CN115952693A - 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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Abstract
The invention belongs to the technical field of gearbox bench tests, and particularly provides a gearbox load spectrum conversion method, a device, equipment and a medium based on big data, wherein the method is characterized in that on the basis of actual user use data of a vehicle end, the gear torque frequency, the gear use probability, the average speed of each gear and the daily working time of a vehicle under the big data are statistically analyzed through vehicle matching analysis in the market; and (3) calculating the rotating speed of the input shaft of the gearbox under each torque of each gear, and converting the rotating speed of the input shaft of the gearbox under each torque of each gear into the rotating speed of the input shaft of the gearbox under full torque of the gearbox according to a 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 influences of factors such as road conditions of vehicles of actual users, real load of the vehicles, driving habits of drivers and the like, the service life of the gearbox gear in the selected market is more truly reflected on the basis of a large amount of user data, and a rack assessment basis is provided for putting a new box type into the 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 an automobile product is an important index for evaluating the quality of an automobile, a gearbox is used as an important component of an automobile power assembly, and the durability examination directly influences the service life of the whole power chain.
At present, the durability evaluation standards of the gearbox are mainly divided into two types: one type is that the endurance capacity of the gearbox is verified by adopting the endurance specifications of national recommended standards or enterprise standards, the standards cannot truly reflect the parameters of the whole vehicle, the use habits of drivers and different requirements of different market subdivision fields on each gear of the gearbox, and the problems of insufficient or over-verification of tests exist; the other type is that the real road load spectrum is collected based on one or a plurality of typical vehicles, the load spectrum of the gearbox is compiled by data extrapolation, the method cannot cover a large amount of vehicle data, the whole market subdividing data is replaced by the data under a certain specific working condition, a large deviation risk exists, and the real road spectrum of the typical vehicle is collected for a long time and high in cost, and the defects also exist.
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 daily running and road test working conditions of a full-life-cycle real vehicle are mainly simulated through the bench test. The effectiveness of the gearbox bench test and the accuracy of design check depend on the relevance of the load spectrum and the service condition of an end customer. How to generate the corresponding gearbox load spectrum according to different parameters is a crucial link.
Disclosure of Invention
In view of the existing problems, the invention provides a gearbox load spectrum conversion method, a gearbox load spectrum conversion device, gearbox load spectrum conversion equipment and gearbox load spectrum conversion media based on big data.
In a first aspect, the technical scheme of the invention provides a gearbox load spectrum conversion method based on big data, which comprises the following steps:
defining the design working life of a target gearbox and the gear ratio of the target gearbox;
acquiring the whole vehicle feedback real-time information of a sample vehicle within a set time period;
performing 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 age of the target gearbox;
calculating the revolution 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 of the gearbox, the gear use probability table and the average speed of each gear;
according to a Minner linear damage accumulation theory, the revolution of the input shaft of the gearbox under each torque of each gear is converted into the revolution of the input shaft of the gearbox under full torque of the gearbox, and a gearbox rack load spectrum is formed.
Preferably, the step of defining the design working life and the gear ratio of the target gearbox comprises the following steps:
considering engine model distribution matched with a target gearbox market, selecting a vehicle with an engine model ratio larger than a set threshold value as a sample vehicle;
and defining the design working life of the target gearbox and the gear ratio of the gearbox.
Preferably, the step of performing 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:
and performing statistical analysis on the acquired real-time information to distinguish different gears, distinguishing different torques in the same gear to form a total gear torque frequency table, and simultaneously forming a gear use probability table, an average speedometer of each gear and calculating the daily working time.
Preferably, the step of calculating the target total operating time of the transmission according to the calculated daily operating time and the defined target design operating life of the transmission comprises:
converting the working days in a set time period into the working days per year in proportion;
and multiplying the defined target gearbox design working life, the annual working days and the daily working duration to obtain the target gearbox total working duration.
Preferably, the step of calculating the revolution number 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 of the gearbox, the gear use probability table and the average speed of each gear comprises the following steps:
calculating the design working time of each gear according to the gear use probability table and the total working time of the target gearbox;
calculating the designed working time under each torque of each gear according to the gear torque frequency table and the designed working time of each gear;
calculating the rotating speed of the input shaft of the gearbox under each gear according to the average speed of each gear and the speed ratio of the sample vehicle axle;
and 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.
Preferably, the method further comprises:
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 of each gear to obtain the designed working time of each torque of each gear.
Preferably, the method further comprises:
the rotating speed of the input shaft of the gearbox under each gear = the average vehicle speed
Speed ratio of the axle>
Target transmission gear ratio/tire circumference;
the number of revolutions of an input shaft of a gearbox under each torque of each gear = the designed working time under each torque of each gear
And the rotating speed of the input shaft of the gearbox under the corresponding gear.
In a second aspect, the technical scheme of the invention provides a gearbox load spectrum conversion device based on big data, which comprises a presetting module, a vehicle data acquisition module, a statistical analysis module, a calculation module and a rack load spectrum generation module;
the presetting module is used for defining the design working life of a target gearbox and the gear ratio of the target gearbox;
the vehicle data acquisition module is used for acquiring the real-time information of the whole vehicle feedback of the sample vehicle within a set time period;
the statistical analysis module is used for performing statistical analysis on the acquired real-time information to generate a gear torque frequency table, a gear use probability table and each gear average speedometer of the sample vehicle and calculating the daily working time;
the calculation module is used for calculating the total working time of the target gearbox according to the calculated daily working time and the defined target gearbox design working age; calculating the revolution 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 of the gearbox, the gear use probability table and the average speed of each gear;
and the rack load spectrum generation module is used for converting the revolution of the input shaft of the gearbox under each torque of each gear into the revolution of the input shaft of the gearbox under full torque of the gearbox according to a Minner linear damage accumulation theory to form a gearbox rack load spectrum.
As a preferred embodiment of the present invention, the preset module includes a sample selection unit and a parameter definition unit;
the sample selection unit is used for selecting a vehicle with the engine model ratio larger than a set threshold value as a sample vehicle by considering 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 ratio of the gearbox.
As an optimization of the technical scheme of the invention, the statistical analysis module is specifically used for performing statistical analysis on the acquired real-time information to distinguish different gears, distinguishing different torques in the same gear to form a total gear torque frequency table, simultaneously forming a gear use probability table, an average speedometer of each gear, calculating daily working time, and further converting the working days in a set time period into the working days per year in proportion.
Preferably, the calculation module comprises a duration calculation unit, a rotating speed calculation unit and a revolution calculation unit;
the time length calculating unit is used for multiplying the defined design working age of the target gearbox, the annual working days and the daily working time length to obtain the total working time length of the target gearbox; the gear design control method is also used for multiplying the use probability of each gear by the total working duration of the target gearbox to obtain the designed working duration of each gear; multiplying the frequency corresponding to each torque of each gear by the designed working time of each gear to obtain the designed 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 sample vehicle axle speed ratio; specifically calculated according to the following formula:
gearbox input shaft speed = average vehicle speed under each gear
Speed ratio of the axle>
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 torque of each gear based on the designed working time under each torque of each gear, and specifically calculating according to the following formula:
the number of revolutions of an input shaft of a gearbox under each torque of each gear = the designed working time under each torque of each gear
And the rotating speed of the input shaft of the gearbox under the corresponding gear.
In a third aspect, a technical solution of 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 gearbox load spectrum translation method according to the first aspect.
In a fourth aspect, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the big-data based transmission load spectrum conversion method according to the first aspect.
According to the technical scheme, the invention has the following advantages: the load spectrum of the gearbox formed by the method comprises the influences of factors such as road conditions of vehicles of actual users, real load of the vehicles, driving habits of drivers and the like, the service life of the gearbox gear in the selected market is more truly reflected on the basis of a large amount of user data, and a rack assessment basis is provided for putting a new box type into the market.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Therefore, compared with the prior art, the invention has prominent substantive features and remarkable progress, and the beneficial effects of the implementation are also obvious.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the present invention.
Fig. 2 is a schematic block diagram of an apparatus of one embodiment of the present invention.
FIG. 3 is a partial view of a gear torque frequency table in an embodiment of the present invention.
FIG. 4 is a chart of gear probabilities in an embodiment of the present invention.
FIG. 5 is a schematic S-N curve of a material according to an embodiment of the present invention.
FIG. 6 is a graphical representation of a transmission bench test load spectrum generated in accordance with an embodiment of the present invention.
Detailed Description
At present, the durability evaluation standards of the gearbox are mainly divided into two types: one type is that the endurance of the gearbox is verified by adopting the endurance standard of national recommended standards or enterprise standards, the standards cannot truly reflect the parameters of the whole vehicle, the use habits of drivers and different requirements of different market subdivision fields on each gear of the gearbox, and the problem of insufficient or over verification of tests exists; the other type is based on one or more typical vehicles, real road load spectrums are collected, data extrapolation is used for compiling gearbox load spectrums, the method cannot cover a large amount of vehicle data, the whole market subdivision data is replaced by certain specific working condition data, large deviation risks exist, and meanwhile, the collection of the real road spectrums of the typical vehicles is long in time and high in cost and has disadvantages.
The invention provides a transmission load spectrum conversion method, which is characterized in that on the basis of actual user use data of a vehicle end, gear torque frequency, gear use probability, average speed of each gear and daily working duration of a vehicle under big data are statistically analyzed through vehicle configuration analysis under a market subdivision; and calculating the rotating speed of the input shaft of the gearbox under each torque of each gear based on a power chain, and converting the rotating speed of the input shaft of the gearbox under each torque of each gear into the rotating speed of the gearbox under full torque according to a Minner linear damage accumulation theory to form a final gearbox rack load spectrum.
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
FIG. 1 is a schematic flow chart diagram of a big data-based gearbox load spectrum conversion method provided by an embodiment of the invention, wherein the method comprises the following steps:
step 1: defining the design working life of a target gearbox and the gear ratio of the target gearbox;
in the step, specifically, the engine model distribution matched with the target gearbox market is considered, and a vehicle with the engine model ratio larger than a set threshold value is selected as a sample vehicle; when a sample vehicle is selected, one or more types of engines with larger occupation ratios capable of covering market demands are selected according to the matching occupation ratio distribution of the engines in the target market; meanwhile, the axle speed ratio of the vehicle is configured by considering the selected engine model, and one or more types with large market occupation ratio are also selected. The server capability is calculated from the data as many vehicle samples as possible.
And defining the design working life of the target gearbox and the gear ratio of the gearbox.
Step 2: acquiring the whole vehicle feedback real-time information of a sample vehicle within a set time period; and (3) calling sample vehicle CAN to feed back real-time information within a set time period (which CAN be one week or 30 days). And (3) regulating the horsepower, torque, gear and axle speed ratio information of the engine.
And step 3: performing 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 torque corresponding to the torque percentage of the gear ordinate, and the abscissa of the gear use probability table is the probability of the gear use of the gear ordinate; the gear torque frequency table is used for distinguishing different gears and calculating the frequency of different torques in the same gear;
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 each gear average speedometer of a sample vehicle and calculating the daily working time length of the sample vehicle comprise:
performing statistical analysis on the acquired real-time information to distinguish different gears, and distinguishing different torques in the same gear to form a total gear torque frequency table, wherein fig. 3 is a partial table view of the gear torque frequency, and specifically is a gear torque frequency table with the torque percentage of 70% -100%; forming a gear use probability table, such as the average vehicle speed table of each gear and calculating the daily working time length, shown in fig. 4.
And 4, step 4: calculating the total working time of the target gearbox according to the calculated daily working time and the defined target gearbox design working age;
in the step, the working days in the set time period need to be converted into the working days per year in proportion;
and multiplying the defined target gearbox design working life, the annual working days and the daily working duration to obtain the target gearbox total working duration.
And 5: calculating the revolution 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 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 total working time of the target gearbox; specifically, the use probability of each gear is multiplied by the total working duration of the target gearbox to obtain the designed working duration of each gear;
calculating the designed working time under each torque of each gear according to the gear torque frequency table and the designed 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 of each gear to obtain the designed working time of each torque of each gear.
Calculating the rotating speed of the input shaft of the gearbox under each gear according to the average speed of each gear and the axle speed ratio of the sample vehicle;
the rotating speed of the input shaft of the gearbox under each gear = the average vehicle speed
Axle speed ratio>
Target transmission gear ratio/tire circumference;
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;specifically, the number of revolutions of the input shaft of the gearbox under each torque of each gear = the designed working time under each torque of each gear
And the rotating speed of the input shaft of the gearbox under the corresponding gear.
And 6: according to a Minner linear damage accumulation theory, the revolution of the input shaft of the gearbox under each torque of each gear is converted into the revolution of the input shaft of the gearbox under the full torque of the gearbox, and a load spectrum of a gearbox rack is formed.
Fig. 5 is an S-N curve, which is the basis of a fatigue analysis, which describes the relationship of material stress to cycle life, and in fig. 5,
on a cycle base, <' > based>
And converting the revolution of the input shaft of the gearbox under each torque of each gear into the revolution of the gearbox under full torque according to a Minner linear damage accumulation theory as 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 needs to be considered.
When the rotating speed conversion of the full torque of the gearbox is calculated, the maximum torque output by the engine of the selected vehicle and the torque of the full torque of the gearbox need to be considered. The gearbox bench test load spectrum generated in the embodiment of the invention is shown in FIG. 6.
The load spectrum of the gearbox formed by the method comprises the influences of factors such as road conditions of vehicles of actual users, real load of the vehicles, driving habits of drivers and the like, the service life of the gearbox gear in the selected market is more truly reflected on the basis of a large amount of user data, and a rack assessment basis is provided for putting a new box type into the market.
FIG. 2 is a schematic block diagram of a big data-based transmission load spectrum conversion apparatus provided in an embodiment of the present invention, the apparatus includes a presetting module, a vehicle data acquisition module, a statistical analysis module, a calculation module, and a rack load spectrum generation module;
the presetting module is used for defining the design working life of a target gearbox and the gear 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 a vehicle with the engine model ratio larger than a set threshold value as a sample vehicle by considering the engine model distribution matched with the target gearbox market; considering engine model distribution matched with a target gearbox market, selecting a vehicle with an engine model ratio larger than a set threshold value as a sample vehicle; when a sample vehicle is selected, one or more types of engines with larger occupation ratios capable of covering market demands are selected according to the matching occupation distribution of the engines in the target market; meanwhile, the axle speed ratio of the vehicle is configured by considering the selected engine model, and one or more types with large market proportion are also selected. The server capability is calculated from the data as many vehicle samples as possible.
And the parameter definition unit is used for defining the design working life of the target gearbox and the gear ratio of the gearbox.
The vehicle data acquisition module is used for acquiring the real-time information of the whole vehicle feedback of the sample vehicle within a set time period; the real-time information comprises engine horsepower, torque, gears and axle speed ratio;
the statistical analysis module is used for performing statistical analysis on the acquired real-time information to generate a gear torque frequency table, a gear use probability table and an average speedometer of each gear of the sample vehicle and calculate the working time of each day;
the method is particularly used for carrying out statistical analysis on the acquired real-time information to distinguish different gears, distinguishing different torques in the same gear to form a total gear torque frequency table, simultaneously forming a gear use probability table and each gear average speedometer, calculating the daily working time and converting the working days in a set time period into the working days per year in proportion.
The calculation 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 age of the target gearbox; calculating the revolution 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 of the gearbox, the gear use probability table and the average speed of each gear;
the device specifically comprises a calculation module, a control module and a control module, wherein the calculation module comprises a duration calculation unit, a rotating speed calculation unit and a revolution calculation unit;
the time length calculating unit is used for multiplying the defined design working age of the target gearbox, the annual working days and the daily working time length to obtain the total working time length of the target gearbox; the gear design control method is also used for multiplying the use probability of each gear by the total working duration of the target gearbox to obtain the designed working duration of each gear; multiplying the frequency corresponding to each torque of each gear by the designed working time of each gear to obtain the designed 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 axle speed ratio of the sample vehicle; specifically calculated according to the following formula:
gearbox input shaft speed = average vehicle speed under each gear
Speed ratio of the axle>
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 torque of each gear based on the designed working time under each torque of each gear, and specifically calculating according to the following formula:
the design working time of the transmission input shaft revolution = the design working time under each torque of each gear
And the rotating speed of the input shaft of the gearbox under the corresponding gear.
And the rack load spectrum generation module is used for converting the revolution of the input shaft of the gearbox under each torque of each gear into the revolution of the input shaft of the gearbox under full torque of the gearbox according to a Minner linear damage accumulation theory to form a gearbox rack load spectrum.
An embodiment of the present invention further provides an electronic device, where the electronic device includes: the system comprises a processor, a communication interface, a memory and a bus, wherein the processor, the communication interface and the memory are communicated 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 the design working life of a target gearbox and the gear speed ratio of the target gearbox; step 2, acquiring the real-time information of the whole vehicle feedback of a sample vehicle within a set time period; and step 3: performing 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; and 4, step 4: calculating the total working time of the target gearbox according to the calculated daily working time and the defined design working age of the target gearbox; and 5: calculating the revolution 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 of the gearbox, the gear use probability table and the average speed of each gear; and 6: according to a Minner linear damage accumulation theory, the revolution of the input shaft of the gearbox under each torque of each gear is converted into the revolution of the input shaft of the gearbox under the full torque of the gearbox, and a load spectrum of a gearbox rack is formed.
Specifically, the processor may call logic instructions in the memory to perform the following method: and performing statistical analysis on the acquired real-time information to distinguish different gears, distinguishing different torques in the same gear to form a total gear torque frequency table, and simultaneously forming a gear use probability table, an average speedometer of each gear and calculating the daily working time.
The processor may call logic instructions in memory to perform the following method: converting the working days in a set time period into the working days per year in proportion; multiplying the defined design working life of the target gearbox, the annual working days and the daily working duration to obtain the total working duration of the target gearbox; calculating the design working time of each gear according to the gear use probability table and the total working time of the target gearbox; calculating the designed working time under each torque of each gear according to the gear torque frequency table and the designed working time of each gear; calculating the rotating speed of the input shaft of the gearbox under each gear according to the average speed of each gear and the speed ratio of the sample vehicle axle; and 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.
In addition, the logic instructions in the memory may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute 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), a magnetic disk or an optical disk, and 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 a method provided by the above method embodiments, for example, including: step 1, defining the design working life of a target gearbox and the gear ratio of the target gearbox; step 2, acquiring the real-time information of the whole vehicle feedback of a sample vehicle within a set time period; and step 3: performing 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; and 4, step 4: calculating the total working time of the target gearbox according to the calculated daily working time and the defined design working age of the target gearbox; and 5: calculating the revolution 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 of the gearbox, the gear use probability table and the average speed of each gear; step 6: according to a Minner linear damage accumulation theory, the revolution of the input shaft of the gearbox under each torque of each gear is converted into the revolution of the input shaft of the gearbox under the full torque of the gearbox, and a load spectrum of a gearbox rack is formed.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. The gearbox load spectrum conversion method based on the big data is characterized by comprising the following steps:
defining the design working life of a target gearbox and the gear ratio of the target gearbox;
acquiring the whole vehicle feedback real-time information of a sample vehicle within a set time period;
performing 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 age of the target gearbox;
calculating the revolution 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 of the gearbox, the gear use probability table and the average speed of each gear;
according to a Minner linear damage accumulation theory, the revolution of the input shaft of the gearbox under each torque of each gear is converted into the revolution of the input shaft of the gearbox under full torque of the gearbox, and a gearbox rack load spectrum is formed.
2. The big-data based transmission load spectrum conversion method according to claim 1, wherein the step of defining a target transmission design operating life and transmission gear ratio comprises:
considering engine model distribution matched with a target gearbox market, selecting a vehicle with an engine model ratio larger than a set threshold value as a sample vehicle;
and defining the design working life of the target gearbox and the gear ratio of the gearbox.
3. The big-data-based transmission load spectrum conversion method according to claim 2, wherein the step of performing statistical analysis on the acquired real-time information to generate a gear torque frequency table, a gear use probability table and an average vehicle speed table of each gear of the sample vehicle and calculating the daily working time comprises the steps of:
and performing statistical analysis on the acquired real-time information to distinguish different gears, distinguishing different torques in the same gear to form a total gear torque frequency table, and simultaneously forming a gear use probability table, an average speedometer of each gear and calculating the daily working time.
4. The big-data based transmission load spectrum conversion method of claim 3, wherein the step of calculating the target total transmission operating duration based on the calculated daily operating duration in combination with the defined target transmission design operating age comprises:
converting the working days in a set time period into the working days per year in proportion;
and multiplying the defined target gearbox design working life, the annual working days and the daily working duration to obtain the target gearbox total working duration.
5. The big-data-based transmission load spectrum conversion method according to claim 4, wherein the step of calculating the number of revolutions of the input shaft of the transmission at each torque of each gear according to the gear use probability table, the target total operating time of the transmission, the gear use probability table and the average vehicle speed of each gear comprises the following steps:
calculating the design working time of each gear according to the gear use probability table and the total working time of the target gearbox;
calculating the designed working time under each torque of each gear according to the gear torque frequency table and the designed working time of each gear;
calculating the rotating speed of the input shaft of the gearbox under each gear according to the average speed of each gear and the speed ratio of the sample vehicle axle;
and 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.
6. The big-data based transmission load spectrum translation method of claim 5, further comprising:
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 of each gear to obtain the designed working time of each torque of each gear.
7. The big-data based transmission load spectrum conversion method according to claim 6, further comprising:
gearbox input shaft speed = average vehicle speed under each gear
Speed ratio of the axle>
Target transmission gear ratio/tire circumference;
the number of revolutions of an input shaft of a gearbox under each torque of each gear = the designed working time under each torque of each gear
And the rotating speed of the input shaft of the gearbox under the corresponding gear.
8. The gearbox load spectrum conversion device based on big data is characterized by comprising a presetting module, a vehicle data acquisition module, a statistical analysis module, a calculation module and a rack load spectrum generation module;
the presetting module is used for defining the design working life of a target gearbox and the gear ratio of the target gearbox;
the vehicle data acquisition module is used for acquiring the real-time information of the whole vehicle feedback of the sample vehicle within a set time period;
the statistical analysis module is used for performing statistical analysis on the acquired real-time information to generate a gear torque frequency table, a gear use probability table and an average speedometer of each gear of the sample vehicle and calculate the working time of each day;
the calculation 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 age of the target gearbox; calculating the revolution 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 of the gearbox, the gear use probability table and the average speed of each gear;
and the rack load spectrum generation module is used for converting the revolution of the input shaft of the gearbox under each torque of each gear into the revolution of the input shaft of the gearbox under full torque of the gearbox according to a Minner linear damage accumulation theory to form a gearbox rack load spectrum.
9. An electronic device, characterized in that the electronic device comprises:
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 translation method of any one of claims 1 to 7.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the big-data based transmission load spectrum conversion method according to any of claims 1 to 7.
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