Engine operating condition development strategy based on big data system

Abstract

The present invention relates to an engine operating condition development strategy based on a big data system, comprising the following steps: collecting vehicle operation data of all vehicles registered in the network, and then uploading it to a cloud database; screening the vehicles to obtain a target vehicle group and a backup vehicle group; extracting vehicle key parameters from the vehicle operation data stored in the cloud database according to defined rules; converting the vehicle key parameters of each vehicle one by one to obtain the operating condition data of each target vehicle; converting the operating condition data into an operating condition sequence file; the operating condition sequence file is the final output result of this development strategy. The present invention implements screening, classification and information extraction for different engine platforms, models, regions, faults, etc.; improves the coverage of the test system and the robustness of the control system; and realizes rapid output of automated test sequences.

Description

Technical Field

[0001] The present invention relates to the technical field of engine electronic control and engine verification control, and in particular to an engine operating condition development strategy based on a big data system. Background Art

[0002] At present, the design of working conditions in engine function or durability tests generally comes from two aspects. One is the verification working conditions of national or industry standards, mainly WHSC / WHTC, ESC / ETC cycles, etc. The second is based on the experimental data of road test vehicles. The relevant data of road test vehicles are collected through acquisition cards and the corresponding working conditions are designed after being sorted and summarized.

[0003] However, at present, the test conditions in both engine benches and semi-physical simulation benches basically use the above scheme, which can basically cover the relevant engine conditions and meet the requirements for engine emissions and certification. However, there are also some shortcomings. For example, it is difficult to achieve real-time monitoring, condition design and verification for the problem of poor fuel economy under national highway conditions. If the condition development and design is carried out through big data, and verification is carried out, it will be more in line with the usage and improve the fuel economy of national highways and enhance product competitiveness;

[0004] In summary, the current prior art has the following defects:

[0005] 1. Since there is no real user's car usage data, it does not conform to the user's actual usage and working conditions;

[0006] 2. Since there is no method to record the data related to the problematic trip, it is difficult to reproduce the problems caused by special working conditions or road sections;

[0007] 3. Due to the lack of vehicle usage data statistics and analysis, it is difficult to understand product problems and trends before customers do, and it is even more impossible to intervene in advance;

[0008] 4. Since there are no standards and methods for converting actual working conditions into automated test cases, it is difficult to conduct diversified robustness verification and improvement of control systems. Summary of the invention

[0009] In response to the above problems, the present invention provides an engine operating condition development strategy based on a big data system, which aims to achieve screening, classification and information extraction for different engine platforms, models, regions, faults, etc.; improve the coverage of the test system and the robustness of the control system; and achieve rapid output of automated test sequences.

[0010] To solve the above problems, the technical solution provided by the present invention is:

[0011] The engine operating condition development strategy based on the big data system includes the following steps:

[0012] S10. According to the preset collection frequency, all vehicles registered in the network will have their T-BOX collect vehicle operation data for each vehicle; then the vehicle operation data will be uploaded to the cloud database and saved; the vehicle operation data includes engine-related data - real-time data, vehicle and environment-related data - real-time data, and non-real-time data;

[0013] S100. In the cloud database, all vehicles registered in the network are screened to obtain a target vehicle group and a backup vehicle group; the target vehicle group includes a plurality of screened target vehicles; the target vehicles include faulty target vehicles, vehicle type-engine type target vehicles, and region-road section type target vehicles; the backup vehicle group includes a plurality of screened backup vehicles; the backup vehicles include faulty backup vehicles, vehicle type-engine type backup vehicles, and region-road section type backup vehicles;

[0014] S200. Extracting key vehicle parameters from the vehicle operation data stored in the cloud database according to manually preset limiting rules;

[0015] Then, according to manually preset limiting rules, extracting key vehicle parameters from the vehicle operation data stored in the cloud database;

[0016] S300. Convert the key vehicle parameters of each vehicle one by one to obtain the operating data of each target vehicle;

[0017] S400. Convert the operating condition data into an operating condition sequence file; the operating condition sequence file is the final output result of this development strategy.

[0018] Preferably, the vehicle screening in S100 specifically includes the following steps:

[0019] S110. Based on the classification of fault types, fault screening is performed; the fault type target vehicle and the fault type backup vehicle are obtained; the data for the fault screening is based on: fault code information, number of reports;

[0020] S120. Based on the classification of vehicle model or engine platform, perform vehicle model-engine type screening; obtain the vehicle model-engine type target vehicle and the vehicle model-engine type backup vehicle; the data basis of the vehicle model-engine type screening is: vehicle model code, engine signal, PN;

[0021] S130. Based on the application scenario or regional classification, perform regional-road segment screening; obtain the regional-road segment type target vehicle and the regional-road segment type backup vehicle; the regional-road segment screening data is based on: regional information, gas stations, service stations;

[0022] S140. Filter the fault-type target vehicles, the vehicle type-engine type target vehicles, and the region-road section type target vehicles obtained in S110 to S130 to obtain the target vehicle group;

[0023] S150. Pack the fault-type backup vehicles, the vehicle type-engine type backup vehicles, and the region-road section type backup vehicles obtained in S110 to S130 to obtain the backup vehicle group.

[0024] Preferably, the engine-related class-real-time data in S200 includes engine speed, engine torque, engine water temperature, calculated fuel consumption, intake air temperature, braking status, engine mode, air-fuel ratio, and fault code status;

[0025] The vehicle and environment related real-time data includes gearbox position, throttle, vehicle speed, battery voltage, altitude, fuel temperature, key switch, and cruise switch;

[0026] The non-real-time data includes driving cycle records, fault code history records, and repair and maintenance records.

[0027] Preferably, the limiting rules in S200 include the following:

[0028] The collection period when collecting the vehicle operation data is one driving cycle or multiple and continuous driving cycles;

[0029] Or, the unit of the collection time interval when collecting the vehicle operation data is day or hour;

[0030] Or, the collection distance interval when collecting the vehicle operation data is a manually preset collection distance value;

[0031] Alternatively, the collection section when collecting the vehicle operation data is a specific section preset manually.

[0032] Preferably, S300 specifically includes the following steps:

[0033] S310. Obtaining the acquisition time and the driving cycle of the key vehicle parameters of each vehicle one by one;

[0034] S320. Fitting and comparing the key vehicle parameters from the target vehicle with the key vehicle parameters from the backup vehicle;

[0035] S330. According to the fitting comparison result of S320, the key vehicle parameters from the backup vehicle are used to compensate and improve the key vehicle parameters from the target vehicle;

[0036] S340. Perform data determination operations on the key vehicle parameters from the target vehicle, and classify the key vehicle parameters from the target vehicle according to manually preset classification standards to obtain the operating condition data of each of the target vehicles.

[0037] Preferably, converting the operating condition data into the operating condition sequence file in S400 specifically comprises the following steps:

[0038] S410. Extracting the operating condition sequence file description from the operating condition data;

[0039] S420. According to the manually set data table format, extract the corresponding data value from the working condition data and fill it into the preset data table;

[0040] The operating condition sequence file description is combined with the data table to obtain the operating condition sequence file.

[0041] Preferably, the table format of the data table is:

[0042] Column 1 is a vehicle state description; the column 1 includes a plurality of driving cycle sub-columns, a plurality of shutdown operation sub-columns and an end mark; and the driving cycle sub-columns and the shutdown operation sub-columns are in a one-to-one correspondence; column 2 is a time step; the step unit and the step interval of the time step are manually preset; column 3 is a vehicle parameter; column 4 is an engine parameter; column 5 is an environmental parameter;

[0043] Line 1 is the parameter classification; Line 2 is the parameter Chinese name; Line 3 is the parameter unit; Line 4 is the parameter software variable name.

[0044] Preferably, S140 further comprises the following steps:

[0045] S141. The fault-type target vehicles, the vehicle type-engine type target vehicles, and the region-road section type target vehicles obtained in S110 to S130 are packaged, and then a preliminary target vehicle selection operation is performed according to a manually preset target vehicle sorting rule or date to obtain a preliminary target vehicle group; the number of the target vehicles in the preliminary target vehicle group is 5 to 8;

[0046] S142. Performing a target vehicle selection operation on the initially selected target vehicle group according to a manually preset vehicle group selection rule to obtain the target vehicle group.

[0047] Preferably, the vehicle group selection rule in S142 specifically includes the following contents:

[0048] The operating condition of the target vehicle meets the manually preset regularity requirements; the regularity requirements specifically include manually preset: vehicle route road condition regularity requirements, seasonal regularity requirements, regional regularity requirements, maintenance frequency regularity requirements, refueling point regularity requirements, operating time regularity requirements, industry regularity requirements, fault reporting frequency regularity requirements, and fault vehicle groups having the same operating experience regularity requirements;

[0049] The working condition of the target vehicle meets the manually preset integrity requirements; the integrity requirements specifically include the manually preset: data integrity requirements, continuity requirements, T-BOX offline times requirements, and data availability requirements within the target time period; the T-BOX offline times requirements are specifically: the number of offline reconnections within the target time period or driving cycle is less than 3 times; the data availability requirements within the target time period are specifically: no missing key data of the engine, vehicle and environment

[0050] The operating condition of the target vehicle meets the manually preset target accuracy requirements; the target accuracy requirements specifically include the manually preset requirements: the number of the target vehicles is not more than 3; and the selected target vehicles meet the regularity requirements and the integrity requirements.

[0051] Preferably, the operating condition sequence file description includes the operating condition strategy name, vehicle model, engine model, test step, and total test time.

[0052] Compared with the prior art, the present invention has the following advantages:

[0053] 1. Since the working condition control design strategy of the present invention is based on a big data cloud platform, it can screen, classify and extract information for different engine platforms, models, regions, faults, etc.;

[0054] 2. The working condition control design strategy of the present invention can be used to conduct targeted verification and control optimization for customers' special application scenarios, thereby improving the coverage of the test system and the robustness of the control system;

[0055] 3. Since the working condition control design strategy of the present invention can be imported into the automated test script editing software when extracting information, it is possible to quickly output the automated test sequence. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A flow chart of a development strategy for a specific embodiment of the present invention;

[0057] Figure 2It is a schematic diagram of a parameter extraction process based on excessive fuel consumption on a certain model of a national highway and the recurrence of an engine failure in a specific embodiment of the present invention;

[0058] Figure 3 It is a schematic diagram of an excerpt of the working condition sequence file data of a specific embodiment of the present invention. DETAILED DESCRIPTION

[0059] The present invention is further explained below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0060] It should be noted in advance that the technical solution of the present invention is based on the vehicle-mounted T-BOX and the cloud big data system. By designing a parameter extraction algorithm, information extraction and data collation are performed on vehicle faults, driving routes, engine operating conditions and other states, and finally the vehicle driving information is converted into test conditions and test scripts.

[0061] It should be further explained in advance that the converted test conditions and scripts can be used in tests such as engine durability, fault reproduction, control algorithm optimization, fuel economy and post-processing control.

[0062] like Figure 1 As shown in the figure, the engine operating condition development strategy based on the big data system includes the following steps:

[0063] S10. According to the preset collection frequency, all vehicles registered in the network will have their T-BOX collect the vehicle operation data of each vehicle; then upload the vehicle operation data to the cloud database and save it; the vehicle operation data includes engine-related data - real-time data, vehicle and environment-related data - real-time data, and non-real-time data;

[0064] In this specific embodiment, the acquisition frequency is 1 / 60 Hz.

[0065] It should be noted that T-BOX needs to transmit the running vehicle data, and the vehicle running data will be uploaded to the cloud database and saved at a certain time interval. The time interval in this specific embodiment is 1 minute, that is, the collection frequency is 1 / 60 Hz.

[0066] S100. In the cloud database, all vehicles registered in the network are screened to obtain a target vehicle group and a backup vehicle group; the target vehicle group includes multiple screened target vehicles; the target vehicles include faulty target vehicles, vehicle type-engine type target vehicles, and region-road section type target vehicles; the backup vehicle group includes multiple screened backup vehicles; the backup vehicles include faulty backup vehicles, vehicle type-engine type backup vehicles, and region-road section type backup vehicles;

[0067] It should be noted that the data platform can filter cloud parameters according to needs, mainly based on the classification of fault types, classification based on vehicle models or engine platforms, and classification based on application scenarios / regions.

[0068] In this specific embodiment, the vehicle screening in S100 specifically includes the following steps:

[0069] S110. Based on the classification of fault types, perform fault screening; obtain a faulty target vehicle and a faulty backup vehicle; the data basis for fault screening is: fault code information and the number of reports.

[0070] S120. Based on the classification of vehicle models or engine platforms, perform vehicle model-engine type screening; obtain vehicle model-engine type target vehicles and vehicle model-engine type backup vehicles; the data basis for vehicle model-engine type screening is: vehicle model code, engine signal, PN.

[0071] S130. Based on the classification of application scenarios or regions, perform region-road section screening; obtain region-road section type target vehicles and region-road section type backup vehicles; the data basis for region-road section screening is: regional information, gas stations, and service stations.

[0072] S140. Screen the fault-type target vehicles, vehicle type-engine type target vehicles, and region-road section type target vehicles obtained in S110 to S130 to obtain a target vehicle group.

[0073] In this specific embodiment, S140 further includes the following steps:

[0074] S141. The faulty target vehicles, vehicle type-engine type target vehicles, and region-road section type target vehicles obtained in S110-S130 are packaged, and then the target vehicles are preliminarily selected according to the manually preset target vehicle sorting rules or dates to obtain a preliminarily selected target vehicle group; the number of target vehicles in the preliminarily selected target vehicle group is 5-8;

[0075] S142. Perform a target vehicle selection operation on the initially selected target vehicle group according to a manually preset vehicle group selection rule to obtain a target vehicle group.

[0076] In this specific embodiment, the vehicle group selection rule in S142 specifically includes the following contents:

[0077] The operating conditions of the target vehicle meet the manually preset regularity requirements; the regularity requirements specifically include the manually preset: regularity requirements for vehicle route conditions, seasonal regularity requirements, regional regularity requirements, regularity requirements for maintenance frequency, regularity requirements for refueling points, regularity requirements for operating hours, regularity requirements for the industry to which they belong, regularity requirements for fault reporting frequency, and regularity requirements that the faulty vehicle group have the same operating experience.

[0078] The working condition of the target vehicle meets the manually preset integrity requirements; the integrity requirements specifically include the manually preset: data integrity requirements, continuity requirements, T-BOX offline times requirements, and data availability requirements within the target time period; the T-BOX offline times requirements are specifically: the number of offline reconnections within the target time period or driving cycle is less than 3 times; the data availability requirements within the target time period are specifically: no missing key data of the engine, vehicle and environment

[0079] The operating conditions of the target vehicles meet the manually preset target accuracy requirements; the target accuracy requirements specifically include the manually preset: the number of target vehicles is not more than 3; and the selected target vehicles meet the regularity requirements and integrity requirements.

[0080] It should be noted that, through practical tests, the number of selected target vehicles is preferably 2 to 3.

[0081] S150. Pack the fault-type backup vehicles, vehicle type-engine type backup vehicles, and region-road section type backup vehicles obtained in S110 to S130 to obtain a backup vehicle group.

[0082] S200. Extract key vehicle parameters from the vehicle operation data stored in the cloud database according to manually preset limiting rules;

[0083] The development of engine operating condition control strategies based on big data is mainly based on engine-related data - real-time data, vehicle and environment-related data - real-time data, and non-real-time data.

[0084] In this specific embodiment, the engine-related real-time data includes engine speed, engine torque, engine water temperature, calculated fuel consumption, intake temperature, braking status, engine mode, air-fuel ratio, and fault code status.

[0085] In this specific embodiment, the vehicle and environment related real-time data includes transmission gear, throttle, vehicle speed, battery voltage, altitude, fuel temperature, key switch, and cruise switch.

[0086] In this specific embodiment, the non-real-time data includes driving cycle records, fault code history records, and repair and maintenance records.

[0087] Then, according to the manually preset limiting rules, the key vehicle parameters are extracted from the vehicle operation data stored in the cloud database;

[0088] It should be noted that after determining the target vehicle group, data extraction of all vehicles is required on the cloud data platform, and the data extraction scope needs to be limited.

[0089] In this specific embodiment, the limiting rules in S200 include the following:

[0090] The collection period for collecting vehicle operation data is one driving cycle or multiple and continuous driving cycles.

[0091] Alternatively, the unit of the collection time interval when collecting vehicle operation data is day or hour.

[0092] Alternatively, the collection distance interval when collecting vehicle operation data is a manually preset collection distance value.

[0093] Alternatively, the collection section when collecting vehicle operation data is a specific section preset manually.

[0094] S300. Convert key vehicle parameters of each vehicle one by one to obtain operating condition data of each target vehicle.

[0095] It should be noted that after obtaining the data of each vehicle in the target vehicle group, the data needs to be converted and processed, and this process can be performed on the cloud or on a local computer.

[0096] In this specific embodiment, S300 specifically includes the following steps:

[0097] S310. Obtain the collection time and driving cycle of key vehicle parameters of each vehicle one by one.

[0098] S320. Fit and compare the key vehicle parameters from the target vehicle with the key vehicle parameters from the backup vehicle.

[0099] S330. Based on the fitting comparison result of S320, the key vehicle parameters from the backup vehicle are used to compensate and improve the key vehicle parameters from the target vehicle.

[0100] S340. Perform data determination operations on the key vehicle parameters from the target vehicle, and classify the key vehicle parameters from the target vehicle according to manually preset classification standards to obtain the operating condition data of each target vehicle.

[0101] like Figure 2As shown, in order to better demonstrate the practical application scenarios and principles of the present invention, this specific embodiment specifically captures an actual parameter extraction process based on excessive fuel consumption on a certain model of national highway and the reproduction of engine failure as an example.

[0102] S400. Convert the operating condition data into an operating condition sequence file; the operating condition sequence file is the final output result of this development strategy.

[0103] In this specific embodiment, converting the operating condition data into an operating condition sequence file in S400 specifically includes the following steps:

[0104] S410. Extract the operating condition sequence file description from the operating condition data.

[0105] S420. According to the table format of the manually set data table, extract the corresponding data values ​​from the working condition data and fill them into the preset data table.

[0106] In this specific embodiment, the table format of the data table is:

[0107] Column 1 is a vehicle state description; Column 1 includes multiple driving cycle sub-columns, multiple shutdown operation sub-columns and an end mark; and the driving cycle sub-columns and the shutdown operation sub-columns are in a one-to-one correspondence; Column 2 is a time step; the step unit and step interval of the time step are manually preset; Column 3 is a vehicle parameter; Column 4 is an engine parameter; Column 5 is an environmental parameter;

[0108] Row 1 is the parameter classification; Row 2 is the parameter Chinese name; Row 3 is the parameter unit; Row 4 is the parameter software variable name.

[0109] Combine the operating sequence file description with the data table to obtain the operating sequence file.

[0110] It should be noted that the function of S400 is: the working condition sequence needs to be converted into EXCEL file format to ensure that the automated test software can be called normally. Usually, automated test software such as TESTstand / ECU-Test / VB / VC / Phyton can call Excel file data normally and execute according to certain steps.

[0111] It should be further explained that the data of the operating sequence file is recorded row by row at corresponding time intervals in Excel.

[0112] In this specific embodiment, the operating condition sequence file description includes the operating condition strategy name, vehicle model, engine model, test step, and total test time.

[0113] It should be noted that the recommended test step is 60 seconds or multiples of 60 seconds; for special operations, a separate test step can be set.

[0114] like Figure 3 As shown, in order to better demonstrate the practical application scenarios and principles of the present invention, this specific embodiment specifically captures a real working condition sequence file as an example.

[0115] After practical verification, it has been found that after using the method of the present invention, the method can quickly extract faulty vehicles or driving environment conditions on the market, can be quickly verified on a test bench to optimize the electronic control system, improve product competitiveness, save time and cost, and can be quickly applied to the optimization and problem reproduction of other control systems other than the engine; on the other hand, it can quickly locate and repair customer problems and reduce problem handling time.

[0116] In the above detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are clearly stated in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0117] The disclosed embodiments are described above to enable any person skilled in the art to implement or use the present invention. Various modifications of these embodiments are obvious to those skilled in the art, and the general principles defined herein may also be applied to other embodiments without departing from the spirit and scope of the present disclosure. Therefore, the present disclosure is not limited to the embodiments given herein, but is consistent with the broadest scope of the principles and novel features disclosed in this application.

[0118] The above description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but it should be recognized by those skilled in the art that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the scope of protection of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, the word is covered in a manner similar to the term "including", just as "including," is explained as a transitional word in the claims. In addition, any term "or" used in the specification of the claims is intended to mean "non-exclusive or".

[0119] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An engine operating condition development strategy based on a big data system, characterized by: The following steps are involved: S10. According to the preset collection frequency, all vehicles registered in the network will have their T-BOX collect vehicle operation data for each vehicle; then the vehicle operation data will be uploaded to the cloud database and saved; the vehicle operation data includes engine-related data - real-time data, vehicle and environment-related data - real-time data, and non-real-time data; S100. In the cloud database, all vehicles registered in the network are screened to obtain a target vehicle group and a backup vehicle group; the target vehicle group includes a plurality of screened target vehicles; The target vehicles include faulty target vehicles, vehicle type-engine type target vehicles, and region-road section type target vehicles; the backup vehicle group includes a plurality of selected backup vehicles; the backup vehicles include faulty backup vehicles, vehicle type-engine type backup vehicles, and region-road section type backup vehicles; S200. Extracting key vehicle parameters from the vehicle operation data stored in the cloud database according to manually preset limiting rules; S300. Convert the key vehicle parameters of each vehicle one by one to obtain the operating data of each target vehicle; S400. Convert the working condition data into a working condition sequence file; the working condition sequence file is the final output result of this development strategy; The vehicle screening in S100 specifically includes the following steps: S110. Perform fault screening based on the classification of fault types; The fault target vehicle and the fault backup vehicle are obtained; the data basis for the fault screening is: fault code information and reporting times; S120. Based on the classification of vehicle model or engine platform, perform vehicle model-engine type screening; obtain the vehicle model-engine type target vehicle and the vehicle model-engine type backup vehicle; the data basis of the vehicle model-engine type screening is: vehicle model code, engine signal, PN; S130. Based on the application scenario or regional classification, perform region-road section screening; Obtaining the region-road section type target vehicle and the region-road section type backup vehicle; The data basis for the region-road section screening is: regional information, gas stations, and service stations; S140. Filter the fault-type target vehicles, the vehicle type-engine type target vehicles, and the region-road section type target vehicles obtained in S110 to S130 to obtain the target vehicle group; S150. Pack the fault-type backup vehicles, the vehicle type-engine type backup vehicles, and the region-road section type backup vehicles obtained in S110 to S130 to obtain the backup vehicle group; The engine-related real-time data in S10 includes engine speed, engine torque, engine water temperature, calculated fuel consumption, intake air temperature, brake status, engine mode, air-fuel ratio, and fault code status; The vehicle and environment related real-time data includes gearbox position, throttle, vehicle speed, battery voltage, altitude, fuel temperature, key switch, and cruise switch; The non-real-time data includes driving cycle records, fault code history records, and repair and maintenance records; The limiting rules in S200 include the following: The collection period when collecting the vehicle operation data is one driving cycle or multiple and continuous driving cycles; Or, the unit of the collection time interval when collecting the vehicle operation data is day or hour; Or, the collection distance interval when collecting the vehicle operation data is a manually preset collection distance value; Or, the collection section when collecting the vehicle operation data is a specific section preset manually; S300 specifically includes the following steps: S310. Obtaining the acquisition time and the driving cycle of the key vehicle parameters of each vehicle one by one; S320. Fitting and comparing the key vehicle parameters from the target vehicle with the key vehicle parameters from the backup vehicle; S330. According to the fitting comparison result of S320, the key vehicle parameters from the backup vehicle are used to compensate and improve the key vehicle parameters from the target vehicle; S340. Perform data determination operations on the key vehicle parameters from the target vehicle, and classify the key vehicle parameters from the target vehicle according to manually preset classification standards to obtain the operating condition data of each of the target vehicles.

2. The engine operating condition development strategy based on the big data system according to claim 1 is characterized by: In S400, converting the operating condition data into the operating condition sequence file specifically comprises the following steps: S410. Extracting the operating condition sequence file description from the operating condition data; S420. According to the manually set data table format, extract the corresponding data value from the working condition data and fill it into the preset data table; The operating condition sequence file description is combined with the data table to obtain the operating condition sequence file.

3. The engine operating condition development strategy based on the big data system according to claim 2 is characterized by: The table format of the data table is: Column 1 is a vehicle state description; the column 1 includes a plurality of driving cycle sub-columns, a plurality of shutdown operation sub-columns and an end mark; and the driving cycle sub-columns and the shutdown operation sub-columns are in a one-to-one correspondence; column 2 is a time step; the step unit and the step interval of the time step are manually preset; column 3 is a vehicle parameter; column 4 is an engine parameter; column 5 is an environmental parameter; Row 1 is the parameter classification; Row 2 is the parameter Chinese name; Row 3 is the parameter unit; Row 4 is the parameter software variable name.

4. The engine operating condition development strategy based on the big data system according to claim 3 is characterized by: S140 also includes the following steps: S141. The fault-type target vehicles, the vehicle type-engine type target vehicles, and the region-road section type target vehicles obtained in S110 to S130 are packaged, and then a preliminary target vehicle selection operation is performed according to a manually preset target vehicle sorting rule or date to obtain a preliminary target vehicle group; the number of the target vehicles in the preliminary target vehicle group is 5 to 8; S142. Performing a target vehicle selection operation on the initially selected target vehicle group according to a manually preset vehicle group selection rule to obtain the target vehicle group.

5. The engine operating condition development strategy based on the big data system according to claim 4 is characterized in that: The vehicle group selection rule in S142 specifically includes the following contents: The operating condition of the target vehicle meets the manually preset regularity requirements; the regularity requirements specifically include manually preset: vehicle route road condition regularity requirements, seasonal regularity requirements, regional regularity requirements, maintenance frequency regularity requirements, refueling point regularity requirements, operating time regularity requirements, industry regularity requirements, fault reporting frequency regularity requirements, and fault vehicle groups having the same operating experience regularity requirements; The working condition of the target vehicle meets the manually preset integrity requirements; the integrity requirements specifically include the manually preset: data integrity requirements, continuity requirements, T-BOX offline times requirements, and data availability requirements within the target time period; the T-BOX offline times requirements are specifically: the number of offline reconnections within the target time period or driving cycle is less than 3 times; the data availability requirements within the target time period are specifically: no missing key data of the engine, vehicle and environment The operating condition of the target vehicle meets the manually preset target accuracy requirements; the target accuracy requirements specifically include the manually preset: the number of the target vehicles is not more than 3; and the selected target vehicles meet the regularity requirements and the integrity requirements.

6. The engine operating condition development strategy based on the big data system according to claim 5 is characterized by: The operating condition sequence file description includes the operating condition strategy name, vehicle model, engine model, test step, and total test time.

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