Fault diagnosis determination method and device, storage medium and electronic equipment
By using an automated fault detection method and a fault exposure and user experience evaluation table, the problem of inconsistent human judgment standards is solved, the efficiency and accuracy of fault detection are improved, and the objectivity and reliability of the results are ensured.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- MOMENTA (SUZHOU) TECHNOLOGY CO LTD
- Filing Date
- 2024-09-20
- Publication Date
- 2026-07-31
AI Technical Summary
In existing technologies, fault detection relies on human experience, which leads to inconsistent judgment standards, low efficiency, and low accuracy.
By acquiring fault data and user tolerance, and using fault exposure assessment tables and user experience assessment tables, the system automatically determines fault eligibility and achieves standardized judgment by combining normal distribution and multi-dimensional assessment values.
It improves the efficiency and accuracy of fault identification, ensures the objectivity and reliability of results, and reduces manpower consumption.
Smart Images
Figure CN121697660B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of intelligent driving technology, and more specifically, to a method, apparatus, storage medium, and electronic device for determining fault exit. Background Technology
[0002] FRA (Fault Release Approval) refers to the process of assessing the positive and false alarms of a fault on a real vehicle after it has been developed according to design requirements. Only when certain standards are met can the fault be applied to specific functions, thus avoiding direct association with functions, which could lead to a decline in the user experience.
[0003] However, the current method for determining whether a fault can be accurately identified mainly relies on manual judgment based on experience. This is not only time-consuming and labor-intensive, but also suffers from inconsistent judgment standards among different people and varying degrees of accuracy. Summary of the Invention
[0004] This application provides a method, apparatus, storage medium, and electronic device for determining fault eligibility, which can provide an automated judgment scheme for standardized fault eligibility that meets accuracy requirements, thereby not only saving manpower and improving efficiency, but also ensuring accuracy.
[0005] The specific technical solution is as follows:
[0006] In a first aspect, embodiments of this application provide a method for determining fault clearance, the method comprising:
[0007] Obtain fault data information of target faults within a preset statistical period and the average tolerance of users for abnormal downgrade frequency per unit time. The fault data information includes the proportion of faulty vehicles, the total number of vehicles, the total intelligent driving information of vehicles with at least one intelligent driving function and its corresponding vehicle takeover information, and the total number of faults associated with the intelligent driving function. The total number of faults associated with the intelligent driving function includes the total number of faults that will cause the intelligent driving function to be downgraded.
[0008] Find the exposure assessment value corresponding to the proportion of vehicles with the target fault in the fault exposure assessment table, wherein the fault exposure assessment table includes the mapping relationship between the range of the proportion of vehicles with the fault and the exposure assessment value.
[0009] For each intelligent driving function, the fault experience evaluation table template is updated based on the preset statistical duration, the total number of vehicles, the total intelligent driving information of the vehicles, the average tolerance, and the total number of faults associated with the intelligent driving function, to obtain the fault experience evaluation table corresponding to the intelligent driving function. The experience evaluation value corresponding to the vehicle takeover information of the intelligent driving function is then found in the fault experience evaluation table corresponding to the intelligent driving function. The fault experience evaluation table template includes a mapping relationship between different vehicle takeover information range expressions and experience evaluation values. The fault experience evaluation table includes a mapping relationship between different vehicle takeover information ranges and experience evaluation values.
[0010] Based on the exposure assessment value of the target fault and the experience assessment value of the target fault in various intelligent driving functions, it is determined whether the target fault is allowed to exit.
[0011] As can be seen from the above scheme, the embodiments of this application can first determine the exposure assessment value corresponding to the target fault based on the proportion of faulty vehicles, and determine the experience assessment value corresponding to each intelligent driving function based on the vehicle takeover information. Then, based on the exposure assessment value and the experience assessment value corresponding to each intelligent driving function, it is determined whether the target fault can be identified, thereby realizing the standardization of automated fault identification, saving manpower, and improving the efficiency of fault identification. Furthermore, the embodiments of this application update the pre-set fault experience assessment table template in real time by statistical fault data information and the average tolerance of users for abnormal degradation frequency per unit time, obtaining a fault experience assessment table corresponding to each intelligent driving function, thereby improving the realism of the experience of the target fault affecting each intelligent driving function, and thus further improving the accuracy and reliability of fault identification.
[0012] In one possible implementation, each of the vehicle takeover information range expressions is generated based on the average tolerance standard parameter and the corresponding average tolerance ratio coefficient;
[0013] For each intelligent driving function, based on the preset statistical duration, the total number of vehicles, the total intelligent driving information of the vehicles, the average tolerance, and the total number of faults associated with the intelligent driving function, the fault experience evaluation form template is updated to obtain the fault experience evaluation form corresponding to the intelligent driving function, including:
[0014] For each of the intelligent driving functions, the average tolerance standard parameter value corresponding to the intelligent driving function is determined based on the preset statistical duration, the total number of vehicles, the total intelligent driving information of the vehicles, the average tolerance, and the total number of faults associated with the intelligent driving function.
[0015] Substitute the average tolerance standard parameter value into each vehicle takeover information range expression in the fault experience evaluation form template to obtain the value of each vehicle takeover information range expression as the vehicle takeover information range.
[0016] A table that includes the mapping relationship between the vehicle takeover information range and the experience evaluation value will be used as the fault experience evaluation table corresponding to the intelligent driving function.
[0017] In one possible implementation, for each of the intelligent driving functions, a standard parameter value for the average tolerance of the intelligent driving function is determined based on the preset statistical duration, the total number of vehicles, the total intelligent driving information of the vehicles, the average tolerance, and the total number of faults associated with the intelligent driving function, including:
[0018] For each of the intelligent driving functions, the average intelligent driving information of a single vehicle within the unit time period is determined based on the preset statistical duration, the total number of vehicles, and the total intelligent driving information of the vehicles.
[0019] The average tolerance standard parameter value corresponding to the intelligent driving function is determined based on the average intelligent driving information of a single vehicle within a unit of time, the average tolerance, and the total number of faults associated with the intelligent driving function.
[0020] In one possible implementation, the average tolerance standard parameter value corresponding to the intelligent driving function is determined based on the average intelligent driving information of a single vehicle within a unit of time, the average tolerance, and the total number of faults associated with the intelligent driving function, including:
[0021] The product of the average intelligent driving information of a single vehicle within a unit of time, the average tolerance, and the total number of faults associated with the intelligent driving function is used as the standard parameter value of the average tolerance corresponding to the intelligent driving function.
[0022] In one possible implementation, after determining the average tolerance standard parameter value corresponding to the intelligent driving function, the method further includes:
[0023] When the total intelligent driving information of the vehicle under the intelligent driving function is less than the average tolerance standard parameter value of the preset multiple, the value of the preset statistical duration is increased, and the fault data information of the target fault within the increased preset statistical duration is re-acquired until the total intelligent driving information of the vehicle under the intelligent driving function is greater than or equal to the average tolerance standard parameter value of the preset multiple. Then, the target fault is judged based on the fault data information acquired last time.
[0024] As can be seen from the above solution, this embodiment of the application can pause the current fault identification task when the total intelligent driving information of the vehicle is less than a preset multiple of the average tolerance standard parameter value. This is to avoid the fault identification result being subjective and inaccurate due to insufficient fault data. In this case, this embodiment of the application can increase the value of the preset statistical duration to reacquire fault data information until the reacquired total intelligent driving information of the vehicle is greater than or equal to the preset multiple of the average tolerance standard parameter value, and then resume the current fault identification task. This ensures the objectivity and accuracy of the fault identification result.
[0025] In one possible implementation, the fault exposure assessment table is obtained by dividing the proportion of faulty vehicles according to different exposure assessment values based on the 3σ principle of normal distribution.
[0026] As can be seen from the above scheme, the embodiments of this application divide the proportion of faulty vehicles according to different exposure assessment values by using the 3σ principle of normal distribution, which can improve the objectivity and accuracy of the fault exposure assessment table.
[0027] In one possible implementation, determining whether to allow the target fault to exit is based on the exposure assessment value of the target fault and the experience assessment value of the target fault in various intelligent driving functions, including:
[0028] For each intelligent driving function to be evaluated, the fault clearance level corresponding to the intelligent driving function to be evaluated is determined based on the exposure evaluation value of the target fault and the experience evaluation value of the target fault in the intelligent driving function to be evaluated.
[0029] The lowest fault clearance level among all fault clearance levels corresponding to intelligent driving functions is taken as the fault clearance level of the target fault, wherein the fault clearance level is positively correlated with the probability of allowing the fault to be cleared.
[0030] Based on the fault clearance level of the target fault, determine whether the target fault is allowed to be cleared.
[0031] As can be seen from the above scheme, the embodiments of this application first determine the fault clearance level corresponding to each intelligent driving function by comprehensively evaluating the values from multiple dimensions, and then determine the fault clearance level of the target fault by taking the lowest level down, which can further improve the reliability of fault clearance judgment.
[0032] Secondly, embodiments of this application provide a fault clearance determination device, the device comprising:
[0033] The acquisition unit is used to acquire fault data information of target faults within a preset statistical time period and the average tolerance of users for abnormal downgrade frequency within a unit time period. The fault data information includes the proportion of faulty vehicles, the total number of vehicles, the total intelligent driving information of vehicles with at least one intelligent driving function and its corresponding vehicle takeover information, and the total number of faults associated with the intelligent driving function. The total number of faults associated with the intelligent driving function includes the total number of faults that will cause the intelligent driving function to be downgraded.
[0034] The first lookup unit is used to look up the exposure assessment value corresponding to the proportion of faulty vehicles of the target fault in the fault exposure assessment table, wherein the fault exposure assessment table includes a mapping relationship between the range of faulty vehicle proportions and the exposure assessment value.
[0035] The update unit is used to update the fault experience evaluation table template for each intelligent driving function based on the preset statistical duration, the total number of vehicles, the total intelligent driving information of the vehicles, the average tolerance, and the total number of faults associated with the intelligent driving function, to obtain the fault experience evaluation table corresponding to the intelligent driving function. The fault experience evaluation table template includes a mapping relationship between different vehicle takeover information range expressions and experience evaluation values, and the fault experience evaluation table includes a mapping relationship between different vehicle takeover information ranges and experience evaluation values.
[0036] The second search unit is used to search for the experience evaluation value corresponding to the vehicle takeover information of the intelligent driving function in the fault experience evaluation table corresponding to the intelligent driving function.
[0037] The determining unit is used to determine whether to allow the target fault to exit based on the exposure assessment value of the target fault and the experience assessment value of the target fault in each intelligent driving function.
[0038] In one possible implementation, each of the vehicle takeover information range expressions is generated based on the average tolerance standard parameter and the corresponding average tolerance ratio coefficient;
[0039] The update unit includes:
[0040] The first calculation module is used to determine the average tolerance standard parameter value corresponding to each intelligent driving function based on the preset statistical duration, the total number of vehicles, the total intelligent driving information of the vehicles, the average tolerance, and the total number of faults associated with the intelligent driving function.
[0041] The second calculation module is used to substitute the average tolerance standard parameter value into each vehicle takeover information range expression in the fault experience evaluation form template to obtain the value of each vehicle takeover information range expression as the vehicle takeover information range.
[0042] The generation module is used to generate a table that includes the mapping relationship between the vehicle takeover information range and the experience evaluation value, as the fault experience evaluation table corresponding to the intelligent driving function.
[0043] In one possible implementation, the first calculation module is configured to, for each of the intelligent driving functions, determine the average intelligent driving information of a single vehicle within a unit time period based on the preset statistical duration, the total number of vehicles, and the total intelligent driving information of the vehicles; and determine the average tolerance standard parameter value corresponding to the intelligent driving function based on the average intelligent driving information of a single vehicle within a unit time period, the average tolerance, and the total number of faults associated with the intelligent driving function.
[0044] In one possible implementation, the first calculation module is used to take the product of the average intelligent driving information of a single vehicle within a unit time, the average tolerance, and the total number of faults associated with the intelligent driving function as the average tolerance standard parameter value corresponding to the intelligent driving function.
[0045] In one possible implementation, the device further includes:
[0046] The adjustment unit is used to, after determining the average tolerance standard parameter value corresponding to the intelligent driving function, increase the value of the preset statistical duration when the total intelligent driving information of the vehicle in the intelligent driving function is less than the average tolerance standard parameter value of the preset multiple, and re-acquire the fault data information of the target fault within the increased preset statistical duration, until the re-acquired total intelligent driving information of the vehicle in the intelligent driving function is greater than or equal to the average tolerance standard parameter value of the preset multiple, and then make an exit judgment on the target fault based on the fault data information acquired last time.
[0047] In one possible implementation, the fault exposure assessment table is obtained by dividing the proportion of faulty vehicles according to different exposure assessment values based on the 3σ principle of normal distribution.
[0048] In one possible implementation, the determining unit includes:
[0049] The first determining module is used to determine the fault clearance level of each intelligent driving function to be evaluated based on the exposure evaluation value of the target fault and the experience evaluation value of the target fault in the intelligent driving function to be evaluated.
[0050] The second determining module is used to take the lowest fault clearance level among all fault clearance levels corresponding to intelligent driving functions as the fault clearance level of the target fault, wherein the fault clearance level is positively correlated with the probability of allowing the fault to be cleared.
[0051] The third determining module is used to determine whether to allow the target fault to be allowed to exit based on the fault exit level of the target fault.
[0052] As can be seen from the above scheme, the embodiments of this application can first determine the exposure assessment value corresponding to the target fault based on the proportion of faulty vehicles, and determine the experience assessment value corresponding to each intelligent driving function based on the vehicle takeover information. Then, based on the exposure assessment value and the experience assessment value corresponding to each intelligent driving function, it is determined whether the target fault can be identified, thereby realizing the standardization of automated fault identification, saving manpower, and improving the efficiency of fault identification. Furthermore, the embodiments of this application update the pre-set fault experience assessment table template in real time by statistical fault data information and the average tolerance of users for abnormal degradation frequency per unit time, obtaining a fault experience assessment table corresponding to each intelligent driving function, thereby improving the realism of the experience of the target fault affecting each intelligent driving function, and thus further improving the accuracy and reliability of fault identification.
[0053] Thirdly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method as described in any possible implementation of the first aspect.
[0054] Fourthly, embodiments of this application provide an electronic device, which includes:
[0055] One or more processors;
[0056] The processor is coupled to a storage device for storing one or more programs;
[0057] When one or more programs are executed by one or more processors, the electronic device performs the method as described in any possible implementation of the first aspect.
[0058] Fifthly, embodiments of this application provide a computer program product containing instructions that, when executed on a computer or processor, cause the computer or processor to perform the method described in any possible implementation of the first aspect. Attached Figure Description
[0059] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.
[0060] Figure 1A flowchart illustrating a method for determining fault clearance provided in an embodiment of this application;
[0061] Figure 2 An example diagram illustrating the division of exposure assessment values provided in this application embodiment;
[0062] Figure 3 A block diagram of a fault clearance determination device provided in an embodiment of this application;
[0063] Figure 4 This is a schematic diagram of the structure of an electronic device or computer device provided in an embodiment of this application. Detailed Implementation
[0064] The technical solutions of the embodiments of this 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 this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0065] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The terms "comprising" and "having," and any variations thereof, in the embodiments and drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device 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 devices.
[0066] Figure 1 This is a flowchart illustrating a method for determining fault eligibility. This method can be applied to electronic or computer equipment, such as terminals or servers. The method includes:
[0067] S110: Obtain fault data information of target faults within a preset statistical period and the average tolerance of users for abnormal degradation frequency per unit time. The fault data information includes the proportion of faulty vehicles, the total number of vehicles, the total intelligent driving information of vehicles with at least one intelligent driving function and its corresponding vehicle takeover information, and the total number of faults associated with intelligent driving functions.
[0068] In practical applications, the preset statistical duration is usually measured in days, such as 30 days, 60 days, 90 days, etc. The target fault refers to any fault that needs to be determined whether it is allowed to proceed, such as a heartbeat timeout fault.
[0069] The percentage of vehicles with malfunctions refers to the ratio of the total number of malfunctioning vehicles to the total number of vehicles in the statistics. Intelligent driving functions include driving functions and parking functions, specifically including intelligent driving functions such as ACC (Adaptive Cruise Control) and APA (Auto Parking Assist). The total intelligent driving information for a specific driving function includes the total mileage taken over by that function across all vehicles. The total intelligent driving information for a specific parking function includes the total number of turns taken over by that parking function across all vehicles.
[0070] The total intelligent driving information for vehicles with ACC (Adaptive Cruise Control) can include the total mileage driven by all vehicles using ACC. The vehicle disengagement information for ACC includes the total mileage taken over by ACC across all vehicles, which can be referred to as ACC MPD (Mileage Per Disengagement). The total intelligent driving information for vehicles with APA (Automatic Parking Assist) can include the total number of times APA has been used by all vehicles. The corresponding vehicle disengagement information includes the total number of rounds taken over by APA across all vehicles, which can be referred to as APA RPD (Rounds Per Disengagement). The total number of faults associated with intelligent driving functions includes the total number of faults that could lead to a degrade of the intelligent driving function. ACC MPD = Total mileage driven by all vehicles using ACC / Number of times faults overlap with ACC; APA RPD = Total number of times APA has been used by all vehicles / Number of times faults overlap with the app.
[0071] In addition, fault data information may also include fault codes, project names, fault names, etc.
[0072] The average tolerance of users for abnormal downgrade frequency within a unit of time can be obtained by statistically analyzing actual survey data from multiple users. In practical applications, multiple tolerance options can be preset for multiple users to choose from, and then a weighted average can be calculated to obtain the average tolerance of users for abnormal downgrade frequency within a unit of time. For example, if the unit of time is set to "month", the average tolerance is 1.5 months.
[0073] It should be noted that the intelligent driving function involved in the embodiments of this application is a function related to the target fault, while intelligent driving functions unrelated to the target fault do not participate in the fault clearance calculation.
[0074] S120: Find the exposure assessment value corresponding to the percentage of vehicles with the target fault in the fault exposure assessment table.
[0075] The fault exposure assessment table includes the mapping relationship between the percentage of faulty vehicles and the exposure assessment value.
[0076] Fault exposure: This refers to the number of vehicles on which the fault has occurred. If too many vehicles are involved, the probability of a batch of after-sales problems will also increase, and the fault exposure assessment value can be smaller.
[0077] Therefore, exposure corresponds to the likelihood that the fault will become an after-sales issue; the higher the percentage of faulty vehicles, the higher the risk of it becoming an after-sales issue. Figure 2 As shown, the proportion of faulty vehicles can be divided according to different exposure assessment values using the 3σ principle of the normal distribution. In this embodiment, the proportion of faulty vehicles can be divided into any number of intervals, meaning there is no limit to the number of exposure assessment values.
[0078] When the exposure assessment values are set to 1, 2, 3, 4, and 5 respectively, the generated fault exposure assessment table is shown in Table 1:
[0079] Table 1
[0080] Percentage of faulty vehicles (31.8,100％] (18.2,31.8％] (4.6,18.2％] (0.3,4.6％] [0,0.3％]
[0081] Specifically, an exposure assessment value of 1 indicates that there is a very low chance that it will not become an after-sales issue, meaning it is highly likely to become an after-sales issue; an exposure assessment value of 2 indicates that there is a very low chance that it will not become an after-sales issue, meaning it is very likely to become an after-sales issue; an exposure assessment value of 3 indicates that it will not become an after-sales issue; an exposure assessment value of 4 indicates that it will almost never become an after-sales issue; and an exposure assessment value of 5 indicates that it will definitely not become an after-sales issue.
[0082] S130: For each intelligent driving function, update the fault experience evaluation form template based on the preset statistical duration, total number of vehicles, total intelligent driving information of vehicles, average tolerance, and total number of faults associated with the intelligent driving function, obtain the fault experience evaluation form corresponding to the intelligent driving function, and find the experience evaluation value corresponding to the vehicle takeover information of the intelligent driving function in the fault experience evaluation form corresponding to the intelligent driving function.
[0083] The fault experience evaluation form template includes a mapping relationship between different vehicle takeover information range expressions and experience evaluation values. Each vehicle takeover information range expression is generated based on the average tolerance standard parameter and the corresponding average tolerance ratio coefficient.
[0084] The method for updating the fault experience evaluation form template may include steps A1-A3:
[0085] A1. For each intelligent driving function, determine the average tolerance standard parameter value corresponding to the intelligent driving function based on the preset statistical duration, total number of vehicles, total intelligent driving information of vehicles, average tolerance, and total number of faults associated with the intelligent driving function.
[0086] Specifically, for each intelligent driving function, the average intelligent driving information of a single vehicle within a unit of time can be determined first based on the preset statistical duration, the total number of vehicles, and the total intelligent driving information of the vehicles; then, based on the average intelligent driving information of a single vehicle within a unit of time, the average tolerance, and the total number of faults associated with the intelligent driving function, the average tolerance standard parameter value corresponding to the intelligent driving function can be determined.
[0087] Electronic devices can calculate the average intelligent driving information of a single vehicle per unit time according to the first formula, and calculate the average tolerance standard parameter value corresponding to the intelligent driving function according to the second formula.
[0088] When the unit time of the average intelligent driving information and the unit time of the preset statistical duration are taken to different values, the first formula will have differences in time conversion.
[0089] For example, if the unit of time is a month and the preset statistical duration is in days, the first formula includes...
[0090]
[0091] The second formula includes: the average tolerance standard parameter value corresponding to the intelligent driving function = the average intelligent driving information of a single vehicle per unit time * the average tolerance * the total number of faults associated with the intelligent driving function.
[0092] A2. Substitute the average tolerance standard parameter value into the various vehicle takeover information range expressions in the fault experience evaluation form template to obtain the value of each vehicle takeover information range expression as the vehicle takeover information range.
[0093] Each vehicle takeover information range expression is generated based on the average tolerance standard parameter and the corresponding average tolerance ratio coefficient. For example, the left and right boundaries of each vehicle takeover information range expression are both formed by the product of the average tolerance standard parameter and the average tolerance ratio coefficient. Average tolerance ratio coefficient = frequency of abnormal downgrades by the user per unit time / average tolerance of the user for abnormal downgrades per unit time.
[0094] As shown in Table 2, when the average tolerance of users for abnormal downgrade frequency within a unit of time is 1.5 months, and the preset frequency includes once a week, once every half month, once a month, once every two months, and once every three months, the average tolerance ratio coefficients are 0.17, 0.33, 0.67, 1.33, and 2, respectively.
[0095] Table 2
[0096]
[0097] When the user experience evaluation values are set to 1, 2, 3, 4, and 5 respectively, and the average tolerance standard parameter is represented by B, the generated fault experience evaluation table template can be shown in Table 3:
[0098] Table 3
[0099]
[0100] Therefore, the higher the value of the vehicle takeover information, the higher the experience evaluation value.
[0101] A3. Use the table that includes the mapping relationship between the range of vehicle takeover information and the experience evaluation value as the fault experience evaluation table for the intelligent driving function.
[0102] As can be seen from Table 3, once B is calculated, substituting B into Table 3 will yield a fault experience evaluation table with specific values.
[0103] S140: Determine whether to allow the target fault to exit based on the exposure assessment value of the target fault and the experience assessment value of the target fault in various intelligent driving functions.
[0104] After obtaining the exposure assessment value of the target fault and the experience assessment value of the target fault in various intelligent driving functions, it is possible to comprehensively judge whether the target fault should be allowed to exit based on the exposure assessment value of the target fault and the experience assessment value of the target fault in various intelligent driving functions.
[0105] One possible comprehensive judgment method includes:
[0106] For each intelligent driving function to be evaluated, the fault admission level corresponding to the intelligent driving function to be evaluated is determined based on the exposure evaluation value of the target fault and the experience evaluation value of the target fault in relation to the intelligent driving function to be evaluated; the lowest fault admission level among all the fault admission levels corresponding to intelligent driving functions is taken as the fault admission level of the target fault; and the target fault admission level is used to determine whether to allow the target fault to be admitted.
[0107] Among them, the fault clearance level is positively correlated with the probability of allowing fault clearance, and the exposure assessment value and the experience assessment value are positively correlated with the fault clearance level.
[0108] This application embodiment can pre-set a two-dimensional classification table based on two dimensions: exposure and user experience. Then, for each intelligent driving function, the fault clearance level corresponding to the (exposure assessment value, user experience assessment value) data is searched. Finally, the lowest fault clearance level is selected from multiple fault clearance levels as the final fault clearance level for the target fault. Based on the clearance level represented by the fault clearance level, it is determined whether the target fault is allowed to proceed. This classification table can be shown in Table 4:
[0109] Table 4
[0110]
[0111] Among them, the fault clearance levels corresponding to A+, A, B, C, and D decrease sequentially.
[0112] A+ can mean: exit is permitted;
[0113] A can indicate: It is recommended to allow entry, but an evaluation should be conducted based on the specific level of exposure and user experience.
[0114] B can mean: Release is pending, and a detailed analysis of actual vehicle data is required before a decision is made on whether to release the vehicle.
[0115] C / D can mean: rejection of the vehicle, requiring the return of real vehicle data, analysis of the fault, implementation of targeted repair measures, and continuous monitoring.
[0116] It should be added that, in order to further improve the accuracy of fault detection, the embodiments of this application may also add at least one evaluation dimension, such as the severity of the fault, in addition to exposure and user experience.
[0117] Specifically, a mapping table can be pre-set, which includes the mapping relationship between exposure assessment value, user experience assessment value, and severity fault clearance level. After determining the exposure assessment value of the target fault and the user experience assessment value of the target fault in each intelligent driving function, in addition to determining the fault clearance level corresponding to each intelligent driving function to be evaluated based on the exposure assessment value of the target fault and the user experience assessment value of the target fault in the intelligent driving function to be evaluated, the severity fault clearance level corresponding to each intelligent driving function can also be determined by looking up the mapping table. Finally, the lowest fault clearance level among all fault clearance levels can be used as the fault clearance level of the target fault.
[0118] The fault admission determination method provided in this application first determines the exposure assessment value corresponding to the target fault based on the proportion of faulty vehicles, and determines the experience assessment value corresponding to each intelligent driving function based on the vehicle takeover information. Then, it comprehensively determines whether the target fault can be admitted based on the exposure assessment value and the experience assessment value corresponding to each intelligent driving function. This achieves standardized automated fault admission judgment, saves manpower, and improves the efficiency of fault admission judgment. Furthermore, this application embodiment updates the pre-set fault experience assessment table template in real time by statistical fault data information and the average tolerance of users for abnormal degradation frequency per unit time, obtaining a fault experience assessment table corresponding to each intelligent driving function. This improves the realism of the experience of the target fault affecting each intelligent driving function, thereby further improving the accuracy and reliability of fault admission judgment.
[0119] In one possible implementation, to improve the accuracy of fault detection, after determining the average tolerance standard parameter value corresponding to the intelligent driving function, it can be first determined whether the total intelligent driving information of the vehicle is less than a preset multiple of the average tolerance standard parameter value. If the total intelligent driving information of the vehicle is less than the preset multiple of the average tolerance standard parameter value, the value of the preset statistical time period is increased, and the fault data information of the target fault within the increased preset statistical time period is reacquired until the reacquired total intelligent driving information of the vehicle is greater than or equal to the preset multiple of the average tolerance standard parameter value. Then, the fault detection is performed based on the last acquired fault data information. The preset multiple of the average tolerance standard parameter value is greater than or equal to the minimum value in the vehicle takeover information range corresponding to the largest experience evaluation value in the fault experience evaluation table. For example, when the minimum value in the vehicle takeover information range corresponding to the largest experience evaluation value is 2B, the preset multiple of the average tolerance standard parameter value is greater than or equal to 2B, thus ensuring that the acquired fault data information meets all vehicle takeover information ranges set in the fault experience evaluation table, thereby enabling a more objective and accurate experience evaluation.
[0120] When B = (total intelligent driving information of vehicles with intelligent driving function / n / T) * 30 * 1.5 * m, if the total intelligent driving information of vehicles with intelligent driving function is required to be ≥ 2B, then n ≥ 90m / T is required. Where n represents the total number of vehicles, T represents the preset statistical duration, m represents the total number of faults managed by intelligent driving function, and 1.5 represents the average tolerance.
[0121] This embodiment of the application can pause the current fault detection task when the total intelligent driving information of the vehicle is less than a preset multiple of the average tolerance standard parameter value. This is to avoid the fault detection result being subjective and inaccurate due to insufficient fault data. In this case, this embodiment of the application can increase the value of the preset statistical duration to reacquire fault data information until the reacquired total intelligent driving information of the vehicle is greater than or equal to the preset multiple of the average tolerance standard parameter value, and then resume the current fault detection task. This ensures the objectivity and accuracy of the fault detection result.
[0122] The following example illustrates the process of determining the fault clearance mentioned above:
[0123] Example 1:
[0124] The fault data information for fault 1 includes the contents shown in Table 5, where the preset statistical duration is 30 days.
[0125] Table 5
[0126]
[0127] (1) Calculation of exposure assessment values:
[0128] Since the proportion of faulty vehicles is 97.75%, according to Table 1, the corresponding exposure assessment value is 1.
[0129] (2) Calculation of the user experience evaluation value for the ACC function:
[0130] The average monthly ACC mileage for a single vehicle is 1,107,174.7 / 7,435 = 148.9 ≈ 150 km;
[0131] B = 150 * 1.5 * 90 = 20250 km.
[0132] Therefore, updating Table 3 yields Table 6:
[0133] Table 6
[0134]
[0135] By referring to Table 6, we can see that the ACC MPD value of 22173.49 in Table 5 corresponds to an experience evaluation value of 3.
[0136] When the exposure assessment value is 1 and the experience assessment value is 3, according to Table 4, the fault clearance level corresponding to the ACC function is C.
[0137] (3) Calculation of the user experience evaluation value of APA function:
[0138] The average number of APAs per vehicle per month is approximately 5.2 times (37 / 7435).
[0139] B = 5.2 * 1.5 * 120 = 936.
[0140] Therefore, updating Table 3 yields Table 7:
[0141] Table 7
[0142]
[0143] By referring to Table 7, we can see that the experience evaluation value corresponding to the APA MPD value of 4798.38 in Table 5 is 5.
[0144] When the exposure assessment value is 1 and the experience assessment value is 5, according to Table 4, the fault clearance level corresponding to the APA function is B.
[0145] When the fault clearance level corresponding to the ACC function is C and the fault clearance level corresponding to the APA function is B, the lower level C is taken as the clearance level for the target fault.
[0146] Example 2:
[0147] The fault data information for fault 2 includes the contents shown in Table 8, where the preset statistical duration is 30 days.
[0148] Table 8
[0149]
[0150] (1) Calculation of exposure assessment values:
[0151] Since the proportion of faulty vehicles is 3.47%, according to Table 1, the corresponding exposure assessment value is 4.
[0152] (2) Calculation of the user experience evaluation value for the CP function:
[0153] Average monthly CP mileage per vehicle = 1,107,174.7 / 7,435 = 148.9 ≈ 150 km;
[0154] B = 150 * 1.5 * 90 = 20250 km.
[0155] Therefore, Table 3 is updated to obtain Table 6. By looking up Table 6, we can find that the experience evaluation value corresponding to the ACC MPD value of 1791.54 in Table 8 is 1.
[0156] When the exposure assessment value is 4 and the experience assessment value is 1, according to Table 4, the fault clearance level corresponding to the ACC function is C.
[0157] This fault is unrelated to the APA function, and the user experience of the APA function does not need to be considered. Therefore, the fault clearance level of this fault can be determined as C.
[0158] Based on the above method embodiments, another embodiment of this application provides a fault clearance determination device, such as... Figure 3 As shown, the device includes:
[0159] The acquisition unit 210 is used to acquire fault data information of target faults within a preset statistical time period and the average tolerance of users for abnormal downgrade frequency within a unit time period. The fault data information includes the proportion of faulty vehicles, the total number of vehicles, the total intelligent driving information of vehicles with at least one intelligent driving function and its corresponding vehicle takeover information, and the total number of faults associated with the intelligent driving function. The total number of faults associated with the intelligent driving function includes the total number of faults that will cause the intelligent driving function to be downgraded.
[0160] The first lookup unit 220 is used to look up the exposure assessment value corresponding to the proportion of faulty vehicles of the target fault in the fault exposure assessment table, wherein the fault exposure assessment table includes a mapping relationship between the range of faulty vehicle proportions and the exposure assessment value.
[0161] The updating unit 230 is used to update the fault experience evaluation table template for each intelligent driving function based on the preset statistical duration, the total number of vehicles, the total intelligent driving information of the vehicles, the average tolerance, and the total number of faults associated with the intelligent driving function, so as to obtain the fault experience evaluation table corresponding to the intelligent driving function. The fault experience evaluation table template includes a mapping relationship between different vehicle takeover information range expressions and experience evaluation values, and the fault experience evaluation table includes a mapping relationship between different vehicle takeover information ranges and experience evaluation values.
[0162] The second search unit 240 is used to search for the experience evaluation value corresponding to the vehicle takeover information of the intelligent driving function in the fault experience evaluation table corresponding to the intelligent driving function.
[0163] The determining unit 250 is used to determine whether to allow the target fault to exit based on the exposure assessment value of the target fault and the experience assessment value of the target fault in each intelligent driving function.
[0164] In one possible implementation, each of the vehicle takeover information range expressions is generated based on the average tolerance standard parameter and the corresponding average tolerance ratio coefficient;
[0165] The update unit 230 includes:
[0166] The first calculation module is used to determine the average tolerance standard parameter value corresponding to each intelligent driving function based on the preset statistical duration, the total number of vehicles, the total intelligent driving information of the vehicles, the average tolerance, and the total number of faults associated with the intelligent driving function.
[0167] The second calculation module is used to substitute the average tolerance standard parameter value into each vehicle takeover information range expression in the fault experience evaluation form template to obtain the value of each vehicle takeover information range expression as the vehicle takeover information range.
[0168] The generation module is used to generate a table that includes the mapping relationship between the vehicle takeover information range and the experience evaluation value, as the fault experience evaluation table corresponding to the intelligent driving function.
[0169] In one possible implementation, the first calculation module is configured to, for each of the intelligent driving functions, determine the average intelligent driving information of a single vehicle within a unit time period based on the preset statistical duration, the total number of vehicles, and the total intelligent driving information of the vehicles; and determine the average tolerance standard parameter value corresponding to the intelligent driving function based on the average intelligent driving information of a single vehicle within a unit time period, the average tolerance, and the total number of faults associated with the intelligent driving function.
[0170] In one possible implementation, the first calculation module is used to take the product of the average intelligent driving information of a single vehicle within a unit time, the average tolerance, and the total number of faults associated with the intelligent driving function as the average tolerance standard parameter value corresponding to the intelligent driving function.
[0171] In one possible implementation, the device further includes:
[0172] The adjustment unit is used to, after determining the average tolerance standard parameter value corresponding to the intelligent driving function, increase the value of the preset statistical duration when the total intelligent driving information of the vehicle in the intelligent driving function is less than the average tolerance standard parameter value of the preset multiple, and re-acquire the fault data information of the target fault within the increased preset statistical duration, until the re-acquired total intelligent driving information of the vehicle in the intelligent driving function is greater than or equal to the average tolerance standard parameter value of the preset multiple, and then make an exit judgment on the target fault based on the fault data information acquired last time.
[0173] In one possible implementation, the fault exposure assessment table is obtained by dividing the proportion of faulty vehicles according to different exposure assessment values based on the 3σ principle of normal distribution.
[0174] In one possible implementation, the determining unit 250 includes:
[0175] The first determining module is used to determine the fault clearance level of each intelligent driving function to be evaluated based on the exposure evaluation value of the target fault and the experience evaluation value of the target fault in the intelligent driving function to be evaluated.
[0176] The second determining module is used to take the lowest fault clearance level among all fault clearance levels corresponding to intelligent driving functions as the fault clearance level of the target fault, wherein the fault clearance level is positively correlated with the probability of allowing the fault to be cleared.
[0177] The third determining module is used to determine whether to allow the target fault to be allowed to exit based on the fault exit level of the target fault.
[0178] The fault detection device provided in this application first determines the exposure assessment value corresponding to the target fault based on the proportion of faulty vehicles, and determines the user experience assessment value corresponding to each intelligent driving function based on the vehicle takeover information. Then, it comprehensively determines whether the target fault can be detected based on the exposure assessment value and the user experience assessment value corresponding to each intelligent driving function. This achieves standardized automated fault detection, saves manpower, and improves the efficiency of fault detection. Furthermore, this application update the pre-set fault experience assessment table template in real time by statistical fault data and the user's average tolerance for abnormal degradation frequency per unit time. This, combined with the fault experience assessment table corresponding to each intelligent driving function, improves the realism of the user experience affected by the target fault on each intelligent driving function, thereby further improving the accuracy and reliability of fault detection.
[0179] Based on the above method embodiments, another embodiment of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any of the above embodiments.
[0180] Based on the above method embodiments, another embodiment of this application provides an electronic device or computer device, such as... Figure 4 As shown, it includes:
[0181] One or more processors 310;
[0182] The processor 310 is coupled to a storage device 320, the storage device 320 being used to store one or more programs;
[0183] When the one or more programs are executed by the one or more processors 310, the electronic device or computer device performs the method as described in any of the above embodiments.
[0184] Based on the above embodiments, another embodiment of this application provides a computer program product, which includes instructions that, when executed on a computer or processor, cause the computer or processor to perform the method described in any of the above embodiments.
[0185] The above-described device and system embodiments correspond to the method embodiments and have the same technical effects. For detailed descriptions, please refer to the method embodiments. The device embodiments are derived from the method embodiments; detailed descriptions can be found in the method embodiments section, and will not be repeated here. Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application.
[0186] Those skilled in the art will understand that the modules in the apparatus of the embodiments can be distributed in the apparatus of the embodiments as described in the embodiments, or they can be located in one or more devices different from this embodiment with corresponding changes. The modules of the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.
[0187] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.
Claims
1. A method of determining a fault clearance, characterized in that, The method includes: Obtain fault data information of target faults within a preset statistical period and the average tolerance of users for abnormal downgrade frequency per unit time. The fault data information includes the proportion of faulty vehicles, the total number of vehicles, the total intelligent driving information of vehicles with at least one intelligent driving function and its corresponding vehicle takeover information, and the total number of faults associated with the intelligent driving function. The total number of faults associated with the intelligent driving function includes the total number of faults that will cause the intelligent driving function to be downgraded. Find the exposure assessment value corresponding to the proportion of vehicles with the target fault in the fault exposure assessment table, wherein the fault exposure assessment table includes the mapping relationship between the range of the proportion of vehicles with the fault and the exposure assessment value. For each intelligent driving function, the fault experience evaluation table template is updated based on the preset statistical duration, the total number of vehicles, the total intelligent driving information of the vehicles, the average tolerance, and the total number of faults associated with the intelligent driving function, to obtain the fault experience evaluation table corresponding to the intelligent driving function. The experience evaluation value corresponding to the vehicle takeover information of the intelligent driving function is then found in the fault experience evaluation table corresponding to the intelligent driving function. The fault experience evaluation table template includes a mapping relationship between different vehicle takeover information range expressions and experience evaluation values. The fault experience evaluation table includes a mapping relationship between different vehicle takeover information ranges and experience evaluation values. Based on the exposure assessment value of the target fault and the experience assessment value of the target fault in various intelligent driving functions, it is determined whether the target fault is allowed to exit.
2. The method of claim 1, wherein, Each of the vehicle takeover information range expressions is generated based on the average tolerance standard parameter and the corresponding average tolerance ratio coefficient; For each intelligent driving function, based on the preset statistical duration, the total number of vehicles, the total intelligent driving information of the vehicles, the average tolerance, and the total number of faults associated with the intelligent driving function, the fault experience evaluation form template is updated to obtain the fault experience evaluation form corresponding to the intelligent driving function, including: For each of the intelligent driving functions, the average tolerance standard parameter value corresponding to the intelligent driving function is determined based on the preset statistical duration, the total number of vehicles, the total intelligent driving information of the vehicles, the average tolerance, and the total number of faults associated with the intelligent driving function. Substitute the average tolerance standard parameter value into each vehicle takeover information range expression in the fault experience evaluation form template to obtain the value of each vehicle takeover information range expression as the vehicle takeover information range. A table that includes the mapping relationship between the vehicle takeover information range and the experience evaluation value will be used as the fault experience evaluation table corresponding to the intelligent driving function.
3. The method of claim 2, wherein, For each of the intelligent driving functions, based on the preset statistical duration, the total number of vehicles, the total intelligent driving information of the vehicles, the average tolerance, and the total number of faults associated with the intelligent driving function, a standard parameter value for the average tolerance corresponding to the intelligent driving function is determined, including: For each of the intelligent driving functions, the average intelligent driving information of a single vehicle within the unit time period is determined based on the preset statistical duration, the total number of vehicles, and the total intelligent driving information of the vehicles. The average tolerance standard parameter value corresponding to the intelligent driving function is determined based on the average intelligent driving information of a single vehicle within a unit of time, the average tolerance, and the total number of faults associated with the intelligent driving function.
4. The method according to claim 3, characterized in that, Based on the average intelligent driving information of a single vehicle within a unit of time, the average tolerance, and the total number of faults associated with the intelligent driving function, the average tolerance standard parameter value corresponding to the intelligent driving function is determined, including: The product of the average intelligent driving information of a single vehicle within a unit of time, the average tolerance, and the total number of faults associated with the intelligent driving function is used as the standard parameter value of the average tolerance corresponding to the intelligent driving function.
5. The method of claim 2, wherein, After determining the average tolerance standard parameter value corresponding to the intelligent driving function, the method further includes: When the total intelligent driving information of the vehicle under the intelligent driving function is less than the average tolerance standard parameter value of the preset multiple, the value of the preset statistical duration is increased, and the fault data information of the target fault within the increased preset statistical duration is re-acquired until the total intelligent driving information of the vehicle under the intelligent driving function is greater than or equal to the average tolerance standard parameter value of the preset multiple. Then, the target fault is judged based on the fault data information acquired last time.
6. The method of claim 1, wherein, The fault exposure assessment table is obtained by dividing the proportion of faulty vehicles according to different exposure assessment values based on the 3σ principle of normal distribution.
7. The method according to any one of claims 1 to 6, characterized in that, Based on the exposure assessment value of the target fault and the experience assessment value of the target fault in various intelligent driving functions, determine whether to allow the target fault to exit, including: For each intelligent driving function to be evaluated, the fault clearance level corresponding to the intelligent driving function to be evaluated is determined based on the exposure evaluation value of the target fault and the experience evaluation value of the target fault in the intelligent driving function to be evaluated. The lowest fault clearance level among all fault clearance levels corresponding to intelligent driving functions is taken as the fault clearance level of the target fault, wherein the fault clearance level is positively correlated with the probability of allowing the fault to be cleared. Based on the fault clearance level of the target fault, determine whether the target fault is allowed to be cleared.
8. A fault-qualified determination device, characterized by The device includes: The acquisition unit is used to acquire fault data information of target faults within a preset statistical time period and the average tolerance of users for abnormal downgrade frequency within a unit time period. The fault data information includes the proportion of faulty vehicles, the total number of vehicles, the total intelligent driving information of vehicles with at least one intelligent driving function and its corresponding vehicle takeover information, and the total number of faults associated with the intelligent driving function. The total number of faults associated with the intelligent driving function includes the total number of faults that will cause the intelligent driving function to be downgraded. The first lookup unit is used to look up the exposure assessment value corresponding to the proportion of faulty vehicles of the target fault in the fault exposure assessment table, wherein the fault exposure assessment table includes a mapping relationship between the range of faulty vehicle proportions and the exposure assessment value. The update unit is used to update the fault experience evaluation table template for each intelligent driving function based on the preset statistical duration, the total number of vehicles, the total intelligent driving information of the vehicles, the average tolerance, and the total number of faults associated with the intelligent driving function, to obtain the fault experience evaluation table corresponding to the intelligent driving function. The fault experience evaluation table template includes a mapping relationship between different vehicle takeover information range expressions and experience evaluation values, and the fault experience evaluation table includes a mapping relationship between different vehicle takeover information ranges and experience evaluation values. The second search unit is used to search for the experience evaluation value corresponding to the vehicle takeover information of the intelligent driving function in the fault experience evaluation table corresponding to the intelligent driving function. The determining unit is used to determine whether to allow the target fault to exit based on the exposure assessment value of the target fault and the experience assessment value of the target fault in each intelligent driving function.
9. A computer readable storage medium having stored thereon a computer program, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-7.
10. An electronic device, comprising: The electronic device includes: One or more processors; The processor is coupled to a storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the electronic device performs the method as described in any one of claims 1-7.