Remote diagnosis method and device, electronic equipment and storage medium

Abstract

The invention discloses a remote diagnosis method and device, electronic equipment and a storage medium, the method is applied to remote diagnosis equipment in a remote diagnosis system, the remote diagnosis system comprises a target vehicle, and the remote diagnosis equipment is connected with the storage medium. The method comprises the following steps: acquiring signal intensity data, data transmission rate data and connection interruption frequency between the target vehicle and the remote diagnosis equipment within a preset time period; determining a target network quality value of the remote diagnosis system based on the signal strength data, the data transmission rate data and the connection interruption frequency; determining a target diagnosis mode based on the target network quality value; and performing remote diagnosis on the target vehicle based on the target diagnosis mode to obtain a target diagnosis result. According to the embodiment of the invention, the reliability of vehicle remote diagnosis is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle remote diagnosis, and in particular to a remote diagnosis method, device, electronic equipment and storage medium. Background Art

[0002] With the rapid development of intelligent and connected vehicles, remote diagnostics have become a core tool for vehicle troubleshooting, performance monitoring, and maintenance services. Existing remote diagnostic methods typically use fixed diagnostic modes without dynamic adaptation to network quality. Poor network quality can lead to data loss, process interruptions, incorrect results, and even safety risks such as delayed vehicle control commands. Therefore, improving the reliability of vehicle remote diagnostics is an urgent issue. Summary of the Invention

[0003] The embodiments of the present application provide a remote diagnosis method, device, electronic device and storage medium, which improve the reliability of vehicle remote diagnosis.

[0004] In a first aspect, embodiments of the present application provide a remote diagnostic method, which is applied to a remote diagnostic device in a remote diagnostic system, wherein the remote diagnostic system includes a target vehicle and the remote diagnostic device. The method includes:

[0005] Acquiring signal strength data, data transmission rate data, and connection interruption frequency between the target vehicle and the remote diagnostic device within a preset time period;

[0006] determining a target network quality value for the remote diagnostic system based on the signal strength data, the data transmission rate data, and the connection interruption frequency;

[0007] determining a target diagnostic method based on the target network quality value;

[0008] The target vehicle is remotely diagnosed based on the target diagnosis method to obtain a target diagnosis result.

[0009] In a second aspect, an embodiment of the present application provides a remote diagnosis device, the device comprising: an acquisition unit and a processing unit;

[0010] The acquisition unit is configured to acquire signal strength data, data transmission rate data, and connection interruption frequency between the target vehicle and the remote diagnostic device within a preset time period;

[0011] the processing unit being configured to determine a target network quality value of the remote diagnostic system based on the signal strength data, the data transmission rate data, and the connection interruption frequency;

[0012] determining a target diagnostic method based on the target network quality value;

[0013] The target vehicle is remotely diagnosed based on the target diagnosis method to obtain a target diagnosis result.

[0014] In a third aspect, an embodiment of the present invention provides an electronic device comprising: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor so that the electronic device performs the method of the first aspect.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method of the first aspect.

[0016] In a fifth aspect, an embodiment of the present invention provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, so that a computer executes the method of the first aspect.

[0017] The implementation of the present invention has the following beneficial effects:

[0018] It can be seen that the remote diagnosis method described in the embodiment of the present invention is applied to a remote diagnosis device in a remote diagnosis system, wherein the remote diagnosis system includes a target vehicle and the remote diagnosis device. The method includes: obtaining signal strength data, data transmission rate data and connection interruption frequency between the target vehicle and the remote diagnosis device within a preset time period, determining a target network quality value of the remote diagnosis system based on the signal strength data, the data transmission rate data and the connection interruption frequency, determining a target diagnosis method based on the target network quality value, performing remote diagnosis on the target vehicle based on the target diagnosis method, obtaining a target diagnosis result, and improving the reliability of vehicle remote diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the implementation methods or background technologies of the present application, the drawings required for use in the implementation methods or background technologies of the present application will be described below.

[0020] Figure 1 This is a schematic diagram of the structure of a remote diagnosis system provided by an embodiment of the present application;

[0021] Figure 2 This is a flow chart of a remote diagnosis method provided by an embodiment of the present application;

[0022] Figure 3 This is a flow chart of determining a target network quality value provided by an embodiment of the present application;

[0023] Figure 4 This is a flow chart of determining a first network quality value provided by an embodiment of the present application;

[0024] Figure 5 This is a flow chart of a method for determining a target diagnosis provided by an embodiment of the present application;

[0025] Figure 6 is a schematic diagram of a target mapping table provided in an embodiment of the present application;

[0026] Figure 7 This is a schematic structural diagram of a remote diagnostic device provided in an embodiment of the present application;

[0027] Figure 8 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the present invention, the following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0029] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0030] Reference herein to an "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0031] See also Figure 1 , Figure 1 1 is a schematic structural diagram of a remote diagnosis system provided in an embodiment of the present application. The remote diagnosis system 100 includes a target vehicle 101 and a remote diagnosis device 102 .

[0032] In this embodiment, the signal strength data, data transmission rate data and connection interruption frequency between the target vehicle 101 and the remote diagnostic device 102 within a preset time period are first obtained, and then the target network quality value of the remote diagnostic system is determined based on the signal strength data, the data transmission rate data and the connection interruption frequency. Then, the target diagnostic method is determined based on the target network quality value. Finally, the target vehicle 101 is remotely diagnosed based on the target diagnostic method to obtain a target diagnostic result.

[0033] It can be seen that by monitoring the three core indicators of signal strength, transmission rate, and connection interruption frequency, the network quality changes of the remote diagnosis system (such as signal attenuation, bandwidth congestion, or temporary disconnection) can be perceived in real time. When the network quality deteriorates (such as entering an underground garage causing signal weakening), the system automatically switches to offline diagnosis or delayed transmission mode to avoid interruption of diagnostic tasks due to sudden network problems, reducing repeated operations and manual intervention costs. In a weak network environment, key diagnostic data (such as fault codes and sensor thresholds) can be transmitted first, and non-critical data (such as historical logs and high-definition videos) can be temporarily stored locally and re-transmitted after the network is restored, ensuring that the core diagnostic process is not interrupted and that the diagnostic process runs stably under various network conditions.

[0034] See also Figure 2 , Figure 2 This is a flowchart of a remote diagnosis method provided by an embodiment of the present application, including but not limited to the following steps:

[0035] S201: Acquire signal strength data, data transmission rate data, and connection interruption frequency between the target vehicle and the remote diagnostic device within a preset time period.

[0036] In this embodiment, the target vehicle's onboard communication module and the remote diagnostic device's wireless communication interface can collect real-time signal strength data, data transmission rate data, and connection interruption frequency data between the target vehicle and the remote diagnostic device over a preset time period. Signal strength data reflects the strength of the wireless signal, data transmission rate data indicates the amount of data transmitted per unit time, and connection interruption frequency data indicates the number of times the communication link is disconnected during the preset time period.

[0037] S202: Determine a target network quality value of the remote diagnosis system based on the signal strength data, the data transmission rate data, and the connection interruption frequency.

[0038] In this implementation, see Figure 3 , Figure 3 This is a flowchart of determining a target network quality value provided by an embodiment of the present application, including but not limited to the following steps:

[0039] S301: Determine a first network quality value corresponding to the signal strength data.

[0040] In this implementation, see Figure 4 , Figure 4 This is a flowchart of determining a first network quality value provided by an embodiment of the present application, including but not limited to the following steps:

[0041] S401: Perform fitting based on the n signal strength values and the recording times of the n signal strength values to obtain a target fitting straight line.

[0042] In this embodiment, the signal strength data includes n signal strength values and n signal strength value recording times for the preset time period, where n is an integer greater than 1, and each signal strength value corresponds to a signal strength value recording time. The n signal strength values and the n signal strength value recording times can be fitted using a least squares method, a weighted least squares method, or a polynomial fitting method to obtain a target fitting line.

[0043] Actual signal strength is often affected by factors such as multipath fading and electromagnetic interference, resulting in random fluctuations. The fitted straight line transforms discrete data points into a smooth trend line through mathematical optimization, filtering out occasional noise and highlighting the long-term patterns of signal changes. For example, the signal strength of a vehicle may jump frequently while driving, but the fitted straight line can reveal whether the overall signal is increasing, decreasing, or remaining stable.

[0044] S402: Determine the slope of the target fitting line to obtain a target slope.

[0045] In this embodiment, by taking any two points in the target fitting line, the slope of the target fitting line can be determined to obtain the target slope. The slope of the target fitting line directly reflects the rate and direction of change of the signal strength. A positive slope indicates that the signal is constantly increasing, a negative slope indicates that the signal is constantly attenuating, and a slope close to zero indicates that the signal is stable.

[0046] S403: Determine a network quality value corresponding to the target slope to obtain the first network quality value.

[0047] In this embodiment, a mapping relationship between a preset slope and a network quality value may be used. Based on the mapping relationship, a network quality value corresponding to the target slope may be determined to obtain the first network quality value.

[0048] As can be seen, by fitting n signal strength values and their corresponding recording times within a preset time period, a target fitted line is obtained, and its slope (target slope) is used to determine the first network quality value. The core advantage of this method is that it converts discrete signal strength data over time into a continuous linear trend model, thereby quantifying the dynamic rate of change of signal strength. Specifically, linear fitting can smooth random noise (such as multipath effects and transient interference) through algorithms such as the least squares method, avoiding misjudgments caused by single-point outliers, making the assessment results more accurate to the true trend of signal changes. As a quantitative indicator, the slope not only reflects whether the signal strength is continuously increasing, stable, or decreasing (for example, a positive slope indicates signal strengthening, a negative slope indicates attenuation, and a slope close to zero indicates stability), but also predicts the direction of network quality evolution by the magnitude of the rate of change (for example, a larger absolute value of the slope indicates more dramatic change). For example, when the slope is negative and its absolute value exceeds a threshold, it can be used to predict impending signal deterioration and trigger an early warning mechanism, prompting the system to adjust its strategy (such as switching network channels or reducing data transmission) in advance, rather than passively responding to failures. In addition, this method converts time series data into a single characteristic parameter (slope), simplifying the complexity of multi-dimensional data processing and facilitating its combination with other network indicators (such as transmission delay and packet loss rate) to form a comprehensive evaluation system. It also provides a basis for dynamic resource allocation by increasing the data collection frequency during the signal enhancement phase and reducing unnecessary transmission during the attenuation phase to reduce traffic consumption, ultimately improving the accuracy, real-time nature of network quality assessment and the overall adaptability of the system. It is particularly suitable for scenarios such as the Internet of Vehicles and the Industrial Internet of Things that have extremely high requirements for communication stability and reliability.

[0049] S302: Determine a second network quality value corresponding to the data transmission rate data.

[0050] In this embodiment, the data transmission rate data includes p data transmission rate values and p data transmission rate value recording moments in the preset time period, where p is an integer greater than 1, and each data transmission rate value corresponds to a data transmission rate value recording moment.

[0051] Exemplarily, fitting can be performed based on the p data transmission rate values and the recording times of the p data transmission rate values to obtain a target fitting straight line. Specifically, a polynomial fitting or exponential fitting method can be used to fit the p data transmission rate values and the recording times of the p data transmission rate values to obtain a first fitting straight line.

[0052] Exemplarily, the slope of the first fitting straight line is determined to obtain the first slope. Specifically, the slope of the first fitting straight line can be determined by arbitrarily selecting two points on the first fitting straight line to obtain the first slope.

[0053] Exemplarily, the network quality value corresponding to the first slope is determined to obtain the second network quality value. Specifically, it can be a mapping relationship between a preset slope and a network quality value. Based on the mapping relationship, the network quality value corresponding to the first slope can be determined to obtain the second network quality value.

[0054] It can be seen that by fitting p data transmission rate values and their corresponding recording moments within a preset time period, the target fitting straight line is obtained, and the second network quality value is determined by its slope (first slope). The core advantage of this method is that it converts the discrete data of transmission rate changes over time into a continuous linear trend model, thereby quantifying the dynamic change characteristics of the transmission rate. Specifically, linear fitting (such as polynomial fitting and exponential fitting) can smooth out random fluctuations in the transmission process (such as rate jitter caused by network congestion and link switching) through algorithms, avoid evaluation bias caused by single-point sudden fluctuations (such as short-term peaks or valleys), and make the results more accurately reflect the long-term trend or typical pattern of the transmission rate. The slope, as a quantitative indicator, can not only reflect whether the transmission rate is continuously increasing, stable, or decreasing (such as a positive slope indicates an increase in rate, a negative slope indicates a decrease in rate, and a slope close to zero indicates a stable rate), but also predict the evolution direction of the network carrying capacity by the magnitude of the change rate (such as the larger the absolute value of the slope, the more drastic the rate change). For example, when the slope is negative and the absolute value exceeds the threshold, the risk of insufficient network bandwidth resources can be identified in advance and the traffic control mechanism (such as priority scheduling and dynamic data compression) can be triggered, rather than lagging behind to deal with the problem of lag.

[0055] S303: Determine a third network quality value corresponding to the connection interruption frequency.

[0056] In this embodiment, it may be a mapping relationship between a preset connection interruption frequency and a preset network quality value, so that the third network quality value corresponding to the connection interruption frequency can be determined based on the mapping relationship.

[0057] S304: Determine a first weight corresponding to the first network quality value, a second weight corresponding to the second network quality value, and a third weight corresponding to the third network quality value.

[0058] In this embodiment, the sum of the first weight, the second weight, and the third weight is 1.

[0059] Exemplarily, a first reference weight corresponding to the first network quality value and a second reference weight corresponding to the second network quality value are determined, wherein the sum of the first reference weight and the second reference weight is less than 1.

[0060] Exemplarily, the length of the signal propagation path between the target vehicle and the remote diagnostic device is obtained. Specifically, since signal strength decays with increasing propagation distance, and the attenuation amplitude is generally proportional to the square or higher power of the path length, the longer the path length, the lower the initial signal strength value. Furthermore, under the same environmental interference, long-distance transmission is more susceptible to factors such as multipath fading and obstacle obstruction, resulting in increased signal strength fluctuations or a trend of attenuation. Therefore, the signal propagation path length will affect the signal strength data, so it is necessary to obtain the signal propagation path length between the target vehicle and the remote diagnostic device.

[0061] Exemplarily, a first optimization factor corresponding to the signal propagation path length is determined. Specifically, it can be a mapping relationship between a preset signal propagation path length and an optimization factor. Based on the mapping relationship, the first optimization factor corresponding to the signal propagation path length can be determined.

[0062] Exemplarily, the first reference weight is optimized based on the first optimization factor to obtain the first weight. Specifically, the first weight is calculated according to the following formula:

[0063] First weight = first reference weight × (1 + first optimization factor);

[0064] According to the above formula, the first reference weight can be optimized based on the first optimization factor to obtain the first weight.

[0065] For example, the number of data transmission channels between the target vehicle and the remote diagnostic device is obtained. Specifically, since the more data transmission channels there are, the greater the amount of data that can be transmitted simultaneously per unit time (similar to how a multi-lane highway improves traffic efficiency), the peak data transmission rate can be directly increased, and more channels mean higher redundancy. When the transmission rate of a channel drops due to problems such as signal interference or bandwidth congestion, the system can automatically divert data to other idle channels to avoid a sudden drop in the rate caused by a single channel failure, thereby maintaining the stability of the transmission rate. The correspondence between the number of channels and the transmission rate is affected by the system scheduling strategy. If the number of channels exceeds the actual demand (such as in low-data-volume scenarios), some of the rate advantage may be offset by idle resources or scheduling overhead (such as channel management and synchronization overhead). Conversely, in high-concurrency data transmission scenarios (such as real-time video stream diagnosis), insufficient channels will lead to increased competition and a reduction in transmission rate due to queuing delays. Therefore, the number of data transmission channels between the target vehicle and the remote diagnostic device will affect the data transmission rate data between the target vehicle and the remote diagnostic device, so the number of data transmission channels between the target vehicle and the remote diagnostic device is obtained.

[0066] Exemplarily, determining a second optimization factor corresponding to the number of data transmission channels may specifically be a mapping relationship between a preset number of data transmission channels and an optimization factor, and based on the mapping relationship, determining the second optimization factor corresponding to the number of data transmission channels.

[0067] Exemplarily, the second reference weight is optimized based on the second optimization factor to obtain the second weight. Specifically, the second weight is calculated according to the following formula:

[0068] Second weight = second reference weight × (1 + second optimization factor);

[0069] According to the above formula, the second reference weight can be optimized based on the second optimization factor to obtain the second weight.

[0070] Exemplarily, the third weight is determined based on the first weight and the second weight. Specifically, since the sum of the first weight, the second weight and the third weight is 1, after determining the first weight and the second weight, the third weight can be determined based on the first weight and the second weight.

[0071] It can be seen that by introducing signal propagation path length and the number of data transmission channels as optimization factors and dynamically adjusting the weights based on a preset mapping relationship and weight calculation formula, it is possible to achieve refined modeling and optimization of the data transmission rate in the remote diagnosis system. Signal propagation path length directly affects signal attenuation and delay (e.g., the longer the path, the lower the signal strength and the higher the interference probability). By adjusting the first optimization factor and weights, the impact of path length on the transmission rate can be quantified, making the model more consistent with actual physical layer transmission patterns and avoiding rate prediction errors caused by ignoring path factors. The number of data transmission channels reflects the system's parallel transmission capability (e.g., multi-link aggregation can increase the bandwidth limit). The second optimization factor, through the linkage between the number of channels and weights, can amplify the rate advantage when channels are sufficient (e.g., increasing peak rate through multi-channel offloading in high-concurrency scenarios) and alert the system to resource bottlenecks when channels are insufficient (e.g., prioritizing critical data transmission) through weight adjustment, balancing channel utilization and scheduling overhead. By constraining the sum of the first, second, and third weights to 1, dynamic weight allocation is achieved for multiple influencing factors (path length, number of channels, and other unspecified factors), making the model flexible and scalable. For example, in dense urban areas, where path length interference is significant, the first weight can be automatically increased to emphasize its dominant role in rate. In VANET scenarios, the number of channels can become a rate bottleneck, so the second weight is correspondingly increased to guide the system to prioritize channel scheduling strategies. By optimizing the multiplicative relationship between factors and weights, qualitative influences are converted into quantitative adjustments, making data rate calculations more scientific and providing a more reliable basis for selecting remote diagnostic methods (such as whether to enable real-time data flow diagnostics). Ultimately, this improves the diagnostic system's ability to dynamically adapt to network quality and enhances resource utilization efficiency.

[0072] It can be seen that by integrating multi-dimensional network quality values such as signal strength, data transmission rate, and connection interruption frequency and combining them with weights to calculate the target network quality value of the remote diagnosis system, the scientific and practical nature of network quality assessment can be improved in terms of comprehensiveness, flexibility, quantitative evaluation, and reliability. Specifically, multi-dimensional indicators can cover core elements such as signal stability, transmission efficiency, and connection reliability, avoiding the one-sidedness of a single parameter. Weight allocation can flexibly adjust the importance of each indicator based on business scenarios (such as real-time diagnosis or stability-prioritized scenarios), making the assessment more tailored to actual needs. Fusion of multi-dimensional data into a single quantitative target value not only facilitates rapid judgment of whether the network meets diagnostic requirements, but also allows for the identification of network bottlenecks through the proportion of each dimension value and weight, guiding optimization direction. In addition, multi-dimensional weighting can reduce the impact of fluctuations or temporary interference of a single indicator through averaging, improving the reliability of the assessment results. Even if one indicator is abnormal or missing, other indicators can still support the assessment, enhancing the system's anti-interference ability and robustness. Ultimately, it provides a scientific basis for resource allocation, task scheduling, and network optimization of the remote diagnosis system, thereby ensuring diagnostic efficiency and high data reliability.

[0073] S305: Perform calculation based on the first network quality value, the second network quality value, the third network quality value, the first weight, the second weight, and the third weight to obtain the target network quality value of the remote diagnosis system.

[0074] In this embodiment, illustratively, a reference network quality value is obtained by performing calculation based on the first network quality value, the second network quality value, the third network quality value, the first weight, the second weight, and the third weight. Specifically, the reference network quality value is calculated according to the following formula:

[0075] Reference network quality value = first network quality value × first weight + second network quality value × second weight + third network quality value × third weight;

[0076] According to the above formula, a reference network quality value can be obtained by performing calculation based on the first network quality value, the second network quality value, the third network quality value, the first weight, the second weight and the third weight.

[0077] Exemplarily, the usage time of the target vehicle is obtained. Specifically, an increase in the usage time of the target vehicle may cause the aging of its built-in vehicle network module hardware, thereby affecting the network quality. The aging antenna, RF components or circuit boards may cause the wireless signal reception sensitivity to decrease, resulting in problems such as weak signal and unstable connection, which directly lowers the network quality value. The old vehicle system may not support the latest network protocol, or there may be compatibility defects with the communication protocol of the remote diagnostic equipment, resulting in reduced data transmission efficiency or increased packet loss rate. Vehicles that have been used for a long time may undergo multiple software upgrades or configuration changes, which affect the quality of network interaction. Therefore, the usage time of the target vehicle will affect the target network quality value of the remote diagnostic system, so the usage time of the target vehicle is obtained.

[0078] Exemplarily, the target fine-tuning parameter corresponding to the usage time is determined. Specifically, it can be a mapping relationship between preset usage time and fine-tuning parameters. Based on the mapping relationship, the target fine-tuning parameter corresponding to the usage time can be determined.

[0079] Exemplarily, the reference network quality value is adjusted based on the target fine-tuning parameter to obtain the target network quality value. Specifically, the target network quality value is calculated according to the following formula:

[0080] Target network quality value = reference network quality value × (1 + target fine-tuning parameter);

[0081] According to the above formula, the reference network quality value can be adjusted based on the target fine-tuning parameter to obtain the target network quality value.

[0082] As can be seen, by combining the first, second, and third network quality values with their corresponding weights to calculate the reference network quality value, a comprehensive evaluation of network indicators from different dimensions and their importance is comprehensively considered, ultimately establishing a holistic assessment of network quality. Furthermore, the key factor of target vehicle usage duration is introduced, and the corresponding target fine-tuning parameters are used to adjust the reference network quality value, fully accounting for the potential impact of factors such as hardware aging and software compatibility on network quality during vehicle use. This approach avoids the one-sidedness of single-metric evaluation, focusing on both the performance of the network itself and dynamically adjusting it based on the actual vehicle usage. The resulting target network quality value is more closely aligned with the actual network quality of the remote diagnostic system in actual applications, providing a scientific basis for optimizing network configuration, improving diagnostic efficiency and accuracy, and comprehensively and accurately evaluating the network quality of the remote diagnostic system.

[0083] S203: Determine a target diagnosis method based on the target network quality value.

[0084] In this implementation, see Figure 5 , Figure 5 This is a flow chart of a method for determining a target diagnosis provided by an embodiment of the present application, including but not limited to the following steps:

[0085] S501: Acquire historical diagnosis data of the remote diagnosis system within a historical time period.

[0086] In this embodiment, the end time of the historical time period is earlier than the start time of the preset time period. Historical data is the basis for subsequent analysis and is used to explore the correlation patterns between network quality and diagnostic methods. By limiting the historical time period to a non-overlapping period with the preset time period, the timeliness and independence of the data are ensured, preventing future data from affecting the summary of historical patterns.

[0087] S502: Determine a mapping table between network quality values and diagnostic methods based on the historical diagnostic data to obtain a target mapping table.

[0088] In this implementation, the network quality value and the diagnostic method used at each diagnosis are extracted from historical data. Through statistical analysis (such as frequency distribution and correlation analysis), the optimal diagnostic method corresponding to different network quality value intervals is found. The above rules are organized into a structured table to clearly define the diagnostic method corresponding to each network quality value range, that is, the target mapping table. Figure 6 , Figure 6 This is a schematic diagram of a target mapping table provided by an embodiment of the present application. Figure 6 In the target mapping table 600, a plurality of diagnostic modes and a network quality value range corresponding to each diagnostic mode are included, wherein the diagnostic modes include real-time data stream diagnosis, intermittent diagnosis and vehicle local diagnosis. When the network quality value range is greater than the first reference network quality value, the diagnostic mode is determined to be real-time data stream diagnosis. When the network quality value range is less than or equal to the first reference network quality value and greater than the second reference network quality value, the diagnostic mode is determined to be intermittent diagnosis. When the network quality value range is less than or equal to the second reference network quality value, the diagnostic mode is determined to be vehicle local diagnosis.

[0089] Real-time data streaming diagnostics feature: Through a live network connection, vehicle operating data (such as sensor signals, control system status, and fault codes) is continuously and in real time transmitted to a remote server or cloud platform for real-time analysis and diagnosis. Data transmission is seamless, enabling real-time monitoring of vehicle status and rapid detection of anomalies (such as engine failure and battery failure). This requires a stable network connection and is suitable for scenarios with good network coverage. The cloud can leverage complex models such as big data analysis and intelligent algorithms to provide highly accurate diagnostic results and even predictive maintenance recommendations.

[0090] Characteristics of intermittent diagnosis: Data is transmitted non-continuously between the vehicle and the remote server, and diagnosis is completed through intermittent connections (such as periodic data transmission and event-triggered transmission). Data transmission is not real-time and continuous, but is carried out according to preset rules (such as sending data every 10 minutes, sending data when anomalies are detected). It does not require a continuous high-bandwidth connection and can work in weak network environments (such as suburbs and tunnel entrances). It reduces network pressure by transmitting data in batches. There is a certain delay in data transmission (minutes), but it can still meet the needs of most non-emergency scenarios. The vehicle first pre-processes the data locally (such as filtering invalid signals and compressing key data) and then sends it to the cloud through an intermittent network to reduce the amount of data transmission.

[0091] Characteristics of local vehicle diagnosis: It relies entirely on the vehicle's local hardware and software for diagnosis, eliminating the need for a remote server or cloud connection. The diagnostic process is independently completed by the vehicle's electronic control unit, embedded system, or locally stored diagnostic program, with results stored locally in the vehicle or output through a physical interface. It does not rely on any network connection and is suitable for extreme environments (such as underground garages and mountainous areas without signal). It relies on pre-programmed diagnostic logic on the vehicle (such as a built-in fault code library and preset threshold judgments) and cannot access cloud-based big data or the latest algorithms. Consequently, diagnostic depth and accuracy are limited. Local diagnostic response speeds are fast (in milliseconds), making it particularly suitable for immediate alerts of critical faults (such as brake system anomalies).

[0092] As can be seen, the optimal diagnostic method is automatically matched based on network quality. For example, in strong network environments, real-time data stream diagnosis is used, leveraging high transmission efficiency for accurate, real-time fault analysis. In weak network environments, intermittent diagnosis is switched to batched transmission and local preprocessing to reduce network pressure and ensure continuity of the diagnostic process. In no network environment, local vehicle diagnosis is enabled to avoid diagnostic interruptions caused by reliance on remote connections, ensuring basic fault detection capabilities in extreme scenarios. The mapping table is generated based on historical data statistics and covers typical scenarios across different network quality ranges, reducing manual intervention costs. By matching diagnostic method characteristics (such as real-time performance, data volume, and network dependency), the diagnostic system improves stability and reliability in complex network environments. In particular, in on-board diagnostic scenarios, it balances real-time monitoring requirements with robustness in weak or no network environments. Real-time diagnosis leverages cloud-based big data and intelligent algorithms to improve accuracy, while local diagnosis addresses availability issues in extreme environments. The combination of the two enables the system to meet the demands of high-demand real-time fault analysis while also covering diagnostic needs in remote or signal-blind areas, forming a multi-layered, full-scenario diagnostic capability.

[0093] S503: Determine a diagnostic method corresponding to the target network quality value based on the target mapping table to obtain the target diagnostic method.

[0094] In this embodiment, according to the rules established in the target mapping table, the current network quality value is judged to which interval it belongs, thereby determining the corresponding diagnostic method and obtaining the optimal diagnostic method for the current network environment to guide the actual operation of the remote diagnostic system.

[0095] It can be seen that by analyzing the correspondence between actual network quality values and diagnostic methods over historical time periods, the target mapping table can reflect the optimal diagnostic strategy under different network conditions in real-world scenarios. Historical data covers network characteristics across different time periods and regions (such as network congestion during peak hours and evening rush hours, and signal differences between suburban and urban areas). This enables the mapping table to capture the cyclical patterns of network quality, prioritize real-time data stream diagnosis, fully utilize high-quality networks to transmit large amounts of real-time data, and leverage the high precision of cloud-based intelligent diagnosis. It proactively switches to local vehicle diagnosis to avoid network congestion or diagnostic timeouts caused by forced data transmission, saving traffic costs while improving diagnostic success rates. Intermittent and local diagnosis reduce the real-time computing power requirements of cloud servers. Validated by historical data, the mapping table can avoid failures caused by mismatches between network quality and diagnostic methods. In situations where there is no network or the network is interrupted (such as in underground garages), local vehicle diagnosis based on historical mapping rules can serve as a backup solution, ensuring that critical faults (such as brake system anomalies) can still be detected promptly, avoiding diagnostic failures caused by reliance on the network. After each preset time period, the newly generated diagnostic data can be added to the historical database to update the target mapping table. When the diagnostic system introduces new network technology (such as satellite communication) or new diagnostic methods, the mapping table can be retrained through historical data to achieve a smooth upgrade of the diagnostic strategy.

[0096] It should be noted that, in this embodiment, when the target network quality value is greater than the preset network quality value, the operation of determining the target diagnostic method based on the target network quality value is performed; when the target network quality value is less than or equal to the preset network quality value, it is necessary to adjust the direct data transmission channel between the target vehicle and the remote diagnostic equipment to improve the network quality, and then perform the operation of determining the target diagnostic method based on the target network quality value.

[0097] Exemplarily, prompt information is generated based on the target network quality value, and m data transmission channels between the target vehicle and the remote diagnostic device are obtained, where m is an integer greater than 1. The prompt information is used to indicate that the network quality between the target vehicle and the remote diagnostic device is poor. Specifically, all feasible data transmission paths between the target vehicle and the remote diagnostic device are comprehensively scanned and identified. These channels may include wireless communication channels and wired communication channels. Through the detection function of the on-board communication module or related hardware equipment, these potential channels are listed one by one to form a list containing m channels, which provides a basis for subsequent channel quality evaluation.

[0098] Exemplarily, the channel quality value corresponding to each of the m data transmission channels is determined to obtain m channel quality values. Specifically, for each data transmission channel obtained previously, real-time performance detection and quantitative evaluation are performed from multiple dimensions such as signal strength, transmission delay, bandwidth capacity, data packet loss rate, and connection stability. These specific performance indicators are converted into a comprehensive channel quality value through specific algorithms or rules. This value can intuitively reflect the ability and reliability of each channel to transmit data in the current state, thereby providing a basis for screening high-quality channels.

[0099] Exemplarily, k channel quality values greater than a preset channel quality value among the m channel quality values are determined, where k is an integer less than or equal to m. Specifically, after obtaining the quality values of all channels, a predefined channel quality threshold (i.e., the preset channel quality value) is set, and the quality value of each channel is compared with the threshold to screen out channels with quality values higher than the threshold. These channels are considered to have the ability to meet the basic requirements of data transmission. The number k of screened channels depends on the comparison result of the actual channel quality with the threshold, and may be equal to m (all channels meet the standard), or may be less than m (some channels meet the standard), or may even be 0 (no channel meets the standard).

[0100] Exemplarily, k data transmission channels corresponding to the k channel quality values are determined. Specifically, after completing the screening of the channel quality values, the k channel quality values that meet the requirements are screened out and matched one-to-one with the actual physical or logical data transmission channels to clarify which channels have passed the quality assessment. For example, it is determined that the quality values of the wireless network channel and the wired channel are greater than a preset threshold, thereby forming a clear list of high-quality channels to prepare for subsequent data transmission.

[0101] Exemplarily, data transmission is performed between the target vehicle and the remote diagnostic device based on the k data transmission channels. When the target network quality value is greater than the preset network quality value, the operation of determining the target diagnostic method based on the target network quality value is performed. Specifically, after the available k high-quality channels are determined, the data transmission process between the target vehicle and the remote diagnostic device is immediately initiated through these channels. According to the diagnostic requirements, the vehicle status data, fault information, etc. are sent to the remote diagnostic device or the cloud system through these channels to complete the diagnostic task. At the same time, the target network quality value is continuously monitored during the data transmission process. Once it is detected that the target network quality value rebounds and exceeds the preset network quality value, it indicates that the network environment has recovered to a state that can support the conventional diagnostic process. At this time, the use of the spare k data transmission channels is immediately stopped, and the operation of determining the target diagnostic method based on the target network quality value is performed instead. That is, according to the pre-established mapping relationship between network quality and diagnostic method, a diagnostic method (such as real-time data stream diagnosis) that matches the current high-quality network is selected to complete the diagnostic work with higher efficiency and accuracy, thereby realizing intelligent switching and adaptive adjustment of the diagnostic process in different network environments.

[0102] It should be explained that a dynamic and adaptive network diagnosis optimization mechanism is constructed that can intelligently adjust data transmission strategies based on real-time network quality to ensure the reliability and efficiency of remote diagnosis: when network quality is good, efficient diagnostic methods are quickly matched based on preset mapping relationships to fully utilize high-quality network performance; when network quality is below the threshold, reliable data transmission channels are screened by generating prompt information, multi-channel scanning, and quality assessment. Parallel transmission improves the stability and efficiency of data transmission and avoids diagnostic interruptions caused by weak signals or congestion on a single channel. At the same time, the network status is continuously monitored during the use of the backup channel, and once the quality returns to standard, it automatically switches back to normal diagnostic mode. This mechanism not only enhances the system's fault tolerance in complex network environments through multi-channel redundancy and reduces the risk of diagnostic interruption, but also optimizes network resource utilization through dynamic weight adjustment and intelligent switching, thereby ensuring that the diagnostic process can maintain efficient operation in different scenarios, further improving the user experience while reducing diagnostic delays or failure rates caused by network problems.

[0103] It can be seen that when the network quality is good, an efficient target diagnosis method is determined based on the target network quality value to ensure the real-time and accuracy of the diagnosis; when the network quality is poor, a prompt message is generated and multiple data transmission channels are automatically obtained. By evaluating the channel quality, reliable channels are screened for data transmission to ensure the continuity of diagnosis and avoid diagnosis interruption due to a single network problem. At the same time, after the network quality is restored, it automatically switches back to the efficient diagnosis mode to achieve optimal utilization of network resources and intelligent adaptation of the diagnosis process, thereby improving the stability, reliability and user experience of the remote diagnosis system in complex network environments. Specifically, when the target network quality is high, the system quickly matches the optimal diagnostic method (such as real-time data stream diagnosis) based on the preset mapping relationship, making full use of the low latency and high bandwidth characteristics of the high-quality network to ensure real-time transmission of diagnostic data and high-precision analysis in the cloud, maximizing diagnostic efficiency and accuracy. When the network quality drops below the threshold, the system immediately triggers a multi-level response: first, it generates a clear prompt message to inform the user of the current network status so that the diagnostic delay can be predicted in advance; at the same time, it automatically scans and obtains all available data transmission channels between the target vehicle and the remote device, and through quantitative evaluation of the quality value of each channel (such as signal strength, delay, stability and other indicators), selects reliable channels that exceed the preset standards to form redundant transmission paths. By transmitting data in parallel through these high-quality backup channels, even if the main network is unstable, it can ensure that key diagnostic data is not lost and transmission is not interrupted, maintain the basic operation of the diagnostic process, and avoid complete stagnation of diagnosis due to a single network failure. In addition, during the operation of the backup channel, the system continuously monitors the quality of the main network. Once it detects that the network has recovered to the standard, it immediately and seamlessly switches back to the efficient diagnostic mode based on the main network, avoiding manual intervention while achieving dynamic adaptation to maintain the bottom line when the network fluctuates and improve efficiency when the network is good. This mechanism not only improves the robustness of the diagnostic system in weak network environments through multi-channel redundancy and reduces the risk of diagnostic failure due to network problems, but also avoids the waste of resources caused by forcibly using high-demand diagnostic methods in low-quality networks through intelligent switching. At the same time, it enhances the user's perception of system status through transparent prompt information, and ultimately achieves comprehensive optimization of diagnostic continuity, data reliability and user experience in complex network environments.

[0104] S204: Performing remote diagnosis on the target vehicle based on the target diagnosis method to obtain a target diagnosis result.

[0105] In this embodiment, the target vehicle is remotely diagnosed based on the target diagnosis method and the target diagnosis result is obtained. The diagnostic process needs to be adapted to the characteristics of the target diagnosis method and the network quality: when the target network quality value is high, real-time data stream diagnosis is adopted, and the real-time data of the vehicle sensor (such as engine speed, battery voltage, etc.) is continuously transmitted to the remote diagnostic equipment or the cloud through a stable and high-speed network. The real-time analysis algorithm (such as a machine learning model) is used to parse the data, identify anomalies in real time and generate diagnostic results. For example, an alarm is immediately issued when a sensor value exceeds the threshold; if the target network quality value is at a medium level, intermittent diagnosis is triggered, and the vehicle first stores the data in the local cache. The system stores the data (e.g., by time window or event trigger), and when the network quality recovers, transmits batch data to the remote end through intermittent connection (e.g., periodic transmission). The remote system analyzes the data offline, compares it with historical trends or industry standards, and generates periodic diagnostic reports, such as analyzing the causes of abnormal fuel consumption fluctuations in the past 24 hours. When the target network quality value is extremely low, the vehicle's local diagnosis is initiated, relying entirely on the on-board diagnostic module and pre-programmed logic to directly analyze the locally stored fault codes and sensor data, output the diagnostic results through local interfaces such as the dashboard indicator lights and the on-board screen (e.g., if a transmission fault is displayed, please stop and check), and synchronize the results to the remote end after the network is restored. The entire process dynamically matches network quality with diagnostic methods to ensure that remote diagnosis can be completed efficiently and reliably in different network environments, ultimately forming target diagnostic results that include real-time fault alarms, performance evaluations, or predictive maintenance recommendations.

[0106] The core benefit of this approach is that by dynamically matching network quality with diagnostic methods, a remote diagnostic capability covering all scenarios is established: under high network quality, real-time data stream diagnosis is used to achieve millisecond-level fault response and high-precision analysis to ensure driving safety; under medium network quality, local caching and batch transmission strategies for intermittent diagnosis are used to balance diagnostic timeliness and network resource consumption, avoiding data loss due to network fluctuations; in low network quality or no network environment, the vehicle's local diagnostic module is used to achieve immediate alarm and local processing of emergency faults, ensuring diagnostic availability in extreme scenarios. This layered diagnostic mechanism not only improves the system's adaptability and robustness to complex network environments, but also reduces the risk of missed diagnosis and misdiagnosis through the dynamic balance between data real-time and diagnostic accuracy, while reducing dependence on a single network channel, ultimately achieving comprehensive optimization of remote diagnostic efficiency, reliability, and user experience.

[0107] As can be seen, a mechanism that flexibly switches diagnostic modes based on real-time network quality is key to ensuring efficient remote diagnosis. In modern in-vehicle network environments, signal coverage is uneven and network quality fluctuates frequently. Using a single diagnostic mode can easily lead to diagnostic interruptions, data loss, or transmission distortion, seriously impacting the accuracy and timeliness of diagnostic results. The three modes of real-time data streaming, intermittent diagnosis, and local diagnosis complement each other and effectively address these challenges. The real-time data streaming diagnostic mode plays a key role when network quality is high. In this scenario, with high network signal strength, fast transmission rates, and low latency, the system continuously transmits massive amounts of real-time data collected by vehicle sensors, such as engine speed, torque output, battery voltage, brake pressure, and other critical information, to remote diagnostic equipment or cloud servers at millisecond speeds via a stable, high-speed network channel. The cloud leverages powerful computing resources and advanced real-time analysis algorithms, such as machine learning models and neural networks, to conduct in-depth analysis of this data. Once abnormal fluctuations in sensor data are detected, such as a sudden deviation from the normal engine speed or an unusual jump in the oxygen sensor signal, the system can quickly identify and locate the fault and issue a timely alert, effectively capturing real-time vehicle faults and ensuring safe operation. Intermittent diagnostic mode is ideal for moderate network quality situations, where there is some signal attenuation, unstable transmission rates, or high latency. In this mode, the vehicle no longer transmits data in real time, but instead caches the collected data locally. This caching strategy can be based on a time window, bundling and storing vehicle operation data within that time period. Alternatively, it can be event-triggered, automatically recording relevant data for a period surrounding specific events such as sudden acceleration, sudden braking, or a sudden change in steering angle. Once network quality improves, the system seizes the window of improved network conditions and transmits the cached data in batches to the remote end. After receiving the data, the remote diagnostic system conducts offline analysis, applying statistical methods, trend analysis models, and other tools to conduct in-depth data mining. For example, by comparing a vehicle's fuel consumption data over the past week, month, or even longer, and analyzing whether fuel consumption trends show abnormal increases or decreases, it can infer potential issues such as engine performance degradation, insufficient tire pressure, or changes in driving habits, providing a scientific basis for preventive maintenance. In extreme environments where network quality is extremely low or even completely interrupted, such as underground garages, remote mountainous areas, and tunnels, the local diagnostic module becomes the last line of defense for vehicle safety. This mode relies entirely on the on-board hardware and pre-programmed diagnostic programs to operate independently, without the need for data exchange with remote servers. The on-board electronic control unit has a rich built-in fault code library and sophisticated diagnostic logic, enabling real-time monitoring of the operating status of various vehicle systems.Once an emergency fault is detected, such as excessive transmission oil temperature, abnormal airbag triggering signals, or insufficient brake system pressure, the local diagnostic module will immediately respond. This includes flashing the instrument panel indicator, popping up warning messages on the onboard display, and voice prompts, prompting the driver to take appropriate measures to ensure driving safety. The diagnostic results are stored on the vehicle's storage device and, once the network is restored, synchronized to the remote diagnostic system for further analysis and processing.

[0108] In summary, the implementation of the present invention has the following beneficial effects:

[0109] It can be seen that the remote diagnosis method described in the embodiment of the present invention is applied to a remote diagnosis device in a remote diagnosis system, wherein the remote diagnosis system includes a target vehicle and the remote diagnosis device. The method includes: obtaining signal strength data, data transmission rate data and connection interruption frequency between the target vehicle and the remote diagnosis device within a preset time period, determining a target network quality value of the remote diagnosis system based on the signal strength data, the data transmission rate data and the connection interruption frequency, determining a target diagnosis method based on the target network quality value, performing remote diagnosis on the target vehicle based on the target diagnosis method, obtaining a target diagnosis result, and improving the reliability of vehicle remote diagnosis.

[0110] See also Figure 7 , Figure 7 is a structural diagram of a remote diagnosis device provided in an embodiment of the present application. The remote diagnosis device 700 includes: an acquisition unit 701 and a processing unit 702;

[0111] The acquisition unit 701 is configured to acquire signal strength data, data transmission rate data, and connection interruption frequency between the target vehicle and the remote diagnostic device within a preset time period;

[0112] The processing unit 702 is configured to determine a target network quality value of the remote diagnosis system based on the signal strength data, the data transmission rate data, and the connection interruption frequency;

[0113] determining a target diagnostic method based on the target network quality value;

[0114] Performing remote diagnosis on the target vehicle based on the target diagnosis method to obtain a target diagnosis result;

[0115] In some possible implementations, in determining the target network quality value of the remote diagnostic system based on the signal strength data, the data transmission rate data, and the connection interruption frequency, the processing unit 702 is specifically configured to:

[0116] Determining a first network quality value corresponding to the signal strength data;

[0117] determining a second network quality value corresponding to the data transmission rate data;

[0118] Determining a third network quality value corresponding to the connection interruption frequency;

[0119] Determine a first weight corresponding to the first network quality value, a second weight corresponding to the second network quality value, and a third weight corresponding to the third network quality value; the sum of the first weight, the second weight, and the third weight is 1;

[0120] The target network quality value of the remote diagnosis system is obtained by performing calculation based on the first network quality value, the second network quality value, the third network quality value, the first weight, the second weight, and the third weight.

[0121] In some possible implementations, the signal strength data includes n signal strength values and n signal strength value recording moments for the preset time period; n is an integer greater than 1, and each signal strength value corresponds to a signal strength value recording moment; and in determining the first network quality value corresponding to the signal strength data, the processing unit 702 is specifically configured to:

[0122] Performing fitting based on the n signal strength values and the recording times of the n signal strength values to obtain a target fitting straight line;

[0123] Determining the slope of the target fitting straight line to obtain a target slope;

[0124] A network quality value corresponding to the target slope is determined to obtain the first network quality value.

[0125] In some possible implementations, in determining a first weight corresponding to the first network quality value, a second weight corresponding to the second network quality value, and a third weight corresponding to the third network quality value, the processing unit 702 is specifically configured to:

[0126] Determine a first reference weight corresponding to the first network quality value and a second reference weight corresponding to the second network quality value; the sum of the first reference weight and the second reference weight is less than 1;

[0127] Obtaining a signal propagation path length between the target vehicle and the remote diagnostic device;

[0128] determining a first optimization factor corresponding to the signal propagation path length;

[0129] Optimizing the first reference weight based on the first optimization factor to obtain the first weight;

[0130] Obtaining the number of data transmission channels between the target vehicle and the remote diagnostic device;

[0131] determining a second optimization factor corresponding to the number of data transmission channels;

[0132] Optimizing the second reference weight based on the second optimization factor to obtain the second weight;

[0133] The third weight is determined based on the first weight and the second weight.

[0134] In some possible implementations, in terms of obtaining the target network quality value of the remote diagnostic system by calculating based on the first network quality value, the second network quality value, the third network quality value, the first weight, the second weight, and the third weight, the processing unit 702 is specifically configured to:

[0135] Performing calculation based on the first network quality value, the second network quality value, the third network quality value, the first weight, the second weight, and the third weight to obtain a reference network quality value;

[0136] Obtaining the usage time of the target vehicle;

[0137] Determining a target fine-tuning parameter corresponding to the usage duration;

[0138] The reference network quality value is adjusted based on the target fine-tuning parameter to obtain the target network quality value.

[0139] In some possible implementations, the processing unit 702 is specifically configured to:

[0140] When the target network quality value is greater than a preset network quality value, performing the operation of determining a target diagnostic method based on the target network quality value;

[0141] When the target network quality value is less than or equal to the preset network quality value, generating a prompt message based on the target network quality value, and obtaining m data transmission channels between the target vehicle and the remote diagnostic device; m is an integer greater than 1, and the prompt message is used to indicate that the network quality between the target vehicle and the remote diagnostic device is poor;

[0142] Determine a channel quality value corresponding to each of the m data transmission channels to obtain m channel quality values;

[0143] Determine k channel quality values greater than a preset channel quality value among the m channel quality values, where k is an integer less than or equal to m;

[0144] Determining k data transmission channels corresponding to the k channel quality values;

[0145] Data transmission is performed between the target vehicle and the remote diagnostic device based on the k data transmission channels, and when the target network quality value is greater than the preset network quality value, the operation of determining the target diagnostic method based on the target network quality value is performed.

[0146] In some possible implementations, in determining a target diagnostic method based on the target network quality value, the processing unit 702 is specifically configured to:

[0147] Acquiring historical diagnostic data of the remote diagnostic system within a historical time period; the end time of the historical time period is earlier than the start time of the preset time period;

[0148] Determine a mapping table between network quality values and diagnostic methods based on the historical diagnostic data to obtain a target mapping table;

[0149] The diagnostic method corresponding to the target network quality value is determined based on the target mapping table to obtain the target diagnostic method.

[0150] See also Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device provided by the embodiment of this application. Figure 8 As shown, electronic device 800 includes a transceiver 801, a processor 802, and a memory 803. These are connected via a bus 804. The memory 803 is used to store computer programs and data, and the transceiver 801 can transmit the data stored in the memory 803 to the processor 802. The above program includes instructions for executing the following steps:

[0151] Acquiring signal strength data, data transmission rate data, and connection interruption frequency between the target vehicle and the remote diagnostic device within a preset time period;

[0152] determining a target network quality value for the remote diagnostic system based on the signal strength data, the data transmission rate data, and the connection interruption frequency;

[0153] determining a target diagnostic method based on the target network quality value;

[0154] The target vehicle is remotely diagnosed based on the target diagnosis method to obtain a target diagnosis result.

[0155] In some possible implementations, in determining a target network quality value of the remote diagnostic system based on the signal strength data, the data transmission rate data, and the connection interruption frequency, the program includes instructions for performing the following steps:

[0156] Determining a first network quality value corresponding to the signal strength data;

[0157] determining a second network quality value corresponding to the data transmission rate data;

[0158] Determining a third network quality value corresponding to the connection interruption frequency;

[0159] Determine a first weight corresponding to the first network quality value, a second weight corresponding to the second network quality value, and a third weight corresponding to the third network quality value; the sum of the first weight, the second weight, and the third weight is 1;

[0160] The target network quality value of the remote diagnosis system is obtained by performing calculation based on the first network quality value, the second network quality value, the third network quality value, the first weight, the second weight, and the third weight.

[0161] In some possible implementations, the signal strength data includes n signal strength values and n signal strength value recording moments for the preset time period; n is an integer greater than 1, and each signal strength value corresponds to a signal strength value recording moment; and in determining the first network quality value corresponding to the signal strength data, the program includes instructions for performing the following steps:

[0162] Performing fitting based on the n signal strength values and the recording times of the n signal strength values to obtain a target fitting straight line;

[0163] Determining the slope of the target fitting straight line to obtain a target slope;

[0164] A network quality value corresponding to the target slope is determined to obtain the first network quality value.

[0165] In some possible implementations, in determining a first weight corresponding to the first network quality value, a second weight corresponding to the second network quality value, and a third weight corresponding to the third network quality value, the program includes instructions for performing the following steps:

[0166] Determine a first reference weight corresponding to the first network quality value and a second reference weight corresponding to the second network quality value; the sum of the first reference weight and the second reference weight is less than 1;

[0167] Obtaining a signal propagation path length between the target vehicle and the remote diagnostic device;

[0168] determining a first optimization factor corresponding to the signal propagation path length;

[0169] Optimizing the first reference weight based on the first optimization factor to obtain the first weight;

[0170] Obtaining the number of data transmission channels between the target vehicle and the remote diagnostic device;

[0171] determining a second optimization factor corresponding to the number of data transmission channels;

[0172] Optimizing the second reference weight based on the second optimization factor to obtain the second weight;

[0173] The third weight is determined based on the first weight and the second weight.

[0174] In some possible implementations, in terms of calculating based on the first network quality value, the second network quality value, the third network quality value, the first weight, the second weight, and the third weight to obtain the target network quality value of the remote diagnostic system, the program includes instructions for performing the following steps:

[0175] Performing calculation based on the first network quality value, the second network quality value, the third network quality value, the first weight, the second weight, and the third weight to obtain a reference network quality value;

[0176] Obtaining the usage time of the target vehicle;

[0177] Determining a target fine-tuning parameter corresponding to the usage duration;

[0178] The reference network quality value is adjusted based on the target fine-tuning parameter to obtain the target network quality value.

[0179] In some possible implementations, the above program includes instructions for performing the following steps:

[0180] When the target network quality value is greater than a preset network quality value, performing the operation of determining a target diagnostic method based on the target network quality value;

[0181] When the target network quality value is less than or equal to the preset network quality value, generating a prompt message based on the target network quality value, and obtaining m data transmission channels between the target vehicle and the remote diagnostic device; m is an integer greater than 1, and the prompt message is used to indicate that the network quality between the target vehicle and the remote diagnostic device is poor;

[0182] Determine a channel quality value corresponding to each of the m data transmission channels to obtain m channel quality values;

[0183] Determine k channel quality values greater than a preset channel quality value among the m channel quality values, where k is an integer less than or equal to m;

[0184] Determining k data transmission channels corresponding to the k channel quality values;

[0185] Data transmission is performed between the target vehicle and the remote diagnostic device based on the k data transmission channels, and when the target network quality value is greater than the preset network quality value, the operation of determining the target diagnostic method based on the target network quality value is performed.

[0186] In some possible implementations, in terms of determining a target diagnostic method based on the target network quality value, the program includes instructions for performing the following steps:

[0187] Acquiring historical diagnostic data of the remote diagnostic system within a historical time period; the end time of the historical time period is earlier than the start time of the preset time period;

[0188] Determine a mapping table between network quality values and diagnostic methods based on the historical diagnostic data to obtain a target mapping table;

[0189] The diagnostic method corresponding to the target network quality value is determined based on the target mapping table to obtain the target diagnostic method.

[0190] It should be understood that the electronic devices in this application may include smartphones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, PDAs, laptops, mobile Internet devices (MIDs) or wearable devices, or servers, edge computing nodes, etc. The above electronic devices are only examples and are not exhaustive, including but not limited to the above electronic devices.

[0191] The embodiments of the present application further provide a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement part or all of the steps of any one of the methods described in the above method embodiments.

[0192] The embodiments of the present application also provide a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any one of the methods described in the above method embodiments.

[0193] It should be noted that for the aforementioned method implementations, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the implementations described in the specification are all optional implementations, and the actions and modules involved are not necessarily required by this application.

[0194] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0195] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0196] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0197] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or in the form of software program modules.

[0198] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various implementation methods of the present application. The aforementioned memory includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0199] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0200] The above is a detailed introduction to the implementation methods of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above implementation methods is only used to help understand the method and core idea of the present application. At the same time, for those skilled in the art, based on the ideas of the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A remote diagnosis method, characterized in that: A remote diagnostic device is used in a remote diagnostic system, wherein the remote diagnostic system includes a target vehicle and the remote diagnostic device, and the method includes: Acquiring signal strength data, data transmission rate data, and connection interruption frequency between the target vehicle and the remote diagnostic device within a preset time period; determining a target network quality value for the remote diagnostic system based on the signal strength data, the data transmission rate data, and the connection interruption frequency; determining a target diagnostic method based on the target network quality value; The target vehicle is remotely diagnosed based on the target diagnosis method to obtain a target diagnosis result.

2. The method according to claim 1, wherein Determining a target network quality value of the remote diagnostic system based on the signal strength data, the data transmission rate data, and the connection interruption frequency includes: Determining a first network quality value corresponding to the signal strength data; determining a second network quality value corresponding to the data transmission rate data; Determining a third network quality value corresponding to the connection interruption frequency; Determine a first weight corresponding to the first network quality value, a second weight corresponding to the second network quality value, and a third weight corresponding to the third network quality value; the sum of the first weight, the second weight, and the third weight is 1; The target network quality value of the remote diagnosis system is obtained by performing calculation based on the first network quality value, the second network quality value, the third network quality value, the first weight, the second weight, and the third weight.

3. The method according to claim 2, wherein The signal strength data includes n signal strength values and n signal strength value recording moments in the preset time period; n is an integer greater than 1, and each signal strength value corresponds to a signal strength value recording moment; Determining a first network quality value corresponding to the signal strength data includes: Performing fitting based on the n signal strength values and the recording times of the n signal strength values to obtain a target fitting straight line; Determining the slope of the target fitting straight line to obtain a target slope; A network quality value corresponding to the target slope is determined to obtain the first network quality value.

4. The method according to claim 3, wherein The determining a first weight corresponding to the first network quality value, a second weight corresponding to the second network quality value, and a third weight corresponding to the third network quality value includes: Determine a first reference weight corresponding to the first network quality value and a second reference weight corresponding to the second network quality value; the sum of the first reference weight and the second reference weight is less than 1; Obtaining a signal propagation path length between the target vehicle and the remote diagnostic device; determining a first optimization factor corresponding to the signal propagation path length; Optimizing the first reference weight based on the first optimization factor to obtain the first weight; Obtaining the number of data transmission channels between the target vehicle and the remote diagnostic device; determining a second optimization factor corresponding to the number of data transmission channels; Optimizing the second reference weight based on the second optimization factor to obtain the second weight; The third weight is determined based on the first weight and the second weight.

5. The method according to claim 4, wherein The calculating based on the first network quality value, the second network quality value, the third network quality value, the first weight, the second weight, and the third weight to obtain the target network quality value of the remote diagnosis system includes: Performing calculation based on the first network quality value, the second network quality value, the third network quality value, the first weight, the second weight, and the third weight to obtain a reference network quality value; Obtaining the usage time of the target vehicle; Determining a target fine-tuning parameter corresponding to the usage duration; The reference network quality value is adjusted based on the target fine-tuning parameter to obtain the target network quality value.

6. The method according to claim 1, wherein The method further comprises: When the target network quality value is greater than a preset network quality value, performing the operation of determining a target diagnostic method based on the target network quality value; When the target network quality value is less than or equal to the preset network quality value, generating a prompt message based on the target network quality value, and obtaining m data transmission channels between the target vehicle and the remote diagnostic device; m is an integer greater than 1, and the prompt message is used to indicate that the network quality between the target vehicle and the remote diagnostic device is poor; Determine a channel quality value corresponding to each of the m data transmission channels to obtain m channel quality values; Determine k channel quality values greater than a preset channel quality value among the m channel quality values, where k is an integer less than or equal to m; Determining k data transmission channels corresponding to the k channel quality values; Data transmission is performed between the target vehicle and the remote diagnostic device based on the k data transmission channels, and when the target network quality value is greater than the preset network quality value, the operation of determining the target diagnostic method based on the target network quality value is performed.

7. The method according to claim 6, wherein The determining a target diagnostic method based on the target network quality value includes: Acquiring historical diagnostic data of the remote diagnostic system within a historical time period; the end time of the historical time period is earlier than the start time of the preset time period; Determine a mapping table between network quality values and diagnostic methods based on the historical diagnostic data to obtain a target mapping table; The diagnostic method corresponding to the target network quality value is determined based on the target mapping table to obtain the target diagnostic method.

8. A remote diagnostic device, characterized in that: The device comprises: an acquisition unit and a processing unit; The acquisition unit is configured to acquire signal strength data, data transmission rate data, and connection interruption frequency between the target vehicle and the remote diagnostic device within a preset time period; the processing unit being configured to determine a target network quality value of the remote diagnostic system based on the signal strength data, the data transmission rate data, and the connection interruption frequency; determining a target diagnostic method based on the target network quality value; The target vehicle is remotely diagnosed based on the target diagnosis method to obtain a target diagnosis result.

9. An electronic device, characterized in that: The method comprises a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the one or more programs include instructions for executing the steps in the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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