CN115952693B - Transmission load spectrum conversion method, device, equipment and medium based on big data - Google Patents
Transmission load spectrum conversion method, device, equipment and medium based on big data Download PDFInfo
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- CN115952693B CN115952693B CN202310231525.5A CN202310231525A CN115952693B CN 115952693 B CN115952693 B CN 115952693B CN 202310231525 A CN202310231525 A CN 202310231525A CN 115952693 B CN115952693 B CN 115952693B
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Abstract
The invention belongs to the technical field of gearbox bench tests, and particularly provides a gearbox load spectrum conversion method, device, equipment and medium based on big data, wherein the method is based on actual user use data at a vehicle end, and the gear torque frequency, gear use probability, average speed of each gear and daily working time of a vehicle under the big data are statistically analyzed through vehicle profile analysis under the market; and calculating the rotation speed of the input shaft of the gearbox under each torque of each gear, and converting the rotation speed of the input shaft of the gearbox under each torque of each gear into the full torsion rotation speed of the gearbox according to the Minner linear damage accumulation theory to form a final gearbox rack load spectrum. The load spectrum of the gearbox formed by the method comprises the influence of factors such as road conditions of actual users, actual loads of the vehicles, driving habits of drivers and the like, and based on a large amount of user data, the assessment of the selected market on the service life of gears of the gearbox is reflected more truly, so that a bench assessment basis is provided for new box type put-on market.
Description
Technical Field
The invention relates to the technical field of gearbox bench tests, in particular to a gearbox load spectrum conversion method, device, equipment and medium based on big data.
Background
The durability of the automobile product is an important index for evaluating the quality of the automobile, the gearbox is used as an important component of the automobile power assembly, and the durability check directly influences the service life of the whole power chain.
At present, the durability assessment standards of gearboxes are mainly divided into two types: the durability of the gearbox is verified by adopting the national recommended standard or the durability standard of the enterprise standard, and the standards cannot truly reflect the parameters of the whole vehicle, the use habits of drivers and the different requirements of different market subdivision fields on each gear of the gearbox, so that the problems of insufficient test verification or over verification exist; the other type is to collect the real road load spectrum based on one or more typical vehicles, and compile the load spectrum of the gearbox by utilizing data extrapolation, so that the method cannot cover a large amount of vehicle data, replaces the whole market segment data for certain specific working condition data, has larger deviation risk, and has the defects of long time and high cost for collecting the real road spectrum of the typical vehicles.
The gearbox bench test refers to a process of testing the endurance strength of each subsystem and parts of the gearbox by using a test bench, and the full life cycle real vehicle daily driving and road test working conditions are simulated mainly through the bench test. The effectiveness of the gearbox bench test and the accuracy of the design verification are largely dependent on the relevance of the load spectrum to the end customer's use. How to generate the corresponding transmission load spectrum according to different parameters is a vital link.
Disclosure of Invention
In view of the above-mentioned problems, the present invention provides a method, apparatus, device, and medium for transforming a load spectrum of a transmission based on big data.
In a first aspect, the present invention provides a method for transforming a load spectrum of a gearbox based on big data, comprising the following steps:
defining a target gearbox design working life and a target gearbox gear speed ratio;
acquiring feedback real-time information of the whole vehicle of the sample vehicle in a set time period;
carrying out statistical analysis on the acquired real-time information to generate a gear torque frequency table, a gear use probability table and an average speed table of each gear of the sample vehicle, and calculating the daily working time;
calculating the total working time of the target gearbox according to the calculated daily working time and the defined design working time of the target gearbox;
calculating the revolution of an input shaft of the gearbox under each torque of each gear according to the gear use probability table, the target total working time length of the gearbox, the gear torque frequency table and the average speed of each gear;
according to the Minner linear damage accumulation theory, the rotation number of the input shaft of the gearbox under each torque of each gear is converted into the full torsion rotation number of the gearbox, and a gearbox rack load spectrum is formed.
As the optimization of the technical scheme of the invention, the steps of defining the design working life of the target gearbox and the gear speed ratio of the gearbox comprise the following steps:
considering engine model distribution matched with a target gearbox market, and selecting a vehicle with an engine model ratio larger than a set threshold as a sample vehicle; a target transmission design operating life and transmission gear speed ratio are defined.
As the optimization of the technical scheme of the invention, the steps of carrying out statistical analysis on the acquired real-time information to generate a gear torque frequency table, a gear use probability table and an average speed table of each gear of the sample vehicle and calculating the daily working time length comprise the following steps:
and carrying out statistical analysis on the acquired real-time information to distinguish different gears, distinguishing different torques of the same gear, forming a total gear torque frequency table, forming a gear use probability table, an average speed table of each gear and calculating daily working time.
As the optimization of the technical scheme of the invention, the step of calculating the target total working time of the gearbox according to the calculated daily working time and the defined target design working time of the gearbox comprises the following steps:
proportionally converting the working days in a set time period into annual working days; multiplying the defined target gearbox design working years, the annual working days and the daily working time to obtain the target gearbox total working time.
As a preferred aspect of the present invention, the step of calculating the number of rotations of the transmission input shaft under each torque of each gear according to the gear use probability table, the target transmission total operation time length, the gear use probability table, and the average vehicle speed of each gear includes:
calculating the design working time of each gear according to the gear use probability table and the target gearbox total working time;
calculating the design working time under each torque of each gear according to the gear torque frequency table and the design working time of each gear;
calculating the rotating speed of an input shaft of the gearbox under each gear according to the average speed of each gear and the bridge speed ratio of a sample vehicle;
and calculating the revolution of the input shaft of the gearbox under each torque of each gear based on the designed working time under each torque of each gear.
As a preferred embodiment of the present invention, the method further includes:
multiplying the use probability of each gear by the total working time of the target gearbox to obtain the designed working time of each gear;
and multiplying the frequency corresponding to each torque of each gear by the designed working time length of each gear to obtain the designed working time length of each torque of each gear.
As a preferred embodiment of the present invention, the method further includes:
speed of transmission input shaft = average vehicle speed per gearBridge speed ratio->Target transmission gear ratio/tire circumference;
transmission input shaft revolution per torque per gear = design operating time per gear per torqueCorresponding to the rotation speed of the input shaft of the gearbox under the gear.
In a second aspect, the technical scheme of the invention provides a transmission load spectrum conversion device based on big data, which comprises a preset module, a vehicle data acquisition module, a statistical analysis module, a calculation module and a rack load spectrum generation module;
the preset module is used for defining the design working life of the target gearbox and the gear speed ratio of the target gearbox;
the vehicle data acquisition module is used for acquiring real-time information of whole vehicle feedback of the sample vehicle in a set time period;
the statistical analysis module is used for carrying out statistical analysis on the acquired real-time information to generate a gear torque frequency table, a gear use probability table and an average speed table of each gear of the sample vehicle and calculating the daily working time;
the calculating module is used for calculating the total working time of the target gearbox according to the calculated daily working time and the defined design working time of the target gearbox; calculating the revolution of an input shaft of the gearbox under each torque of each gear according to the gear use probability table, the target total working time length of the gearbox, the gear use probability table and the average speed of each gear;
the rack load spectrum generation module is used for converting the rotation number of the input shaft of the gearbox under each torque of each gear into the rotation number of the full torque of the gearbox according to the Minner linear damage accumulation theory to form a rack load spectrum of the gearbox.
As an optimization of the technical scheme of the invention, the preset module comprises a sample selection unit and a parameter definition unit;
the sample selection unit is used for selecting vehicles with the engine model ratio larger than a set threshold value as sample vehicles in consideration of the engine model distribution matched with the target gearbox market;
and the parameter definition unit is used for defining the design working life of the target gearbox and the gear speed ratio of the gearbox.
As the optimization of the technical scheme of the invention, the statistical analysis module is particularly used for carrying out statistical analysis on the acquired real-time information to distinguish different gears, distinguishing different torques of the same gear, forming a total gear torque frequency table, simultaneously forming a gear use probability table, an average gear speed table of each gear and calculating daily working time length, and further being used for converting working days in a set time period into working days of each year in proportion.
As an optimization of the technical scheme of the invention, the calculation module comprises a duration calculation unit, a rotation speed calculation unit and a rotation number calculation unit;
the time length calculation unit is used for multiplying the defined design working time limit, the defined annual working days and the defined daily working time length of the target gearbox to obtain the total working time length of the target gearbox; the method is also used for multiplying the use probability of each gear by the total working time of the target gearbox to obtain the designed working time of each gear; multiplying the frequency corresponding to each torque of each gear by the design working time of each gear to obtain the design working time of each torque of each gear;
the rotating speed calculating unit is used for calculating the rotating speed of the input shaft of the gearbox under each gear according to the average speed of each gear and the bridge speed ratio of the sample vehicle; specifically calculated according to the following formula:
speed of transmission input shaft = average vehicle speed per gearBridge speed ratio->Target transmission gear ratio/tire circumference;
the revolution calculating unit is used for calculating the revolution of the input shaft of the gearbox under each gear and each torque based on the designed working time under each gear and each torque, and specifically calculates according to the following formula:
transmission input shaft revolution per torque per gear = design operating time per gear per torqueCorresponding to the rotation speed of the input shaft of the gearbox under the gear.
In a third aspect, the present invention further provides an electronic device, where the electronic device includes:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores computer program instructions executable by the at least one processor to enable the at least one processor to perform the big data based transmission load spectrum conversion method as described in the first aspect.
In a fourth aspect, the present disclosure further provides a non-transitory computer readable storage medium, where the non-transitory computer readable storage medium stores computer instructions, where the computer instructions cause the computer to execute the transmission load spectrum conversion method based on big data according to the first aspect.
From the above technical scheme, the invention has the following advantages: the load spectrum of the gearbox formed by the method comprises the influence of factors such as road conditions of actual users, actual loads of the vehicles, driving habits of drivers and the like, and based on a large amount of user data, the assessment of the selected market on the service life of gears of the gearbox is reflected more truly, so that a bench assessment basis is provided for new box type put-on market.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
It can be seen that the present invention has outstanding substantial features and significant advances over the prior art, as well as its practical advantages.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic flow chart of a method of one embodiment of the invention.
Fig. 2 is a schematic block diagram of an apparatus of one embodiment of the invention.
FIG. 3 is a partial view of a gear torque frequency chart in an embodiment of the invention.
Fig. 4 is a chart of gear probabilities in an embodiment of the present invention.
FIG. 5 is a schematic diagram of S-N curve of a material according to an embodiment of the present invention.
FIG. 6 is a graph of a test load spectrum of a gearbox stage generated by an embodiment of the present invention.
Detailed Description
At present, the durability assessment standards of gearboxes are mainly divided into two types: the durability of the gearbox is verified by adopting the national recommended standard or the durability standard of the enterprise standard, and the standards cannot truly reflect the parameters of the whole vehicle, the use habits of drivers and the different requirements of different market subdivision fields on each gear of the gearbox, so that the problems of insufficient test verification or over verification exist; the other type is to collect the real road load spectrum based on one or more typical vehicles, and compile the load spectrum of the gearbox by utilizing data extrapolation, so that the method cannot cover a large amount of vehicle data, replaces the whole market segment data for certain specific working condition data, has larger deviation risk, and has the defects of long time and high cost for collecting the real road spectrum of the typical vehicles.
The invention provides a transmission load spectrum conversion method, which is based on actual user use data at a vehicle end, and statistically analyzes gear torque frequency, gear use probability, average speed of each gear and daily working time of a vehicle under big data by analyzing the vehicle model under a subdivision market; and calculating the rotation speed of the input shaft of the gearbox under each torque of each gear based on a power chain, and converting the rotation speed of the input shaft of the gearbox under each torque of each gear into the rotation speed of the full torque of the gearbox according to the Minner linear damage accumulation theory to form a final gearbox rack load spectrum.
In order to make the technical solution of the present invention better understood by those skilled in the art, the technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
FIG. 1 is a flow chart of a method for transforming a load spectrum of a transmission based on big data, which includes the steps of:
step 1: defining a target gearbox design working life and a target gearbox gear speed ratio;
in the step, specifically, considering the engine model distribution matched with the target gearbox market, and selecting a vehicle with the engine model ratio larger than a set threshold as a sample vehicle; when a sample vehicle is selected, selecting one or more than one engine model which can cover market demands according to matching duty ratio distribution of the target market engines; meanwhile, the bridge speed ratio of the vehicle is configured by considering the selected engine model, and one or more types with large market ratio are selected. As many vehicle samples as possible of server capabilities are calculated from the data.
A target transmission design operating life and transmission gear speed ratio are defined.
Step 2: acquiring feedback real-time information of the whole vehicle of the sample vehicle in a set time period; the real-time information is fed back by the CAN of the whole vehicle within a set period (which CAN be one week or 30 days) of taking the sample vehicle. Engine horsepower, torque, gear and bridge speed ratio information are retrieved.
Step 3: carrying out statistical analysis on the acquired real-time information to generate a gear torque frequency table, a gear use probability table and an average speed table of each gear of the sample vehicle, and calculating the daily working time;
the abscissa of the gear torque frequency table is the frequency of the torque corresponding to the percentage of the torque, and the abscissa of the gear use probability table is the probability that the ordinate of the gear is used; a gear torque frequency table is used for distinguishing different gears and calculating the frequency of different torques under the same gear;
the step of carrying out statistical analysis on the acquired real-time information to generate a gear torque frequency table, a gear use probability table and an average speed table of each gear of the sample vehicle and calculating the daily working time comprises the following steps:
carrying out statistical analysis on the acquired real-time information to distinguish different gears, distinguishing different torques by the same gear, and forming a total gear torque frequency table, wherein fig. 3 is a view of a gear torque frequency part table, specifically a gear torque frequency table with the torque percentage of 70% -100%; the gear use probability table is shown in fig. 4, the average speed table of each gear and the daily working time length are calculated.
Step 4: calculating the total working time of the target gearbox according to the calculated daily working time and the defined design working time of the target gearbox;
in the step, the working days in a set time period are required to be converted into annual working days in proportion;
multiplying the defined target gearbox design working years, the annual working days and the daily working time to obtain the target gearbox total working time.
Step 5: calculating the revolution of an input shaft of the gearbox under each torque of each gear according to the gear use probability table, the target total working time length of the gearbox, the gear use probability table and the average speed of each gear;
the method comprises the following steps: calculating the design working time of each gear according to the gear use probability table and the target gearbox total working time; specifically, multiplying the use probability of each gear by the total working time of the target gearbox to obtain the designed working time of each gear;
calculating the design working time under each torque of each gear according to the gear torque frequency table and the design working time of each gear; specifically, the frequency corresponding to each torque of each gear in the gear torque frequency table is multiplied by the designed working time length of each gear to obtain the designed working time length under each torque of each gear.
Calculating the rotating speed of an input shaft of the gearbox under each gear according to the average speed of each gear and the bridge speed ratio of a sample vehicle;
speed of transmission input shaft = average vehicle speed per gearBridge speed ratio->Target transmission gear ratio/tire circumference;
calculating the revolution of an input shaft of the gearbox under each gear under each torque based on the designed working time under each torque of each gear; specifically, the number of rotations of the input shaft of the transmission under each torque of each gear=the designed operating time duration under each torque of each gearCorresponding to the rotation speed of the input shaft of the gearbox under the gear.
Step 6: according to the Minner linear damage accumulation theory, the rotation number of the input shaft of the gearbox under each torque of each gear is converted into the full torsion rotation number of the gearbox, and a gearbox rack load spectrum is formed.
Fig. 5 is an S-N curve, which is the basis for fatigue analysis, depicting material stress versus cycle life, and, in fig. 5,for the circulation radix>And (3) converting the rotation number of the input shaft of the gearbox under each torque of each gear into the full torsion rotation number of the gearbox according to the Minner linear damage accumulation theory for the fatigue stress limit of the material. When the maximum output torque of the engine is inconsistent with the maximum torque of the gearbox, the checking torque of the gearbox rack is considered.
When the full torque revolution conversion of the gearbox is calculated, the maximum torque output by the engine of the selected sample vehicle and the full torque moment of the gearbox are considered. The load spectrum of the gearbox bench test generated in the embodiment of the invention is shown in fig. 6.
The load spectrum of the gearbox formed by the method comprises the influence of factors such as road conditions of actual users, actual loads of the vehicles, driving habits of drivers and the like, and based on a large amount of user data, the assessment of the selected market on the service life of gears of the gearbox is reflected more truly, so that a bench assessment basis is provided for new box type put-on market.
FIG. 2 is a schematic block diagram of a big data based gearbox load spectrum conversion device provided by an embodiment of the invention, wherein the device comprises a preset module, a vehicle data acquisition module, a statistical analysis module, a calculation module and a rack load spectrum generation module;
the preset module is used for defining the design working life of the target gearbox and the gear speed ratio of the target gearbox;
it should be noted that, the preset module includes a sample selection unit and a parameter definition unit;
the sample selection unit is used for selecting vehicles with the engine model ratio larger than a set threshold value as sample vehicles in consideration of the engine model distribution matched with the target gearbox market; considering engine model distribution matched with a target gearbox market, and selecting a vehicle with an engine model ratio larger than a set threshold as a sample vehicle; when a sample vehicle is selected, selecting one or more than one engine model which can cover market demands according to matching duty ratio distribution of the target market engines; meanwhile, the bridge speed ratio of the vehicle is configured by considering the selected engine model, and one or more types with large market ratio are selected. As many vehicle samples as possible of server capabilities are calculated from the data.
And the parameter definition unit is used for defining the design working life of the target gearbox and the gear speed ratio of the gearbox.
The vehicle data acquisition module is used for acquiring real-time information of whole vehicle feedback of the sample vehicle in a set time period; the real-time information comprises engine horsepower, torque, gear and bridge speed ratio;
the statistical analysis module is used for carrying out statistical analysis on the acquired real-time information to generate a gear torque frequency table, a gear use probability table and an average speed table of each gear of the sample vehicle and calculating the daily working time;
the method is particularly used for carrying out statistical analysis on the acquired real-time information to distinguish different gears, distinguishing different torques of the same gear, forming a total gear torque frequency table, simultaneously forming a gear use probability table, an average speed table of each gear and calculating daily working time, and further being used for converting working days in a set time period into annual working days in proportion.
The calculating module is used for calculating the total working time of the target gearbox according to the calculated daily working time and the defined design working time of the target gearbox; calculating the revolution of an input shaft of the gearbox under each torque of each gear according to the gear use probability table, the target total working time length of the gearbox, the gear use probability table and the average speed of each gear;
the calculation module comprises a duration calculation unit, a rotation speed calculation unit and a rotation number calculation unit;
the time length calculation unit is used for multiplying the defined design working time limit, the defined annual working days and the defined daily working time length of the target gearbox to obtain the total working time length of the target gearbox; the method is also used for multiplying the use probability of each gear by the total working time of the target gearbox to obtain the designed working time of each gear; multiplying the frequency corresponding to each torque of each gear by the design working time of each gear to obtain the design working time of each torque of each gear;
the rotating speed calculating unit is used for calculating the rotating speed of the input shaft of the gearbox under each gear according to the average speed of each gear and the bridge speed ratio of the sample vehicle; specifically calculated according to the following formula:
speed of transmission input shaft = average vehicle speed per gearBridge speed ratio->Target transmission gear ratio/tire circumference;
the revolution calculating unit is used for calculating the revolution of the input shaft of the gearbox under each gear and each torque based on the designed working time under each gear and each torque, and specifically calculates according to the following formula:
transmission input shaft revolution per torque per gear = design operating time per gear per torqueCorresponding to the rotation speed of the input shaft of the gearbox under the gear.
The rack load spectrum generation module is used for converting the rotation number of the input shaft of the gearbox under each torque of each gear into the rotation number of the full torque of the gearbox according to the Minner linear damage accumulation theory to form a rack load spectrum of the gearbox.
The embodiment of the invention also provides electronic equipment, which comprises: the device comprises a processor, a communication interface, a memory and a bus, wherein the processor, the communication interface and the memory are in communication with each other through the bus. The bus may be used for information transfer between the electronic device and the sensor. The processor may call logic instructions in memory to perform the following method: step 1, defining a design working life of a target gearbox and a gear speed ratio of the target gearbox; step 2, acquiring feedback real-time information of the whole vehicle of the sample vehicle in a set time period; step 3: carrying out statistical analysis on the acquired real-time information to generate a gear torque frequency table, a gear use probability table and an average speed table of each gear of the sample vehicle, and calculating the daily working time; step 4: calculating the total working time of the target gearbox according to the calculated daily working time and the defined design working time of the target gearbox; step 5: calculating the revolution of an input shaft of the gearbox under each torque of each gear according to the gear use probability table, the target total working time length of the gearbox, the gear use probability table and the average speed of each gear; step 6: according to the Minner linear damage accumulation theory, the rotation number of the input shaft of the gearbox under each torque of each gear is converted into the full torsion rotation number of the gearbox, and a gearbox rack load spectrum is formed.
Specifically, the processor may call logic instructions in memory to perform the following method: and carrying out statistical analysis on the acquired real-time information to distinguish different gears, distinguishing different torques of the same gear, forming a total gear torque frequency table, forming a gear use probability table, an average speed table of each gear and calculating daily working time.
The processor may call logic instructions in memory to perform the following method: proportionally converting the working days in a set time period into annual working days; multiplying the defined target gearbox design working years, the annual working days and the daily working time to obtain the target gearbox total working time; calculating the design working time of each gear according to the gear use probability table and the target gearbox total working time; calculating the design working time under each torque of each gear according to the gear torque frequency table and the design working time of each gear; calculating the rotating speed of an input shaft of the gearbox under each gear according to the average speed of each gear and the bridge speed ratio of a sample vehicle; and calculating the revolution of the input shaft of the gearbox under each torque of each gear based on the designed working time under each torque of each gear.
Further, the logic instructions in the memory described above may be implemented in the form of software functional units and stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Embodiments of the present invention provide a non-transitory computer readable storage medium storing computer instructions that cause a computer to perform the methods provided by the method embodiments described above, for example, including: step 1, defining a design working life of a target gearbox and a gear speed ratio of the target gearbox; step 2, acquiring feedback real-time information of the whole vehicle of the sample vehicle in a set time period; step 3: carrying out statistical analysis on the acquired real-time information to generate a gear torque frequency table, a gear use probability table and an average speed table of each gear of the sample vehicle, and calculating the daily working time; step 4: calculating the total working time of the target gearbox according to the calculated daily working time and the defined design working time of the target gearbox; step 5: calculating the revolution of an input shaft of the gearbox under each torque of each gear according to the gear use probability table, the target total working time length of the gearbox, the gear use probability table and the average speed of each gear; step 6: according to the Minner linear damage accumulation theory, the rotation number of the input shaft of the gearbox under each torque of each gear is converted into the full torsion rotation number of the gearbox, and a gearbox rack load spectrum is formed.
Although the present invention has been described in detail by way of preferred embodiments with reference to the accompanying drawings, the present invention is not limited thereto. Various equivalent modifications and substitutions may be made in the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and it is intended that all such modifications and substitutions be within the scope of the present invention/be within the scope of the present invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (4)
1. The transmission load spectrum conversion method based on big data is characterized by comprising the following steps of:
defining a target gearbox design working life and a target gearbox gear speed ratio;
acquiring feedback real-time information of the whole vehicle of the sample vehicle in a set time period;
carrying out statistical analysis on the acquired real-time information to generate a gear torque frequency table, a gear use probability table and an average speed table of each gear of the sample vehicle, and calculating the daily working time;
calculating the total working time of the target gearbox according to the calculated daily working time and the defined design working time of the target gearbox;
calculating the revolution of an input shaft of the gearbox under each torque of each gear according to the gear use probability table, the target total working time length of the gearbox, the gear torque frequency table and the average speed of each gear;
according to the Minner linear damage accumulation theory, converting the rotation number of the input shaft of the gearbox under each torque of each gear into the rotation number of the full torque of the gearbox, and forming a gearbox rack load spectrum;
wherein, the step of defining the design working life of the target gearbox and the gear speed ratio of the gearbox comprises the following steps:
considering engine model distribution matched with a target gearbox market, and selecting a vehicle with an engine model ratio larger than a set threshold as a sample vehicle; defining a target gearbox design working life and a gearbox gear speed ratio;
the step of carrying out statistical analysis on the acquired real-time information to generate a gear torque frequency table, a gear use probability table and an average speed table of each gear of the sample vehicle and calculating the daily working time comprises the following steps:
carrying out statistical analysis on the acquired real-time information to distinguish different gears, distinguishing different torques by the same gear, forming a total gear torque frequency table, forming a gear use probability table, an average speed table of each gear and calculating daily working time;
the step of calculating a target transmission total operating time based on the calculated daily operating time in combination with the defined target transmission design operating time includes:
proportionally converting the working days in a set time period into annual working days; multiplying the defined target gearbox design working years, the annual working days and the daily working time to obtain the target gearbox total working time;
the step of calculating the number of revolutions of the input shaft of the gearbox under each torque of each gear according to the gear use probability table, the target total working time length of the gearbox, the gear torque frequency table and the average speed of each gear comprises the following steps:
calculating the rotating speed of an input shaft of the gearbox under each gear according to the average speed of each gear and the bridge speed ratio of a sample vehicle; calculating the revolution of an input shaft of the gearbox under each gear under each torque based on the designed working time under each torque of each gear; multiplying the use probability of each gear by the total working time of the target gearbox to obtain the designed working time of each gear;
multiplying the frequency corresponding to each torque of each gear by the design working time length of each gear to obtain the design working time length under each torque of each gear; speed of transmission input shaft = average vehicle speed per gearBridge speed ratio->Target transmission gear ratio/tire circumference; the number of revolutions of the transmission input shaft in each gear under each torque=the design operating time length in each gear under each torque>Corresponding to the rotation speed of the input shaft of the gearbox under the gear.
2. The transmission load spectrum conversion device based on big data is characterized by comprising a preset module, a vehicle data acquisition module, a statistical analysis module, a calculation module and a rack load spectrum generation module;
the preset module is used for defining the design working life of the target gearbox and the gear speed ratio of the target gearbox; the method specifically comprises the following steps: considering engine model distribution matched with a target gearbox market, and selecting a vehicle with an engine model ratio larger than a set threshold as a sample vehicle; defining a target gearbox design working life and a gearbox gear speed ratio;
the vehicle data acquisition module is used for acquiring real-time information of whole vehicle feedback of the sample vehicle in a set time period;
the statistical analysis module is used for carrying out statistical analysis on the acquired real-time information to generate a gear torque frequency table, a gear use probability table and an average speed table of each gear of the sample vehicle and calculating the daily working time; the method specifically comprises the following steps: carrying out statistical analysis on the acquired real-time information to distinguish different gears, distinguishing different torques by the same gear, forming a total gear torque frequency table, forming a gear use probability table, an average speed table of each gear and calculating daily working time;
the calculating module is used for calculating the total working time of the target gearbox according to the calculated daily working time and the defined design working time of the target gearbox; the method specifically comprises the following steps: proportionally converting the working days in a set time period into annual working days; multiplying the defined target gearbox design working years, the annual working days and the daily working time to obtain the target gearbox total working time; calculating the design working time of each gear according to the gear use probability table and the target gearbox total working time; calculating the design working time under each torque of each gear according to the gear torque frequency table and the design working time of each gear; calculating the rotating speed of an input shaft of the gearbox under each gear according to the average speed of each gear and the bridge speed ratio of a sample vehicle; calculating the revolution of an input shaft of the gearbox under each gear under each torque based on the designed working time under each torque of each gear; multiplying the use probability of each gear by the total working time of the target gearbox to obtain the designed working time of each gear; multiplying the frequency corresponding to each torque of each gear by the design working time length of each gear to obtain the design working time length under each torque of each gear; speed of transmission input shaft = average vehicle speed per gearBridge speed ratio->Target changeGearbox gear ratio/tire circumference; the number of revolutions of the transmission input shaft in each gear under each torque=the design operating time length in each gear under each torque>The rotation speed of an input shaft of the gearbox under corresponding gear;
the rack load spectrum generation module is used for converting the rotation number of the input shaft of the gearbox under each torque of each gear into the rotation number of the full torque of the gearbox according to the Minner linear damage accumulation theory to form a rack load spectrum of the gearbox.
3. An electronic device, the electronic device comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores computer program instructions executable by the at least one processor to enable the at least one processor to perform the big data based transmission load spectrum conversion method of claim 1.
4. A non-transitory computer-readable storage medium storing computer instructions that cause the computer to perform the big data based transmission load spectrum conversion method of claim 1.
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