Vehicle data stream marking method and device, electronic equipment and storage medium

Abstract

The application discloses a vehicle data stream marking method and device, electronic equipment and a storage medium. The method comprises the following steps: when a diagnostic device performs a data stream recording operation on a target vehicle, firstly, determining n sets of data stream parameters corresponding to n vehicle systems of the target vehicle at a current time; then, determining an abnormality degree value corresponding to each set of data stream parameters in the n sets of data stream parameters, obtaining n abnormality degree values; then, determining k abnormality degree values greater than a preset abnormality degree threshold value in the n abnormality degree values; then, determining k sets of data stream parameters corresponding to the k abnormality degree values; finally, performing a marking operation on the k sets of data stream parameters, and obtaining k marking signals corresponding to the current time. The application improves the fault diagnosis efficiency of the vehicle.

Description

Technical Field

[0001] This invention relates to the field of vehicle diagnostic technology, and in particular to a vehicle data stream marking method, apparatus, electronic device, and storage medium. Background Technology

[0002] When a vehicle malfunctions, specialized automotive diagnostic equipment is often used in repair scenarios to read fault codes and gain an initial understanding of the problem. However, some vehicle faults exhibit characteristics such as dynamic changes, intermittent triggering, and soft, latent faults. Relying solely on fault codes is insufficient to fully characterize the cause of the fault; in-depth analysis based on vehicle operating data streams is essential. Existing diagnostic equipment generally supports recording and storing data stream information frame by frame during continuous vehicle operation, facilitating subsequent playback and review by repair personnel.

[0003] However, during the dynamic recording of the data stream, simply completing data collection and storage fails to effectively correlate and match the data stream with the actual feedback phenomena of the vehicle. In the massive recorded data stream, normal data and potentially abnormal data are mixed together, making it difficult for maintenance personnel to quickly locate the key data frames corresponding to the feedback phenomena of the vehicle. They can only manually check and analyze frame by frame, resulting in low efficiency in vehicle fault diagnosis. Therefore, how to improve the efficiency of vehicle fault diagnosis is an urgent problem to be solved. Summary of the Invention

[0004] This application provides a vehicle data stream marking method, apparatus, electronic device, and storage medium, which improves the efficiency of vehicle fault diagnosis.

[0005] In a first aspect, embodiments of this application provide a vehicle data stream marking method applied to a diagnostic device, the method comprising: When the diagnostic device performs a data stream recording operation on the target vehicle, it determines n sets of data stream parameters corresponding to the n vehicle systems of the target vehicle at the current time; each set of data stream parameters corresponds to one vehicle system, and n is a positive integer; Determine the anomaly level value corresponding to each of the n sets of data stream parameters to obtain n anomaly level values; Determine k abnormality values ​​from the n abnormality values ​​that are greater than a preset abnormality threshold; k is an integer less than or equal to n; Determine the k sets of data stream parameters corresponding to the k anomaly severity values; A marking operation is performed on the k sets of data stream parameters to obtain the k marking signals corresponding to the current time.

[0006] Secondly, embodiments of this application provide a vehicle data stream marking device for use in diagnostic equipment, the device comprising: an acquisition unit and a processing unit; The acquisition unit is used to determine n sets of data stream parameters corresponding to n vehicle systems of the target vehicle at the current time when the diagnostic device performs data stream recording operation on the target vehicle; each set of data stream parameters corresponds to one vehicle system, and n is a positive integer; The processing unit is used to determine the anomaly level value corresponding to each of the n sets of data stream parameters, and obtain n anomaly level values. Determine k abnormality values ​​from the n abnormality values ​​that are greater than a preset abnormality threshold; k is an integer less than or equal to n; Determine the k sets of data stream parameters corresponding to the k anomaly severity values; A marking operation is performed on the k sets of data stream parameters to obtain the k marking signals corresponding to the current time.

[0007] Thirdly, embodiments of the present invention provide an electronic device, including: 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 to cause the electronic device to perform the method as described in the first aspect.

[0008] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that is executed by a processor to implement the method as described in the first aspect.

[0009] Fifthly, embodiments of the present invention provide a computer program product including a non-transitory computer-readable storage medium storing a computer program, such that a computer performs the method as described in the first aspect.

[0010] Implementing the embodiments of the present invention has the following beneficial effects: As can be seen, the vehicle data stream marking method described in this embodiment of the invention, applied to a diagnostic device, firstly determines n sets of data stream parameters corresponding to n vehicle systems of the target vehicle at the current time when the diagnostic device performs a data stream recording operation on the target vehicle. Then, it determines the anomaly level value corresponding to each of the n sets of data stream parameters, obtaining n anomaly level values. Next, it determines k anomaly level values ​​among the n anomaly level values ​​that are greater than a preset anomaly level threshold. Then, it determines k sets of data stream parameters corresponding to the k anomaly level values. Finally, it performs a marking operation on the k sets of data stream parameters to obtain k marking signals corresponding to the current time. Using the embodiment of this application improves the efficiency of vehicle fault diagnosis. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application or the background art will be described below.

[0012] Figure 1 This is a flowchart of a vehicle data stream marking method provided in an embodiment of this application; Figure 2 This is a flowchart of a method for determining an anomaly level value provided in an embodiment of this application; Figure 3 This is a flowchart provided by an embodiment of the present application for determining the anomaly level value corresponding to the first set of data stream parameters; Figure 4 This is another flowchart for determining the degree of abnormality provided in the embodiments of this application; Figure 5 This is a flowchart provided in an embodiment of the present application for determining the anomaly level value corresponding to the second set of data stream parameters; Figure 6 This is a flowchart of a data stream parameter display method provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of a vehicle data stream marking device provided in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present application.

[0014] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0015] In this document, the term "implementation" means that a specific feature, structure, or characteristic described in connection with an implementation may be included in at least one implementation of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same implementation, nor is it a separate or alternative implementation mutually exclusive with other implementations. It will be explicitly and implicitly understood by those skilled in the art that the implementations described herein can be combined with other implementations.

[0016] Please see Figure 1 , Figure 1 This is a flowchart of a vehicle data stream marking method provided in an embodiment of this application. The method includes, but is not limited to, the following steps: S101: When the diagnostic device performs a data stream recording operation on the target vehicle, it determines the n sets of data stream parameters corresponding to the n vehicle systems of the target vehicle at the current time.

[0017] In this embodiment, each set of data stream parameters corresponds to a vehicle system, where n is a positive integer. The diagnostic equipment is a dedicated testing instrument specifically designed to establish communication and interaction with the target vehicle's on-board diagnostic interface and various electronic control systems. It can access the vehicle bus network via wired or wireless means and is capable of real-time acquisition and recording of operating condition data stream parameters for various vehicle systems such as the engine, transmission, brakes, and body.

[0018] Vehicle data stream recording refers to the diagnostic equipment establishing a communication connection with the on-board diagnostic interface of the target vehicle through a physical interface or wireless communication, accessing the bus network of various vehicle electronic control systems in real time, and continuously collecting real-time operating parameters, status values, and operating message data of various vehicle electronic control systems such as engine, transmission, braking system, and body control system during operation according to the set acquisition frequency. The data is then continuously stored, archived in time sequence, and recorded in real time according to the time axis, completely preserving the original data stream information of each system at every moment of the vehicle.

[0019] The vehicle system refers to all the electronic and mechanical related functional systems installed in the target vehicle. It mainly covers the engine control system, automatic transmission control system, anti-lock braking system, vehicle stability control system, body control system, air conditioning control system, airbag control system, power steering control system, battery management system, vehicle control system, chassis suspension control system, lighting control system, and instrument control system. Each vehicle system is a functional module managed by an independent electronic control unit, and each outputs real-time operating data independently. Thus, each vehicle system corresponds to a set of exclusive data stream parameters.

[0020] Specifically, the data stream parameters corresponding to the engine control system mainly include engine speed, coolant temperature, intake air temperature, intake air pressure, throttle opening, injection pulse width, ignition advance angle, oxygen sensor voltage, fuel correction coefficient, idle load, battery voltage, intake air flow, exhaust gas recirculation opening, and engine operating condition parameters, comprehensively reflecting the power supply, combustion state, and environmental adaptation operating data under different operating conditions such as engine start-stop, idling, acceleration, and constant speed.

[0021] The data stream parameters corresponding to the automatic transmission control system include transmission oil temperature, current gear, target gear, shift solenoid valve status, lock-up clutch status, input shaft speed, output shaft speed, transmission oil pressure, shift timing parameters, transmission fault status codes, coasting mode status, gear shift delay parameters, etc., which can fully reflect the transmission shift logic, transmission status, oil temperature conditions and the working data of each solenoid valve.

[0022] The data stream parameters corresponding to the anti-lock braking system include the wheel speeds of the four wheels, the brake pedal switch status, the brake master cylinder pressure, the yaw rate, the lateral acceleration, the working status of the anti-lock braking system solenoid valves, the working status of the pump motor, the brake system fault indicators, wheel slip ratio parameters, etc., and collect data on the rotational speed of each wheel, the braking pressure, and the execution status of the anti-lock control in real time during the vehicle braking process.

[0023] The data stream parameters corresponding to the vehicle stability control system cover vehicle speed, steering wheel angle, steering wheel speed, vehicle yaw rate, lateral acceleration, longitudinal acceleration, vehicle roll angle, traction control status, stability intervention status, and braking force distribution parameters of each wheel, etc., which are used to monitor the vehicle's driving posture, steering action, and related operating parameters of vehicle dynamic stability adjustment.

[0024] The data stream parameters corresponding to the vehicle body control system include door open / close status, window lift position and status, central locking / unlocking status, rearview mirror folding status, windshield wiper operating mode, horn operating status, door open warning signal, vehicle electrical load status, indoor and outdoor ambient temperature sensing parameters, etc. It mainly collects real-time status data of vehicle auxiliary electrical appliances and vehicle opening / closing components.

[0025] The data stream parameters corresponding to the air conditioning control system include the set temperature inside the vehicle, the actual temperature inside the vehicle, the ambient temperature outside the vehicle, the evaporator temperature, the working status of the air conditioning compressor, the blower fan speed, the air outlet mode status, the air conditioning pressure value, the opening degree of the hot and cold air damper, and the internal and external circulation status of the circulation mode, which fully reflect the operating parameters of the air conditioning cooling and heating, air volume regulation, pressure and damper control.

[0026] The data stream parameters corresponding to the airbag control system include the airbag system readiness status, the resistance of each airbag ignition circuit, the collision sensor sensing values, the seat belt pretensioner status, the airbag malfunction warning light status, the passenger airbag deactivation status parameters, and the impact sensing threshold monitoring data, etc., which are used to monitor the circuit status, sensor data, and operational readiness status of the airbag and restraint system.

[0027] The data stream parameters corresponding to the power steering control system include steering wheel torque, steering wheel angle, power steering motor current, power steering motor temperature, vehicle speed matching power steering level, steering system fault status, return torque parameters, power steering mode switching status, etc. It collects steering operation force, motor operating conditions, and power steering adjustment parameters that change with vehicle speed in real time.

[0028] The data stream parameters corresponding to the battery management system include the total voltage of the battery pack, the voltage of individual cells, the charging and discharging current of the battery, the remaining battery capacity, the temperature of individual cells, the insulation resistance of the battery, the charging and discharging limit power, the battery health, the equalization control status, the working status of the high-voltage relay, etc., comprehensively monitoring the voltage, current, temperature, capacity and safety operation parameters of the power battery.

[0029] The data stream parameters corresponding to the vehicle control system include the total vehicle speed, vehicle operating mode, high-voltage system readiness status, instantaneous power consumption of the vehicle, drive request signal, accelerator pedal opening, brake pedal opening, total vehicle fault identifier, power output limit parameters, etc., which coordinate the data related to the vehicle operating mode, driving operation input and overall vehicle power control.

[0030] The data stream parameters corresponding to the chassis suspension control system include various suspension travel heights, shock absorber damping adjustment values, vehicle height sensor values, suspension load pressure, chassis level adjustment status, bumpy road sensing parameters, suspension lock mode status, etc., collecting relevant operating data on chassis suspension height, damping, load, and adaptive adjustment.

[0031] The data stream parameters corresponding to the lighting control system include the on / off status of low beam headlights, high beam headlights, turn signals, side marker lights, brake lights, and reversing lights; the automatic headlight illumination intensity; the headlight height adjustment setting; and the daytime running light status. It monitors the on / off status of all types of lights on the vehicle in real time and automatically adjusts the sensing parameters.

[0032] The data stream parameters corresponding to the instrument control system include the vehicle speed displayed on the instrument panel, the engine tachometer value, the fuel level value, the water temperature value, the remaining driving range value, the status of various fault indicator lights, the maintenance reminder status, and the brightness level of the instrument panel backlight, etc. It mainly carries the various operating conditions and warning status parameters required for the display of the entire vehicle's instruments.

[0033] The reason for recording data streams of the target vehicle using diagnostic equipment is that the operating parameters of the vehicle's various electronic control systems change dynamically in real time during driving and idling. Static reading of fault codes alone cannot fully capture instantaneous operating condition fluctuations, latent faults, intermittent anomalies, and the inter-system linkage status. Recording continuous time-series data streams can completely preserve the original operating parameters of all vehicle systems, such as the engine, transmission, brakes, and body, at every moment. This provides continuous and complete raw data support for subsequent extraction of multiple sets of data stream parameters by system, quantification of the abnormality value of each set of parameters, screening of abnormal data exceeding the threshold, accurate location of the abnormal system, and generation of marker signals. At the same time, it can also reconstruct the changes in operating conditions before and after the vehicle fault occurred, facilitating the investigation of intermittent faults, latent faults, and operating anomalies caused by the coupling of various systems. This enables fault tracing, operating condition analysis, and accurate judgment of vehicle status, overcoming the shortcomings of single fault code diagnosis, which is one-sided and unable to reproduce fault scenarios. Therefore, it is necessary to record data streams of each vehicle system of the target vehicle using the aforementioned diagnostic equipment.

[0034] S102: Determine the anomaly level value corresponding to each group of data stream parameters in the n groups of data stream parameters, and obtain n anomaly level values.

[0035] In this embodiment, normal baseline ranges and standard reference values ​​can be pre-set for each data stream parameter of each vehicle system. The diagnostic equipment compares and calculates the deviation amplitude, rate of change, and operating condition adaptation deviation of each set of data stream parameters collected in real time with the corresponding preset normal baseline range and standard reference value. Combined with the linkage matching status of multiple related parameters in the same system, the equipment performs weighted conversion and normalization processing according to the preset anomaly quantification calculation model, quantifies the operating deviation status of each set of data stream parameters into the corresponding anomaly degree value, and sequentially quantifies and solves the n sets of data stream parameters respectively, thereby obtaining n anomaly degree values ​​that correspond one-to-one with the n sets of data stream parameters.

[0036] S103: Determine k abnormality values ​​among the n abnormality values ​​that are greater than the preset abnormality threshold.

[0037] In this embodiment, k is an integer less than or equal to n. The preset anomaly threshold can be comprehensively calibrated based on the factory standard operating parameters of each vehicle system, historical normal operation big data samples, parameter fluctuation range under different driving conditions, and fault triggering critical characteristics. Adaptive fixed thresholds or adaptive thresholds that are dynamically adjusted according to operating conditions can be set for different vehicle systems. Differential corrections can also be made based on vehicle configuration, service life, and environmental conditions. By defining a safe upper limit for the distribution range of anomaly values ​​under normal fault-free conditions, this safe upper limit is set as the anomaly threshold, serving as the critical judgment criterion for distinguishing between normal and abnormal states of vehicle system data flow parameters.

[0038] After obtaining n anomaly levels corresponding to n vehicle systems, each anomaly level value is compared with a pre-set anomaly level threshold. All anomaly levels with values ​​higher than the pre-set anomaly level threshold are selected from all anomaly levels, thus obtaining k anomaly levels.

[0039] S104: Determine the k sets of data stream parameters corresponding to the k anomaly severity values.

[0040] In this embodiment, after filtering out k anomaly values ​​that are greater than the preset anomaly threshold by numerical comparison, the k sets of data flow parameters corresponding to the k anomaly values ​​can be determined by relying on the pre-established one-to-one correspondence between each vehicle system, data flow parameter group and anomaly value.

[0041] S105: Perform a marking operation on the k sets of data stream parameters to obtain the k marking signals corresponding to the current time.

[0042] In this embodiment, the marker signal is a structured digital signal generated by the diagnostic equipment for abnormal data stream parameters. It includes time information, vehicle system information, data stream parameter identifier, abnormality level, and abnormality degree value. It is mainly used to accurately identify the vehicle system and corresponding data stream parameters that are currently experiencing operational abnormalities. It can be stored locally on the diagnostic equipment for subsequent fault retrospective review, fault cause analysis, and vehicle condition tracing. At the same time, it can also intuitively distinguish between normal and abnormal data streams, providing accurate signal basis and data positioning support for automatic vehicle fault diagnosis, abnormal data filtering and display, and subsequent maintenance and repair.

[0043] After accurately identifying k groups of data stream parameters with abnormal operating conditions, the diagnostic equipment uses the current timestamp as a reference identifier to bind the corresponding vehicle system number, parameter type information, abnormality level value, and operating condition acquisition information to each group of abnormal data stream parameters. According to the preset marking rules, it adds a unique abnormality identifier, timing identifier, and system traceability identifier to each group of data stream parameters, completing the structured marking processing of the k groups of data stream parameters. Each time a group of data stream parameters is marked, a corresponding marking signal is generated. Finally, k marking signals corresponding one-to-one with the k groups of data stream parameters at the current acquisition time are generated synchronously.

[0044] As can be seen, by using diagnostic equipment to record the target vehicle's data stream while simultaneously collecting multiple sets of data stream parameters at corresponding times according to the vehicle system, quantifying and calculating the degree of abnormality for each set, and accurately filtering out the abnormality values ​​exceeding the standard based on the preset abnormality threshold, and then back-matching and locating the corresponding abnormal data stream parameter groups and uniformly executing the marking to generate time-series marking signals, it is possible to achieve layered collection, quantitative evaluation, accurate filtering, and automatic marking of real-time operating data of various vehicle electronic control systems. It can quickly identify instantaneous, intermittent, and hidden non-code-reported vehicle operating abnormalities, overcoming the shortcomings of traditional diagnosis that relies solely on fault codes and cannot capture subtle deviations in data streams and transient abnormalities in operating conditions. It achieves accurate correlation and traceability between abnormal data and the vehicle system to which it belongs and the time of collection, which is convenient for subsequent fault review, operating condition retrospection, fault cause analysis, and for maintenance personnel to quickly locate faulty systems and abnormal parameters. This improves the real-time performance, accuracy, and automation of vehicle fault diagnosis. At the same time, the structured marking signals facilitate data storage, retrieval, and secondary processing by subsequent intelligent diagnostic algorithms, effectively reducing the difficulty of vehicle fault diagnosis and maintenance time.

[0045] When the first set of data stream parameters is the data stream parameter corresponding to the power battery system, and when the first set of data stream parameters is one of the n sets of data stream parameters, please refer to [link to relevant documentation]. Figure 2 , Figure 2 This is a flowchart illustrating a method for determining an anomaly level value, provided in an embodiment of this application. The method includes, but is not limited to, the following steps: S201: Determine the power battery temperature, power battery voltage, and power battery state of charge value corresponding to the first set of data stream parameters.

[0046] In this embodiment, the power battery temperature is the operating temperature value of the power battery cell or the entire battery pack detected in real time at the current acquisition time. It can directly reflect the heat generation state, environmental adaptation state and cell thermal equilibrium state of the power battery during charging and discharging operation. It is the core basic parameter for judging whether the thermal condition of the power battery is normal and whether there is overheating or low temperature abnormality.

[0047] The power battery voltage is the total voltage of the battery pack or the real-time voltage of a single cell collected by the power battery system at the current moment. It represents the current power output capability of the power battery, the aging status of the cells, and the power supply stability of the high-voltage circuit. It can intuitively reflect whether the power battery voltage is too high, too low, or has abnormal voltage fluctuations.

[0048] The state of charge (SOC) value of a power battery is a quantitative value that represents the proportion of the current remaining charge of the power battery to the rated total charge. It can reflect the remaining energy storage level, charging and discharging process status, and charge decay changes of the power battery in real time. It is a key parameter for judging whether the power battery charge is within a reasonable range and whether there are abnormal charge jumps or charging and discharging control abnormalities.

[0049] S202: Determine the temperature difference between the power battery temperature and the preset temperature, the voltage difference between the power battery voltage and the preset voltage, and the state of charge difference between the power battery state of charge value and the preset state of charge value.

[0050] In this embodiment, the temperature difference between the power battery temperature and the preset temperature is used to characterize the extent to which the current actual operating temperature of the power battery deviates from the standard suitable operating temperature range specified by the manufacturer. It can intuitively reflect the degree to which the battery is currently overheated, underheated, or deviates from normal operating conditions, providing a basic basis for quantitatively assessing whether the thermal state of the power battery is abnormal.

[0051] The voltage difference between the power battery voltage and the preset voltage is used to characterize the degree of deviation of the real-time operating voltage of the power battery from the standard rated voltage or the reference voltage under normal operating conditions. It can reflect whether the battery cell or battery pack has a voltage that is too high, too low, unbalanced, or abnormal in the power supply circuit, and reflect the deviation level of the power battery voltage operating conditions.

[0052] The state of charge difference between the power battery's state of charge value and the preset state of charge value is used to characterize the difference between the battery's current actual remaining charge percentage and the standard reasonable state of charge range. It can reflect whether the power battery has excessively high charge, abnormal charge decay, abnormal charging and discharging capacity matching, and the degree to which charge fluctuations deviate from the normal control range, reflecting abnormal deviations in the battery's charge operation status.

[0053] S203: Determine the anomaly level value corresponding to the first set of data stream parameters based on the temperature difference, the voltage difference, and the state of charge difference.

[0054] In this embodiment, the temperature difference, voltage difference, and state of charge difference can be normalized according to the weighting coefficients pre-configured for each. This eliminates the influence of different parameter dimensions. Then, each normalized difference is multiplied by its corresponding weighting coefficient and summed. Alternatively, an over-limit correction coefficient can be introduced for each difference to compensate for the error. A comprehensive quantitative score is obtained through weighted fusion calculation. This comprehensive quantitative score is set as the abnormality value of the first set of data stream parameters corresponding to the power battery system. This achieves a precise quantification of the overall operational abnormality of the power battery system by combining the three key deviation dimensions of temperature, voltage, and state of charge.

[0055] As can be seen, by selecting power battery temperature, power battery voltage, and power battery state of charge (SOC) as core evaluation parameters, calculating the temperature difference, voltage difference, and SOC difference corresponding to preset standard values, and integrating these three differences to jointly determine the anomaly level of the power battery system data stream parameters, the actual operating conditions of the power battery can be comprehensively characterized from three key dimensions: thermal state, electrical state, and charge state. This avoids the one-sidedness and misjudgment defects of single-parameter evaluation. Through multi-dimensional deviation synergistic quantification, it accurately reflects the degree of deviation of the overall operation of the power battery from the normal benchmark, effectively improving the accuracy and rigor of the calculation of the anomaly level of the power battery system. It can comprehensively identify various explicit and implicit operating faults such as abnormal battery temperature, voltage deviation, and abnormal SOC, providing a reliable quantitative judgment basis for subsequent anomaly threshold comparison, anomaly parameter location, and data stream marking. At the same time, it adapts to the anomaly assessment needs under different driving conditions and battery aging states, significantly reducing the probability of missed and misjudged power battery faults.

[0056] Please see Figure 3 , Figure 3 This application provides a flowchart for determining the anomaly level value corresponding to the first set of data stream parameters, including but not limited to the following steps: S301: Determine the first degree of abnormality corresponding to the temperature difference, the second degree of abnormality corresponding to the voltage difference, and the third degree of abnormality corresponding to the state of charge difference.

[0057] In this embodiment, it can be a first mapping relationship between a preset temperature difference value and an anomaly degree value, and the first anomaly degree value corresponding to the temperature difference value can be determined based on the first mapping relationship.

[0058] It can be a preset second mapping relationship between voltage difference and abnormality level value, and based on this second mapping relationship, the second abnormality level value corresponding to the voltage difference can be determined.

[0059] It can be a third mapping relationship between a preset difference in the state of charge and an anomaly level value. Based on this third mapping relationship, the third anomaly level value corresponding to the difference in the state of charge can be determined.

[0060] S302: Determine a first reference anomaly value based on the first anomaly value, the second anomaly value, and the third anomaly value.

[0061] In this embodiment, the first weight corresponding to the first anomaly degree value, the second weight corresponding to the second anomaly degree value, and the third weight corresponding to the third anomaly degree value can be determined first, wherein the sum of the first weight, the second weight, and the third weight is 1. Then, the first reference anomaly degree value is calculated as follows: First reference anomaly score = First anomaly score × First weight + Second anomaly score × Second weight + Third anomaly score × Third weight; The first reference anomaly value can be determined based on the first anomaly value, the second anomaly value, and the third anomaly value in the manner described above.

[0062] S303: Obtain the insulation resistance corresponding to the power battery system.

[0063] In this embodiment, the insulation resistance of the power battery system can accurately reflect the insulation status between the high-voltage circuit of the power battery and the chassis, as well as the degree of high-voltage leakage risk. The insulation resistance value directly determines the overall operational safety level and the severity of abnormal operating conditions of the power battery. Under the premise of calculating the basic reference abnormality value from the deviations of temperature, voltage, and state of charge, the smaller the insulation resistance, the worse the insulation performance of the high-voltage system and the higher the risk of leakage, which will simultaneously amplify the overall operational abnormality level of the power battery system. The larger the insulation resistance, the better the insulation status and the absence of leakage risk, without additionally aggravating the abnormality assessment level of the system. This allows the final abnormality value of the power battery system to take into account both the deviation of normal operating parameters and the two key factors of high-voltage insulation safety risks, making the abnormality value more consistent with the actual operational risk state of the power battery. Therefore, it is necessary to first obtain the insulation resistance corresponding to the power battery system.

[0064] S304: Determine the first adjustment parameter corresponding to the insulation resistance.

[0065] In this embodiment, it can be a preset mapping relationship between insulation resistance and adjustment parameters. Based on this mapping relationship, a first adjustment parameter corresponding to the insulation resistance can be determined.

[0066] S305: Adjust the first reference anomaly level value based on the first adjustment parameter to obtain the anomaly level value corresponding to the first set of data stream parameters.

[0067] In this embodiment, the anomaly level value corresponding to the first set of data stream parameters is determined in the following manner: The anomaly severity value corresponding to the first set of data stream parameters = the first reference anomaly severity value × (1 + the first adjustment parameter); The first reference anomaly level value can be adjusted based on the first adjustment parameter in the manner described above to obtain the anomaly level value corresponding to the first set of data stream parameters.

[0068] As can be seen, by first generating first, second, and third anomaly severity values ​​based on temperature difference, voltage difference, and state of charge difference respectively, and then combining the three to obtain the first reference anomaly severity value, the basic anomaly level can be quantified from the three core operating conditions of battery temperature, voltage, and charge. Then, the insulation resistance of the power battery system, a key parameter for high-voltage safety, is introduced to obtain the first adjustment parameter. This adjustment parameter is used to correct and optimize the first reference anomaly severity value. This not only achieves a comprehensive assessment of the deviation of the power battery's operating conditions from the level of conventional operating parameters, but also takes into account the impact of high-voltage insulation leakage safety on the anomaly level. It makes up for the shortcomings of relying solely on temperature, pressure, and charge parameters to judge and ignore high-voltage insulation hazards. The final calculated anomaly severity value of the power battery system takes into account both conventional operating condition anomalies and high-voltage insulation safety anomalies. The evaluation dimensions are more comprehensive, and the quantitative results are more in line with the actual operating state of the power battery. This effectively improves the accuracy and rationality of the anomaly severity value calculation, reduces omissions and misjudgments caused by single-dimensional evaluation, and provides a more reliable basis for subsequent anomaly threshold comparison, anomaly data stream screening, and marking operations.

[0069] When the second set of data stream parameters is the data stream parameter corresponding to the engine system, and the second set of data stream parameters is one of the n sets of data stream parameters, please refer to [link to relevant documentation]. Figure 4 , Figure 4 This is another flowchart for determining the degree of anomaly provided in this application, including but not limited to the following steps: S401: Determine the engine vibration amplitude and engine operating noise decibel value corresponding to the engine system.

[0070] In this embodiment, engine vibration amplitude is the magnitude of mechanical vibration generated by the engine body and surrounding structure under normal operating conditions at the current moment. It can intuitively reflect the matching status of internal engine components, the stability of combustion and power output, the vibration damping status of engine mounts, and whether there is looseness, wear, or imbalance in transmission connections. It is a key physical parameter characterizing whether the engine's mechanical operating conditions are stable and whether there are any potential mechanical abnormalities.

[0071] Engine operating noise in decibels is a quantitative value of the acoustic noise intensity radiated outward by the engine under current operating conditions. It can reflect the quality of engine combustion, the working status of the intake and exhaust systems, and whether there are abnormal noises in the operation of valves and transmission components. It can reflect whether the overall engine operating status deviates from the standard quiet range from an acoustic perspective, and provides a core basis for judging whether there are abnormal noise faults, poor combustion, or abnormal components in the engine.

[0072] It should be explained that the selection of engine vibration amplitude and engine operating noise decibel value corresponding to the engine system is based on the inherent working characteristics of engine mechanical transmission and combustion operation. Characteristic parameters that can intuitively reflect the smoothness of engine operation, the wear and fit of parts, and the quality of combustion conditions are selected from two physical characterization dimensions: mechanical vibration and acoustic noise.

[0073] It should be noted that, based on the working mechanism of electrochemical energy storage and high-voltage power supply, the power battery system selects power battery temperature, power battery voltage, and power battery state of charge values ​​from three electrochemical and electrical operating dimensions: thermal state, electrical voltage state, and remaining charge state. Since the power battery system and engine system differ fundamentally in their operating principles, fault manifestations, and input / output characteristics from the vehicle systems they belong to, the engine system focuses on selecting mechanical and physical characterization parameters, while the power battery system focuses on selecting electrochemical and electrical operating condition characterization parameters. These parameters are tailored to the independent operating rules and abnormal fault generation mechanisms of their respective systems, allowing different vehicle systems to use exclusive characteristic parameters adapted to their own characteristics to quantify the degree of abnormality, ensuring the accuracy and adaptability of the abnormality value calculation for each system.

[0074] It should be explained that, in this embodiment, for the various subsystems of the vehicle, electrochemical, high-voltage electrical, and closed-loop electronic control systems, represented by the power battery system, vehicle electronic control system, transmission control system, and braking control system, are suitable for selecting internal electrical parameters, thermodynamic parameters, and state parameters that reflect their own working conditions as evaluation criteria. Based on the internal operating mechanism of the system, operational anomalies are quantified from intrinsic indicators such as voltage, temperature, charge, pressure, and speed. For mechanical transmission, power operation, and structural motion systems, represented by the engine system, chassis suspension system, and vehicle running gear, vibration... Externally perceptible and detectable physical characteristics such as amplitude, operating noise, deformation displacement, and attitude tilt angle provide feedback on the coordination of internal components, smooth operation, and structural working status through external physical features. Each vehicle system, based on its own electro-chemical or mechanical physical operation attributes, matches corresponding internal operating parameters or external physical characteristics parameters to quantify the degree of anomaly. This aligns with the working principles and fault presentation patterns of different systems, enabling all types of vehicle systems to conduct anomaly assessments using parameter dimensions adapted to their own characteristics. This ensures the rationality, adaptability, and accuracy of the anomaly degree calculations for all vehicle systems.

[0075] S402: Determine the vibration amplitude difference between the engine vibration amplitude and the preset vibration amplitude, and the operating noise decibel difference between the engine operating noise decibel value and the preset operating noise decibel value.

[0076] In this embodiment, the actual engine vibration amplitude and engine operating noise decibel value of the engine system at the current moment are collected. The vibration amplitude difference is calculated by subtracting the actual engine vibration amplitude from the pre-calibrated standard preset vibration amplitude. Simultaneously, the operating noise decibel value is calculated by subtracting the actual engine operating noise decibel value from the pre-set standard preset operating noise decibel value. The vibration amplitude difference characterizes the degree to which the actual mechanical vibration amplitude of the engine deviates from the factory-standard stable vibration range, directly reflecting the severity of abnormal mechanical vibration caused by internal engine component wear, uneven combustion, and engine mount damping failure. The operating noise decibel difference characterizes the magnitude of the deviation of the actual engine operating noise intensity from the normal quiet operating condition benchmark, accurately reflecting the degree of acoustic abnormalities such as abnormal engine intake and exhaust, abnormal valve mechanism noise, and poor combustion conditions. These two differences quantify the deviation level of the engine's actual operating conditions from the standard operating conditions from both mechanical vibration and acoustic noise dimensions.

[0077] S403: Determine the degree of abnormality corresponding to the second set of data stream parameters based on the difference in vibration amplitude and the difference in operating noise in decibels.

[0078] In this embodiment, the vibration amplitude difference and the operating noise decibel difference can be divided into corresponding abnormality level intervals and configured with independent weight coefficients. The two differences are first normalized to eliminate the difference in dimensions, and then weighted and fused according to their respective weight coefficients. Alternatively, an operating condition correction coefficient can be added to adapt and correct the current operating condition of the engine. After comprehensive calculation, a normalized comprehensive quantitative score is obtained. This score is used as the abnormality level value corresponding to the second set of data stream parameters of the engine system. Thus, based on the two core deviation dimensions of vibration and noise, the overall abnormality level of the engine's mechanical operation and acoustic condition is comprehensively quantified.

[0079] It can be seen that by selecting engine vibration amplitude and operating noise decibel value as the core evaluation parameters of the engine system, the difference in vibration amplitude and operating noise decibel value between the actual parameters and the corresponding preset standard parameters are calculated respectively. Then, the abnormality value of the engine system data stream parameter is determined by combining the two differences. The engine's overall operating status is comprehensively characterized from two independent and complementary dimensions: mechanical vibration smoothness and operating acoustic characteristics. This breaks through the limitations of traditionally relying solely on electronic control electrical parameters to evaluate engine operating conditions. It can effectively capture hidden mechanical faults such as engine component wear, loose assembly, uneven combustion, and abnormal intake and exhaust. Through the dual-dimensional deviation collaborative quantification method, it accurately reflects the degree to which the actual engine operating conditions deviate from the standard operating conditions, greatly improving the comprehensiveness and accuracy of the quantitative results of the engine system abnormality value. It avoids the one-sidedness, omissions, and misjudgments caused by single parameter evaluation. It provides a reliable quantitative judgment basis for subsequent abnormal threshold comparison, abnormal data stream location, and tag generation. At the same time, it conforms to the inherent working mechanism of engine mechanical operation and acoustic radiation, and is suitable for the abnormal assessment needs under different operating conditions such as speed and load.

[0080] Please see Figure 5 , Figure 5 This application provides a flowchart for determining the anomaly level value corresponding to the second set of data stream parameters, including but not limited to the following steps: S501: Determine the fourth abnormality value corresponding to the vibration amplitude difference and the fifth abnormality value corresponding to the operating noise decibel difference.

[0081] In this embodiment, it can be a fourth mapping relationship between a preset vibration amplitude difference and an anomaly degree value. Based on this fourth mapping relationship, the fourth anomaly degree value corresponding to the vibration amplitude difference can be determined.

[0082] It can be a fifth mapping relationship between a preset operating noise decibel difference and an anomaly level value. Based on this fifth mapping relationship, the fifth anomaly level value corresponding to the operating noise decibel difference can be determined.

[0083] S502: Determine a second reference anomaly value based on the fourth anomaly value and the fifth anomaly value.

[0084] In this embodiment, the fourth weight corresponding to the fourth abnormality value and the second weight corresponding to the fifth abnormality value can be determined first, wherein the sum of the fourth weight and the fifth weight is 1.

[0085] Specifically, the second reference anomaly level value can be calculated as follows: Second reference anomaly score = Fourth anomaly score × Fourth weight + Fifth anomaly score × Fifth weight; The second reference anomaly value can be determined based on the fourth anomaly value and the fifth anomaly value in the manner described above.

[0086] S503: Obtain the instantaneous deviation of the engine system's oil pressure at the current moment.

[0087] In this embodiment, oil pressure is a core operating parameter that ensures the normal operation and lubrication of moving parts such as crankshaft connecting rod mechanism, valve mechanism and bearings inside the engine. The instantaneous deviation of oil pressure can reflect in real time whether there are hidden operating conditions such as insufficient oil supply pressure, excessive pressure, and abnormal oil pressure fluctuation in the engine's lubrication supply system at the current moment. These lubrication abnormalities cannot be fully reflected by the engine vibration amplitude and decibel value of operating noise alone. Introducing the instantaneous deviation of oil pressure can supplement the evaluation of potential abnormalities in the overall operation of the engine from the perspective of engine lubrication system operating conditions, make up for the inadequacy of evaluating the engine status solely by external physical parameters such as vibration and noise, and take into account both the engine's external operating performance and the actual operating conditions of internal lubrication supply. This makes the assessment of the degree of abnormality of the engine system more consistent with the actual operating mechanism of the whole machine, and comprehensively covers potential fault hazards in mechanical vibration, acoustic performance and lubrication supply. Therefore, it is necessary to first obtain the instantaneous deviation of oil pressure of the engine system at the current moment.

[0088] S504: Determine the second adjustment parameter corresponding to the instantaneous deviation of the oil pressure.

[0089] In this embodiment, it can be a preset mapping relationship between the instantaneous deviation of the oil pressure and the adjustment parameter. Based on this mapping relationship, a second adjustment parameter corresponding to the instantaneous deviation of the oil pressure can be determined.

[0090] S505: Adjust the second reference anomaly level value based on the second adjustment parameter to obtain the anomaly level value corresponding to the second set of data stream parameters.

[0091] In this embodiment, the anomaly level value corresponding to the second set of data stream parameters is calculated in the following manner: The anomaly severity value corresponding to the second set of data stream parameters = the second reference anomaly severity value × (1 + the second adjustment parameter); The second reference anomaly level value can be adjusted based on the second adjustment parameter in the manner described above to obtain the anomaly level value corresponding to the second set of data stream parameters.

[0092] As can be seen, the fourth and fifth anomaly levels are obtained based on the vibration amplitude difference and the decibel difference of the operating noise, respectively. The two are then combined to obtain the second reference anomaly level. This allows for the preliminary quantification of the basic anomaly level of the engine's overall operation from two external physical characterization dimensions: mechanical vibration and operating noise. Then, the instantaneous deviation of the engine system's oil pressure at the current moment is introduced, and the second reference anomaly level is corrected by the corresponding second adjustment parameter. This supplements the consideration of potential engine operation hazards from the perspective of internal lubrication and oil supply conditions, making up for the inadequacy of relying solely on external parameters such as vibration and noise to identify hidden faults in the lubrication system. It takes into account both the engine's external operating performance and the real-time working status of internal oil pressure, covering the fault causes of engine mechanical operation, acoustic characteristics, and lubrication and oil supply from multiple dimensions. This makes the final determined engine system anomaly level value more consistent with the actual working mechanism and significantly improves the accuracy of anomaly quantification assessment.

[0093] Please see Figure 6 , Figure 6 This is a flowchart of a data stream parameter display method provided in an embodiment of this application, including but not limited to the following steps: S601: After the diagnostic device completes the data stream recording operation of the target vehicle within a preset time period, it obtains the data stream viewing request of the target user for the target vehicle system.

[0094] In this embodiment, the target vehicle system is any one of the n vehicle systems, and the data stream viewing request is used by the target user to view the abnormal data stream parameters of the target vehicle system within the preset time period, wherein the current time is within the preset time period.

[0095] A user's request to view the data stream of a specific vehicle system is a request initiated by the user to the diagnostic equipment after the equipment has recorded the overall data stream of the target vehicle for a preset time period. This request specifically refers to the user selecting any vehicle subsystem such as the power battery system, engine system, or braking system, and only requesting to retrieve and view the data stream parameters of the selected vehicle system that are marked as abnormal within the preset time period, rather than viewing all the regular data stream parameters of all vehicle systems. For example, the user can select the engine system on the diagnostic equipment interface and initiate a viewing request. The equipment will not need to display the regular data stream of the engine system without abnormalities within that time period, but will only selectively filter out relevant data stream parameters such as abnormal vibration amplitude, operating noise, and oil pressure that have generated marked signals. This enables on-demand, targeted retrieval of abnormal data for a specific vehicle system, thereby accurately meeting the user's need to quickly troubleshoot fault data of a single vehicle system.

[0096] S602: Based on the data stream viewing request, determine all data stream parameters and a marker signals corresponding to the target vehicle system.

[0097] In this embodiment, 'a' is an integer. After the diagnostic device receives a data stream viewing request initiated by the target user, it accurately locates and extracts the complete original data stream parameters corresponding to the target vehicle system from the vehicle data stream and marker signal data recorded and stored within a preset time period, based on the vehicle system identity identifier specified in the request. At the same time, it filters out all marker signals belonging to the vehicle system and generated within the preset time period, and obtains a set of marker signals of quantity 'a', thus completing the integration of all original data stream parameters of the target vehicle system and associated abnormal marker signals.

[0098] S603: Based on the a-marked signals, search through all data stream parameters to obtain a set of data stream parameters corresponding to the a-marked signals.

[0099] In this embodiment, the diagnostic device uses the selected a-marked signals as search indexes. It uses the vehicle system identifier, acquisition time information and data stream parameter association identifier carried by each marked signal to perform a one-to-one matching search in all data stream parameters corresponding to the target vehicle system, locates the original abnormal data stream parameter group corresponding to each marked signal one by one, and finally matches and collects a groups of data stream parameters that correspond one-to-one with the a-marked signals.

[0100] By uniquely binding each marker signal with its corresponding vehicle system number, data acquisition timestamp, data stream parameter group index number, and parameter type identifier, a one-to-one exclusive association mapping relationship is formed between the marker signal and the corresponding data stream parameter group. At the same time, when storing all data stream parameters, a hierarchical index storage structure is established according to vehicle system, time series, and parameter group number. When searching, the unique identifier information carried by the marker signal is used as the search keyword, and the location is matched and located step by step according to the hierarchical index. This avoids confusion between data from different systems, different times, and different parameter groups. Through multiple mechanisms such as unique identifier binding, structured index storage, and precise keyword matching, the uniqueness and accuracy of the marker signal in finding and matching all data stream parameters are guaranteed.

[0101] S604: Display the data stream parameters of group a on the display interface of the diagnostic device.

[0102] In this embodiment, after the diagnostic device retrieves a group of abnormal data stream parameters corresponding to a marked signals, it can display the abnormal data stream parameters of the target vehicle system within a preset time period on the diagnostic device's display interface according to preset interface layout rules, in the form of a classification list, time series curve, or parameter detail entries. At the same time, it can synchronously associate the corresponding marked signal time, abnormality type, and parameter deviation from the baseline value information, remove normal data entries without abnormalities, and only display the filtered group of a abnormal data stream parameters, so that users can intuitively view and quickly identify the abnormal operating condition data of the target vehicle system, and achieve accurate visualization of abnormal data.

[0103] As can be seen, after the diagnostic equipment completes the recording of vehicle data streams within a preset time period, it responds to the user's request to view the data stream of any vehicle system. First, it locates all data stream parameters and corresponding marker signals of the target vehicle system. Then, it accurately retrieves and matches abnormal data stream parameters based on the marker signals and displays them specifically on the display interface. It eliminates the need to list a massive amount of normal data stream parameters without anomalies for the whole vehicle and the target system. It can selectively filter and intuitively present the abnormal data of a specified vehicle system within a preset time period as needed, greatly reducing the interference of invalid data, simplifying the user's fault data search process, and reducing the time spent on manual troubleshooting. At the same time, the association and matching mechanism between marker signals and data stream parameters ensures the accuracy of abnormal data retrieval, making it easier for maintenance personnel to quickly locate the fault points of the vehicle system and analyze the evolution of abnormal operating conditions, effectively improving the efficiency and accuracy of vehicle fault diagnosis and data analysis.

[0104] In summary, implementing the embodiments of the present invention has the following beneficial effects: As can be seen, the vehicle data stream marking method described in this embodiment of the invention, applied to a diagnostic device, firstly determines n sets of data stream parameters corresponding to n vehicle systems of the target vehicle at the current time when the diagnostic device performs a data stream recording operation on the target vehicle. Then, it determines the anomaly level value corresponding to each of the n sets of data stream parameters, obtaining n anomaly level values. Next, it determines k anomaly level values ​​among the n anomaly level values ​​that are greater than a preset anomaly level threshold. Then, it determines k sets of data stream parameters corresponding to the k anomaly level values. Finally, it performs a marking operation on the k sets of data stream parameters to obtain k marking signals corresponding to the current time. Using the embodiment of this application improves the efficiency of vehicle fault diagnosis.

[0105] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of a vehicle data stream marking device provided in an embodiment of this application. The vehicle data stream marking device is applied to diagnostic equipment. The vehicle data stream marking device 700 includes: an acquisition unit 701 and a processing unit 702. The acquisition unit 701 is used to determine n sets of data stream parameters corresponding to n vehicle systems of the target vehicle at the current time when the diagnostic device performs data stream recording operation on the target vehicle; each set of data stream parameters corresponds to one vehicle system, and n is a positive integer; The processing unit 702 is used to determine the anomaly degree value corresponding to each group of data stream parameters in the n groups of data stream parameters, and obtain n anomaly degree values. Determine k abnormality values ​​from the n abnormality values ​​that are greater than a preset abnormality threshold; k is an integer less than or equal to n; Determine the k sets of data stream parameters corresponding to the k anomaly severity values; A marking operation is performed on the k sets of data stream parameters to obtain the k marking signals corresponding to the current time.

[0106] In some possible implementations, when the first set of data stream parameters is the data stream parameter corresponding to the power battery system; the first set of data stream parameters is a set of data stream parameters among the n sets of data stream parameters, in determining the anomaly degree value corresponding to each set of data stream parameters in the n sets of data stream parameters, to obtain n anomaly degree values, the processing unit 702 is specifically used for: Determine the power battery temperature, power battery voltage, and power battery state of charge values ​​corresponding to the first set of data stream parameters; Determine the temperature difference between the power battery temperature and the preset temperature, the voltage difference between the power battery voltage and the preset voltage, and the state of charge difference between the power battery state of charge value and the preset state of charge value; The anomaly level value corresponding to the first set of data stream parameters is determined based on the temperature difference, the voltage difference, and the state of charge difference.

[0107] In some possible implementations, in determining the degree of anomaly corresponding to the first set of data stream parameters based on the temperature difference, the voltage difference, and the state of charge difference, the processing unit 702 is specifically used for: Determine the first degree of anomaly corresponding to the temperature difference, the second degree of anomaly corresponding to the voltage difference, and the third degree of anomaly corresponding to the state of charge difference; A first reference anomaly value is determined based on the first anomaly value, the second anomaly value, and the third anomaly value; Obtain the insulation resistance corresponding to the power battery system; Determine the first adjustment parameter corresponding to the insulation resistance; Based on the first adjustment parameter, the first reference anomaly level value is adjusted to obtain the anomaly level value corresponding to the first set of data stream parameters.

[0108] In some possible implementations, when the second set of data stream parameters is the data stream parameter corresponding to the engine system; the second set of data stream parameters is a set of data stream parameters among the n sets of data stream parameters; in determining the anomaly degree value corresponding to each set of data stream parameters in the n sets of data stream parameters, to obtain n anomaly degree values, the processing unit 702 is specifically used for: Determine the engine vibration amplitude and engine operating noise decibel value corresponding to the engine system; Determine the vibration amplitude difference between the engine vibration amplitude and the preset vibration amplitude, and the operating noise decibel difference between the engine operating noise decibel value and the preset operating noise decibel value; The abnormality level value corresponding to the second set of data stream parameters is determined based on the difference in vibration amplitude and the difference in operating noise in decibels.

[0109] In some possible implementations, in determining the anomaly level value corresponding to the second set of data stream parameters based on the vibration amplitude difference and the operating noise decibel difference, the processing unit 702 is specifically used for: Determine the fourth abnormality value corresponding to the vibration amplitude difference and the fifth abnormality value corresponding to the operating noise decibel difference; A second reference anomaly value is determined based on the fourth anomaly value and the fifth anomaly value; Obtain the instantaneous deviation of the engine system's oil pressure at the current moment; Determine the second adjustment parameter corresponding to the instantaneous deviation of the oil pressure; Based on the second adjustment parameter, the second reference anomaly level value is adjusted to obtain the anomaly level value corresponding to the second set of data stream parameters.

[0110] In some possible implementations, the processing unit 702 is further specifically used for: After the diagnostic device completes the data stream recording operation of the target vehicle within a preset time period, it obtains the data stream viewing request of the target user for the target vehicle system; the target vehicle system is any one of the n vehicle systems, and the data stream viewing request is used by the target user to view the abnormal data stream parameters of the target vehicle system within the preset time period, and the current time is within the preset time period; Based on the data stream viewing request, determine all data stream parameters and a marker signals corresponding to the target vehicle system; a is an integer. Based on the a-marked signals, a search is performed in all data stream parameters to obtain a set of data stream parameters corresponding to the a-marked signals; The data stream parameters of group a are displayed on the display interface of the diagnostic device.

[0111] Please see Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device is used in diagnostic equipment, such as... Figure 8 As shown, the electronic device 800 includes a transceiver 801, a processor 802, and a memory 803. These are connected via a bus 804. The memory 803 stores computer programs and data, and the transceiver 801 can transmit data stored in the memory 803 to the processor 802. The program includes instructions for performing the following steps: When the diagnostic device performs a data stream recording operation on the target vehicle, it determines n sets of data stream parameters corresponding to the n vehicle systems of the target vehicle at the current time; each set of data stream parameters corresponds to one vehicle system, and n is a positive integer; Determine the anomaly level value corresponding to each of the n sets of data stream parameters to obtain n anomaly level values; Determine k abnormality values ​​from the n abnormality values ​​that are greater than a preset abnormality threshold; k is an integer less than or equal to n; Determine the k sets of data stream parameters corresponding to the k anomaly severity values; A marking operation is performed on the k sets of data stream parameters to obtain the k marking signals corresponding to the current time.

[0112] In some possible implementations, when the first set of data stream parameters is the data stream parameter corresponding to the power battery system; the first set of data stream parameters is a set of data stream parameters among the n sets of data stream parameters, and in determining the anomaly degree value corresponding to each set of data stream parameters in the n sets of data stream parameters to obtain n anomaly degree values, the above procedure includes instructions for performing the following steps: Determine the power battery temperature, power battery voltage, and power battery state of charge values ​​corresponding to the first set of data stream parameters; Determine the temperature difference between the power battery temperature and the preset temperature, the voltage difference between the power battery voltage and the preset voltage, and the state of charge difference between the power battery state of charge value and the preset state of charge value; The anomaly level value corresponding to the first set of data stream parameters is determined based on the temperature difference, the voltage difference, and the state of charge difference.

[0113] In some possible implementations, the above procedure includes instructions for performing the following steps in determining the degree of anomaly corresponding to the first set of data stream parameters based on the temperature difference, the voltage difference, and the state of charge difference: Determine the first degree of anomaly corresponding to the temperature difference, the second degree of anomaly corresponding to the voltage difference, and the third degree of anomaly corresponding to the state of charge difference; A first reference anomaly value is determined based on the first anomaly value, the second anomaly value, and the third anomaly value; Obtain the insulation resistance corresponding to the power battery system; Determine the first adjustment parameter corresponding to the insulation resistance; Based on the first adjustment parameter, the first reference anomaly level value is adjusted to obtain the anomaly level value corresponding to the first set of data stream parameters.

[0114] In some possible implementations, when the second set of data stream parameters is the data stream parameter corresponding to the engine system; the second set of data stream parameters is one set of data stream parameters among the n sets of data stream parameters; in determining the anomaly degree value corresponding to each set of data stream parameters in the n sets of data stream parameters, to obtain n anomaly degree values, the above procedure includes instructions for performing the following steps: Determine the engine vibration amplitude and engine operating noise decibel value corresponding to the engine system; Determine the vibration amplitude difference between the engine vibration amplitude and the preset vibration amplitude, and the operating noise decibel difference between the engine operating noise decibel value and the preset operating noise decibel value; The abnormality level value corresponding to the second set of data stream parameters is determined based on the difference in vibration amplitude and the difference in operating noise in decibels.

[0115] In some possible implementations, in determining the degree of anomaly corresponding to the second set of data stream parameters based on the vibration amplitude difference and the operating noise decibel difference, the above procedure includes instructions for performing the following steps: Determine the fourth abnormality value corresponding to the vibration amplitude difference and the fifth abnormality value corresponding to the operating noise decibel difference; A second reference anomaly value is determined based on the fourth anomaly value and the fifth anomaly value; Obtain the instantaneous deviation of the engine system's oil pressure at the current moment; Determine the second adjustment parameter corresponding to the instantaneous deviation of the oil pressure; Based on the second adjustment parameter, the second reference anomaly level value is adjusted to obtain the anomaly level value corresponding to the second set of data stream parameters.

[0116] In some possible implementations, the above procedure includes instructions for performing the following steps: After the diagnostic device completes the data stream recording operation of the target vehicle within a preset time period, it obtains the data stream viewing request of the target user for the target vehicle system; the target vehicle system is any one of the n vehicle systems, and the data stream viewing request is used by the target user to view the abnormal data stream parameters of the target vehicle system within the preset time period, and the current time is within the preset time period; Based on the data stream viewing request, determine all data stream parameters and a marker signals corresponding to the target vehicle system; a is an integer. Based on the a-marked signals, a search is performed in all data stream parameters to obtain a set of data stream parameters corresponding to the a-marked signals; The data stream parameters of group a are displayed on the display interface of the diagnostic device.

[0117] It should be understood that the electronic devices mentioned in this application may include smartphones (such as Android phones, iOS phones, Windows Phones, etc.), tablets, PDAs, laptops, mobile internet devices (MIDs) or wearable devices, servers, edge computing nodes, etc. The above-mentioned electronic devices are merely examples and not exhaustive, and include, but are not limited to, the electronic devices described above.

[0118] This application also provides a computer-readable storage medium storing a computer program that is executed by a processor to implement some or all of the steps of any of the methods described in the above method embodiments.

[0119] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments.

[0120] It should be noted that, for the sake of simplicity, the aforementioned methods are described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are optional, and the actions and modules involved are not necessarily essential to this application.

[0121] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0123] The units described as separate components may or may not be physically separate. The 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 the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0124] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0125] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, 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. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0126] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0127] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The above description of the embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for marking vehicle data streams, characterized in that, Applied to diagnostic devices, the method includes: When the diagnostic device performs a data stream recording operation on the target vehicle, it determines n sets of data stream parameters corresponding to the n vehicle systems of the target vehicle at the current time; each set of data stream parameters corresponds to one vehicle system, and n is a positive integer; Determine the anomaly level value corresponding to each of the n sets of data stream parameters to obtain n anomaly level values; Determine k abnormality values ​​from the n abnormality values ​​that are greater than a preset abnormality threshold; k is an integer less than or equal to n; Determine the k sets of data stream parameters corresponding to the k anomaly severity values; A marking operation is performed on the k sets of data stream parameters to obtain the k marking signals corresponding to the current time.

2. The method as described in claim 1, characterized in that, When the first set of data stream parameters is the data stream parameter corresponding to the power battery system; the first set of data stream parameters is one set of data stream parameters among the n sets of data stream parameters; The step of determining the anomaly severity value corresponding to each of the n sets of data stream parameters yields n anomaly severity values, including: Determine the power battery temperature, power battery voltage, and power battery state of charge values ​​corresponding to the first set of data stream parameters; Determine the temperature difference between the power battery temperature and the preset temperature, the voltage difference between the power battery voltage and the preset voltage, and the state of charge difference between the power battery state of charge value and the preset state of charge value; The anomaly level value corresponding to the first set of data stream parameters is determined based on the temperature difference, the voltage difference, and the state of charge difference.

3. The method as described in claim 2, characterized in that, The step of determining the anomaly level value corresponding to the first set of data stream parameters based on the temperature difference, the voltage difference, and the state of charge difference includes: Determine the first degree of anomaly corresponding to the temperature difference, the second degree of anomaly corresponding to the voltage difference, and the third degree of anomaly corresponding to the state of charge difference; A first reference anomaly value is determined based on the first anomaly value, the second anomaly value, and the third anomaly value; Obtain the insulation resistance corresponding to the power battery system; Determine the first adjustment parameter corresponding to the insulation resistance; Based on the first adjustment parameter, the first reference anomaly level value is adjusted to obtain the anomaly level value corresponding to the first set of data stream parameters.

4. The method as described in claim 1, characterized in that, When the second set of data stream parameters is the data stream parameter corresponding to the engine system; the second set of data stream parameters is one set of data stream parameters among the n sets of data stream parameters; The step of determining the anomaly severity value corresponding to each of the n sets of data stream parameters yields n anomaly severity values, including: Determine the engine vibration amplitude and engine operating noise decibel value corresponding to the engine system; Determine the vibration amplitude difference between the engine vibration amplitude and the preset vibration amplitude, and the operating noise decibel difference between the engine operating noise decibel value and the preset operating noise decibel value; The abnormality level value corresponding to the second set of data stream parameters is determined based on the difference in vibration amplitude and the difference in operating noise in decibels.

5. The method as described in claim 4, characterized in that, The step of determining the anomaly level value corresponding to the second set of data stream parameters based on the vibration amplitude difference and the operating noise decibel difference includes: Determine the fourth abnormality value corresponding to the vibration amplitude difference and the fifth abnormality value corresponding to the operating noise decibel difference; A second reference anomaly value is determined based on the fourth anomaly value and the fifth anomaly value; Obtain the instantaneous deviation of the engine system's oil pressure at the current moment; Determine the second adjustment parameter corresponding to the instantaneous deviation of the oil pressure; Based on the second adjustment parameter, the second reference anomaly level value is adjusted to obtain the anomaly level value corresponding to the second set of data stream parameters.

6. The method as described in claim 1, characterized in that, The method further includes: After the diagnostic device completes the data stream recording operation of the target vehicle within a preset time period, it obtains the data stream viewing request of the target user for the target vehicle system; the target vehicle system is any one of the n vehicle systems, and the data stream viewing request is used by the target user to view the abnormal data stream parameters of the target vehicle system within the preset time period, and the current time is within the preset time period; Based on the data stream viewing request, determine all data stream parameters and a marker signals corresponding to the target vehicle system; a is an integer. Based on the a-marked signals, a search is performed in all data stream parameters to obtain a set of data stream parameters corresponding to the a-marked signals; The data stream parameters of group a are displayed on the display interface of the diagnostic device.

7. A vehicle data stream marking device, characterized in that, Applied to diagnostic equipment, the device includes: an acquisition unit and a processing unit; The acquisition unit is used to determine n sets of data stream parameters corresponding to n vehicle systems of the target vehicle at the current time when the diagnostic device performs data stream recording operation on the target vehicle; each set of data stream parameters corresponds to one vehicle system, and n is a positive integer; The processing unit is used to determine the anomaly level value corresponding to each of the n sets of data stream parameters, and obtain n anomaly level values. Determine k abnormality values ​​from the n abnormality values ​​that are greater than a preset abnormality threshold; k is an integer less than or equal to n; Determine the k sets of data stream parameters corresponding to the k anomaly severity values; A marking operation is performed on the k sets of data stream parameters to obtain the k marking signals corresponding to the current time.

8. An electronic device, characterized in that, The method includes 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 performing the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, When the computer program product is run on a computer, it causes the computer to perform the method as described in any one of claims 1-6.

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