Network optimization method and equipment based on vehicle-mounted remote information control unit, and medium
By caching and analyzing vehicle network data through the onboard telematics control unit (TCU), the problem of signal blind spots in vehicle networks is solved, enabling fast and effective network optimization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 联友智连科技有限公司
- Filing Date
- 2025-12-02
- Publication Date
- 2026-04-21
AI Technical Summary
In the Internet of Vehicles (IoV), signal blind spots or weak coverage frequently occur in some areas, leading to network outages. Operators find it difficult to accurately locate base stations that need optimization through conventional means, resulting in low efficiency in network optimization.
By caching vehicle-to-everything (V2X) communication data packets through the onboard remote information control unit (TCU), abnormal network conditions are detected and abnormal records are generated and uploaded to the cloud for analysis to determine the base station areas that need optimization.
It provides a continuous and authentic source of data, significantly reducing network optimization costs and timelines for operators, and improving the efficiency of responding to and resolving network issues.
Smart Images
Figure CN121908239A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of network optimization technology, and specifically to a network optimization method, device, and medium based on an in-vehicle remote information control unit. Background Technology
[0002] With the rapid popularization of vehicle-to-everything (V2X) technology, its penetration rate continues to climb, and the density of connected vehicles on the road has increased significantly. At the same time, end-users' expectations for network connection stability and quality are also rising, directly driving the industry to upgrade communication service standards. Furthermore, the diversified development of V2X applications further strengthens users' demand for seamless connectivity, making network performance a key factor affecting user experience.
[0003] However, despite the significant resources invested by Chinese telecom operators in base station infrastructure construction, resulting in a substantial total number of base stations, actual coverage still suffers from numerous shortcomings. Specifically, some areas, due to complex terrain or insufficient planning, frequently experience signal blind spots or weak coverage, leading to network outages for vehicles in motion. Consequently, user dissatisfaction often erupts through customer complaint channels, placing continuous pressure on the service quality of both automakers and operators.
[0004] Meanwhile, when dealing with such customer complaints, automakers typically attribute the cause to insufficient external network coverage and passively wait for operators to optimize the network. However, operators, lacking specific fault location data, find it difficult to accurately pinpoint the base stations requiring optimization through conventional internal road tests or monitoring methods, resulting in low efficiency in troubleshooting. In summary, this information asymmetry and imperfect collaboration mechanism often lead to delays and limited effectiveness in resolving related network quality issues. Summary of the Invention
[0005] In view of this, embodiments of the present invention provide a network optimization method, device, and medium based on an in-vehicle telematics control unit.
[0006] The first aspect of this invention provides a network optimization method based on an in-vehicle telematics control unit, comprising the following steps: Cache vehicle network interaction data packets; The current network status of the vehicle network is determined based on the vehicle's interconnected data packets. When the current network status of the vehicle network is abnormal, an abnormal record is generated; After the current network status of the vehicle network is restored, the abnormal record will be uploaded to the vehicle network cloud. Perform anomaly analysis in the cloud.
[0007] Furthermore, the caching of vehicle network interaction data packets specifically includes the following steps: Set the time range for vehicle-to-everything (V2X) interactive data packets based on the current time point, and cache the V2X interactive data packets within the time range; The timing range of vehicle-to-everything (V2X) interaction data packets is updated over time, and V2X interaction data packets that are outside the timing range are discarded.
[0008] Furthermore, the vehicle-to-everything (V2X) data interaction information recorded in the V2X interactive data packet specifically includes: vehicle location information, vehicle communication module log information, and data packet generation time.
[0009] Furthermore, determining the current network status of the vehicle network based on the vehicle's vehicle network interaction data packets specifically includes the following steps: The signal strength, connection status, and error codes of the vehicle network are analyzed from the latest acquired vehicle network interaction data packets. The current network status of the vehicle network is determined based on predefined network status anomaly rules.
[0010] Furthermore, the predefined network state anomaly rules specifically include: When the signal strength of the vehicle network is continuously lower than a preset threshold within a preset time, the current network status of the vehicle network is judged to be abnormal. When the vehicle network connection status is in a disconnected state, the current network status of the vehicle network is determined to be abnormal. When a specific error code is recorded in the vehicle-to-everything (V2X) communication data packet, it is determined that the current network status of the V2X is abnormal.
[0011] Furthermore, the anomaly record specifically records the time, location, and specific type of the anomaly in the vehicle network status.
[0012] Furthermore, the anomaly analysis performed in the cloud specifically includes the following steps: Divide the satellite map into multiple regions and identify the target areas that require network optimization. For each target area, optimization suggestions are matched based on anomaly records, and these suggestions are applied to the base stations in the target area and / or distributed to the network maintenance team in the target area.
[0013] Furthermore, determining the target area requiring network optimization specifically includes the following steps: Record the number of abnormal records uploaded in each region; Get the vehicle traffic in each area, and calculate the upload frequency of abnormal records in each area based on the number of uploaded abnormal records and the vehicle traffic. The regions where the upload frequency of abnormal records exceeds a preset frequency threshold are identified as target regions that require network optimization.
[0014] A second aspect of the present invention discloses an electronic device, including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the aforementioned network optimization method based on an in-vehicle remote information control unit.
[0015] A third aspect of the present invention discloses a computer-readable storage medium storing a program that is executed by a processor to implement the above-described network optimization method based on an in-vehicle telematics control unit.
[0016] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned method.
[0017] The embodiments of the present invention have the following beneficial effects: The present invention provides a network optimization method, device, and medium based on an in-vehicle telematics control unit. This method uses the in-vehicle telematics control unit to record anomalies in the location of vehicle network status abnormalities during normal vehicle operation. After network recovery, the anomalies are uploaded to the operating platform of the vehicle manufacturer or operator. This allows operators to identify base stations requiring network optimization through a large amount of anomaly record data, enabling rapid and targeted network optimization. The present invention provides operators with a continuous, authentic, and widely covered source of effective data for vehicle network optimization. It not only significantly reduces the manpower and financial costs required for operators to conduct independent road tests and network optimization tests, but also efficiently transforms network problems encountered by end users in real-world scenarios into actionable optimization criteria, thereby significantly shortening the response and resolution cycle for network quality issues.
[0018] Additional aspects and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description or may be learned by practice of the invention. Attached Figure Description
[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0020] Figure 1 This is a flowchart illustrating the implementation steps of a network optimization method based on an in-vehicle remote information control unit. Figure 2 This is a schematic diagram of the vehicle network interaction data packet format cached by the present invention; Figure 3 This is a schematic diagram of the abnormal data recording format formed by the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device according to the present invention; Figure 5 This is a schematic diagram of a computer-readable storage medium structure according to the present invention. Detailed Implementation
[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0022] like Figure 1 As shown, the first embodiment of the present invention provides a network optimization method based on an in-vehicle telematics control unit (TCU), comprising the following steps: S1. Cache vehicle network interaction data packets; S2. Determine the current network status of the vehicle network based on the vehicle network interaction data packets; S3. Generate an anomaly record when the current network status of the vehicle network is abnormal; S4. After the current network status of the vehicle network is restored, upload the abnormal record to the vehicle network cloud; S5. Perform anomaly analysis in the cloud.
[0023] This invention utilizes a remote information control unit installed inside the vehicle to record locations where the vehicle network status is abnormal during normal vehicle operation. After the network is restored, the abnormal records are uploaded to the operating platform of the vehicle manufacturer or operator. This allows the operator to identify base stations that require network optimization through a large amount of abnormal record data and perform rapid and targeted network optimization.
[0024] The implementation process of each step of this invention is described in detail below: S1. Cache vehicle network interaction data packets.
[0025] In step S1, caching the vehicle's vehicle-to-everything (V2X) communication data packets specifically includes the following steps: S1-1. Set the time range for vehicle-to-everything (V2X) interactive data packets based on the current time point, and cache the V2X interactive data packets within the time range; S1-2. Update the time range of vehicle-to-everything (V2X) interactive data packets over time, and discard V2X interactive data packets that are outside the time range.
[0026] The vehicle-to-everything (V2X) data interaction information recorded in the cached V2X interaction data packets in this embodiment of the invention specifically includes vehicle location information, vehicle communication module log information, and data packet generation time. The location information is represented by latitude and longitude values from a satellite positioning system, and the log information records V2X signal quality indicators (such as RSRP and RSRQ for LTE / 5G), connection events (such as successful attachment, failure, and handover records), and error codes (if any).
[0027] Specifically, the format of vehicle-to-everything (V2X) data exchange information is as follows: Figure 2 As shown, 8 bytes are UTC timestamps, used to indicate the time when the data packet was generated. The precision and latitude are each recorded using 4 bytes of floating-point format. The log information of the communication module is followed by the data field.
[0028] In this embodiment of the invention, vehicle network interaction data packets are stored using a rolling storage method. The initial storage is based on a fixed time range (e.g., 5s, 10s), and the vehicle network interaction data packets within this time range before the current time point are stored in a rolling manner. For example, when the time range is 10s, assuming the data packet delivery frequency is one per second, there will always be 10 data packets within the time range, while data packets older than 11s are discarded. The size of the time range is specifically determined based on anomaly detection requirements and hardware storage capacity; this embodiment of the invention does not impose any limitations on this.
[0029] S2. Determine the current network status of the vehicle network based on the vehicle network interaction data packets.
[0030] In step S2, the current network status of the vehicle network is determined based on the vehicle network interaction data packets, specifically including the following steps: S2-1. Parse the signal strength, connection status, and error codes of the vehicle network from the latest acquired vehicle network interaction data packets; S2-2. Determine the current network status of the vehicle network based on predefined network status anomaly rules.
[0031] In this embodiment of the invention, the current network status of the vehicle-to-everything (V2X) network is determined by real-time parsing of the interaction information recorded in the data packets. For example, log data from the communication module in the data packets can be parsed to extract key network indicators, including signal strength, connection status, and error codes. Signal strength can be determined by the RSRP value of the log information; for example, in 4G / 5G communication, an RSRP below -110dBm indicates a poor signal. Connection status is determined by connection events in the data field. Error codes are identified by examining the data field to confirm whether any error codes are recorded. After parsing the interaction information, this embodiment of the invention matches it against predefined network status anomaly rules.
[0032] For example, the predefined network state anomaly rules specifically include the following rules: When the signal strength of the vehicle network is continuously lower than a preset threshold within a preset time, the current network status of the vehicle network is judged to be abnormal. When the vehicle network connection status is in a disconnected state, the current network status of the vehicle network is determined to be abnormal. When a specific error code is recorded in the vehicle-to-everything (V2X) communication data packet, it is determined that the current network status of the V2X is abnormal.
[0033] It should be noted that the above rules are examples of network status anomaly rules and do not represent all network status anomaly rules. The specific network status anomaly rules used can be added or reduced based on these.
[0034] S3. Generate an anomaly record when the current network status of the vehicle network is abnormal.
[0035] In this embodiment of the invention, when the current network status of the vehicle network is abnormal, an abnormality record is generated. The abnormality record specifically records the time, location and specific type of the abnormality in the vehicle network status.
[0036] like Figure 3 As shown, the abnormal record generated in this embodiment of the invention includes 8 bytes of UTC time, used to characterize the time node when the abnormality occurred; latitude and longitude information in floating-point format, each occupying 4 bytes; and 1 byte of abnormality type code.
[0037] In some embodiments, after generating an anomaly record, the TCU continues to collect real-time data for the next 10 seconds from the communication module and GNSS module (as additional input) to ensure that the complete 20-second anomaly context is recorded, ultimately enclosing a complete binary block, which is then prepared for uploading as an anomaly record.
[0038] S4. After the current network status of the vehicle network is restored, upload the abnormal record to the vehicle network cloud.
[0039] In this embodiment of the invention, when the TCU detects that the network connection has been restored, it automatically uploads the locally stored anomaly log file to the cloud platform via a secure transmission protocol. Specifically, chunked transmission and retry mechanisms can be used to ensure the reliability of data transmission, and file status management is performed after successful upload, achieving efficient aggregation of anomaly data from the vehicle terminal to the cloud.
[0040] S5. Perform anomaly analysis in the cloud.
[0041] In this embodiment of the invention, anomaly analysis is performed in the cloud, specifically including the following steps: S5-1. Divide the satellite map into multiple regions and determine the target regions that require network optimization; S5-2. For each target area, perform optimization suggestions matching based on anomaly records, and apply the optimization suggestions to the base stations in the target area and / or distribute them to the network maintenance team in the target area.
[0042] In this embodiment of the invention, the specific optimization suggestions may include base station parameter adjustments (such as antenna azimuth / downtilt adjustment, power adjustment), hardware adjustments (such as antenna replacement, base station expansion, etc.), and network configuration changes (frequency point optimization, neighbor cell relationship optimization), etc. The optimization suggestions are applied to the base stations in the target area and / or distributed to the network maintenance team in the target area to carry out rapid and targeted network optimization.
[0043] In some embodiments, step 5-1 determines the target region that needs network optimization, specifically including the following steps: S5-1-1. Record the number of abnormal records uploaded in each region; S5-1-2. Obtain vehicle traffic in each area, and calculate the upload frequency of abnormal records in each area based on the number of uploaded abnormal records and vehicle traffic. S5-1-3. Identify areas where the upload frequency of abnormal records exceeds a preset frequency threshold as target areas that require network optimization.
[0044] In this embodiment of the invention, the map is divided into multiple electronic fences (e.g., a 500m × 500m grid) based on a geographic grid or dynamic clustering (such as the DBSCAN algorithm). Within each electronic fence, the number of anomalous events per unit time (e.g., per day) is calculated and divided by the vehicle traffic flow within the fenced area to obtain the upload frequency of anomalous records. In this embodiment, introducing vehicle traffic flow to calculate the anomalous frequency avoids false high-frequency anomalies caused by high traffic volume. By comparing the anomalous frequency with a frequency threshold, areas within the electronic fence that continuously report anomalies are identified, and targeted optimizations are performed.
[0045] In summary, this invention provides operators with a continuous, authentic, and widely covered source of effective data for optimizing vehicle-to-everything (V2X) networks. It not only significantly reduces the manpower and financial costs required for operators to conduct independent road tests and network optimization tests, but also efficiently transforms network problems encountered by end users in real-world scenarios into actionable optimization criteria, thereby greatly shortening the response and resolution cycle for network quality issues.
[0046] Figure 4This is a schematic diagram of the electronic device proposed in the second embodiment of the present invention. In this embodiment, the memory stores program instructions for implementing the network optimization method based on the vehicle-mounted telematics control unit (VMT) of any of the above embodiments. The processor executes the program instructions stored in the memory to perform network optimization based on the VMT. The processor may also be referred to as a CPU (Central Processing Unit). The processor may be an integrated circuit chip with signal processing capabilities. The processor may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor may be a microprocessor or any conventional processor.
[0047] The methods described in the first embodiment of the present invention are applicable to the embodiments of the present electronic device. The specific functions implemented by the embodiments of the present electronic device are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above methods.
[0048] Figure 5 This is a schematic diagram of the structure of a computer-readable storage medium according to the third embodiment of the present invention. The computer-readable storage medium of the fourth embodiment of the present invention stores program instructions capable of implementing the above-described network optimization method based on a vehicle-mounted remote information control unit. These program instructions can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned computer-readable storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.
[0049] The methods described in the first embodiment of the present invention are applicable to the computer-readable storage medium embodiment. The specific functions implemented by the computer-readable storage medium embodiment are the same as those in the above method embodiment, and the beneficial effects achieved are also the same as those achieved by the above method.
[0050] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned related steps to implement the network optimization method based on the vehicle-mounted remote information control unit provided in the above embodiment.
[0051] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of the present invention are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.
[0052] Those skilled in the art will understand that modules in the device of the embodiments of the present invention can be adaptively modified and placed in one or more devices different from those embodiments. Modules, units, or components in the embodiments of the present invention can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the corresponding claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the corresponding claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.
[0053] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.
[0054] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.
[0055] Furthermore, the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. In particular, for embodiments such as apparatus and devices, since they are basically similar to the method embodiments, the relevant parts can be referred to the description of the method embodiments. The apparatus, devices, and other embodiments described above are merely illustrative, and the modules, units, etc., described as separate components may or may not be physically separate, that is, they may be located in one place or distributed in multiple places, such as nodes in a system network. Specifically, some or all of the modules and units can be selected according to actual needs to achieve the purpose of the above-described embodiment solutions. Those skilled in the art can understand and implement this without creative effort.
[0056] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.
[0057] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0058] Furthermore, the terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying relative importance, or implicitly specifying the number of technical features indicated in this embodiment. Therefore, features defined with terms such as "first" and "second" in the embodiments of this invention can explicitly or implicitly indicate that the embodiment includes at least one of those features. In the description of this invention, the word "multiple" means at least two or more, such as two, three, four, etc., unless otherwise explicitly specified in the embodiments.
[0059] In embodiments of the present invention, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of the present invention may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.
[0060] Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention. Other embodiments of the present invention will readily conceive of by considering the specification and practicing the invention. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.
Claims
1. A network optimization method based on an in-vehicle telematics control unit, characterized in that, Includes the following steps: Cache vehicle network interaction data packets; The current network status of the vehicle network is determined based on the vehicle's interconnected data packets. When the current network status of the vehicle network is abnormal, an abnormal record is generated; After the current network status of the vehicle network is restored, the abnormal record will be uploaded to the vehicle network cloud. Perform anomaly analysis in the cloud.
2. The network optimization method based on an in-vehicle remote information control unit according to claim 1, characterized in that, The cached vehicle network interaction data packets specifically include the following steps: Set the time range for vehicle-to-everything (V2X) interactive data packets based on the current time point, and cache the V2X interactive data packets within the time range; The timing range of vehicle-to-everything (V2X) interaction data packets is updated over time, and V2X interaction data packets that are outside the timing range are discarded.
3. The network optimization method based on an on-board remote information control unit according to claim 1, characterized in that, The vehicle-to-everything (V2X) data interaction information recorded in the V2X data packet specifically includes: vehicle location information, vehicle communication module log information, and data packet generation time.
4. The network optimization method based on an in-vehicle remote information control unit according to claim 1, characterized in that, Determining the current network status of the vehicle network based on the vehicle's vehicle network interaction data packets specifically includes the following steps: The signal strength, connection status, and error codes of the vehicle network are analyzed from the latest acquired vehicle network interaction data packets. The current network status of the vehicle network is determined based on predefined network status anomaly rules.
5. A network optimization method based on an on-board remote information control unit according to claim 1, characterized in that, The predefined network state anomaly rules specifically include: When the signal strength of the vehicle network is continuously lower than a preset threshold within a preset time, the current network status of the vehicle network is judged to be abnormal. When the vehicle network connection status is in a disconnected state, the current network status of the vehicle network is determined to be abnormal. When a specific error code is recorded in the vehicle-to-everything (V2X) communication data packet, it is determined that the current network status of the V2X is abnormal.
6. The network optimization method based on an in-vehicle remote information control unit according to claim 1, characterized in that, The anomaly log specifically records the time, location, and type of the anomaly in the vehicle network status.
7. The network optimization method based on an on-board telematics control unit according to claim 1, characterized in that, The anomaly analysis performed in the cloud specifically includes the following steps: Divide the satellite map into multiple regions and identify the target areas that require network optimization. For each target area, optimization suggestions are matched based on anomaly records, and these suggestions are applied to the base stations in the target area and / or distributed to the network maintenance team in the target area.
8. A network optimization method based on an in-vehicle remote information control unit according to claim 7, characterized in that, Determining the target area requiring network optimization specifically includes the following steps: Record the number of abnormal records uploaded in each region; Get the vehicle traffic in each area, and calculate the upload frequency of abnormal records in each area based on the number of uploaded abnormal records and the vehicle traffic. The regions where the upload frequency of abnormal records exceeds a preset frequency threshold are identified as target regions that require network optimization.
9. An electronic device, characterized in that, Including the processor and memory; The memory is used to store programs; The processor executes the program to implement a network optimization method based on an in-vehicle telematics control unit as described in any one of claims 1-8.
10. A computer-readable storage medium, characterized in that, The storage medium stores a program, which is executed by a processor to implement a network optimization method based on an in-vehicle remote information control unit as described in any one of claims 1-8.