzzy level of the rule antecedent in the membership degree vector; weighting and aggregating the utility levels output by each rule according to the activation intensity and performing defuzzification processing to obtain a comprehensive utility score; and performing business-differentiated weighted adjustment on the comprehensive utility score according to the service quality requirements of the current active business to obtain an adjusted utility score.

[0018] The utility gain is calculated by subtracting the adjusted utility score of each candidate network from the utility score of the current serving network. A corresponding handover hysteresis threshold is selected based on the type relationship between the candidate network and the current serving network. The utility gain is compared with the handover hysteresis threshold. When the utility gain exceeds the handover hysteresis threshold, a handover decision result that triggers handover is generated.

[0019] Furthermore, it also includes: selecting the candidate network with the greatest utility gain as the target network based on the handover decision result, initiating an access request to the target network and completing the access authentication process to obtain the address resources and transmission bearers allocated by the target network to obtain the target network connection; after the target network connection is established, migrating the data traffic of active services from the source network path to the target network path.

[0020] For service scenarios that support concurrent connections across multiple networks, parallel transmission sub-streams are established on multiple network interfaces to obtain multi-path transmission sessions. The sub-stream status table of the multi-path session is maintained to record the associated network interface, local address, remote address, current congestion window, and round-trip delay estimate of each sub-stream. The sub-stream configuration of the multi-path transmission session is dynamically adjusted according to the availability status of the network interfaces.

[0021] Furthermore, it also includes: obtaining the real-time available bandwidth and transmission delay of the network associated with each sub-flow from the network quality database; calculating the basic traffic allocation weight of each sub-flow based on the proportion of available bandwidth; when there is a difference in the transmission delay of each sub-flow, performing delay difference compensation adjustment on the basic traffic allocation weight to obtain the compensated weight; and performing congestion response adjustment on the compensated weight based on the congestion window change trend of each sub-flow to obtain the traffic allocation weight.

[0022] When sending data packets, a weighted round-robin algorithm is used to select transmission sub-streams based on the traffic allocation weights to complete load balancing. For service data that is sensitive to reliability, the same data packet is sent concurrently through multiple sub-streams to perform redundant transmission. The number of bytes sent, the number of acknowledgments, and the number of retransmissions in each sub-stream are periodically statistically summarized for dynamic adjustment of traffic allocation weights.

[0023] Secondly, this application provides a vehicle network switching and load balancing device based on a lightweight terminal, comprising:

[0024] The network monitoring module is used to perform periodic scanning based on the multi-type wireless network interfaces configured in the vehicle terminal to obtain a candidate network list, and to collect multi-dimensional quality indicators for each candidate network in the candidate network list to obtain a network quality indicator set. The network quality indicator set includes signal quality indicators, network load indicators, transmission latency indicators, and available bandwidth indicators, and writes the network quality indicator set into the network quality database.

[0025] The switching decision module is used to read the network quality index set of each candidate network from the network quality database and perform normalization processing to obtain a normalized index set. The normalized index set is mapped by a fuzzy membership function to obtain a membership vector. Fuzzy inference is performed on the membership vector based on a preset fuzzy inference rule base to obtain a comprehensive utility score. The difference between the comprehensive utility score and the utility score of the current service network is calculated to obtain a utility gain. The utility gain is compared with a preset switching hysteresis threshold to obtain a switching decision result.

[0026] The load balancing module is used to perform access authentication and bearer establishment on target networks that meet the switching conditions based on the switching decision results to obtain target network connections. In the case of multiple network concurrent connections, it establishes multi-path transmission sub-streams based on each network interface, obtains the available bandwidth and transmission delay of each sub-stream associated network from the network quality database to calculate traffic allocation weights, and performs weighted round-robin scheduling on data packets based on the traffic allocation weights to complete load balancing allocation.

[0027] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the aforementioned vehicle network switching and load balancing method based on a lightweight terminal.

[0028] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned vehicle network switching and load balancing method based on a lightweight terminal.

[0029] Fifthly, this application provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the aforementioned lightweight terminal-based vehicle network handover and load balancing method.

[0030] As can be seen from the above technical solution, this application provides a method and apparatus for vehicular network handover and load balancing based on a lightweight terminal. Through multi-dimensional index collection and normalization processing, it achieves accurate quality assessment. A handover mechanism is constructed, combining fuzzy inference and utility scoring to establish a reliable decision-making strategy. Load balancing optimization is introduced, ensuring continuous improvement in transmission through multi-path management and weight allocation. This method effectively solves the shortcomings of traditional technologies in quality assessment, decision-making, and load balancing, providing technical support for vehicular network management. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a flowchart illustrating the vehicle network switching and load balancing method based on a lightweight terminal in this application embodiment. Figure 2 This is a structural diagram of the vehicle network switching and load balancing device based on a lightweight terminal in an embodiment of this application. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0034] The acquisition, storage, use, and processing of data in this application comply with relevant laws and regulations.

[0035] To address the problems existing in current technologies, this application provides a method and apparatus for vehicular network handover and load balancing based on a lightweight terminal. Through multi-dimensional index collection and normalization processing, it achieves accurate quality assessment. A handover mechanism is constructed, combining fuzzy inference and utility scoring to establish a reliable decision-making strategy. Load balancing optimization is introduced, ensuring continuous transmission improvement through multi-path management and weight allocation. This method effectively solves the shortcomings of traditional technologies in quality assessment, decision-making, and load balancing, providing technical support for vehicular network management.

[0036] To effectively address the shortcomings of traditional technologies in quality assessment, decision-making, and load balancing, and to provide technical support for vehicular network management, this application provides an embodiment of a vehicular network handover and load balancing method based on a lightweight terminal. See [link to embodiment]. Figure 1 The method for vehicle network handover and load balancing based on lightweight terminals specifically includes the following:

[0037] Step S101: Perform periodic scanning based on the multi-type wireless network interfaces configured in the vehicle terminal to obtain a candidate network list. Perform multi-dimensional quality index collection on each candidate network in the candidate network list to obtain a network quality index set. The network quality index set includes signal quality index, network load index, transmission delay index, and available bandwidth index. Write the network quality index set into the network quality database.

[0038] This embodiment implements unified management of the cellular network interface, vehicle-to-everything (V2X) direct communication interface, and wireless LAN interface configured on the vehicle terminal. The network interface management module maintains the enabled and connected status of each interface. After the vehicle terminal starts up, the network interface management module performs network scanning on each interface at a preset cycle. The scanning cycle is adaptively adjusted according to the vehicle's speed. When the vehicle is traveling at high speed, the scanning cycle is shortened to improve the timeliness of network discovery, and when the vehicle is traveling at low speed, the scanning cycle is extended to reduce power consumption.

[0039] The results of the network scan are filtered and written into a candidate network list. The data structure of the candidate network list includes five fields: network identifier, network type, access point identifier, signal strength indicator, and scan timestamp. For example, when a vehicle passes through a commercial area, the scan can detect multiple cellular network cells and several wireless LAN hotspots, and the basic information of each network is registered one by one in the candidate network list.

[0040] Based on the candidate network list, this embodiment performs multi-dimensional quality indicator collection on each candidate network. Signal quality indicators are obtained by calling the underlying driver interface of the network interface, and the collected content includes received signal strength indication, signal-to-interference-plus-noise ratio, and reference signal received power. Network load indicators are obtained by parsing system information broadcast by the network, including the utilization rate of cell resource blocks for cellular networks and the channel utilization rate and number of associated terminals for wireless LANs.

[0041] Accordingly, this embodiment uses an active probing method to obtain transmission delay and available bandwidth metrics. The transmission delay probing module periodically sends small-volume probing packets to a preset network probing server, calculates the round-trip time based on the packet round-trip time, and simultaneously calculates the packet loss rate as a supplementary metric for link quality. The available bandwidth metric employs a lightweight algorithm based on packet pair probing. The sending end continuously sends back-to-back packet pairs, and the receiving end estimates the bottleneck link bandwidth based on the packet interval. This method has low probing overhead and is suitable for resource-constrained lightweight terminal platforms.

[0042] The signal quality indicators, network load indicators, transmission latency indicators, and available bandwidth indicators are aggregated and assembled into a network quality indicator set. This embodiment performs a validity check on the network quality indicator set, removing abnormal data that exceeds a preset valid range and retaining valid indicator data that passes the check.

[0043] Specifically, the effective indicator data is indexed and organized according to network identifier and collection timestamp, and then written into the network quality database. The network quality database adopts a time-series storage structure, supports the retrieval of the latest quality data by network identifier, and retains historical collection records for subsequent connection stability statistics. The set of network quality indicators stored in the network quality database is read by the subsequent step S201 as the input data source for normalization processing and fuzzy utility evaluation.

[0044] Step S102: Read the network quality index set of each candidate network from the network quality database and perform normalization processing to obtain a normalized index set. The normalized index set is mapped by a fuzzy membership function to obtain a membership vector. Based on a preset fuzzy inference rule base, fuzzy inference is performed on the membership vector to obtain a comprehensive utility score. The difference between the comprehensive utility score and the current service network utility score is calculated to obtain a utility gain. The utility gain is compared with a preset handover hysteresis threshold to obtain a handover decision result.

[0045] In this embodiment, the network quality index set of each candidate network written in step S101 is read from the network quality database, and normalization processing is performed on the quality indexes of each dimension to eliminate the difference in dimensions. The signal quality index adopts a piecewise linear normalization method. According to the preset excellent threshold, qualified threshold and unusable threshold, the original index value is mapped to the range of zero to one. The signal strength is mapped to one if it is higher than the excellent threshold, and to zero if it is lower than the unusable threshold. The normalized value is calculated by linear interpolation for the values ​​within the threshold range.

[0046] The network load and transmission delay metrics are inversely normalized; lower load corresponds to higher normalized values, and lower delay corresponds to higher normalized values. The available bandwidth metric is relative normalized using the maximum available bandwidth among all candidate networks as a benchmark. In this embodiment, the signal quality normalized value, load normalized value, delay normalized value, and bandwidth normalized value are assembled into a set of normalized metrics.

[0047] Based on the normalized index set, this embodiment defines a fuzzy membership function for each quality dimension. The fuzzy membership function maps the normalized values ​​of each dimension to membership degrees of four fuzzy levels: poor, average, good, and excellent, with membership degrees ranging from zero to one. For example, when the normalized signal quality value of a candidate network is in the transition range between good and excellent, the network has non-zero membership degrees at both the good and excellent levels. The fuzzy membership function defines the boundaries and transition slopes of each level using a trapezoidal function.

[0048] Accordingly, this embodiment assembles the membership degrees of each quality dimension into a membership degree vector, and performs fuzzy inference on the membership degree vector based on a preset fuzzy inference rule base. The fuzzy inference rule base contains multiple rules to describe the mapping relationship between the combination of fuzzy levels of each quality dimension and the overall utility level. For example, if the signal quality is good, the network load is medium, and the transmission delay is excellent, then the overall utility is good. This embodiment traverses all rules in the rule base and calculates the activation strength of each rule. The activation strength is the minimum value of the membership degree corresponding to each fuzzy level of the rule's antecedent in the membership degree vector.

[0049] After the activation intensity calculation is completed, this embodiment aggregates the utility levels output by each rule according to the activation intensity and performs defuzzification to obtain a comprehensive utility score. The defuzzification process uses the centroid method to convert the fuzzy output into a precise value, and the comprehensive utility score ranges from zero to one. This embodiment performs service-differentiated weighting adjustments on the comprehensive utility score based on the service quality requirements of currently active services. For latency-sensitive services, the weight of the transmission latency dimension is increased; for bandwidth-sensitive services, the weight of the available bandwidth dimension is increased, resulting in an adjusted utility score.

[0050] Specifically, in this embodiment, the utility gain is calculated by subtracting the adjusted utility score of each candidate network from the utility score of the current serving network. A corresponding handover hysteresis threshold is selected based on the type relationship between the candidate network and the current serving network. A smaller hysteresis threshold is used for horizontal handover between networks of the same type, while a larger hysteresis threshold is used for vertical handover between heterogeneous networks to reflect the higher handover cost of vertical handover. When the utility gain exceeds the handover hysteresis threshold, this embodiment generates a handover decision result that triggers handover. This handover decision result is read by subsequent step S103 to perform access authentication and bearer establishment for the target network.

[0051] Step S103: Based on the handover decision result, perform access authentication and bearer establishment on the target network that meets the handover conditions to obtain the target network connection. For multi-network concurrent connection scenarios, establish multi-path transmission sub-streams based on each network interface. Obtain the available bandwidth and transmission delay of each sub-stream associated network from the network quality database to calculate the traffic allocation weight. Perform weighted round-robin scheduling on the data packets based on the traffic allocation weight to complete the load balancing allocation.

[0052] In this embodiment, the handover decision result generated in step S102 is read, and candidate networks that meet the handover conditions are sorted by utility gain. The candidate network with the largest utility gain is selected as the target network. The handover execution module initiates an access request to the target network, completes the access authentication process, obtains the address resources and transmission bearers allocated by the target network, and establishes a connection to the target network.

[0053] After the target network connection is established, this embodiment migrates the data traffic of active services from the source network path to the target network path. Sub-stream migration is performed for service sessions using transport layer multipath protocols, and address switching is performed for service sessions using single-path protocols. When there are real-time service sessions sensitive to switching, this embodiment adopts a soft switching mode of establishing the connection before disconnecting it, delaying the release of the source network connection to retain switching rollback capability.

[0054] Based on the target network connection, this embodiment establishes a multi-path transmission session for service scenarios that support concurrent connections across multiple networks. The multi-path connection management module establishes parallel transmission sub-streams on multiple network interfaces, with each sub-stream jointly carrying the data transmission of the same service session. This embodiment maintains a sub-stream status table for the multi-path session, recording the associated network interface, local address, remote address, current congestion window, and round-trip delay estimate for each sub-stream.

[0055] Accordingly, this embodiment obtains the real-time available bandwidth and transmission latency of each sub-flow's associated network from the network quality database, and calculates the traffic allocation weight based on the comprehensive relationship between bandwidth contribution and latency penalty. This embodiment uses the following weight calculation function:

[0056] G = a1×H / Hmax + a2×Dmin / D - a3×L.

[0057] Where G is the traffic allocation weight of a single sub-flow; a1, a2, and a3 are non-negative coefficients that remain fixed within the current scheduling period; H is the real-time available bandwidth of the network associated with the sub-flow, and Hmax is the maximum available bandwidth among all sub-flows; D is the measured round-trip delay of the sub-flow, and Dmin is the minimum round-trip delay among all sub-flows; L is the normalized packet loss rate of the sub-flow within the most recent statistical window. G is used to determine the transmission opportunity ratio of each sub-flow in the weighted round-robin scheduling.

[0058] After the traffic allocation weight G is calculated, this embodiment dynamically adjusts it based on the congestion window trends of each sub-stream. Sub-streams with shrinking congestion windows have their traffic allocation weight G reduced, while sub-streams with expanding congestion windows have their traffic allocation weight G increased. When sending data packets, this embodiment uses a weighted round-robin algorithm based on the adjusted traffic allocation weights to select the transmission sub-streams, with higher-weight sub-streams receiving more transmission opportunities.

[0059] Specifically, this embodiment supports a redundant transmission mode for reliability-sensitive service data, sending the same data packet concurrently through multiple sub-streams. The receiving end uses the first arriving data packet and discards duplicate packets. This embodiment performs periodic statistical summaries on the number of bytes sent, acknowledgments, and retransmissions for each sub-stream. The statistical data is used for the periodic recalculation of the traffic allocation weight G, and is also read by the subsequent mobility-aware predictive handover module to evaluate the actual transmission performance of each network.

[0060] As described above, the vehicular network handover and load balancing method based on lightweight terminals provided in this application can achieve accurate quality assessment through multi-dimensional index collection and normalization processing. A handover mechanism is constructed, combining fuzzy inference and utility scoring to establish a reliable decision-making strategy. Load balancing optimization is introduced, ensuring continuous transmission improvement through multi-path management and weight allocation. This method effectively addresses the shortcomings of traditional technologies in quality assessment, decision-making, and load balancing, providing technical support for vehicular network management.

[0061] In one embodiment of the vehicle network handover and load balancing method based on a lightweight terminal in this application, the method may further include the following:

[0062] Step S201: Based on the cellular network interface, vehicle-to-everything (V2X) direct communication interface, and wireless LAN interface configured in the vehicle terminal, periodic scanning is performed. The scanning period is adaptively adjusted according to the vehicle's moving speed to obtain scanning configuration parameters. The scanning configuration parameters control each network interface to perform network discovery according to the corresponding period and write the discovered available network information into the candidate network list. The candidate network list includes the network identifier, network type, access point identifier, signal strength indicator, and scanning timestamp of each candidate network.

[0063] Step S202: For each candidate network in the candidate network list, signal quality indicators are collected by calling the underlying driver interface of the network interface; network load indicators are collected by parsing the system information of the network broadcast; transmission delay indicators are obtained by sending probe messages to a preset network probe server and measuring round-trip delay and packet loss rate; available bandwidth indicators are obtained by sending back-to-back message pairs and estimating the bottleneck link bandwidth based on the message interval of the receiving end; and the signal quality indicators, network load indicators, transmission delay indicators, and available bandwidth indicators are assembled into a network quality indicator set.

[0064] This embodiment performs periodic scanning on the cellular network interface, vehicle-to-everything (V2X) communication interface, and wireless LAN interface configured on the vehicle terminal. The cellular network interface supports dual-mode operation of 4G and 5G mobile communication, the V2X communication interface is used for short-range direct communication between the vehicle and roadside equipment, and the wireless LAN interface is used to access hotspot networks deployed along the route.

[0065] The scanning cycle of the periodic scan is adaptively adjusted according to the vehicle's moving speed. In this embodiment, real-time vehicle speed information is obtained from the vehicle bus. When the vehicle speed is higher than a preset high-speed threshold, the scanning cycle is shortened to improve the timeliness of network discovery; when the vehicle speed is lower than a preset low-speed threshold, the scanning cycle is extended to reduce power consumption. The correspondence between the scanning cycle and vehicle speed is stored in the scanning configuration parameters, which are loaded by the network interface management module when the on-board terminal starts and dynamically updated when the vehicle speed changes.

[0066] Under the control of the scanning configuration parameters, each network interface performs network discovery according to the corresponding cycle. The cellular network interface discovers available cells by measuring neighboring cell signals, the vehicle-to-everything (V2X) direct communication interface discovers available roadside devices by listening to broadcast messages, and the wireless LAN interface discovers available hotspots by scanning beacon frames. In this embodiment, the available network information discovered by each interface is aggregated and written into a candidate network list. The candidate network list includes five fields for each candidate network: network identifier, network type, access point identifier, signal strength indicator, and scan timestamp.

[0067] Accordingly, this embodiment performs multi-dimensional quality indicator collection on each candidate network in the candidate network list. Signal quality indicators are obtained by calling the underlying driver interface of the network interface, and the collected content includes received signal strength indication, signal-to-interference-plus-noise ratio, and reference signal received power. Each indicator reflects the radio frequency propagation quality of the wireless link. Network load indicators are obtained by parsing system information broadcast by the network. For cellular networks, cell resource block utilization is extracted from system information blocks, and for wireless LANs, channel utilization and the number of associated terminals are extracted from beacon frames.

[0068] The transmission latency metric is obtained through active probing. In this embodiment, small-volume probe messages are periodically sent to a preset network probing server. The round-trip latency is calculated based on the difference between the message sending time and the response receiving time. Simultaneously, the packet loss rate of the probe messages is used as a measure of link reliability. The sending period and message size of the probe messages are adaptively configured according to the current network type and service load status. The probing frequency is appropriately increased when the network is idle to obtain a more accurate latency estimate.

[0069] The available bandwidth metric is obtained by sending back-to-back message pairs and estimating the bottleneck link bandwidth based on the message interval at the receiving end. The sending end continuously sends two adjacent probe messages, and the receiving end records the arrival time interval of the two messages, estimating the bottleneck bandwidth of the link based on the ratio of message size to time interval. In this embodiment, the signal quality metric, network load metric, transmission delay metric, and available bandwidth metric are assembled into a network quality metric set. The network quality metric set is indexed by network identifier and collection timestamp and written into a network quality database for subsequent normalization processing and fuzzy utility evaluation in step S102.

[0070] In one embodiment of the vehicle network handover and load balancing method based on a lightweight terminal in this application, the method may further include the following:

[0071] Step S301: The signal quality indicators include received signal strength indication, signal-to-interference-plus-noise ratio and reference signal received power; the network load indicators include cell resource block utilization and channel utilization; the transmission delay indicators include round-trip delay and packet loss rate; and the available bandwidth indicators are calculated by a bandwidth estimation algorithm to obtain the bottleneck link bandwidth estimate.

[0072] Step S302: Perform validity checks on the signal quality index, network load index, transmission delay index, and available bandwidth index to remove abnormal data that exceeds the preset valid range and obtain valid index data. Then, index and organize the valid index data according to the network identifier and the collection timestamp and write it into the network quality database.

[0073] This embodiment refines the definition and data organization of the various quality indicators collected in step S202 above. The signal quality indicators include three sub-items: received signal strength indicator, signal-to-interference-plus-noise ratio, and reference signal received power. The received signal strength indicator reflects the power level of the radio frequency signal received by the terminal, the signal-to-interference-plus-noise ratio reflects the relative strength of the useful signal and the interference noise, and the reference signal received power reflects the reception quality of the cellular network reference signal.

[0074] The network load metrics include two sub-items: cell resource block utilization and channel utilization. Cell resource block utilization is applicable to cellular network scenarios and represents the proportion of radio resource occupancy in the current cell. This metric is obtained by parsing system information blocks from network broadcasts. Channel utilization is applicable to wireless LAN scenarios and represents the proportion of time occupancy in the current channel. This metric is extracted from the load information field of beacon frames.

[0075] Based on the aforementioned signal quality and network load metrics, this embodiment provides a structured definition of transmission delay and available bandwidth metrics. The transmission delay metric includes two sub-items: round-trip time (RTD) and packet loss rate. RTD is calculated from the difference between the transmission time of a probe message and the reception time of an acknowledgment. Packet loss rate is calculated as the ratio of the number of messages for which no acknowledgment was received within the probe period to the total number of messages sent. The available bandwidth metric calculates the bottleneck link bandwidth estimate using a bandwidth estimation algorithm. This bandwidth estimation algorithm estimates the link transmission capacity based on the reception interval of back-to-back message pairs.

[0076] Accordingly, this embodiment performs validity checks on the signal quality indicators, network load indicators, transmission delay indicators, and available bandwidth indicators. For each sub-item, a preset valid range is set. The valid range for the received signal strength indicator is determined based on the receiver sensitivity and saturation power; the valid range for the round-trip delay is determined based on the typical delay characteristics of the network type; and the valid range for the available bandwidth is determined based on the upper limit of the interface physical layer rate. Abnormal data exceeding the preset valid range is marked and removed, retaining only the valid indicator data that passes the check.

[0077] The effective indicator data is indexed and organized according to network identifier and collection timestamp, and then written into the network quality database. The network identifier serves as the primary index to distinguish the quality records of different candidate networks, while the collection timestamp serves as an auxiliary index to support time-series queries and historical data statistics. The network quality database uses a ring buffer structure to store the recent quality records of each network, and automatically overwrites the oldest historical data when the storage space reaches a preset capacity. The effective indicator data stored in the network quality database is read by the normalization processing module in subsequent step S102, and is also used by the connection stability statistics module to calculate the signal strength fluctuation variance and average connection duration.

[0078] In one embodiment of the vehicle network handover and load balancing method based on a lightweight terminal in this application, the method may further include the following:

[0079] Step S401: Read the network quality index set of each candidate network from the network quality database. Apply piecewise linear normalization to the signal quality index and map it to the zero-to-one interval according to the preset excellent threshold, qualified threshold and unusable threshold to obtain the signal quality normalized value. Apply reverse normalization to the network load index and transmission delay index to obtain the load normalized value and delay normalized value. Apply relative normalization to the available bandwidth index with the maximum available bandwidth value in each candidate network as the benchmark to obtain the bandwidth normalized value. Assemble the signal quality normalized value, the load normalized value, the delay normalized value and the bandwidth normalized value into a normalized index set.

[0080] Step S402: Define a fuzzy membership function for each quality dimension. The fuzzy membership function maps the normalized values ​​of each dimension in the normalized index set to the membership degrees of four fuzzy levels: poor, average, good, and excellent. The membership degrees of each quality dimension are then assembled into a membership vector.

[0081] This embodiment reads the set of network quality indicators for each candidate network written in step S302 from the network quality database, performs normalization processing on the quality indicators of each dimension to eliminate differences in dimensions and establish a unified evaluation scale. The signal quality indicators adopt a piecewise linear normalization method, mapping the original indicator values ​​to the zero-to-one interval according to preset excellent thresholds, qualified thresholds, and unusable thresholds.

[0082] In the piecewise linear normalization method, when the signal quality index is higher than the excellent threshold, the normalized signal quality value is 1; when the signal quality index is lower than the unusable threshold, the normalized signal quality value is zero; when the signal quality index is between the excellent threshold and the acceptable threshold, the normalized value is calculated by linear interpolation; when the signal quality index is between the acceptable threshold and the unusable threshold, it is also calculated by linear interpolation, but with a different slope. For example, when the received power of the reference signal of a candidate network is at the midpoint between the acceptable threshold and the excellent threshold, its normalized signal quality value is approximately 0.75.

[0083] Accordingly, this embodiment employs inverse normalization to obtain normalized load and normalized latency values ​​for network load and transmission latency metrics. Lower network load corresponds to a higher normalized load value, and lower transmission latency corresponds to a higher normalized latency value. This processing method ensures that the normalized values ​​for each dimension maintain a consistent direction; that is, a higher normalized value indicates better network quality. Inverse normalization is achieved by dividing the difference between the original metric value and a preset upper limit by the metric's value range.

[0084] Based on the load normalization value and delay normalization value, this embodiment performs relative normalization processing on the available bandwidth index using the maximum available bandwidth value among all candidate networks as a benchmark. The bandwidth normalization value is equal to the available bandwidth of the network divided by the maximum available bandwidth among all candidate networks. The candidate network with the highest bandwidth has a bandwidth normalization value of one, and the other networks are proportionally normalized. This embodiment assembles the signal quality normalization value, load normalization value, delay normalization value, and bandwidth normalization value into a set of normalized indices.

[0085] The normalized index set serves as the input for the fuzzy membership mapping. In this embodiment, a fuzzy membership function is defined for each quality dimension. The fuzzy membership function uses a trapezoidal function to map the normalized values ​​of each dimension to membership degrees for four fuzzy levels: poor, average, good, and excellent. The trapezoidal function defines membership boundaries by setting core and transition intervals for each level. When a normalized value falls within the core interval of a level, the membership degree for that level is one. When a normalized value falls within the transition interval of an adjacent level, the membership degree between the two levels is calculated using linear interpolation.

[0086] After mapping each quality dimension based on the fuzzy membership function, this embodiment assembles the membership degrees of each quality dimension into a membership vector. The membership vector structure includes four levels of membership degrees for the signal quality dimension, load dimension, delay dimension, and bandwidth dimension. This membership vector is read by the fuzzy inference module in subsequent step S501 as input data for rule matching and activation strength calculation.

[0087] In one embodiment of the vehicle network handover and load balancing method based on a lightweight terminal in this application, the method may further include the following:

[0088] Step S501: Based on the preset fuzzy inference rule base, traverse each fuzzy rule and calculate the activation strength of each rule. The activation strength is the minimum value of the membership degree corresponding to each fuzzy level of the rule antecedent in the membership degree vector. The utility level output by each rule is weighted and aggregated according to the activation strength and defuzzification is performed to obtain a comprehensive utility score. According to the service quality requirements of the current active business, the comprehensive utility score is adjusted by business differentiation weighting to obtain an adjusted utility score.

[0089] Step S502: Calculate the utility gain by comparing the adjusted utility score of each candidate network with the utility score of the current serving network. Select the corresponding handover hysteresis threshold according to the type relationship between the candidate network and the current serving network. Compare the utility gain with the handover hysteresis threshold. When the utility gain exceeds the handover hysteresis threshold, generate a handover decision that triggers handover.

[0090] This embodiment reads the membership vector generated in step S402, traverses each fuzzy rule based on the preset fuzzy inference rule base, and calculates the activation strength of each rule. The fuzzy inference rule base contains multiple rules to describe the mapping relationship between the combination of fuzzy levels of each quality dimension and the overall utility level. For example, if the signal quality is good, the network load is medium, the transmission latency is excellent, and the available bandwidth is good, then the overall utility is good.

[0091] The activation strength is calculated using a minimum-value operation. For each fuzzy rule, this embodiment extracts the membership values ​​corresponding to each fuzzy level involved in the rule's antecedent from the membership vector, and takes the minimum value among these membership values ​​as the activation strength of the rule. For example, if the antecedent of a rule requires excellent signal quality and good network load, and if the current candidate network has a membership degree of 0.8 for the excellent level in the signal quality dimension and a membership degree of 0.6 for the good level in the load dimension, then the activation strength of the rule is taken as the minimum of the two, 0.6.

[0092] Accordingly, in this embodiment, the utility levels of each rule's output are aggregated using a weighted average based on the activation intensity. Each activated rule contributes the product of its output utility level and its activation intensity, and the sum of the contributions from all rules forms a fuzzy output distribution. In this embodiment, the fuzzy output distribution is defuzzified to obtain a comprehensive utility score. The defuzzification uses the centroid method to convert the fuzzy output into a precise numerical value, and the comprehensive utility score ranges from zero to one.

[0093] Based on the comprehensive utility score, this embodiment performs differentiated weighted adjustments according to the service quality requirements of currently active services. The service type identification module detects the categories of currently active data services. For latency-sensitive services, the contribution weight of the latency dimension in the utility calculation is increased; for bandwidth-sensitive services, the contribution weight of the bandwidth dimension is increased; and for reliability-sensitive services, the contribution weight of the signal quality dimension is increased. This embodiment uses the weighted adjustment result as the adjusted utility score.

[0094] After the adjusted utility score is calculated, this embodiment calculates the utility gain by comparing the adjusted utility score of each candidate network with the utility score of the current service network. A positive utility gain indicates that the overall utility of the candidate network is better than that of the current service network, and the absolute value of the utility gain reflects the degree of utility gap between the two networks.

[0095] Based on the aforementioned utility gain, this embodiment selects a corresponding handover hysteresis threshold according to the type relationship between the candidate network and the current serving network. A smaller hysteresis threshold is used for horizontal handover between similar networks, while a larger hysteresis threshold is used for vertical handover between heterogeneous networks to reflect the higher handover cost caused by protocol stack differences involved in vertical handover. This embodiment compares the utility gain with the handover hysteresis threshold. When the utility gain exceeds the handover hysteresis threshold, a handover decision result triggering handover is generated. This handover decision result is read by subsequent step S601 to perform access authentication and bearer establishment of the target network.

[0096] In one embodiment of the vehicle network handover and load balancing method based on a lightweight terminal in this application, the method may further include the following:

[0097] Step S601: Based on the handover decision result, select the candidate network with the largest utility gain as the target network, initiate an access request to the target network and complete the access authentication process to obtain the address resources and transmission bearers allocated by the target network to obtain the target network connection. After the target network connection is established, migrate the data traffic of active services from the source network path to the target network path.

[0098] Step S602: For service scenarios that support concurrent connections across multiple networks, establish parallel transmission sub-streams on multiple network interfaces to obtain multi-path transmission sessions. Maintain a sub-stream status table for the multi-path sessions to record the associated network interface, local address, remote address, current congestion window, and round-trip delay estimate for each sub-stream. Dynamically adjust the sub-stream configuration of the multi-path transmission sessions based on the availability status of the network interfaces.

[0099] In this embodiment, the handover decision result generated in step S502 is read, and the candidate networks that meet the handover triggering conditions are sorted according to their utility gain. The candidate network with the largest utility gain is selected as the target network. The handover execution module obtains the network identifier and access point identifier of the target network and initiates an access request to the target network.

[0100] After the access request is initiated, this embodiment executes the corresponding access authentication process according to the target network type. Cellular network access authentication completes authorization and key negotiation through the user identity module, while wireless LAN access authentication completes identity verification through a pre-shared key or an enterprise-level authentication protocol. After the access authentication process is completed, this embodiment obtains the address resources and transport bearers allocated by the target network. The address resources include network layer addresses and transport layer ports, and the transport bearers include quality of service parameters and bearer identifiers, thereby establishing a connection to the target network.

[0101] After the target network connection is established, this embodiment migrates the data traffic of active services from the source network path to the target network path. For service sessions using transport layer multipath protocols, this embodiment establishes a new transport sub-stream on the target network and gradually schedules data to the new sub-stream. Once the transmission of the new sub-stream is stable, the source network sub-stream is closed. For service sessions using single-path protocols, this embodiment performs address switching and notifies the peer to update the session binding relationship. When there are real-time service sessions that are sensitive to switching, this embodiment adopts a soft switching mode of establishing a connection before disconnecting to reduce the duration of service interruption.

[0102] Accordingly, this embodiment establishes parallel transmission substreams on multiple network interfaces to obtain multipath transmission sessions for service scenarios that support concurrent connections across multiple networks. The multipath connection management module detects the number of currently available network interfaces and the connection status of each interface. When the conditions for establishing a multipath are met, it initiates a multipath session negotiation with the remote server. After successful negotiation, transmission substreams are established on each network interface.

[0103] After the multipath transmission session is established, this embodiment maintains a sub-stream state table to record the operating parameters of each sub-stream. The data structure of the sub-stream state table includes five fields for each sub-stream: associated network interface, local address, remote address, current congestion window, and round-trip delay estimate. The congestion window reflects the current transmission rate control status of the sub-stream, and the round-trip delay estimate reflects the end-to-end transmission delay of the sub-stream. Both parameters are periodically updated to the sub-stream state table by the congestion control module of the transmission protocol.

[0104] Based on the sub-flow status table, this embodiment dynamically adjusts the sub-flow configuration of the multipath transmission session according to the availability status of the network interface. When the network interface management module reports that a new network interface has become available, this embodiment evaluates whether to establish a new sub-flow on that interface to increase aggregate bandwidth; when a network interface fails or its quality deteriorates, this embodiment shuts down the corresponding sub-flow and redistributes its data traffic to the remaining sub-flows. The sub-flow status table is read by the load balancing scheduling module in subsequent step S701 as input data for calculating traffic allocation weights.

[0105] In one embodiment of the vehicle network handover and load balancing method based on a lightweight terminal in this application, the method may further include the following:

[0106] Step S701: Obtain the real-time available bandwidth and transmission delay of the network associated with each sub-flow from the network quality database, calculate the basic traffic allocation weight of each sub-flow according to the proportion of available bandwidth, and when there is a difference in the transmission delay of each sub-flow, perform delay difference compensation adjustment on the basic traffic allocation weight to obtain the compensated weight. Perform congestion response adjustment on the compensated weight according to the congestion window change trend of each sub-flow to obtain the traffic allocation weight.

[0107] Step S702: When sending data packets, a weighted round-robin algorithm is used to select transmission sub-streams based on the traffic allocation weights to complete load balancing. For service data that is sensitive to reliability, the same data packet is sent concurrently through multiple sub-streams to perform redundant transmission. The number of bytes sent, the number of acknowledgments, and the number of retransmissions in each sub-stream are periodically statistically summarized for dynamic adjustment of traffic allocation weights.

[0108] In this embodiment, the real-time available bandwidth and transmission latency of each sub-flow associated network established in step S602 are obtained from the network quality database, and the traffic allocation weight is calculated by combining the congestion window parameters recorded in the sub-flow status table maintained in step S602. The basic traffic allocation weight of each sub-flow is determined according to the proportion of available bandwidth, and the sub-flow with higher available bandwidth receives a larger basic weight.

[0109] Based on the basic traffic allocation weights, this embodiment detects whether there are differences in the transmission delays of each sub-stream. When the estimated round-trip delays of each sub-stream differ significantly, the receiver requires a larger buffer capacity to cope with the rearrangement of out-of-order data packets. Therefore, this embodiment performs delay difference compensation adjustments on the basic traffic allocation weights. Sub-streams with lower delays have their allocation weights increased, while sub-streams with higher delays have their allocation weights decreased. The compensation amount is calculated based on the relative difference between the delay of each sub-stream and the minimum delay to obtain the compensated weights.

[0110] Accordingly, this embodiment adjusts the compensated weights based on the congestion window change trends of each sub-flow. The congestion window change trend is obtained by comparing the current congestion window value with the congestion window value of the previous statistical period. A continuously shrinking congestion window indicates congestion in the network where the sub-flow resides, while a continuously growing congestion window indicates smooth transmission in the network where the sub-flow resides. This embodiment reduces the allocation weight of sub-flows with shrinking congestion windows and increases the allocation weight of sub-flows with growing congestion windows, thereby obtaining the traffic allocation weights.

[0111] After the traffic allocation weights are calculated, this embodiment uses a weighted round-robin algorithm to select the transmission sub-stream when sending data packets. The weighted round-robin algorithm maintains a weight counter for each sub-stream. When sending a data packet, it selects the sub-stream with the largest weight counter for transmission. After transmission, it subtracts a fixed step size from the weight counter of that sub-stream and increments the weight counters of the remaining sub-streams by the corresponding traffic allocation weight value. This scheduling method ensures that the proportion of transmission opportunities obtained by each sub-stream is consistent with the traffic allocation weights.

[0112] After load balancing is achieved based on the weighted round-robin scheduling, this embodiment supports a redundant transmission mode for reliability-sensitive service data. In redundant transmission mode, the same data packet is sent concurrently through multiple substreams. The receiving end uses the first arriving data packet for delivery and discards subsequent duplicate packets. The service type identification module determines whether to enable redundant transmission based on the service quality flag of the data packet. Redundant transmission is enabled for vehicle control and safety warning data to achieve higher transmission reliability.

[0113] In the aforementioned load balancing process, this embodiment periodically summarizes the transmission statistics for each sub-stream. The statistics include the number of bytes sent, bytes acknowledged, and bytes retransmitted for each sub-stream. The number of bytes sent reflects the traffic load borne by the sub-stream, the number of bytes acknowledged reflects the effective throughput of the sub-stream, and the number of bytes retransmitted reflects the transmission loss of the sub-stream. These statistics are used for the periodic recalculation of the traffic allocation weights and are also read by the subsequent mobility-aware predictive handover module to evaluate the actual transmission performance of each network.

[0114] To effectively address the shortcomings of traditional technologies in quality assessment, decision-making, and load balancing, and to provide technical support for vehicular network management, this application provides an embodiment of a lightweight terminal-based vehicular network handover and load balancing device for implementing all or part of the aforementioned lightweight terminal-based vehicular network handover and load balancing method. See [link to embodiment]. Figure 2 The vehicle network switching and load balancing device based on a lightweight terminal specifically includes the following components:

[0115] Network monitoring module 10 is used to perform periodic scanning based on the multi-type wireless network interfaces configured in the vehicle terminal to obtain a candidate network list, perform multi-dimensional quality index collection on each candidate network in the candidate network list to obtain a network quality index set, the network quality index set includes signal quality index, network load index, transmission delay index and available bandwidth index, and write the network quality index set into the network quality database.

[0116] The switching decision module 20 is used to read the network quality index set of each candidate network from the network quality database and perform normalization processing to obtain a normalized index set. The normalized index set is mapped by a fuzzy membership function to obtain a membership vector. Fuzzy inference is performed on the membership vector based on a preset fuzzy inference rule base to obtain a comprehensive utility score. The difference between the comprehensive utility score and the utility score of the current service network is calculated to obtain a utility gain. The utility gain is compared with a preset switching hysteresis threshold to obtain a switching decision result.

[0117] The load balancing module 30 is used to perform access authentication and bearer establishment on the target network that meets the switching conditions based on the switching decision result to obtain the target network connection. In the case of multiple network concurrent connection, it establishes multi-path transmission sub-streams based on each network interface, obtains the available bandwidth and transmission delay of each sub-stream associated network from the network quality database to calculate the traffic allocation weight, and performs weighted round-robin scheduling on the data packets based on the traffic allocation weight to complete the load balancing allocation.

[0118] As described above, the vehicle network handover and load balancing device based on a lightweight terminal provided in this application can achieve accurate quality assessment through multi-dimensional index collection and normalization processing. A handover mechanism is constructed, combining fuzzy inference and utility scoring to establish a reliable decision-making strategy. Load balancing optimization is introduced, ensuring continuous improvement in transmission through multi-path management and weight allocation. This method effectively addresses the shortcomings of traditional technologies in quality assessment, decision-making, and load balancing, providing technical support for vehicle network management.

[0119] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the vehicle network switching and load balancing method based on a lightweight terminal.

[0120] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for vehicle network switching and load balancing based on a lightweight terminal.

[0121] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method for vehicle network switching and load balancing based on a lightweight terminal.

[0122] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0124] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0125] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0126] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for vehicle network handover and load balancing based on a lightweight terminal, characterized in that, The method includes: Based on the multi-type wireless network interfaces configured in the vehicle terminal, a candidate network list is obtained by periodic scanning. Multi-dimensional quality indicators are collected for each candidate network in the candidate network list to obtain a network quality indicator set. The network quality indicator set includes signal quality indicators, network load indicators, transmission latency indicators, and available bandwidth indicators. The network quality indicator set is written into the network quality database. The network quality index set of each candidate network is read from the network quality database and normalized to obtain a normalized index set. The normalized index set is mapped by a fuzzy membership function to obtain a membership vector. Fuzzy inference is performed on the membership vector based on a preset fuzzy inference rule base to obtain a comprehensive utility score. The difference between the comprehensive utility score and the current service network utility score is calculated to obtain a utility gain. The utility gain is compared with a preset handover hysteresis threshold to obtain a handover decision result. Based on the handover decision result, access authentication and bearer establishment are performed on the target network that meets the handover conditions to obtain the target network connection. For multi-network concurrent connection scenarios, multi-path transmission sub-streams are established based on each network interface. The available bandwidth and transmission delay of each sub-stream associated network are obtained from the network quality database to calculate the traffic allocation weight. Based on the traffic allocation weight, weighted round-robin scheduling is performed on the data packets to complete the load balancing allocation.

2. The method for vehicle network handover and load balancing based on a lightweight terminal according to claim 1, characterized in that, The multi-type wireless network interface configured based on the vehicle terminal performs periodic scanning to obtain a candidate network list. Multi-dimensional quality index collection is then performed on each candidate network in the candidate network list to obtain a set of network quality indicators, including: The cellular network interface, vehicle-to-everything (V2X) direct communication interface, and wireless local area network (WLAN) interface configured on the vehicle terminal perform periodic scanning. The scanning period is adaptively adjusted according to the vehicle's moving speed to obtain scanning configuration parameters. These scanning configuration parameters control each network interface to perform network discovery according to the corresponding period and write the discovered available network information into a candidate network list. The candidate network list includes the network identifier, network type, access point identifier, signal strength indicator, and scanning timestamp of each candidate network. For each candidate network in the candidate network list, signal quality indicators are collected by calling the underlying driver interface of the network interface, network load indicators are collected by parsing the system information of the network broadcast, transmission delay indicators are obtained by sending probe messages to a preset network probe server and measuring round-trip delay and packet loss rate, and available bandwidth indicators are obtained by sending back-to-back message pairs and estimating the bottleneck link bandwidth based on the message interval of the receiving end. The signal quality indicators, network load indicators, transmission delay indicators and available bandwidth indicators are assembled into a network quality indicator set.

3. The method for vehicle network handover and load balancing based on a lightweight terminal according to claim 1, characterized in that, The network quality metric set includes signal quality metrics, network load metrics, transmission latency metrics, and available bandwidth metrics. The network quality metric set is written into the network quality database, including: The signal quality indicators include received signal strength indication, signal-to-interference-plus-noise ratio, and reference signal received power; the network load indicators include cell resource block utilization and channel utilization; the transmission delay indicators include round-trip delay and packet loss rate; and the available bandwidth indicators are calculated using a bandwidth estimation algorithm to obtain the estimated bottleneck link bandwidth. The signal quality index, network load index, transmission delay index, and available bandwidth index are validated to remove abnormal data that exceeds the preset valid range, and the valid index data is obtained. The valid index data is then indexed and organized according to the network identifier and the collection timestamp and written into the network quality database.

4. The method for vehicle network handover and load balancing based on a lightweight terminal according to claim 1, characterized in that, The process involves reading the network quality index set of each candidate network from the network quality database and performing normalization processing to obtain a normalized index set. The normalized index set is then mapped using a fuzzy membership function to obtain a membership vector, which includes: The network quality index set of each candidate network is read from the network quality database. The signal quality index is mapped to the zero-to-one interval using a piecewise linear normalization method based on preset excellent threshold, qualified threshold and unusable threshold to obtain the signal quality normalized value. The network load index and transmission delay index are processed by inverse normalization to obtain the load normalized value and delay normalized value. The available bandwidth index is processed by relative normalization based on the maximum available bandwidth value in each candidate network to obtain the bandwidth normalized value. The signal quality normalized value, the load normalized value, the delay normalized value and the bandwidth normalized value are assembled into a normalized index set. A fuzzy membership function is defined for each quality dimension. The fuzzy membership function maps the normalized values ​​of each dimension in the normalized index set to four fuzzy levels of membership: poor, average, good, and excellent. The membership degrees of each quality dimension are then assembled into a membership vector.

5. The method for vehicle network handover and load balancing based on a lightweight terminal according to claim 1, characterized in that, The process involves performing fuzzy inference on the membership vector based on a preset fuzzy inference rule base to obtain a comprehensive utility score, calculating the difference between the comprehensive utility score and the current service network utility score to obtain a utility gain, and comparing the utility gain with a preset handover hysteresis threshold to obtain a handover decision result, including: Based on a preset fuzzy inference rule base, each fuzzy rule is traversed and the activation intensity of each rule is calculated. The activation intensity is the minimum value of the membership degree corresponding to each fuzzy level of the rule antecedent in the membership degree vector. The utility level output by each rule is weighted and aggregated according to the activation intensity and defuzzification is performed to obtain a comprehensive utility score. According to the service quality requirements of the current active business, the comprehensive utility score is adjusted by business differentiation weighting to obtain an adjusted utility score. The utility gain is calculated by subtracting the adjusted utility score of each candidate network from the utility score of the current serving network. A corresponding handover hysteresis threshold is selected based on the type relationship between the candidate network and the current serving network. The utility gain is compared with the handover hysteresis threshold. When the utility gain exceeds the handover hysteresis threshold, a handover decision result that triggers handover is generated.

6. The method for vehicle network handover and load balancing based on a lightweight terminal according to claim 1, characterized in that, The process of performing access authentication and bearer establishment on the target network that meets the handover conditions based on the handover decision result to obtain the target network connection, and establishing multi-path transmission sub-streams based on each network interface for multi-network concurrent connection scenarios, includes: Based on the handover decision results, the candidate network with the greatest utility gain is selected as the target network. An access request is initiated to the target network and the access authentication process is completed to obtain the address resources and transmission bearers allocated by the target network to obtain the target network connection. After the target network connection is established, the data traffic of active services is migrated from the source network path to the target network path. For service scenarios that support concurrent connections across multiple networks, parallel transmission sub-streams are established on multiple network interfaces to obtain multi-path transmission sessions. The sub-stream status table of the multi-path session is maintained to record the associated network interface, local address, remote address, current congestion window, and round-trip delay estimate of each sub-stream. The sub-stream configuration of the multi-path transmission session is dynamically adjusted according to the availability status of the network interfaces.

7. The method for vehicle network handover and load balancing based on a lightweight terminal according to claim 1, characterized in that, The step of obtaining the available bandwidth and transmission delay of each sub-flow associated network from the network quality database, calculating the traffic allocation weight, and performing weighted round-robin scheduling on data packets based on the traffic allocation weight to complete load balancing allocation includes: The system obtains the real-time available bandwidth and transmission latency of the networks associated with each sub-flow from the network quality database. It calculates the basic traffic allocation weight of each sub-flow based on the proportion of available bandwidth. When there is a difference in the transmission latency of each sub-flow, it performs latency difference compensation adjustment on the basic traffic allocation weight to obtain the compensated weight. It performs congestion response adjustment on the compensated weight based on the congestion window change trend of each sub-flow to obtain the traffic allocation weight. When sending data packets, a weighted round-robin algorithm is used to select transmission sub-streams based on the traffic allocation weights to complete load balancing. For service data that is sensitive to reliability, the same data packet is sent concurrently through multiple sub-streams to perform redundant transmission. The number of bytes sent, the number of acknowledgments, and the number of retransmissions in each sub-stream are periodically statistically summarized for dynamic adjustment of traffic allocation weights.

8. A vehicle network switching and load balancing device based on a lightweight terminal, characterized in that, The device includes: The network monitoring module is used to perform periodic scanning based on the multi-type wireless network interfaces configured in the vehicle terminal to obtain a candidate network list, and to collect multi-dimensional quality indicators for each candidate network in the candidate network list to obtain a network quality indicator set. The network quality indicator set includes signal quality indicators, network load indicators, transmission latency indicators, and available bandwidth indicators, and writes the network quality indicator set into the network quality database. The switching decision module is used to read the network quality index set of each candidate network from the network quality database and perform normalization processing to obtain a normalized index set. The normalized index set is mapped by a fuzzy membership function to obtain a membership vector. Fuzzy inference is performed on the membership vector based on a preset fuzzy inference rule base to obtain a comprehensive utility score. The difference between the comprehensive utility score and the utility score of the current service network is calculated to obtain a utility gain. The utility gain is compared with a preset switching hysteresis threshold to obtain a switching decision result. The load balancing module is used to perform access authentication and bearer establishment on target networks that meet the switching conditions based on the switching decision results to obtain target network connections. In the case of multiple network concurrent connections, it establishes multi-path transmission sub-streams based on each network interface, obtains the available bandwidth and transmission delay of each sub-stream associated network from the network quality database to calculate traffic allocation weights, and performs weighted round-robin scheduling on data packets based on the traffic allocation weights to complete load balancing allocation.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the vehicle network switching and load balancing method based on a lightweight terminal as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the vehicle network switching and load balancing method based on a lightweight terminal as described in any one of claims 1 to 7.

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