A method and system for vehicle-mounted data acquisition and transmission

Abstract

This invention relates to the field of vehicle data acquisition and transmission technology, specifically a method and system for vehicle data acquisition and transmission. The method includes: acquiring multiple vehicle condition parameter sequences within a single driving trip using an aftermarket OBD acquisition device; calculating multidimensional change intensity based on the change rate of at least two vehicle condition parameter sequences and extracting vehicle condition change segments; calculating interaction response values ​​and diagnostic retention values ​​based on the vehicle condition change segments; determining an interaction response window and obtaining a lightweight interaction field based on the interaction response value; determining a diagnostic traceability window and obtaining a diagnostic context field based on the diagnostic retention value; generating a real-time interaction data package and a trip diagnostic segment; sending the real-time interaction data package to an in-vehicle emotional interaction terminal; and uploading the complete trip data package to a cloud server after the driving trip ends. This invention enables the same vehicle condition data to be matched with both in-vehicle emotional interaction instant feedback and cloud-based trip-level diagnostic needs, reducing real-time interaction data redundancy.

Description

Technical Field

[0001] This invention relates to the field of vehicle-mounted data acquisition and transmission technology, specifically a vehicle-mounted data acquisition and transmission method and system. Background Technology

[0002] Vehicle data acquisition and transmission typically involves OBD devices reading vehicle operating data at fixed intervals or through fault triggering, and uploading it to mobile terminals or cloud platforms for vehicle status monitoring, fault code reading, and trip statistics. Existing in-vehicle interactive robots mostly rely on voice, images, or user behavior for feedback, and rarely utilize vehicle operating data to generate real-time feedback corresponding to changes in vehicle conditions.

[0003] In scenarios where aftermarket OBD data acquisition devices work in conjunction with in-vehicle emotional interaction terminals, the same vehicle condition data needs to both drive the in-vehicle emotional interaction terminal to generate anthropomorphic real-time feedback within a short period of time, and form complete trip evidence after the trip to support cloud-based driving behavior diagnosis and vehicle sub-health diagnosis. Since real-time interaction focuses on short-term vehicle condition changes and lightweight transmission, while cloud-based diagnosis focuses on trip integrity, fault correlation, and pre- and post-event states, existing single-path, fixed-cycle, or fault-triggered data upload methods cannot simultaneously meet the needs of both types of data usage. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for vehicle data acquisition and transmission, in order to solve the problem that when aftermarket OBD acquisition devices and in-vehicle emotional interaction terminals work together, the same vehicle condition data is difficult to simultaneously satisfy both real-time feedback of in-vehicle emotional interaction and cloud-based trip-level diagnosis.

[0005] To achieve the above objectives, in one aspect, the present invention provides a method for vehicle-mounted data acquisition and transmission, the method comprising: Step S1: Collect multiple vehicle condition parameter sequences within a single driving trip using an aftermarket OBD acquisition device; calculate the multidimensional change intensity based on the change rate of at least two vehicle condition parameter sequences; extract continuous time periods where the multidimensional change intensity exceeds a preset change threshold as vehicle condition change segments.

[0006] Step S2: Calculate the interactive response value based on the extreme deviation of vehicle condition parameters, trend establishment rate, and driver operation-related parameters within the vehicle condition change segment; calculate the diagnostic retention value based on the duration of the vehicle condition change segment, fault code-related parameters, and trip stage weight.

[0007] Step S3: Determine the interaction response window based on the interaction response value, and extract the first subset of parameters falling into the interaction response window from the multiple vehicle condition parameter sequences as a lightweight interaction field; determine the diagnostic traceability window based on the diagnostic retention value, and extract the second subset of parameters falling into the diagnostic traceability window from the multiple vehicle condition parameter sequences as a diagnostic context field.

[0008] Step S4: Generate a real-time interactive data packet containing lightweight interactive fields and carrying a fragment identifier, and a trip diagnostic fragment containing diagnostic context fields and carrying the same fragment identifier; send the real-time interactive data packet to the in-vehicle emotion interaction terminal via a low-latency wireless link; after the driving trip ends, encapsulate the trip diagnostic fragment and the basic trip data of a single driving trip into a complete trip data packet and upload it to the cloud server.

[0009] Furthermore, the method for calculating the multidimensional change intensity based on the rate of change of vehicle condition parameters from at least two vehicle condition parameter sequences includes: Based on the vehicle speed parameter sequence, engine speed parameter sequence, and driving operation parameter sequence within a single driving trip, the vehicle operating condition type corresponding to each sampling time is determined. The vehicle operating condition type includes starting condition, idling condition, acceleration condition, deceleration condition, and stable driving condition.

[0010] Based on the vehicle condition parameter identifiers read by the aftermarket OBD acquisition device at the vehicle's OBD interface, a set of available parameters is generated; from the set of available parameters, a set of condition-sensitive parameters corresponding to the vehicle's operating condition type is selected; and the time-series change rate of each vehicle condition parameter sequence in the set of condition-sensitive parameters is calculated.

[0011] Based on the vehicle operating condition type, determine the operating condition weight corresponding to each vehicle condition parameter sequence in the set of operating condition sensitive parameters; calculate the multidimensional change intensity based on the time-series change rate of each vehicle condition parameter sequence in the set of operating condition sensitive parameters and the operating condition weight.

[0012] Furthermore, the method for determining the operating condition weights corresponding to each vehicle condition parameter sequence in the set of operating condition sensitive parameters based on the vehicle operating condition type includes: Read the basic weight set corresponding to the vehicle operating condition type from the preset operating condition weight table. The basic weight set includes the basic weights corresponding to each candidate vehicle condition parameter.

[0013] Candidate vehicle condition parameters that belong to the set of operating condition sensitive parameters in the basic weight set are taken as effective weight parameters, and candidate vehicle condition parameters that do not belong to the set of operating condition sensitive parameters in the basic weight set are taken as missing weight parameters.

[0014] Based on the parameter substitution relationship between missing weight parameters and valid weight parameters, the basic weights corresponding to the missing weight parameters are allocated to the valid weight parameters according to a preset allocation ratio, thereby obtaining the operating condition weights corresponding to each vehicle condition parameter sequence in the set of operating condition sensitive parameters, wherein the sum of the preset allocation ratios corresponding to the same missing weight parameter is one.

[0015] Furthermore, the method for calculating the interactive response value based on the extreme deviation of vehicle condition parameters, trend establishment rate, and driver operation correlation parameters within the vehicle condition change segment includes: Identify the vehicle condition parameter sequence with the largest rate of change of dimensionless parameters within the vehicle condition change segment as the primary changing vehicle condition parameter sequence; then, based on the segment extreme values ​​of the primary changing vehicle condition parameter sequence... Travel reference value Calculate the deviation of extreme values ;in, This is the standard scale for parameter variation.

[0016] Based on the parameter values ​​of the main change vehicle condition parameter sequence at the beginning of the segment Parameter values ​​at the peak of the segment Segment start time and peak moments of segments Calculate the rate of trend establishment .

[0017] Calculate the unexpected change value based on the driving operation parameter sequence and vehicle response parameter sequence from multiple vehicle condition parameter sequences. ,in, This represents the rate of change of the vehicle response parameter sequence from the start time of the segment to the peak time of the segment. The rate of change of driving operation parameters in the sequence of driving operation parameters from the start time of the segment to the peak time of the segment. The operating response coefficient is the one corresponding to the vehicle operating condition type. The interactive response value is obtained by weighted summing of the dimensionless values ​​of extreme deviation, trend establishment rate and unexpected change value.

[0018] Furthermore, the method for calculating the diagnostic retention value based on the duration of the vehicle condition change segment, fault code association parameters, and trip stage weights includes: Based on the start time of the segment showing changes in vehicle condition and the end of the segment The duration was calculated. Based on the time of occurrence of the fault code in the fault code status sequence Fault code duration Duration of vehicle condition change segments Calculate the associated parameters of the fault codes ,in, The time when the fault code appeared Fault codes that are assigned a value of 1 when falling within a segment of vehicle condition change and a value of 0 when not falling within a segment of vehicle condition change are marked in the same segment. The duration of overlap between the fault code state sequence and the vehicle condition change segment. , and Preset the associated weights for fault codes.

[0019] Determine the weight of each stage of the journey based on the stage to which the vehicle condition change segment belongs in a single driving trip. The aforementioned travel phases include the start-up phase, idling phase, driving phase, and end phase; the continuous duration during which the intensity of multidimensional changes in vehicle condition before a segment is lower than a preset steady-state threshold is defined as the preceding steady-state duration. The duration during which the intensity of multidimensional changes following a vehicle condition change segment is below a preset steady-state threshold is taken as the subsequent steady-state duration. Based on the preceding steady-state duration Post-steady-state duration and baseline reference duration Calculate baseline demand The diagnostic retention value is obtained by weighting and summing the duration, fault code associated parameters, trip stage weights, and baseline demand.

[0020] Furthermore, the method of determining the interaction response window based on the interaction response value and extracting a first subset of parameters falling into the interaction response window from the plurality of vehicle condition parameter sequences as a lightweight interaction field includes: The interaction level is determined based on the interaction response value, and the pre-capture duration and post-capture duration are determined based on the interaction level; the peak time of the segment is used as the criterion. Centered on the window, based on the preceding capture duration and the duration of the post-capture segment Determine the interactive response window .

[0021] From the multiple vehicle condition parameter sequences falling into the interactive response window, the main variable vehicle condition parameter sequence, driving operation parameter sequence, and vehicle response parameter sequence are selected to obtain the first parameter subset.

[0022] Based on the first parameter subset, generate the main change parameter identifier, change direction marker, change level, unexpected change level, and relative time offset; arrange the main change parameter identifier, change direction marker, change level, unexpected change level, and relative time offset in a preset field order to obtain lightweight interactive fields.

[0023] Further, the method of determining the diagnostic traceability window based on the diagnostic retention value and extracting a second subset of parameters falling into the diagnostic traceability window from the plurality of vehicle condition parameter sequences as a diagnostic context field includes: The pre-action duration and post-action duration are determined based on the diagnostic retention values, using the start time of the vehicle condition change segment. and the end of the segment Define the behavior event window as the boundary. ,in, Pre-action duration, The duration after the action.

[0024] The trend lead-out and trend follow-out durations are determined based on diagnostic retention values ​​and baseline demand, using the segment start time of the vehicle condition change segment. and the end of the segment Use boundaries to define the sub-health trend window ,in, For trend lead time, The duration of the trend is set later.

[0025] The behavior event window and the sub-health trend window are merged into a diagnostic traceability window; from the multiple vehicle condition parameter sequences falling into the behavior event window, the driving operation parameter sequence, the main change vehicle condition parameter sequence, and the vehicle dynamic response parameter sequence are extracted to generate a behavior diagnosis field.

[0026] From multiple vehicle condition parameter sequences falling into the sub-health trend window, extract the main changing vehicle condition parameter sequence, fault code status sequence, associated vehicle condition parameter sequence, and steady-state baseline parameter sequence to generate a sub-health diagnosis field; combine the behavior diagnosis field, sub-health diagnosis field, trip stage marker, and segment identifier into a diagnosis context field.

[0027] Based on the same inventive concept, this invention also provides an in-vehicle data acquisition and transmission system, the system comprising: The change perception module is used to collect multiple vehicle condition parameter sequences within a single driving trip through an aftermarket OBD acquisition device; calculate the multidimensional change intensity based on the change rate of at least two vehicle condition parameter sequences; and extract continuous time periods where the multidimensional change intensity exceeds a preset change threshold as vehicle condition change segments.

[0028] The demand assessment module is used to calculate the interaction response value based on the extreme deviation of vehicle condition parameters, trend establishment rate, and driver operation-related parameters within the vehicle condition change segment; and to calculate the diagnostic retention value based on the duration of the vehicle condition change segment, fault code-related parameters, and trip stage weights.

[0029] The window capture module is used to determine the interaction response window based on the interaction response value, and to capture a first subset of parameters falling into the interaction response window from the multiple vehicle condition parameter sequences as a lightweight interaction field; and to determine the diagnostic traceability window based on the diagnostic retention value, and to capture a second subset of parameters falling into the diagnostic traceability window from the multiple vehicle condition parameter sequences as a diagnostic context field.

[0030] The data encapsulation module is used to generate real-time interactive data packets containing lightweight interactive fields and carrying fragment identifiers, as well as trip diagnostic fragments containing diagnostic context fields and carrying the same fragment identifiers; it sends the real-time interactive data packets to the in-vehicle emotion interaction terminal via a low-latency wireless link; after the driving trip ends, it encapsulates the trip diagnostic fragments and the basic trip data of a single driving trip into a complete trip data packet and uploads it to the cloud server.

[0031] Compared with the prior art, the beneficial effects of the present invention are: 1. Calculate the interaction response value and diagnostic retention value for vehicle condition change segments within a single driving trip, and form real-time interaction data packets and trip diagnostic segments accordingly. This allows the same vehicle condition data to be matched with the data requirements of in-vehicle emotional interaction instant feedback and cloud-based trip-level diagnosis, reducing real-time interaction data redundancy while retaining the event context required for cloud diagnosis.

[0032] 2. Generate a set of sensitive parameters based on the vehicle's operating condition type. When there are missing parameters in the available parameter set, allocate the basic weight of the missing weight parameters through parameter substitution relationships. This enables the aftermarket OBD acquisition device to calculate the intensity of multidimensional changes even when the readable parameters for different vehicle models are inconsistent, thereby improving the stability and adaptability of vehicle condition change segment recognition. Attached Figure Description

[0033] Figure 1 This is a flowchart of a vehicle-mounted data acquisition and transmission method according to the present invention; Figure 2 This is a block diagram of a vehicle-mounted data acquisition and transmission system according to the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Before giving examples, it is necessary to describe the application scenarios of the present invention. The present invention is applicable to vehicle driving scenarios where aftermarket OBD data acquisition devices and in-vehicle emotional interaction terminals work together.

[0036] Example 1: As Figure 1 As shown in the figure, this embodiment provides a method for vehicle-mounted data acquisition and transmission, the method including: Multiple vehicle condition parameter sequences are collected during a single driving trip using an aftermarket OBD acquisition device. Based on the vehicle speed parameter sequence, engine speed parameter sequence, and driving operation parameter sequence during a single driving trip, the vehicle operating condition type corresponding to each sampling moment is determined. The vehicle operating condition type includes starting condition, idling condition, acceleration condition, deceleration condition, and stable driving condition.

[0037] Based on the vehicle condition parameter identifiers read by the aftermarket OBD acquisition device at the vehicle's OBD interface, a set of available parameters is generated; from the set of available parameters, a set of condition-sensitive parameters corresponding to the vehicle's operating condition type is selected; and the time-series change rate of each vehicle condition parameter sequence in the set of condition-sensitive parameters is calculated.

[0038] Read the basic weight set corresponding to the vehicle operating condition type from the preset operating condition weight table. The basic weight set includes the basic weights corresponding to each candidate vehicle condition parameter.

[0039] Candidate vehicle condition parameters that belong to the set of operating condition sensitive parameters in the basic weight set are taken as effective weight parameters, and candidate vehicle condition parameters that do not belong to the set of operating condition sensitive parameters in the basic weight set are taken as missing weight parameters.

[0040] Based on the parameter substitution relationship between missing weight parameters and valid weight parameters, the basic weights corresponding to the missing weight parameters are allocated to the valid weight parameters according to a preset allocation ratio, thereby obtaining the operating condition weights corresponding to each vehicle condition parameter sequence in the set of operating condition sensitive parameters, wherein the sum of the preset allocation ratios corresponding to the same missing weight parameter is one.

[0041] The multidimensional change intensity is calculated based on the time-series change rate of each vehicle condition parameter sequence in the set of sensitive operating conditions and the operating condition weight.

[0042] Taking a gasoline passenger vehicle as an example, a retrofitted OBD data acquisition device is plugged into the vehicle's OBD interface. A single driving trip begins at 08:10:00 when the ignition state switches from off to on and ends at 08:32:40 when the ignition state switches back to off. The retrofitted OBD data acquisition device records multiple vehicle condition parameter sequences at 1-second sampling intervals, including vehicle speed parameter sequences, engine speed parameter sequences, driving operation parameter sequences, throttle opening parameter sequences, braking status parameter sequences, engine load parameter sequences, battery voltage parameter sequences, and fault code status sequences. In this embodiment, the driving operation parameter sequences are represented by the throttle opening parameter sequences; when the throttle opening parameter sequences are not returned by the vehicle's OBD interface, the driving operation parameter sequences are represented by the throttle opening parameter sequences.

[0043] Vehicle operating condition type is determined point-by-point according to sampling time. The first 12 seconds when the vehicle speed is less than 2 km / h, the engine speed increases from 0 r / min to over 600 r / min, and the ignition remains on, are marked as starting condition; the sampling time when the vehicle speed is less than 3 km / h, the engine speed is between 600 r / min and 1000 r / min, and the driving operation parameters change by less than 8% is marked as idling condition; the sampling time when the vehicle speed change rate is greater than 0.8 km / h / s and the driving operation parameter change rate is greater than 1.5% / s is marked as acceleration condition; braking status parameters are returned by the vehicle's OBD interface. Sampling times where the vehicle speed change rate is less than -0.8 km / h / s and the braking status parameter is 1 are marked as deceleration conditions. When the braking status parameter is not returned by the vehicle's OBD interface, sampling times where the vehicle speed change rate is less than -0.8 km / h / s and the driving operation parameter change rate is less than -1.2% / s are marked as deceleration conditions. Sampling times where the vehicle speed is not less than 3 km / h, the absolute value of the vehicle speed change rate is not greater than 0.8 km / h / s, and the absolute value of the engine speed change rate is not greater than 120 r / min / s are marked as stable driving conditions. When a sampling time simultaneously meets the judgment conditions for two vehicle operating condition types, one vehicle operating condition type is retained in the order of starting condition, deceleration condition, acceleration condition, idling condition, and stable driving condition.

[0044] The aftermarket OBD data acquisition device initiates a parameter reading request to the vehicle's OBD interface within the first 5 seconds after the start of a single driving trip. The vehicle condition parameter identifier is the parameter identifier returned by the vehicle's OBD interface; in this embodiment, it is represented by the standard OBD-II PID. The vehicle's OBD interface returns vehicle condition parameter identifiers PID 0D, PID 0C, PID 11, PID 04, PID 42, and PID 03, representing vehicle speed parameters, engine speed parameters, throttle opening parameters, engine load parameters, battery voltage parameters, and fault code status parameters, respectively. The vehicle's OBD interface does not return throttle opening parameter identifiers or braking status parameter identifiers. The usable parameter set generated by the aftermarket OBD data acquisition device is {vehicle speed parameter sequence, engine speed parameter sequence, throttle opening parameter sequence, engine load parameter sequence, battery voltage parameter sequence, and fault code status sequence}.

[0045] A preset operating condition sensitivity parameter table records the correspondence between vehicle operating condition types and candidate vehicle condition parameters, determined by the physical characteristics of each operating condition. The candidate vehicle condition parameters for the starting condition include engine speed parameter sequence, battery voltage parameter sequence, and fault code status sequence; for the idling condition, they include engine speed parameter sequence, engine load parameter sequence, battery voltage parameter sequence, and fault code status sequence; for the acceleration condition, they include vehicle speed parameter sequence, engine speed parameter sequence, throttle opening parameter sequence, throttle valve opening parameter sequence, and engine load parameter sequence; for the deceleration condition, they include vehicle speed parameter sequence, braking status parameter sequence, engine speed parameter sequence, and engine load parameter sequence; and for the stable driving condition, they include vehicle speed parameter sequence, engine speed parameter sequence, battery voltage parameter sequence, and engine load parameter sequence.

[0046] Taking the period from 08:15:20 to 08:15:28 as an example, the vehicle speed parameter sequence increased from 42 km / h to 58 km / h, the engine speed parameter sequence increased from 1550 r / min to 2360 r / min, the throttle opening parameter sequence increased from 18% to 41%, the engine load parameter sequence increased from 31% to 62%, and the battery voltage parameter sequence changed from 13.9V to 13.8V. Each sampling time within the period from 08:15:20 to 08:15:28 was marked as an acceleration condition. The intersection of the candidate vehicle condition parameters corresponding to the acceleration condition with the available parameter set yields the condition-sensitive parameter set {vehicle speed parameter sequence, engine speed parameter sequence, throttle opening parameter sequence, engine load parameter sequence}. The throttle opening parameter sequence did not appear in the available parameter set and was not included in the condition-sensitive parameter set.

[0047] The temporal change rate is calculated as the ratio of the difference between adjacent sampled values ​​to the sampling interval. When a sampling interval is missing, the time difference between adjacent valid sampling times is used; if the time difference between adjacent valid sampling times is greater than 3 seconds, the temporal change rate is not calculated for the corresponding time period. Between 08:15:23 and 08:15:24, vehicle speed parameters increased from 48 km / h to 51 km / h, engine speed parameters increased from 1840 r / min to 1980 r / min, throttle opening parameters increased from 27% to 32%, and engine load parameters increased from 43% to 50%. The temporal change rates for the vehicle speed parameter sequence, engine speed parameter sequence, throttle opening parameter sequence, and engine load parameter sequence are 3 km / h / s, 140 r / min / s, 5% / s, and 7% / s, respectively.

[0048] In the preset operating condition weight table, the basic weight set for acceleration operating conditions includes a basic weight of 0.25 for the vehicle speed parameter sequence, 0.20 for the engine speed parameter sequence, 0.25 for the throttle opening parameter sequence, 0.20 for the throttle valve opening parameter sequence, and 0.10 for the engine load parameter sequence. These weights are determined by ranking the sensitivity of each parameter to changes in engine operating conditions. The parameter substitution relationship indicates that when a candidate vehicle condition parameter is missing, its basic weight is transferred to the effective weight parameter according to the vehicle's physical state correlation. The parameter substitution relationship table records: when the throttle opening parameter sequence is missing, its basic weight is allocated 70% to the throttle valve opening parameter sequence and 30% to the engine load parameter sequence; when the intake manifold absolute pressure parameter sequence is missing, its basic weight is allocated 100% to the engine load parameter sequence; when the braking state parameter sequence is missing, its basic weight is allocated 80% to the vehicle speed parameter sequence and 20% to the engine speed parameter sequence.

[0049] The effective weighted parameters under acceleration conditions are the vehicle speed parameter sequence, engine speed parameter sequence, throttle opening parameter sequence, and engine load parameter sequence; the missing weighted parameter is the throttle opening parameter sequence. The basic weights of the throttle opening parameter sequence are allocated to the throttle opening parameter sequence and the engine load parameter sequence, with the vehicle speed parameter sequence having a weight of 0.25, the engine speed parameter sequence having a weight of 0.20, the throttle opening parameter sequence having a weight of 0.20 + 0.25 × 0.70 = 0.375, and the engine load parameter sequence having a weight of 0.10 + 0.25 × 0.30 = 0.175.

[0050] Before calculating the multidimensional variation intensity, the time-series variation rate of each vehicle condition parameter sequence in the set of sensitive operating conditions is converted into a dimensionless parameter variation rate according to the standard variation scale of the parameters (determined by the standard deviation of normal fluctuations of each parameter under stable driving conditions in the historical journey of the same vehicle model). The standard variation scale of the vehicle speed parameter sequence is 5 km / h / s, the standard variation scale of the engine speed parameter sequence is 300 r / min / s, the standard variation scale of the throttle opening parameter sequence is 10% / s, and the standard variation scale of the engine load parameter sequence is 15% / s. Between 08:15:23 and 08:15:24, the dimensionless parameter variation rates of the vehicle speed parameter sequence, engine speed parameter sequence, throttle opening parameter sequence, and engine load parameter sequence are calculated to be 0.60, 0.47, 0.50, and 0.47, respectively. The intensity of multidimensional change is the sum of the products of the change rates of each dimensionless parameter and the weight of the operating condition. The intensity of multidimensional change corresponding to 08:15:24 is 0.25×0.60+0.20×0.47+0.375×0.50+0.175×0.47=0.514. The preset change threshold is 0.45 under acceleration conditions. The intensity of multidimensional change corresponding to 08:15:24 exceeds the preset change threshold.

[0051] Continuous time periods where the intensity of multidimensional changes exceeds a preset threshold are extracted as vehicle condition change segments; the vehicle condition parameter sequence with the largest rate of change of dimensionless parameters within the vehicle condition change segment is identified as the main changing vehicle condition parameter sequence; based on the segment extreme values ​​of the main changing vehicle condition parameter sequence... Travel reference value Calculate the deviation of extreme values ;in, This is the standard scale for parameter variation.

[0052] Based on the parameter values ​​of the main change vehicle condition parameter sequence at the beginning of the segment Parameter values ​​at the peak of the segment Segment start time and peak moments of segments Calculate the rate of trend establishment .

[0053] Calculate the unexpected change value based on the driving operation parameter sequence and vehicle response parameter sequence from multiple vehicle condition parameter sequences. ,in, This represents the rate of change of the vehicle response parameter sequence from the start time of the segment to the peak time of the segment. The rate of change of driving operation parameters in the sequence of driving operation parameters from the start time of the segment to the peak time of the segment. The operating response coefficient is the one corresponding to the vehicle operating condition type. The interactive response value is obtained by weighted summing of the dimensionless values ​​of extreme deviation, trend establishment rate and unexpected change value.

[0054] Based on the start time of the segment showing changes in vehicle condition and the end of the segment The duration was calculated. Based on the time of occurrence of the fault code in the fault code status sequence Fault code duration Duration of vehicle condition change segments Calculate the associated parameters of the fault codes ,in, The time when the fault code appeared Fault codes that are assigned a value of 1 when falling within a segment of vehicle condition change and a value of 0 when not falling within a segment of vehicle condition change are marked in the same segment. The duration of overlap between the fault code state sequence and the vehicle condition change segment. , and Preset the associated weights for fault codes.

[0055] Determine the weight of each stage of the journey based on the stage to which the vehicle condition change segment belongs in a single driving trip. The aforementioned travel phases include the start-up phase, idling phase, driving phase, and end phase; the continuous duration during which the intensity of multidimensional changes in vehicle condition before a segment is lower than a preset steady-state threshold is defined as the preceding steady-state duration. The duration during which the intensity of multidimensional changes following a vehicle condition change segment is below a preset steady-state threshold is taken as the subsequent steady-state duration. Based on the preceding steady-state duration Post-steady-state duration and baseline reference duration Calculate baseline demand The diagnostic retention value is obtained by weighting and summing the duration, fault code associated parameters, trip stage weights, and baseline demand.

[0056] For example, the multidimensional change intensities corresponding to each sampling time between 08:15:20 and 08:15:28 are 0.18, 0.32, 0.47, 0.50, 0.514, 0.49, 0.46, 0.34, and 0.22, respectively. With a preset change threshold of 0.45 under acceleration conditions, the period from 08:15:22 to 08:15:26 is extracted as a vehicle condition change segment, with the segment starting at... The segment ends at 08:15:22. The timeframe is 08:15:26. The multidimensional change intensity reaches a peak value of 0.514 at 08:15:24. The peak time of the segment is 08:15:24. This refers to the sampling moment when the intensity of multidimensional changes within a vehicle condition change segment reaches its peak. If the intensity of multidimensional changes at only one sampling moment within a continuous time period is lower than the preset change threshold, the corresponding sampling moment is merged into the same vehicle condition change segment; if the duration of the continuous time period is less than 2 seconds, it is not extracted as a vehicle condition change segment.

[0057] Between 08:15:22 and 08:15:26, the maximum time-series change rates of the vehicle speed parameter sequence, engine speed parameter sequence, throttle opening parameter sequence, and engine load parameter sequence were 3 km / h / s, 180 r / min / s, 5% / s, and 7% / s, respectively. After conversion according to the parameter standard change scale, the maximum dimensionless parameter change rates of the four vehicle condition parameter sequences were 0.60, 0.60, 0.50, and 0.47, respectively. The maximum dimensionless parameter change rates of the vehicle speed parameter sequence and the engine speed parameter sequence are the same. When the maximum dimensionless parameter change rates of the two vehicle condition parameter sequences are the same, the engine speed parameter sequence is determined to be the primary changing vehicle condition parameter sequence according to the preset primary changing parameter priority of the engine speed parameter sequence, vehicle speed parameter sequence, throttle opening parameter sequence, and engine load parameter sequence.

[0058] Extrema of segments of the main variable vehicle condition parameter sequence The parameter value that deviates most from the travel reference value within the vehicle condition change segment of the main variable vehicle condition parameter sequence is 2190 r / min in this embodiment. Travel reference value of the main variable vehicle condition parameter sequence. The median engine speed parameters corresponding to stable driving conditions within the first 30 seconds of the vehicle condition change segment were taken. From 08:14:52 to 08:15:21, the median engine speed parameters corresponding to stable driving conditions were 1500 r / min. The standard scale of change for the engine speed parameter sequence was then determined. Take 300 r / min. The deviation from the extreme value is... .

[0059] The parameter values ​​of the main variable vehicle condition parameter sequence at the beginning of the segment The value of the main variable vehicle condition parameter sequence at the peak of the segment is 1710 r / min. The rate is 1980 r / min. The trend establishment rate is... .

[0060] The driving operation parameter sequence is represented by the throttle opening parameter sequence. The vehicle response parameter sequence is represented by the engine speed parameter sequence. The throttle opening parameter sequence is... to The percentage of changes in driving operation increased from 23% to 32%. The engine speed parameter sequence is in to The vehicle response rate changes as the speed increases from 1710 r / min to 1980 r / min. The operating response coefficient corresponding to the acceleration condition. The value of 28 r / min / % is determined by the average slope of the linear regression of the rate of change of throttle opening and the rate of change of engine speed in 20 normal acceleration samples of the example vehicle. The expected rate of change of the vehicle response parameter sequence relative to the driving operation parameter sequence is... Unexpected change value The extreme value deviation is weighted at 0.35, the trend establishment rate at 0.30, and the unexpected change value at 0.35. The trend establishment rate is converted to a dimensionless quantity at 300 r / min / s, and the unexpected change value is converted to a dimensionless quantity at 150 r / min / s. Interactive response value. .

[0061] A single driving trip is determined by the trip's time, location, and vehicle operating status. The first 30 seconds after the ignition switches from off to on is the starting phase; the continuous period when the vehicle speed is less than 3 km / h and the engine speed is between 600 rpm and 1000 rpm is the idling phase; the continuous period outside the starting and idling phases, with a vehicle speed not less than 3 km / h, is the driving phase; the last 30 seconds before the ignition switches from on to off is the ending phase. When two driving phases overlap, one driving phase is retained in the order of starting phase, ending phase, idling phase, and driving phase.

[0062] If the fault code P0300 for a random fire fault persists from 08:15:25 to 08:15:31, then the time when the fault code appears... The fault code duration is 08:15:25. The duration is 6 seconds. When the fault code appeared. If the timeframe falls between 08:15:22 and 08:15:26, the fault code will be marked in the same segment. Take 1, overlap duration The time is 1 second. Preset fault code association weight. , , We take values ​​of 0.50, 0.30, and 0.20 respectively and substitute them into the calculation of fault code association parameters. In this embodiment, the preset thresholds, weighting coefficients, and scale parameters are determined based on 20 historical trip data segments of the same model of the example vehicle; the weighting coefficients are jointly determined by expert scores and principal component contribution rates, the thresholds are determined by the statistical distribution quantiles of the normal operating condition data, and the scale parameters are determined by the normal operating range of the parameters or the historical fluctuation standard deviation.

[0063] The vehicle condition change segment belongs to the driving stage within a single driving trip. The trip stage weight table records the starting stage weight as 0.85, the idling stage weight as 0.70, the driving stage weight as 0.60, and the ending stage weight as 0.75. Therefore, the corresponding trip stage weight for the vehicle condition change segment is... It is 0.60.

[0064] The preset steady-state threshold is set to 0.25. Before 08:15:22, the continuous period from 08:15:12 to 08:15:19 during which the intensity of multidimensional changes is lower than the preset steady-state threshold is defined as the preceding steady-state duration. The duration is 7 seconds. After 08:15:26, the continuous period from 08:15:29 to 08:15:34 during which the intensity of multidimensional changes is lower than the preset steady-state threshold, then the subsequent steady-state duration is... The baseline reference duration is 5 seconds. Taking 20 seconds, we substitute it into the calculation of the baseline demand. The value is 0 if the preceding steady-state duration or the following steady-state duration does not exist.

[0065] The duration weight is set to 0.25, the fault code association weight to 0.35, the trip phase weight coefficient to 0.15, and the baseline requirement weight to 0.25. Duration is converted to a dimensionless quantity based on the baseline reference duration. These values ​​are then used to calculate the diagnostic retained value. .

[0066] The interaction level is determined based on the interaction response value, and the pre-capture duration and post-capture duration are determined based on the interaction level; the peak time of the segment is used as the criterion. Centered on the window, based on the preceding capture duration and the duration of the post-capture segment Determine the interactive response window .

[0067] From the multiple vehicle condition parameter sequences falling into the interactive response window, the main variable vehicle condition parameter sequence, driving operation parameter sequence, and vehicle response parameter sequence are selected to obtain the first parameter subset.

[0068] Based on the first parameter subset, generate the main change parameter identifier, change direction marker, change level, unexpected change level, and relative time offset; arrange the main change parameter identifier, change direction marker, change level, unexpected change level, and relative time offset in a preset field order to obtain lightweight interactive fields.

[0069] The pre-action duration and post-action duration are determined based on the diagnostic retention values, using the start time of the vehicle condition change segment. and the end of the segment Define the behavior event window as the boundary. ,in, Pre-action duration, The duration after the action.

[0070] The trend lead-out and trend follow-out durations are determined based on diagnostic retention values ​​and baseline demand, using the segment start time of the vehicle condition change segment. and the end of the segment Use boundaries to define the sub-health trend window ,in, For trend lead time, The duration of the trend is set later.

[0071] The behavior event window and the sub-health trend window are merged into a diagnostic traceability window; from the multiple vehicle condition parameter sequences falling into the behavior event window, the driving operation parameter sequence, the main change vehicle condition parameter sequence, and the vehicle dynamic response parameter sequence are extracted to generate a behavior diagnosis field.

[0072] From multiple vehicle condition parameter sequences falling into the sub-health trend window, extract the main changing vehicle condition parameter sequence, fault code status sequence, associated vehicle condition parameter sequence, and steady-state baseline parameter sequence to generate a sub-health diagnosis field; combine the behavior diagnosis field, sub-health diagnosis field, trip stage marker, and segment identifier into a diagnosis context field.

[0073] The interaction level table records that an interaction response value less than 0.40 is classified as low, an interaction response value between 0.40 and 0.80 is classified as medium, and an interaction response value greater than 0.80 is classified as high. The interception duration table records the preceding interception duration corresponding to the high level. 1 second, post-capture duration The pre-capture duration is 1 second, corresponding to the medium level. 2s, post-capture duration The duration is 1 second, which corresponds to the pre-capture time for lower levels. 1 second, post-capture duration The value is 0s. In this embodiment, the interaction level of the vehicle condition change segment is set to the high level, and the peak time of the segment is 0s. If the time is 08:15:24, then the interactive response window will be [08:15:23, 08:15:25].

[0074] Within the interactive response window, the main changing vehicle condition parameter sequence is the engine speed parameter sequence, the driving operation parameter sequence is the throttle opening parameter sequence, and the vehicle response parameter sequence is the engine speed parameter sequence. The main changing vehicle condition parameter sequence is the same as the vehicle response parameter sequence, and the first parameter subset retains one engine speed parameter sequence. Therefore, the first parameter subset includes the engine speed parameter values ​​of 1840 r / min, 1980 r / min, and 2110 r / min corresponding to the three sampling times of 08:15:23, 08:15:24, and 08:15:25, and the throttle opening parameter values ​​of 27%, 32%, and 35%.

[0075] The preset parameter coding table records the engine speed parameter sequence as code 0x02, the vehicle speed parameter sequence as code 0x01, the throttle opening parameter sequence as code 0x03, and the engine load parameter sequence as code 0x04. The main changing vehicle condition parameter sequence is the engine speed parameter sequence, and the main changing parameter identifier is 0x02. The change direction marker is determined based on the parameter difference between the main changing vehicle condition parameter sequence at the beginning of the segment and the peak time of the segment. A parameter difference greater than 0 is assigned 0x01, a parameter difference less than 0 is assigned 0x02, and a parameter difference equal to 0 is assigned 0x00. The engine speed parameter sequence increases from 1710 r / min to 1980 r / min, and the change direction marker is 0x01.

[0076] The variation level table records that when the extreme value deviation is less than 0.80, it is level 1; when the extreme value deviation is not less than 0.80 and less than 1.60, it is level 2; when the extreme value deviation is not less than 1.60 and less than 2.40, it is level 3; and when the extreme value deviation is not less than 2.40, it is level 4. In this embodiment, the extreme value deviation D is 2.30, and the variation level is level 3. The unexpected variation level table records that when the ratio of the unexpected variation value to 150 r / min / s is less than 0.20, it is level 1; when it is not less than 0.20 and less than 0.60, it is level 2; and when it is not less than 0.60, it is level 3. In this embodiment, the unexpected variation value U is 9 r / min / s, 9 / 150 = 0.06, and the unexpected variation level is level 1. Both the variation level and the unexpected variation level are encoded using a 1-byte unsigned integer.

[0077] Relative time offset based on the peak time of the segment The relative time offsets for 08:15:23, 08:15:24, and 08:15:25 are -1s, 0s, and 1s, respectively, with zero point. The relative time offset is represented by a signed 1-byte integer, with a unit of 1s. The preset field order is: segment identifier, main change parameter identifier, change direction marker, change level, unexpected change level, and relative time offset. The segment identifier for the vehicle condition change segment is TRIP20240518-001-0003, and the lightweight interaction field is {TRIP20240518-001-0003, 0x02, 0x01, 3, 1, [-1, 0, 1]}. The lightweight interaction field does not include the raw PID header, raw checksum field, and diagnostic context field returned by the vehicle OBD interface.

[0078] When the behavior window duration table records a diagnostic retention value less than 0.30, the behavior pre-event duration is... 1 second, duration of post-action The duration is 1 second; when the diagnostic retention value is not less than 0.30 and less than 0.60, the behavior pre-emptive time is... 2s, duration of action follow-up The duration is 2 seconds; when the diagnostic retention value is not less than 0.60, the behavioral pre-emptive time is... 3s, duration of action follow-up The duration is 3 seconds. This is the lead-up time for the action corresponding to the vehicle condition change segment. Take 2 seconds, the duration of the behavior after execution. Take 2 seconds. Segment start time. The segment ends at 08:15:22. The time is 08:15:26, and the behavior event window is [08:15:20, 08:15:28].

[0079] When the diagnostic retention value recorded in the trend window duration table is not less than 0.30 and less than 0.60, the baseline trend lead time is 6 seconds and the baseline trend follow time is 6 seconds. For every 0.10 increase in baseline demand B, the trend lead time and trend follow time each increase by 1 second. If the diagnostic retention value G is 0.511 and the baseline demand B is 0.40, then the trend lead time... The duration is 10 seconds, which is the post-trend duration. The timeframe is 10 seconds, and the sub-health trend window is [08:15:12, 08:15:36]. The diagnostic retrospective window after merging the behavioral event window and the sub-health trend window is [08:15:12, 08:15:36].

[0080] Within the behavior event window [08:15:20, 08:15:28], the driving operation parameter sequence uses the throttle opening parameter sequence, the main vehicle condition parameter sequence uses the engine speed parameter sequence, and the vehicle dynamic response parameter sequence uses the vehicle speed parameter sequence and the engine load parameter sequence. The behavior diagnosis field records the throttle opening parameter sequence, engine speed parameter sequence, vehicle speed parameter sequence, engine load parameter sequence, and segment start time within the behavior event window. End of segment Peak moments of segments The interaction response value E and interaction level are also considered. Specifically, the throttle opening parameter sequence changes from 18% to 41%, the engine speed parameter sequence changes from 1550 r / min to 2360 r / min, the vehicle speed parameter sequence changes from 42 km / h to 58 km / h, and the engine load parameter sequence changes from 31% to 62%.

[0081] Within the sub-health trend window [08:15:12, 08:15:36], the window correlation coefficient is the absolute value of the Pearson correlation coefficient between the main change vehicle condition parameter sequence and other vehicle condition parameter sequences. Vehicle condition parameter sequences with a window correlation coefficient of not less than 0.50 are included in the associated vehicle condition parameter sequences. The window correlation coefficients between the engine speed parameter sequence and the engine load parameter sequence, battery voltage parameter sequence, and vehicle speed parameter sequence are 0.78, 0.52, and 0.81, respectively. The associated vehicle condition parameter sequences include the engine load parameter sequence, battery voltage parameter sequence, and vehicle speed parameter sequence. The fault code status sequence records that fault code P0300 appeared at 08:15:25 and disappeared at 08:15:31. The steady-state baseline parameter sequence takes the previous steady-state duration. and post-steady-state duration The corresponding vehicle condition parameter sequence segment, the duration of the initial steady state. Corresponding to 08:15:12 to 08:15:19, the duration of the subsequent steady state. This corresponds to 08:15:29 to 08:15:34. The sub-health diagnosis field records the engine speed parameter sequence, fault code status sequence, engine load parameter sequence, battery voltage parameter sequence, vehicle speed parameter sequence, pre-steady-state duration, post-steady-state duration, baseline demand, and fault code association parameters within the sub-health trend window.

[0082] The trip phase marker represents the driving phase. The diagnostic context field is composed of the behavioral diagnostic field, the sub-health diagnostic field, the trip phase marker, and the segment identifier. The segment identifier in the diagnostic context field and the segment identifier in the lightweight interaction field are both TRIP20240518-001-0003. The behavioral diagnostic field retains the original sampled values ​​within the behavioral event window; the sub-health diagnostic field retains the sampled values ​​within the sub-health trend window after resampling for 2 seconds. Resampling uses the arithmetic mean of two adjacent original sampled values, and the fault code status sequence is Boolean-merged based on whether a fault code occurs within a 2-second interval.

[0083] Generate a real-time interactive data packet containing lightweight interactive fields and carrying a fragment identifier, and a trip diagnostic fragment containing a diagnostic context field and carrying the same fragment identifier; send the real-time interactive data packet to the in-vehicle emotion interaction terminal via a low-latency wireless link; after the driving trip ends, encapsulate the trip diagnostic fragment and the basic trip data of a single driving trip into a complete trip data packet and upload it to the cloud server.

[0084] The real-time interactive data packet consists of a fixed header field, a lightweight interactive field, and an instant check field. The fixed header field includes the packet type identifier 0xA1, the field version number 0x01, and the sequence number 36. The lightweight interactive field is {TRIP20240518-001-0003, 0x02, 0x01, 3, 1, [-1, 0, 1]}. The instant check field uses a byte-by-byte XOR checksum of the fixed header field and the lightweight interactive field. The real-time interactive data packet is written to the send buffer at 08:15:24.120.

[0085] The low-latency wireless link uses a BLE connection link with a connection interval of 15ms and a maximum transmission unit length of 185 bytes. The byte length of the real-time interactive data packet is less than the maximum transmission unit length, and the real-time interactive data packet is not fragmented. When both the real-time interactive data packet and the trip diagnostic segment cache copy exist in the transmission buffer, the transmission priority of the real-time interactive data packet is higher than that of the trip diagnostic segment cache copy. The in-vehicle emotion interaction terminal receives the real-time interactive data packet at 08:15:24.150 and completes the parsing of the real-time interactive data packet at 08:15:24.158. The time taken from writing the real-time interactive data packet to the completion of parsing by the in-vehicle emotion interaction terminal is 38ms. Based on the main change parameter identifier 0x02, the change direction marker 0x01, the change level 3, and the unexpected change level 1, the in-vehicle emotion interaction terminal reads the smooth acceleration feedback expression number EXP-ACC-03 from the local expression index table.

[0086] The trip diagnostic segment consists of a segment identifier, a segment time field, a diagnostic context field, and a segment summary field. The segment time field includes the segment start time (08:15:22), segment peak time (08:15:24), and segment end time (08:15:26). The segment summary field includes the multidimensional change intensity peak value (0.514), interaction response value (0.961), diagnostic retention value (0.511), fault code association parameter (0.775), and baseline demand (0.40). The trip diagnostic segment is written to the local trip buffer at 08:15:36.200. The segment identifier in the trip diagnostic segment and the segment identifier in the real-time interaction data packet are both TRIP20240518-001-0003.

[0087] The single driving trip ended at 08:32:40. Basic trip data includes trip identifier TRIP20240518-001, trip start time 08:10:00, trip end time 08:32:40, trip duration 1360s, total trip distance 13.8km, average vehicle speed 36.5km / h, maximum vehicle speed 72km / h, average engine speed 1680r / min, maximum engine speed 3100r / min, a fault code summary field, and a segment index field. The fault code summary field records that fault code P0300 appeared once, with the first appearance time at 08:15:25 and the last disappearance time at 08:15:31. The segment index field records the segment identifier TRIP20240518-001-0003, segment number 3, segment start time 08:15:22, segment peak time 08:15:24, segment end time 08:15:26, real-time interactive data packet sending time 08:15:24.120, and in-vehicle emotional interaction terminal receiving time 08:15:24.150.

[0088] A complete trip data packet consists of a trip header, basic trip data, a set of trip diagnostic segments, a segment index field, and a trip verification field. The trip header includes a packet type identifier (0xB1), a trip identifier (TRIP20240518-001), a vehicle anonymity identifier (VH-27A9), a data format version number (0x01), and a trip diagnostic segment count of 1. The trip diagnostic segment set includes the trip diagnostic segments corresponding to segment identifier TRIP20240518-001-0003. The trip verification field uses a CRC16 checksum.

[0089] The aftermarket OBD data acquisition device detected an available cellular network connection at 08:32:43 and wrote the complete trip data packet to the upload queue at 08:32:43:500. After the complete trip data packet was uploaded to the cloud server, the cloud server returned an acknowledgment message ACK-TRIP20240518-001. Upon receiving the acknowledgment message ACK-TRIP20240518-001, the aftermarket OBD data acquisition device moved the complete trip data packet from the upload queue to the acknowledged queue. When the cellular network connection is unavailable, the complete trip data packet remains in the upload queue for 7 days.

[0090] Example 2: Based on the same inventive concept, such as Figure 2 As shown, this embodiment also provides an in-vehicle data acquisition and transmission system, the system comprising: The change perception module is used to collect multiple vehicle condition parameter sequences within a single driving trip through an aftermarket OBD acquisition device; calculate the multidimensional change intensity based on the change rate of at least two vehicle condition parameter sequences; and extract continuous time periods where the multidimensional change intensity exceeds a preset change threshold as vehicle condition change segments.

[0091] The demand assessment module is used to calculate the interaction response value based on the extreme deviation of vehicle condition parameters, trend establishment rate, and driver operation-related parameters within the vehicle condition change segment; and to calculate the diagnostic retention value based on the duration of the vehicle condition change segment, fault code-related parameters, and trip stage weights.

[0092] The window capture module is used to determine the interaction response window based on the interaction response value, and to capture a first subset of parameters falling into the interaction response window from the multiple vehicle condition parameter sequences as a lightweight interaction field; and to determine the diagnostic traceability window based on the diagnostic retention value, and to capture a second subset of parameters falling into the diagnostic traceability window from the multiple vehicle condition parameter sequences as a diagnostic context field.

[0093] The data encapsulation module is used to generate real-time interactive data packets containing lightweight interactive fields and carrying fragment identifiers, as well as trip diagnostic fragments containing diagnostic context fields and carrying the same fragment identifiers; it sends the real-time interactive data packets to the in-vehicle emotion interaction terminal via a low-latency wireless link; after the driving trip ends, it encapsulates the trip diagnostic fragments and the basic trip data of a single driving trip into a complete trip data packet and uploads it to the cloud server.

[0094] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0095] Finally, it should be noted that although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for vehicle-mounted data acquisition and transmission, characterized in that, The method includes: Multiple vehicle condition parameter sequences are collected during a single driving trip using an aftermarket OBD acquisition device; the multidimensional change intensity is calculated based on the change rate of at least two vehicle condition parameter sequences; and continuous time periods where the multidimensional change intensity exceeds a preset change threshold are extracted as vehicle condition change segments. The interactive response value is calculated based on the extreme deviation of vehicle condition parameters, trend establishment rate, and driver operation-related parameters within the vehicle condition change segment; the diagnostic retention value is calculated based on the duration of the vehicle condition change segment, fault code-related parameters, and trip stage weight. An interaction response window is determined based on the interaction response value, and a first subset of parameters falling into the interaction response window is extracted from the multiple vehicle condition parameter sequences as a lightweight interaction field; a diagnostic traceability window is determined based on the diagnostic retention value, and a second subset of parameters falling into the diagnostic traceability window is extracted from the multiple vehicle condition parameter sequences as a diagnostic context field. Generate a real-time interactive data packet containing lightweight interactive fields and carrying a fragment identifier, and a trip diagnostic fragment containing a diagnostic context field and carrying the same fragment identifier; send the real-time interactive data packet to the in-vehicle emotion interaction terminal via a low-latency wireless link; after the driving trip ends, encapsulate the trip diagnostic fragment and the basic trip data of a single driving trip into a complete trip data packet and upload it to the cloud server.

2. The vehicle-mounted data acquisition and transmission method according to claim 1, characterized in that, The method for calculating the multidimensional change intensity based on the rate of change of vehicle condition parameters from at least two vehicle condition parameter sequences includes: Based on the vehicle speed parameter sequence, engine speed parameter sequence, and driving operation parameter sequence within a single driving trip, the vehicle operating condition type corresponding to each sampling time is determined. The vehicle operating condition type includes starting condition, idling condition, acceleration condition, deceleration condition, and stable driving condition. Based on the vehicle condition parameter identifiers read by the aftermarket OBD acquisition device at the vehicle's OBD interface, a set of available parameters is generated; a set of condition-sensitive parameters corresponding to the vehicle's operating condition type is selected from the set of available parameters; and the time-series change rate of each vehicle condition parameter sequence in the set of condition-sensitive parameters is calculated. Based on the vehicle operating condition type, determine the operating condition weight corresponding to each vehicle condition parameter sequence in the set of operating condition sensitive parameters; calculate the multidimensional change intensity based on the time-series change rate of each vehicle condition parameter sequence in the set of operating condition sensitive parameters and the operating condition weight.

3. The vehicle-mounted data acquisition and transmission method according to claim 2, characterized in that, The method for determining the operating condition weights corresponding to each vehicle condition parameter sequence in the set of operating condition sensitive parameters based on the vehicle operating condition type includes: Read the basic weight set corresponding to the vehicle operating condition type from the preset operating condition weight table. The basic weight set includes the basic weights corresponding to each candidate vehicle condition parameter. Candidate vehicle condition parameters that belong to the set of working condition sensitive parameters in the basic weight set are taken as effective weight parameters, and candidate vehicle condition parameters that do not belong to the set of working condition sensitive parameters in the basic weight set are taken as missing weight parameters. Based on the parameter substitution relationship between missing weight parameters and valid weight parameters, the basic weights corresponding to the missing weight parameters are allocated to the valid weight parameters according to a preset allocation ratio, thereby obtaining the operating condition weights corresponding to each vehicle condition parameter sequence in the set of operating condition sensitive parameters, wherein the sum of the preset allocation ratios corresponding to the same missing weight parameter is one.

4. The vehicle-mounted data acquisition and transmission method according to claim 3, characterized in that, The method for calculating the interaction response value based on the extreme deviation of vehicle condition parameters, trend establishment rate, and driver operation correlation parameters within the vehicle condition change segment includes: Identify the vehicle condition parameter sequence with the largest rate of change of dimensionless parameters within the vehicle condition change segment as the primary changing vehicle condition parameter sequence; then, based on the segment extreme values ​​of the primary changing vehicle condition parameter sequence... Travel reference value Calculate the deviation of extreme values ;in, The standard scale for parameter variation; Based on the parameter values ​​of the main change vehicle condition parameter sequence at the beginning of the segment Parameter values ​​at the peak of the segment Segment start time and peak moments of segments Calculate the rate of trend establishment ; Calculate the unexpected change value based on the driving operation parameter sequence and vehicle response parameter sequence from multiple vehicle condition parameter sequences. ,in, This represents the rate of change of the vehicle response parameter sequence from the start time of the segment to the peak time of the segment. The rate of change of driving operation parameters in the sequence of driving operation parameters from the start time of the segment to the peak time of the segment. The operating response coefficient is the one corresponding to the vehicle operating condition type. The interactive response value is obtained by weighted summing of the dimensionless values ​​of extreme deviation, trend establishment rate and unexpected change value.

5. The vehicle-mounted data acquisition and transmission method according to claim 4, characterized in that, The method for calculating diagnostic retention values ​​based on the duration of vehicle condition change segments, fault code association parameters, and trip stage weights includes: Based on the start time of the segment showing changes in vehicle condition and the end of the segment The duration was calculated. Based on the time of occurrence of the fault code in the fault code status sequence Fault code duration Duration of vehicle condition change segments Calculate the associated parameters of the fault codes ,in, The time when the fault code appeared Fault codes that are assigned a value of 1 when falling within a segment of vehicle condition change and a value of 0 when not falling within a segment of vehicle condition change are marked in the same segment. The overlap duration between the fault code state sequence and the vehicle condition change segment. , and Preset fault code association weights; Determine the weight of each stage of the journey based on the stage to which the vehicle condition change segment belongs in a single driving trip. The aforementioned travel phases include the start-up phase, idling phase, driving phase, and end phase; the continuous duration during which the intensity of multidimensional changes in vehicle condition before a segment is lower than a preset steady-state threshold is defined as the preceding steady-state duration. The duration during which the intensity of multidimensional changes following a vehicle condition change segment remains below a preset steady-state threshold is taken as the subsequent steady-state duration. Based on the preceding steady-state duration Post-steady-state duration and baseline reference duration Calculate baseline demand The diagnostic retention value is obtained by weighting and summing the duration, fault code associated parameters, trip stage weights, and baseline demand.

6. The vehicle-mounted data acquisition and transmission method according to claim 5, characterized in that, The method of determining the interaction response window based on the interaction response value and extracting a first subset of parameters falling into the interaction response window from the plurality of vehicle condition parameter sequences as a lightweight interaction field includes: The interaction level is determined based on the interaction response value, and the pre-capture duration and post-capture duration are determined based on the interaction level; the peak time of the segment is used as the criterion. Centered on the window, based on the preceding capture duration and the duration of the post-capture segment Determine the interactive response window ; From the multiple vehicle condition parameter sequences falling into the interactive response window, the main changing vehicle condition parameter sequence, the driving operation parameter sequence, and the vehicle response parameter sequence are selected to obtain the first parameter subset; Based on the first parameter subset, generate the main change parameter identifier, change direction marker, change level, unexpected change level, and relative time offset; arrange the main change parameter identifier, change direction marker, change level, unexpected change level, and relative time offset in a preset field order to obtain lightweight interactive fields.

7. The vehicle-mounted data acquisition and transmission method according to claim 6, characterized in that, The method of determining the diagnostic traceability window based on the diagnostic retention value and extracting a second subset of parameters falling into the diagnostic traceability window from the plurality of vehicle condition parameter sequences as a diagnostic context field includes: The pre-action duration and post-action duration are determined based on the diagnostic retention values, using the start time of the vehicle condition change segment. and the end of the segment Define the behavior event window as the boundary. ,in, Pre-action duration, The duration after the action; The trend lead-out and trend follow-out durations are determined based on diagnostic retention values ​​and baseline demand, using the segment start time of the vehicle condition change segment. and the end of the segment Use boundaries to define the sub-health trend window ,in, For trend lead time, The duration of the trend; The behavior event window and the sub-health trend window are merged into a diagnostic traceability window; from the multiple vehicle condition parameter sequences falling into the behavior event window, the driving operation parameter sequence, the main change vehicle condition parameter sequence, and the vehicle dynamic response parameter sequence are extracted to generate behavior diagnostic fields; From multiple vehicle condition parameter sequences falling into the sub-health trend window, extract the main changing vehicle condition parameter sequence, fault code status sequence, associated vehicle condition parameter sequence, and steady-state baseline parameter sequence to generate a sub-health diagnosis field; combine the behavior diagnosis field, sub-health diagnosis field, trip stage marker, and segment identifier into a diagnosis context field.

8. A vehicle-mounted data acquisition and transmission system, used to perform the method according to any one of claims 1 to 7, characterized in that, The system includes: The change perception module is used to collect multiple vehicle condition parameter sequences within a single driving trip through an aftermarket OBD acquisition device; calculate the multidimensional change intensity based on the change rate of vehicle condition parameters in at least two vehicle condition parameter sequences; and extract continuous time periods where the multidimensional change intensity exceeds a preset change threshold as vehicle condition change segments. The demand assessment module is used to calculate the interaction response value based on the extreme deviation of vehicle condition parameters, trend establishment rate, and driver operation-related parameters within the vehicle condition change segment; and to calculate the diagnostic retention value based on the duration of the vehicle condition change segment, fault code-related parameters, and trip stage weight. The window capture module is used to determine the interaction response window based on the interaction response value, and to capture a first subset of parameters falling into the interaction response window from the multiple vehicle condition parameter sequences as a lightweight interaction field; and to determine the diagnostic traceability window based on the diagnostic retention value, and to capture a second subset of parameters falling into the diagnostic traceability window from the multiple vehicle condition parameter sequences as a diagnostic context field. The data encapsulation module is used to generate real-time interactive data packets containing lightweight interactive fields and carrying fragment identifiers, as well as trip diagnostic fragments containing diagnostic context fields and carrying the same fragment identifiers; it sends the real-time interactive data packets to the in-vehicle emotion interaction terminal via a low-latency wireless link; after the driving trip ends, it encapsulates the trip diagnostic fragments and the basic trip data of a single driving trip into a complete trip data packet and uploads it to the cloud server.

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