OBD (On-Board Diagnostic) II-based intelligent vehicle networking detection management method and system under mobile internet

Abstract

The invention relates to the technical field of vehicle networking detection management, and particularly discloses an intelligent vehicle networking detection management method and system based on OBD II under the mobile internet. According to the invention, the mobile terminal generates and sends the vehicle networking detection instruction to judge whether direct detection transmission can be carried out or not; when direct detection transmission cannot be carried out, vehicle networking relation identification is carried out; carrying out transition forwarding on the vehicle detection data; and uploading the vehicle detection data to a cloud, and carrying out multi-dimensional big data processing on the vehicle detection data. According to the invention, direct detection transmission judgment analysis can be carried out on a target detection vehicle through a mobile terminal, and when direct detection transmission cannot be carried out, vehicle connection relation identification, target vehicle connection vehicle screening and transition forwarding of vehicle detection data are carried out, and then the vehicle detection data are uploaded to a cloud terminal for multi-dimensional big data processing. Effective combination and application of the OBD and the mobile terminal are realized, and popular, convenient and intelligent requirements of automobile detection are met.

Description

Technical Field

[0001] This invention belongs to the field of vehicle connectivity detection and management technology, and particularly relates to an intelligent vehicle connectivity detection and management method and system based on OBD II under the mobile Internet. Background Technology

[0002] With the increasing frequency of traffic accidents in recent years, people are placing greater emphasis on vehicle safety when purchasing and driving cars, especially for many car owners who lack knowledge of vehicle inspection and repair, often encountering situations where minor problems result in significant financial losses. Consequently, vehicle fault diagnosis, fuel consumption analysis, trip analysis, and other safety-related features are receiving increasing attention from car owners. With the development of the Internet of Vehicles (IoV), maintenance reminders, remote diagnostics, vehicle anti-theft alarms, and map-based services are becoming new options for safe driving, and OBD (On-Board Diagnostics) is the best platform for this.

[0003] In existing technologies, OBD still has significant limitations in application. Although OBD can collect information such as engine status, fault codes, fuel consumption data, and mileage during vehicle operation, this data can often only be read and analyzed through specialized diagnostic instruments or in repair shops, relying on professional personnel and specialized equipment. It cannot be effectively integrated and applied with mobile devices, and cannot be easily, intuitively, and in real-time by drivers using mobile phones or other portable terminals in their daily use. Therefore, it cannot meet the needs of popularization, convenience, and intelligence in vehicle inspection. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent vehicle connectivity detection and management method and system based on OBD II under the mobile Internet, aiming to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions: A method for intelligent vehicle connectivity detection and management based on OBD II under the mobile internet, the method specifically includes the following steps: The system generates and sends vehicle-to-everything (V2X) detection commands to the target vehicle via a mobile device, receives the current signal data transmitted back by the target vehicle, and determines whether direct detection transmission is possible. When direct detection and transmission are not possible, vehicle-to-vehicle relationship identification is performed via mobile terminal to identify multiple online connected vehicles and filter target connected vehicles; The vehicle detection data of the target vehicle is read based on the OBD II interface, and the vehicle detection data is forwarded through the target vehicle-to-vehicle connection. The vehicle detection data is uploaded to the cloud via mobile internet, and then processed in multiple dimensions using cloud computing technology.

[0006] A mobile internet-based intelligent vehicle connectivity detection and management system using OBD II, comprising a signal data analysis unit, a vehicle connectivity relationship identification unit, a data transfer and forwarding unit, and a detection data processing unit, wherein: The signal data analysis unit is used to generate and send vehicle-to-everything (V2X) detection commands to the target detection vehicle via the mobile terminal, receive the current signal data transmitted back by the target detection vehicle, and determine whether direct detection transmission is possible. The vehicle-to-vehicle relationship identification unit is used to identify vehicle-to-vehicle relationships via a mobile terminal when direct detection and transmission are not possible, thereby identifying multiple online connected vehicles and filtering target connected vehicles. The data transition forwarding unit is used to read the vehicle detection data of the target detection vehicle based on the OBD II interface, and to perform transition forwarding of the vehicle detection data through the target vehicle-to-vehicle connection. The detection data processing unit is used to upload the vehicle detection data to the cloud via the mobile Internet, and to perform multi-dimensional big data processing on the vehicle detection data through cloud computing technology.

[0007] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention extracts and fuses multi-dimensional features from signal data fed back by the target vehicle through a mobile terminal, and innovatively combines the real-time vehicle status with a dynamic adjustment evaluation strategy to achieve intelligent and accurate judgment of direct detection transmission capability. This effectively overcomes the drawback of poor reliability of single signal strength judgment in complex mobile environments, and provides a solid and reliable decision basis for subsequent vehicle-to-everything (V2X) relationship identification and data transition forwarding, greatly improving the success rate and system robustness in the initial connection stage. 2. This invention introduces a comprehensive relay performance evaluation method that includes distance, equipment status, and network performance among numerous online vehicle-to-everything (V2X) vehicles, and intelligently selects the optimal and backup relay nodes. This ensures that the data transfer and forwarding task is not only undertaken by the geographically closest vehicle, but also by the vehicle with the most suitable comprehensive capabilities. This significantly improves the success rate and efficiency of data relay in weak network or signal blind spot environments, and ensures the integrity and real-time performance of the detection data. 3. This invention constructs a complete cloud-based big data processing chain, from data cleaning and integration, multi-dimensional feature correlation analysis, spatiotemporal trend modeling to environmental impact correction. Based on this chain, it combines historical benchmarks and real-time fluctuations to conduct risk probability assessments, ultimately generating in-depth vehicle condition evaluation results that far exceed simple fault diagnosis. This provides users with accurate, quantitative, and risk-visualized vehicle health status reports, realizing a shift from passive maintenance to proactive early warning, and greatly enhancing the intelligence level of vehicle inspection and the value of user services. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0009] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0010] Figure 2 The flowchart illustrating the method for determining whether direct detection transmission can be performed according to an embodiment of the present invention is shown.

[0011] Figure 3 A flowchart illustrating vehicle-to-vehicle relationship identification is shown in the method provided by an embodiment of the present invention.

[0012] Figure 4 A flowchart illustrating the transitional forwarding of vehicle detection data in the method provided by an embodiment of the present invention is shown.

[0013] Figure 5 A flowchart illustrating multi-dimensional big data processing in the method provided by an embodiment of the present invention is shown.

[0014] Figure 6 An application architecture diagram of the system provided in an embodiment of the present invention is shown.

[0015] Figure 7 A structural block diagram of the signal data analysis unit in the system provided by an embodiment of the present invention is shown.

[0016] Figure 8 The diagram shows the structural block diagram of the vehicle-to-vehicle relationship identification unit in the system provided by an embodiment of the present invention.

[0017] Figure 9 A structural block diagram of the detection data processing unit in the system provided by an embodiment of the present invention is shown. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] Understandably, current OBD technology still has significant limitations in application. Although OBD can collect information such as engine status, fault codes, fuel consumption data, and mileage during vehicle operation, this data can often only be read and analyzed through specialized diagnostic instruments or in repair shops, relying on professional personnel and specialized equipment. It cannot be effectively integrated and applied with mobile devices, and cannot be easily, intuitively, and in real-time by drivers using mobile phones or other portable terminals in their daily use. Therefore, it cannot meet the needs of popularization, convenience, and intelligence in vehicle inspection.

[0020] To address the aforementioned issues, this invention generates and sends vehicle-to-everything (V2X) detection commands to the target vehicle via a mobile terminal. It receives current signal data from the target vehicle and determines whether direct detection transmission is possible. If direct transmission is not possible, the mobile terminal identifies V2X relationships, identifies multiple online V2X vehicles, and selects the target V2X vehicle. Vehicle detection data from the target vehicle is read via the OBD II interface and forwarded through the target V2X vehicle. The vehicle detection data is then uploaded to the cloud via the mobile internet and processed using cloud computing technology for multi-dimensional big data processing. This invention enables the mobile terminal to analyze and determine whether direct detection transmission is possible, identify V2X relationships when direct transmission is not possible, select target V2X vehicles, forward vehicle detection data, and upload the data to the cloud for multi-dimensional big data processing. This achieves effective integration and application of OBD and mobile terminals, meeting the needs for popularization, convenience, and intelligence in vehicle inspection.

[0021] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0022] Specifically, the intelligent vehicle connectivity detection and management method based on OBD II under the mobile Internet includes the following steps: Step S101: Generate and send vehicle-to-everything (V2X) detection instructions to the target detection vehicle via the mobile terminal, receive the current signal data transmitted in feedback from the target detection vehicle, and determine whether direct detection transmission is possible.

[0023] In this embodiment of the invention, when a driver needs to perform intelligent vehicle connectivity detection, the detection operation can be performed using a mobile terminal. The mobile terminal receives vehicle detection requests, generates vehicle connectivity detection instructions, identifies the target vehicle, and sends the vehicle connectivity detection instructions to the target vehicle. The mobile terminal also receives the current signal data transmitted by the target vehicle and then identifies the signal strength of the current signal data to determine whether direct detection transmission is possible.

[0024] Specifically, Figure 2 The flowchart illustrating the method for determining whether direct detection transmission can be performed according to an embodiment of the present invention is shown.

[0025] In a preferred embodiment of the present invention, the step of generating and sending a vehicle-to-everything (V2X) detection command to the target vehicle via a mobile terminal, receiving the current signal data transmitted back by the target vehicle, and determining whether direct detection transmission is possible specifically includes the following steps: Step S1011: Receive a vehicle detection request via mobile device; Step S1012: Generate vehicle connectivity detection instructions via mobile device; Step S1013: Determine the target detection vehicle and send the vehicle detection command to the target detection vehicle via the mobile terminal; Step S1014: Receive current signal data transmitted by the target detection vehicle via the mobile terminal; Step S1015: Perform strength identification on the current signal data to determine whether direct detection and transmission are possible; In step S1015, the current signal data is subjected to strength identification to determine whether direct detection and transmission are possible. This specifically includes the following steps: Step S10151: Perform preliminary filtering on the signal data to eliminate environmental noise interference and obtain a purified signal data set. Step S10152: Extract signal strength features, signal stability features, and transmission rate features from the purified signal data set to generate a multi-dimensional transmission feature set; Step S10153: Dynamically adjust the feature weight allocation strategy according to the current driving state of the vehicle to obtain a dynamic weight allocation scheme; the feature weight allocation strategy includes: emphasizing signal stability feature weights when the vehicle is in motion, emphasizing signal strength feature weights when stationary, and increasing transmission rate feature weights when moving at high speed. Step S10154: Compare and analyze the multi-dimensional transmission feature set with the preset transmission quality benchmark to identify abnormal transmission features such as signal strength below the strength threshold (85dBm), signal stability exceeding the fluctuation range (±5dBm), or transmission rate (<1Mbps) not meeting the standard, and generate a transmission anomaly identifier set. Step S10155: The transmission anomaly identifier set is weighted and evaluated based on the dynamic weight allocation scheme, and a comprehensive transmission quality score is calculated. When the score is lower than the transmittable threshold (60 points), the transmission obstacle identifier is activated. When the score is higher than the transmittable threshold, the direct transmission permission identifier is activated. Step S10156: Verify the transmission obstacle identifier by combining historical connection success rate data: when the historical connection success rate is higher than the preset threshold (90%), it is downgraded to a temporary transmission obstacle; when the historical connection success rate is lower than the preset threshold, it is confirmed as a permanent transmission obstacle, and a transmission reliability verification result is generated. Step S10157: Combine the transmission reliability verification results with the real-time transmission status: if there is a temporary transmission obstacle and the current signal strength is trending upward, it is determined that direct transmission can be attempted; if there is a permanent transmission obstacle or the signal strength is continuously decreasing, it is determined that direct transmission cannot be performed, so as to generate a determination result on whether direct detection transmission can be performed.

[0026] It should be noted that the preset threshold is established based on a large sample statistical analysis of historical connection data. Its core principle is to distinguish between accidental connection failures and structural connection barriers. Specifically, the preset threshold is set at a critical level that can effectively distinguish between failures caused by accidental factors such as instantaneous signal fluctuations and brief network congestion and failures caused by structural reasons such as equipment hardware failures and persistent strong signal shielding.

[0027] Furthermore, the intelligent vehicle connectivity detection and management method based on OBD II under the mobile Internet also includes the following steps: Step S102: When direct detection and transmission are not possible, vehicle-to-vehicle relationship identification is performed through the mobile terminal to identify multiple online connected vehicles and filter the target connected vehicle.

[0028] In this embodiment of the invention, when it is determined that direct detection and transmission cannot be performed, vehicle-to-everything (V2X) relationship information is obtained through a mobile terminal. Based on the V2X relationship information, online V2X identification is performed to identify multiple online V2X vehicles that have a V2X relationship with the driver. The online vehicle locations of the multiple online V2X vehicles are then obtained. At the same time, the detection vehicle location of the target detection vehicle is obtained. Then, based on the detection vehicle location and the multiple online vehicle locations, the online V2X distance between the target detection vehicle and the multiple online V2X vehicles is calculated. By comparing the multiple online V2X distances, the target V2X vehicle with the shortest corresponding online V2X distance is selected from the multiple online V2X vehicles.

[0029] It is understandable that vehicle-to-everything (V2X) relationships can be communication and friendship relationships based on mobile devices, with all parties binding their vehicles.

[0030] Specifically, Figure 3 A flowchart illustrating vehicle-to-vehicle relationship identification is shown in the method provided by an embodiment of the present invention.

[0031] In a preferred embodiment of the present invention, the step of identifying vehicle-to-vehicle relationships through a mobile terminal, determining multiple online connected vehicles, and filtering target connected vehicles when direct detection and transmission are not possible specifically includes the following steps: Step S1021: When direct detection and transmission are not possible, obtain vehicle-to-everything (V2X) relationship information through the mobile terminal; Step S1022: Based on the vehicle-to-everything (V2X) relationship information, perform online vehicle-to-everything (V2X) identification to determine multiple online V2X vehicles; Step S1023: Obtain the online vehicle locations of multiple online connected vehicles; Step S1024: Obtain the detection vehicle position of the target detection vehicle; Step S1025: Calculate the online vehicle-to-vehicle distance between the target detected vehicle and the multiple online vehicle locations based on the detected vehicle location and the multiple online vehicle locations; Step S1026: Compare multiple online vehicle-to-vehicle distances and select a target vehicle from the multiple online vehicle-to-vehicle vehicles; In step S1026, the multiple online vehicle-to-everything (V2X) distances are compared, and the target V2X vehicle is selected from the multiple online V2X vehicles. This specifically includes the following steps: Step S10261: Based on the online vehicle-to-vehicle distances between the target detection vehicle and the multiple online vehicle-to-vehicle vehicles, construct an online vehicle-to-vehicle distance set; Step S10262: Compare each online vehicle-to-everything distance in the online vehicle-to-everything distance set with the preset effective communication radius threshold (500 meters), and remove all online vehicle-to-everything distances that are greater than the effective communication radius threshold and their corresponding online vehicle-to-everything vehicles, so as to obtain the preliminary screening of the candidate vehicle-to-everything vehicle set and its corresponding candidate vehicle-to-everything distance set. Step S10263: Obtain the real-time signal relay capability parameters of each candidate vehicle in the candidate vehicle-to-everything (V2X) vehicle set; the signal relay capability parameters include the current remaining battery power, current CPU load rate, and current network uplink bandwidth. Step S10264: Based on the candidate vehicle-to-vehicle distance and its real-time signal relay capability parameters corresponding to each candidate vehicle-to-vehicle, a dynamic weight allocation strategy is adopted to calculate its comprehensive relay efficiency index to obtain the comprehensive relay efficiency index; the dynamic weight allocation strategy includes: the smaller the candidate vehicle-to-vehicle distance, the higher the weight corresponding to the candidate vehicle-to-vehicle distance; the more sufficient the remaining power of the device, the lower the CPU load rate, and the larger the network uplink bandwidth, the higher the weight of the signal relay capability parameter. Step S10265: Sort all candidate vehicle-to-vehicle connections in descending order based on the calculated comprehensive relay efficiency index to generate a relay efficiency priority queue. Step S10266: Select the candidate vehicle with the highest comprehensive relay efficiency index from the relay efficiency priority queue as the optimal target vehicle, and at the same time select the candidate vehicles with the second and third highest comprehensive relay efficiency index as backup vehicle sequences. Step S10267: The optimal target vehicle is determined as the final target vehicle for subsequent data transition forwarding, and the backup vehicle sequence is associated with it and stored in order to dynamically switch when the optimal target vehicle cannot complete the forwarding task.

[0032] Furthermore, the intelligent vehicle connectivity detection and management method based on OBD II under the mobile Internet also includes the following steps: Step S103: Read the vehicle detection data of the target detection vehicle based on the OBD II interface, and perform transition forwarding of the vehicle detection data through the target vehicle-to-vehicle connection.

[0033] In this embodiment of the invention, vehicle detection data of the target detection vehicle is read based on the OBD II interface, then the vehicle-to-vehicle communication channel between the target detection vehicle and the target vehicle-to-vehicle is activated. After that, the target detection vehicle is controlled to transmit the vehicle detection data to the target vehicle-to-vehicle through the vehicle-to-vehicle communication channel, and then the target vehicle-to-vehicle is controlled to forward the vehicle detection data to the mobile terminal.

[0034] Specifically, Figure 4 A flowchart illustrating the transitional forwarding of vehicle detection data in the method provided by an embodiment of the present invention is shown.

[0035] In a preferred embodiment of the present invention, the step of reading the vehicle detection data of the target detection vehicle based on the OBD II interface and performing transitional forwarding of the vehicle detection data through the target vehicle-to-vehicle connection specifically includes the following steps: Step S1031: Read the vehicle detection data of the target detection vehicle based on the OBD II interface; Step S1032: Activate the vehicle-to-vehicle communication channel between the target detection vehicle and the target vehicle-to-vehicle; Step S1033: Control the target detection vehicle to transmit the vehicle detection data to the target vehicle-to-everything (V2X) vehicle through the vehicle-to-everything (V2X) communication channel; Step S1034: Control the target vehicle to forward the vehicle detection data to the mobile terminal.

[0036] Furthermore, the intelligent vehicle connectivity detection and management method based on OBD II under the mobile Internet also includes the following steps: Step S104: Upload the vehicle detection data to the cloud via mobile internet, and perform multi-dimensional big data processing on the vehicle detection data using cloud computing technology.

[0037] In this embodiment of the invention, after the mobile terminal obtains the forwarded vehicle detection data, it uploads the vehicle detection data to the cloud via the mobile internet. Then, through cloud computing technology, it performs multi-dimensional data analysis on the vehicle detection data, including data dimensions, vehicle type dimensions, time dimensions, and regional dimensions, to obtain multi-dimensional analysis results. The multi-dimensional analysis results are then used for big data evaluation to obtain vehicle evaluation results. Finally, the vehicle evaluation results are quantitatively modeled to generate an overall vehicle condition score.

[0038] Specifically, Figure 5 A flowchart illustrating multi-dimensional big data processing in the method provided by an embodiment of the present invention is shown.

[0039] In a preferred embodiment of the present invention, the step of uploading the vehicle detection data to the cloud via the mobile internet and performing multi-dimensional big data processing on the vehicle detection data using cloud computing technology specifically includes the following steps: Step S1041: Upload the vehicle detection data to the cloud via mobile internet; Step S1042: Using cloud computing technology, perform multi-dimensional analysis on the vehicle detection data to obtain multi-dimensional analysis results; In step S1042, the vehicle detection data is analyzed in multiple dimensions using cloud computing technology to obtain multi-dimensional analysis results, specifically including the following steps: Step S10421: Clean the vehicle detection data using cloud computing technology, remove invalid fields and outliers to obtain a standardized vehicle dataset. Step S10422: Extract features from the standardized vehicle dataset and establish a dynamic weight mapping relationship between features through correlation analysis to obtain a data feature set with weight labels; the feature extraction includes: separating engine parameters, fault code status, fuel consumption curve and driving trajectory segment; Step S10423: Call the cloud vehicle model database to perform multi-level matching between the current vehicle model and historical data of the same model to obtain a vehicle model benchmarking report containing differentiated indicators; the multi-level matching includes: first-level matching of the average performance indicators of the same model vehicles; second-level matching of the energy consumption benchmark of the same power configuration vehicle group (such as turbocharged / hybrid); and third-level matching of the fault mode feature library of the same model year vehicles. Step S10424: The vehicle detection data is segmented according to the time axis, and a dynamic monitoring window is constructed to obtain a performance degradation trend map with timestamps; the dynamic monitoring window includes: a short-term window (72 hours) to analyze instantaneous fault fluctuations; a medium-term window (30 days) to fit the slope of fuel consumption change; and a long-term window (1 year) to identify the inflection point of component degradation. Step S10425: Combine the cloud-based geographic information database to associate vehicle location with environmental parameters: match the intake oxygen concentration compensation coefficient in high-altitude areas; activate the cooling system load threshold in high-temperature / high-humidity areas; superimpose the start-stop frequency on the engine wear index in congested road sections; to obtain a set of environmental impact correction factors. Step S10426: Input the data feature set, vehicle model benchmarking report, performance degradation trend map, and environmental impact correction factor set into the verification engine for processing to obtain a cross-validation matrix containing anomaly marker bits; the verification engine includes: verifying the spatiotemporal correlation between fault codes and environmental factors; checking whether the deviation between actual fuel consumption and vehicle model benchmark is within the environmental correction range; and detecting whether the performance degradation trend breaks through the boundary of the same type of vehicle statistical model. Step S10427: Perform final aggregation based on the cross-validation matrix to obtain multi-dimensional analysis results including health index, risk item list, and maintenance suggestions; the final aggregation includes: merging indicators for items without abnormality markers according to dimension weights; triggering secondary judgment by expert rule base for items with abnormality markers; Step S1043: Perform big data evaluation on the multi-dimensional analysis results to obtain vehicle evaluation results; In step S1043, the multi-dimensional analysis results are subjected to big data evaluation to obtain vehicle evaluation results, which specifically includes the following steps: Step S10431: Call the historical operating data of the same model of vehicle stored in the cloud, and extract three core indicators: health index, fault code distribution and fuel consumption fluctuation range; generate dynamic statistical benchmark ranges according to vehicle mileage segments (0-50,000 km, 50,000-100,000 km, and over 100,000 km) to establish a vehicle condition historical baseline database. Step S10432: Analyze the health index fluctuation value, the list of newly added risk items, and the urgency of maintenance recommendations from the multi-dimensional analysis results; combine the short-term monitoring data in the performance degradation trend graph to identify abnormal fluctuation points where the health index fluctuates significantly (health index changes by more than ±15% within 72 hours) in order to generate a real-time fluctuation feature set; Step S10433: Based on the differentiated indicators in the vehicle model benchmarking report, dynamically adjust the evaluation weights to obtain an evaluation weight configuration table with priority markings; the dynamic adjustment of evaluation weights includes: increasing the weight of engine-related parameters for models with significant differences in powertrain (increased to 40%); strengthening the weight of fault code status analysis for high failure rate vehicle groups (increased to 35%); and increasing the weight of start-stop wear for vehicles with congestion coefficients exceeding the threshold (>0.8) in the environmental impact correction factor set (20%). Step S10434: Align the real-time fluctuation feature set with the vehicle condition history baseline database according to mileage segments to generate a deviation detection matrix containing three types of markers; the three types of markers include: a deviation marker is activated when the health index exceeds the baseline range of the same mileage; an anomaly marker is activated when the matching degree between the newly added risk item and the fault mode feature database of the same type of vehicle is lower than the threshold (<60%); and a conflict marker is activated when the urgency of the maintenance recommendation contradicts the environmental impact correction factor. Step S10435: Perform risk modeling based on the deviation detection matrix to obtain a probability assessment report containing graded risk levels; the risk modeling includes: calculating the basic risk value according to the assessment weight configuration table with a single label; triggering the correlation superposition algorithm with double labels (such as health index deviation + new risk item anomaly); activating the historical accident case matching mechanism with triple labels; Step S10436: Integrate the probability assessment report with the multi-dimensional analysis results: For vehicles with no risk, directly output the health index as the evaluation value; for vehicles with medium or low risk, adjust the health index according to the weight configuration table; freeze the score of high-risk vehicles and attach a warning code; to generate a vehicle evaluation result containing a numerical score, risk level, and warning code. Step S1044: Quantitatively model the vehicle evaluation results to generate an overall vehicle condition score.

[0040] Furthermore, Figure 6 An application architecture diagram of the system provided in an embodiment of the present invention is shown.

[0041] In another preferred embodiment of the present invention, the intelligent vehicle connectivity detection and management system based on OBD II under the mobile Internet includes: The signal data analysis unit 101 is used to generate and send vehicle-to-everything (V2X) detection commands to the target detection vehicle via a mobile terminal, receive the current signal data transmitted back by the target detection vehicle, and determine whether direct detection transmission is possible.

[0042] In this embodiment of the invention, when a driver needs to perform intelligent vehicle connectivity detection, the detection operation can be performed using a mobile terminal. The signal data analysis unit 101 receives the vehicle detection request through the mobile terminal, generates a vehicle connectivity detection command through the mobile terminal, determines the target vehicle, sends the vehicle connectivity detection command to the target vehicle through the mobile terminal, and receives the current signal data transmitted by the target vehicle through the mobile terminal. Then, it identifies the signal strength of the current signal data and determines whether direct detection transmission is possible.

[0043] Specifically, Figure 7 A structural block diagram of the signal data analysis unit 101 in the system provided in an embodiment of the present invention is shown.

[0044] In a preferred embodiment provided by the present invention, the signal data analysis unit 101 specifically includes: The request receiving control module 1011 is used to receive vehicle detection requests via a mobile terminal; The instruction generation control module 1012 is used to generate vehicle connectivity detection instructions via a mobile terminal; The instruction sending control module 1013 is used to determine the target detection vehicle and send the vehicle-to-everything (V2X) detection instruction to the target detection vehicle via a mobile terminal. The signal data receiving and control module 1014 is used to receive the current signal data transmitted by the target detection vehicle via a mobile terminal. The direct detection transmission judgment module 1015 is used to identify the strength of the current signal data and determine whether direct detection transmission can be performed.

[0045] Furthermore, the intelligent vehicle connectivity detection and management system based on OBD II under the mobile Internet also includes: The vehicle-to-vehicle relationship identification unit 102 is used to identify vehicle-to-vehicle relationships through a mobile terminal when direct detection and transmission are not possible, to identify multiple online connected vehicles, and to filter target connected vehicles.

[0046] In this embodiment of the invention, when it is determined that direct detection and transmission cannot be performed, the vehicle-to-everything (V2X) relationship identification unit 102 obtains V2X relationship information through a mobile terminal, performs online V2X identification based on the V2X relationship information, identifies multiple online V2X vehicles that have a V2X relationship with the driver, obtains the online vehicle locations of the multiple online V2X vehicles, and simultaneously obtains the detection vehicle location of the target detection vehicle. Then, based on the detection vehicle location and the multiple online vehicle locations, it calculates the online V2X distance between the target detection vehicle and the multiple online V2X vehicles. By comparing the multiple online V2X distances, it selects the target V2X vehicle with the shortest corresponding online V2X distance from the multiple online V2X vehicles.

[0047] Specifically, Figure 8 The diagram shows the structural block diagram of the vehicle-to-vehicle relationship identification unit 102 in the system provided by an embodiment of the present invention.

[0048] In a preferred embodiment of the present invention, the vehicle-to-everything (V2X) relationship identification unit 102 specifically includes: The relationship information acquisition and control module 1021 is used to acquire vehicle-to-everything (V2X) relationship information via a mobile terminal when direct detection and transmission are not possible. The vehicle connectivity online identification module 1022 is used to perform vehicle connectivity online identification based on the vehicle connectivity relationship information to identify multiple online connected vehicles; The online location acquisition module 1023 is used to acquire the online vehicle locations of multiple online vehicle-to-everything (V2X) vehicles. The detection location acquisition module 1024 is used to acquire the detection vehicle location of the target detection vehicle; The online vehicle-to-everything (V2X) distance calculation module 1025 is used to calculate the online vehicle-to-everything (V2X) distance between the target detected vehicle and the multiple online vehicle locations based on the detected vehicle location and the multiple online vehicle locations. The comparison and filtering module 1026 is used to compare multiple online vehicle-to-everything (V2X) distances and filter target V2X vehicles from the multiple online V2X vehicles.

[0049] Furthermore, the intelligent vehicle connectivity detection and management system based on OBD II under the mobile Internet also includes: The data transition forwarding unit 103 is used to read the vehicle detection data of the target detection vehicle based on the OBD II interface, and to perform transition forwarding of the vehicle detection data through the target vehicle-to-vehicle connection.

[0050] In this embodiment of the invention, the data transition forwarding unit 103 reads the vehicle detection data of the target detection vehicle based on the OBD II interface, then activates the vehicle-to-vehicle communication channel between the target detection vehicle and the target vehicle-to-vehicle connected vehicle, then controls the target detection vehicle to transmit the vehicle detection data to the target vehicle-to-vehicle connected vehicle through the vehicle-to-vehicle communication channel, and then controls the target vehicle-to-vehicle connected vehicle to forward the vehicle detection data to the mobile terminal.

[0051] The detection data processing unit 104 is used to upload the vehicle detection data to the cloud via the mobile Internet, and to perform multi-dimensional big data processing on the vehicle detection data through cloud computing technology.

[0052] In this embodiment of the invention, after the mobile terminal obtains the forwarded vehicle detection data, the detection data processing unit 104 uploads the vehicle detection data to the cloud via the mobile Internet. Then, through cloud computing technology, it performs multi-dimensional data analysis on the vehicle detection data, including data dimensions, vehicle type dimensions, time dimensions, and regional dimensions, to obtain multi-dimensional analysis results. The multi-dimensional analysis results are then subjected to big data evaluation to obtain vehicle evaluation results. Finally, the vehicle evaluation results are quantitatively modeled to generate an overall vehicle condition score.

[0053] Specifically, Figure 9 A structural block diagram of the detection data processing unit 104 in the system provided by an embodiment of the present invention is shown.

[0054] In a preferred embodiment of the present invention, the detection data processing unit 104 specifically includes: The data upload module 1041 is used to upload the vehicle detection data to the cloud via the mobile Internet; The multi-dimensional analysis module 1042 is used to perform multi-dimensional analysis on the vehicle detection data using cloud computing technology to obtain multi-dimensional analysis results; The big data evaluation module 1043 is used to perform big data evaluation on the multi-dimensional analysis results and obtain vehicle evaluation results. The overall vehicle condition score generation module 1044 is used to quantitatively model the vehicle evaluation results and generate an overall vehicle condition score.

[0055] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0056] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0057] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0058] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0059] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent vehicle connectivity detection and management based on OBD II under the mobile internet, characterized in that, The method specifically includes the following steps: The system generates and sends vehicle-to-everything (V2X) detection commands to the target vehicle via a mobile device, receives the current signal data transmitted back by the target vehicle, and determines whether direct detection transmission is possible. When direct detection and transmission are not possible, vehicle-to-vehicle relationship identification is performed via mobile terminal to identify multiple online connected vehicles and filter target connected vehicles; The vehicle detection data of the target vehicle is read based on the OBD II interface, and the vehicle detection data is forwarded through the target vehicle-to-vehicle connection. The vehicle detection data is uploaded to the cloud via mobile internet, and then processed in multiple dimensions using cloud computing technology.

2. The intelligent vehicle connectivity detection and management method based on OBD II under mobile Internet according to claim 1, characterized in that, The process of generating and sending vehicle-to-everything (V2X) detection commands to the target vehicle via a mobile terminal, receiving current signal data transmitted back from the target vehicle, and determining whether direct detection transmission is possible specifically includes the following steps: Receive vehicle inspection requests via mobile device; Generate vehicle connectivity detection commands via mobile device; The target vehicle is identified, and the vehicle-to-everything (V2X) detection command is sent to the target vehicle via a mobile device. The mobile device receives current signal data transmitted back by the target detection vehicle. The current signal data is subjected to strength identification to determine whether direct detection and transmission are possible.

3. The intelligent vehicle connectivity detection and management method based on OBD II under mobile Internet according to claim 2, characterized in that, The step of identifying the strength of the current signal data and determining whether direct detection and transmission are possible specifically includes the following steps: The signal data is initially filtered to eliminate environmental noise interference, resulting in a purified signal data set. The signal strength features, signal stability features, and transmission rate features are extracted from the purified signal data set to generate a multi-dimensional transmission feature set. The feature weight allocation strategy is dynamically adjusted according to the current driving state of the vehicle to obtain a dynamic weight allocation scheme. The feature weight allocation strategy includes: emphasizing signal stability feature weight in the driving state, emphasizing signal strength feature weight in the stationary state, and increasing transmission rate feature weight in the high-speed moving state. The multi-dimensional transmission feature set is compared and analyzed with the preset transmission quality benchmark to identify abnormal transmission features such as signal strength below the strength threshold, signal stability exceeding the fluctuation range, or transmission rate not meeting the standard, and to generate a set of transmission anomaly identifiers. The transmission anomaly identifier set is weighted and evaluated based on a dynamic weight allocation scheme to calculate a comprehensive transmission quality score. When the score is lower than the transmittable threshold, the transmission obstacle identifier is activated, and when the score is higher than the transmittable threshold, the direct transmission permission identifier is activated. The transmission fault identification is verified by combining historical connection success rate data: when the historical connection success rate is higher than a preset threshold, it is downgraded to a temporary transmission fault; when the historical connection success rate is lower than a preset threshold, it is confirmed as a permanent transmission fault, and a transmission reliability verification result is generated. Combining the transmission reliability verification results with the real-time transmission status: when there is a temporary transmission obstacle and the current signal strength is trending upward, it is determined that direct transmission can be attempted; when there is a permanent transmission obstacle or the signal strength is continuously decreasing, it is determined that direct transmission cannot be performed, so as to generate a judgment result on whether direct detection transmission can be performed.

4. The intelligent vehicle connectivity detection and management method based on OBD II under mobile Internet according to claim 1, characterized in that, When direct detection and transmission are not possible, the method of identifying vehicle-to-vehicle relationships through a mobile terminal, determining multiple online connected vehicles, and filtering target connected vehicles specifically includes the following steps: When direct detection and transmission are not possible, vehicle-to-everything (V2X) relationship information is obtained via mobile devices. Based on the vehicle-to-everything (V2X) relationship information, perform online V2X identification to determine multiple online V2X vehicles; Obtain the online vehicle locations of multiple of the aforementioned online connected vehicles; Obtain the location of the target detection vehicle; Based on the detected vehicle location and the multiple online vehicle locations, calculate the online vehicle-to-vehicle distance between the target detected vehicle and the multiple online vehicle-to-vehicles; The distances of multiple online vehicle-to-vehicle connections are compared, and the target vehicle-to-vehicle connection is selected from the multiple online vehicle-to-vehicle connections.

5. The intelligent vehicle connectivity detection and management method based on OBD II under mobile Internet according to claim 4, characterized in that, The step of comparing multiple online vehicle-to-vehicle distances and selecting a target vehicle from among the multiple online connected vehicles specifically includes the following steps: Based on the online vehicle-to-vehicle distances between the target detection vehicle and multiple online vehicle-to-vehicle vehicles, an online vehicle-to-vehicle distance set is constructed; Each online vehicle-to-everything (V2X) distance in the online vehicle-to-everything (V2X) distance set is compared with a preset effective communication radius threshold. All online V2X distances that are greater than the effective communication radius threshold and their corresponding online V2X vehicles are removed to obtain a preliminary set of candidate V2X vehicles and their corresponding candidate V2X distances. Obtain the real-time signal relay capability parameters for each candidate vehicle in the candidate vehicle-to-everything (V2X) vehicle set; the real-time signal relay capability parameters include the current remaining battery power, current CPU load rate, and current network uplink bandwidth. Based on the candidate vehicle-to-vehicle distance and real-time signal relay capability parameters corresponding to each candidate vehicle-to-vehicle, a dynamic weight allocation strategy is adopted to calculate its comprehensive relay efficiency index, so as to obtain the comprehensive relay efficiency index. Based on the calculated comprehensive relay efficiency index, all candidate connected vehicles are sorted in descending order to generate a relay efficiency priority queue. The candidate vehicle with the highest comprehensive relay efficiency index is selected from the relay efficiency priority queue as the optimal target vehicle, and several candidate vehicles with the second-best comprehensive relay efficiency index are selected as backup vehicle sequences. The optimal target vehicle is determined as the final target vehicle for subsequent data transfer and forwarding, and the backup vehicle sequence is stored in association with it to dynamically switch over when the optimal target vehicle cannot complete the forwarding task.

6. The intelligent vehicle connectivity detection and management method based on OBD II under mobile Internet according to claim 1, characterized in that, The process of reading vehicle detection data from the target vehicle via the OBD II interface and then forwarding the vehicle detection data through the target vehicle-to-vehicle connection specifically includes the following steps: Vehicle detection data of the target vehicle is read based on the OBD II interface; Activate the vehicle-to-vehicle communication channel between the target detection vehicle and the target vehicle-to-vehicle; Control the target detection vehicle and transmit the vehicle detection data to the target vehicle-to-everything (V2X) vehicle through the vehicle-to-everything (V2X) communication channel; Control the target vehicle and forward the vehicle detection data to the mobile terminal.

7. The intelligent vehicle connectivity detection and management method based on OBD II under mobile Internet according to claim 1, characterized in that, The process of uploading the vehicle detection data to the cloud via mobile internet and performing multi-dimensional big data processing on the vehicle detection data using cloud computing technology specifically includes the following steps: The vehicle detection data is uploaded to the cloud via mobile internet; The vehicle detection data is analyzed in multiple dimensions using cloud computing technology to obtain multi-dimensional analysis results. The multi-dimensional analysis results are used for big data evaluation to obtain vehicle evaluation results; The vehicle evaluation results are quantitatively modeled to generate an overall vehicle condition score; The multi-dimensional analysis includes: data dimension, vehicle model dimension, time dimension, and geographical dimension.

8. The intelligent vehicle connectivity detection and management method based on OBD II under mobile Internet according to claim 7, characterized in that, The step of using cloud computing technology to perform multi-dimensional analysis on the vehicle detection data and obtain the multi-dimensional analysis results specifically includes the following steps: Vehicle inspection data is cleaned using cloud computing technology to remove invalid fields and outliers in order to obtain a standardized vehicle dataset. Feature extraction is performed on a standardized vehicle dataset, and a dynamic weight mapping relationship between features is established through correlation analysis to obtain a data feature set with weight labels. The system calls upon the cloud-based vehicle model database to perform multi-level matching between the current vehicle model and historical data of the same model, in order to obtain a vehicle model benchmarking report that includes differentiated indicators. Vehicle testing data is segmented along a time axis, and a dynamic monitoring window is constructed to obtain a performance degradation trend map with timestamps. By combining a cloud-based geographic information database with vehicle location and environmental parameters, the following are used to obtain a set of environmental impact correction factors: matching the intake oxygen concentration compensation coefficient for high-altitude areas; activating the cooling system load threshold for high-temperature / high-humidity areas; and superimposing the engine wear index based on start-stop frequency in congested road sections. The data feature set, vehicle model benchmarking report, performance degradation trend map, and environmental impact correction factor set are input into the validation engine for processing to obtain a cross-validation matrix containing anomaly marker bits. The final aggregation is performed based on the cross-validation matrix to obtain multi-dimensional analysis results including health index, risk item list, and maintenance suggestions. The final aggregation includes: merging indicators for items without abnormality markers according to dimension weights; and triggering secondary judgment from the expert rule base for items with abnormality markers.

9. The intelligent vehicle connectivity detection and management method based on OBD II under mobile Internet according to claim 8, characterized in that, The process of conducting big data evaluation on the multi-dimensional analysis results to obtain vehicle evaluation results specifically includes the following steps: By accessing historical operating data of the same model of vehicles stored in the cloud, three core indicators are extracted: health index, fault code distribution, and fuel consumption fluctuation range. Dynamic statistical benchmark intervals are generated in segments according to vehicle mileage to establish a historical vehicle condition baseline database. The analysis results from multiple dimensions are used to analyze the fluctuation values ​​of the health index, the list of newly added risk items, and the urgency of maintenance recommendations. Combined with short-term monitoring data in the performance degradation trend graph, abnormal fluctuation points with significant fluctuations in the health index are identified to generate a real-time fluctuation feature set. Based on the differentiated indicators in the vehicle benchmarking report, the evaluation weights are dynamically adjusted to obtain an evaluation weight configuration table with priority markings. The dynamic adjustment of evaluation weights includes: increasing the weight of engine-related parameters for vehicles with significant differences in powertrain systems; strengthening the weight of fault code status analysis for high failure rate vehicle groups; and increasing the weight of start-stop wear for vehicles with congestion coefficients exceeding the threshold in the environmental impact correction factor set. The real-time fluctuation feature set is aligned with the vehicle condition history baseline library by mileage segment to generate a deviation detection matrix containing three types of markers. The three types of markers include: a deviation marker is activated when the health index exceeds the baseline range of the same mileage; an anomaly marker is activated when the matching degree between the newly added risk item and the fault mode feature library of the same type of vehicle is lower than a threshold; and a conflict marker is activated when the urgency of the maintenance recommendation contradicts the environmental impact correction factor. Risk modeling is performed based on the deviation detection matrix to obtain a probability assessment report containing graded risk levels; the risk modeling includes: calculating the basic risk value according to the assessment weight configuration table with a single label; triggering the correlation superposition algorithm with double labeling; and activating the historical accident case matching mechanism with triple labeling. The probability assessment report is integrated with the results of multi-dimensional analysis: the health index of risk-free vehicles is directly output as the evaluation value; the health index of medium- and low-risk vehicles is adjusted according to the weight configuration table; the score of high-risk vehicles is frozen and a warning code is attached; so as to generate a vehicle evaluation result containing a numerical score, risk level and warning code.

10. A mobile internet-based intelligent vehicle connectivity detection and management system based on OBD II, characterized in that: The system applies the intelligent vehicle connectivity detection and management method based on OBD II under the mobile Internet as described in any one of claims 1 to 9, characterized in that the system includes a signal data analysis unit, a vehicle connectivity relationship identification unit, a data transition and forwarding unit, and a detection data processing unit, wherein: The signal data analysis unit is used to generate and send vehicle-to-everything (V2X) detection commands to the target detection vehicle via the mobile terminal, receive the current signal data transmitted back by the target detection vehicle, and determine whether direct detection transmission is possible. The vehicle-to-vehicle relationship identification unit is used to identify vehicle-to-vehicle relationships via a mobile terminal when direct detection and transmission are not possible, thereby identifying multiple online connected vehicles and filtering target connected vehicles. The data transition forwarding unit is used to read the vehicle detection data of the target detection vehicle based on the OBD II interface, and to perform transition forwarding of the vehicle detection data through the target vehicle-to-vehicle connection. The detection data processing unit is used to upload the vehicle detection data to the cloud via the mobile Internet, and to perform multi-dimensional big data processing on the vehicle detection data through cloud computing technology.

11. The intelligent vehicle connectivity detection and management system based on OBD II under the mobile Internet according to claim 10, characterized in that, The signal data analysis unit specifically includes: The request receiving control module is used to receive vehicle detection requests via a mobile terminal; The instruction generation control module is used to generate vehicle-to-everything (V2X) detection instructions via a mobile device. The instruction sending control module is used to determine the target detection vehicle and send the vehicle-to-everything (V2X) detection instruction to the target detection vehicle via a mobile terminal. The signal data receiving and control module is used to receive the current signal data transmitted by the target detection vehicle via a mobile terminal; The direct detection transmission judgment module is used to identify the strength of the current signal data and determine whether direct detection transmission is possible. The vehicle-to-everything (V2X) relationship identification unit specifically includes: The relationship information acquisition and control module is used to acquire vehicle-to-everything (V2X) relationship information via a mobile terminal when direct detection and transmission are not possible. The vehicle-to-everything (V2X) online identification module is used to perform V2X online identification based on the V2X relationship information to identify multiple online V2X vehicles. An online location acquisition module is used to acquire the online vehicle locations of multiple online connected vehicles; The detection location acquisition module is used to acquire the detection vehicle location of the target detection vehicle; The online vehicle-to-everything (V2X) distance calculation module is used to calculate the online vehicle-to-everything (V2X) distance between the target detected vehicle and the multiple online vehicle locations based on the detected vehicle location and the multiple online vehicle locations. The comparison and filtering module is used to compare multiple online vehicle-to-everything (V2X) distances and filter target V2X vehicles from the multiple online V2X vehicles. The detection data processing unit specifically includes: The data upload module is used to upload the vehicle detection data to the cloud via mobile internet; The multi-dimensional analysis module is used to perform multi-dimensional analysis on the vehicle detection data using cloud computing technology to obtain multi-dimensional analysis results; The big data evaluation module is used to perform big data evaluation on the multi-dimensional analysis results and obtain vehicle evaluation results; The overall vehicle condition score generation module is used to quantitatively model the vehicle evaluation results and generate an overall vehicle condition score.

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