Recommended gear position for excavator mode

Abstract

The invention relates to excavator control, and in order to solve the problem that there is no reference for the existing excavator mode gear selection, a method for recommending an excavator mode gear based on dynamic big data of the Internet of Vehicles is provided, wherein the steps include collecting excavator operation data at a predetermined frequency and uploading the data to a big data platform; the big data platform analyzes the main working mode, throttle gear and average fuel consumption of each excavator within a predetermined time according to a preset analysis frequency; the big data platform determines a target excavator and a reference excavator according to the average fuel consumption and a preset value of the fuel consumption of the machine model; the working mode and throttle gear information mainly used by the reference excavator are sent to each target excavator; each target excavator displays the recommended working mode and throttle gear pushed by the big data platform on a meter; the invention screens out excavators with high fuel consumption through the big data platform, and recommends reasonable working modes and throttle gears to them according to the analysis data, so that the excavators work under relatively reasonable fuel consumption.

Description

Technical Field

[0001] The present invention relates to excavator control, and more particularly to an excavator mode gear recommendation method. Background Art

[0002] Excavators are usually equipped with different working modes, such as heavy load mode and light load mode, for the driver to choose. The driver selects the corresponding working mode and throttle gear according to the public. For a certain working condition, there is an appropriate working mode and throttle gear, which makes the machine have lower fuel consumption when performing the working condition. The working mode and throttle gear of the excavator are selected by the driver based on his own experience, but the experience of each driver varies. If the selected working mode and throttle gear are inappropriate and do not match the current working condition, its fuel consumption will be higher than that of the excavator with the appropriate working mode and throttle gear. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an excavator mode gear recommendation method for the problem of no reference in the existing excavator mode gear selection, so that the operator has a reference adjustment when the mode gear selected by the operator is inappropriate when operating the machine, so as to achieve a more reasonable fuel consumption.

[0004] The technical solution of the present invention to achieve its purpose is as follows: a method for recommending gear positions of an excavator mode is provided, which is characterized by the following steps:

[0005] S1: Collect excavator operation data at a predetermined frequency and upload it to the Internet of Vehicles dynamic big data platform;

[0006] S2: The big data platform analyzes the main working mode, throttle position, and average fuel consumption of each excavator within the scheduled time according to the pre-set analysis frequency;

[0007] S3: The big data platform selects excavators whose average fuel consumption is greater than the preset value of the corresponding model fuel consumption as target excavators, and uses the model and working condition of the target excavator as screening conditions to select excavators with the same model and working condition and average fuel consumption less than the preset value of the model fuel consumption as reference excavators; the main working mode and throttle gear information of the reference excavator are sent to each target excavator;

[0008] S4: Each target excavator displays the recommended working mode and throttle gear position pushed by the big data platform on the instrument panel;

[0009] The operating data includes machine model information, throttle gear information, working mode information, and working condition information and fuel consumption information used to determine the working condition.

[0010] In the above recommendation method, in step S4, when the current working mode and throttle gear of the target excavator are different from the recommended working mode and throttle gear pushed by the big data platform, the target excavator sends a prompt message to remind to switch the working mode and throttle gear.

[0011] In the above recommendation method, in step S3, the reference excavator is selected based on the fact that the difference between the altitude of the reference excavator and the altitude of the target excavator is within a predetermined range as a screening condition.

[0012] In the above recommendation method, the operating data also includes positioning information of the longitude and latitude coordinates of the target excavator. In step S3, the reference excavator is selected based on the longitude and latitude coordinates of the reference excavator and the longitude and latitude coordinates of the target excavator being in the same area as the screening condition.

[0013] In the above recommendation method, in step S3, the working mode and throttle gear information mainly used by the reference excavator with the lowest fuel consumption are sent to each target excavator.

[0014] In the above-mentioned recommended method, in step S4, an operation interface for the operator to switch the working mode and the throttle gear with one key is displayed on the instrument, and the target excavator controls the working mode and the throttle gear according to the input instructions on the operation interface.

[0015] In the above recommendation method, when the recommended working mode and throttle gear pushed by the big data platform correspond to the current working mode and throttle gear of the target excavator, a prompt message of abnormal fuel consumption of the target excavator is pushed on the big data platform monitoring and management terminal.

[0016] In the above recommendation method, when the average fuel consumption is less than or equal to the preset fuel consumption value of the corresponding model, the excavator displays on its instrument the reminder information pushed by the big data platform to the excavator to maintain the current working mode and throttle gear.

[0017] In the above recommendation method, the preset fuel consumption value of the model is the comprehensive average fuel consumption of the excavator of this type working under various working conditions. Or the preset fuel consumption value of the model is the average fuel consumption of the excavator of this type working under a specific working condition; when selecting the target excavator in step S3, the working condition of each excavator is first determined, and the big data platform selects the excavator of the same model as the excavator and the average fuel consumption greater than the preset fuel consumption value of the model as the target excavator.

[0018] Excavators work in different working conditions, such as excavation, leveling, crushing, etc., and have different fuel consumption under different working conditions. For a certain type of excavator, its specific working condition fuel consumption under a certain working condition can be obtained by testing the excavator under the corresponding working condition before it leaves the factory. The comprehensive average fuel consumption is the average fuel consumption obtained by making the excavator work under different working conditions before it leaves the factory.

[0019] Compared with the prior art, the present invention screens out target excavators with high fuel consumption and then reference excavators with reasonable fuel consumption through a big data platform, and recommends the working mode and throttle gear of the reference excavator with reasonable fuel consumption to the target excavator, so that the target excavator works at a more reasonable fuel consumption, and each excavator is in a reasonable fuel consumption working state when the fleet is operating. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of the excavator mode gear recommendation method of the present invention. DETAILED DESCRIPTION

[0021] The specific implementation scheme is described below with reference to the accompanying drawings.

[0022] like Figure 1 As shown, the excavator mode gear recommendation method in this embodiment is based on the dynamic big data of the Internet of Vehicles and is applicable to cluster operations. The steps are as follows:

[0023] Step S1: Each excavator controller in the fleet collects excavator operation data through each sensor at a predetermined frequency and uploads it to the big data platform. The collected operation data includes machine model information, throttle gear information, working mode information, working condition information for determining the working condition, and positioning information for locating the longitude and latitude coordinates of the excavator. The excavator controller collects these operation data at a predetermined frequency, such as 20 milliseconds / time, 50 milliseconds / time or other frequencies, and uploads them to the big data platform through T-BOX.

[0024] Step S2: The big data platform analyzes the main working mode, throttle gear, and average fuel consumption of each excavator within the scheduled time according to the pre-set analysis frequency. The big data platform analyzes the data sent back by T-BOX. The analysis can select different analysis frequencies according to different needs and models, such as periodic analysis in units of days, weeks or months. In an analysis cycle, the excavator may operate in different working modes and different working gears. The working time in various modes and various throttle gears is counted, and the working mode and throttle gear with the longest duration are counted as the main working mode and throttle gear, and the average fuel consumption of each excavator is calculated. The scheduled time can be the previous analysis cycle of the excavator, or it can be a predetermined time interval. The average fuel consumption can be the average fuel consumption within the predetermined time, or it can be the average fuel consumption of the excavator working in the main working mode and throttle gear.

[0025] Step S3: The big data platform selects excavators whose average fuel consumption is greater than the preset value of the corresponding model fuel consumption as target excavators. Each model of excavator will determine its fuel consumption range through experiments before leaving the factory. A model fuel consumption preset value can be set according to the fuel consumption range. When the excavator is working, its fuel consumption exceeds the preset value of the model fuel consumption, then it can be considered that the fuel consumption of the excavator is too high and unreasonable. In view of the fact that the excavator works in different working conditions, its fuel consumption also varies. For accurate comparison, a specific fuel consumption preset value can also be set for each model of excavator according to its working condition, that is, each model of excavator sets a specific working condition average fuel consumption for various working conditions. When screening the target excavator, first determine the working condition of the excavator, select the specific working condition average fuel consumption corresponding to the excavator model and working condition according to the excavator model and working condition as the model fuel consumption preset value, and analyze the excavator whose average fuel consumption is greater than the model fuel consumption preset value as the target excavator. The preset fuel consumption value of a model may also be the comprehensive average fuel consumption of the model.

[0026] After the target excavator is determined, the model and working condition of the target excavator are used as screening conditions to select an excavator with the same model and working condition and an average fuel consumption less than the preset value of the model fuel consumption as the reference excavator. The main working mode and throttle gear information used by the reference excavator are sent to each target excavator.

[0027] When screening reference excavators, the longitude and latitude coordinates and altitude of the target excavator can also be used as screening conditions. The average fuel consumption of the reference excavator is less than the preset value of the model fuel consumption. At the same time, the longitude and latitude coordinates of the reference excavator are in the same area as the longitude and latitude coordinates of the target excavator, and the altitude difference between the reference excavator and the target excavator is within a predetermined range. The longitude and latitude coordinate area and the altitude difference can be set according to the specific working site of the target excavator.

[0028] The big data platform sends the working mode and throttle gear information mainly used by the reference excavator to each target excavator. Through the above-mentioned screening, there may be multiple reference excavators that meet the conditions. At this time, the working mode and throttle gear information mainly used by the reference excavator with the lowest average fuel consumption can be sent to each target excavator.

[0029] S4: Each target excavator displays the recommended working mode and throttle gear position pushed by the big data platform on the instrument. When the current working mode and throttle gear position of the target excavator are different from the recommended working mode and throttle gear position pushed by the big data platform, the target excavator sends a prompt message to remind the operator to switch the working mode and throttle gear position to achieve the purpose of energy saving.

[0030] For some target excavators that do not require manual switching of working modes and throttle gears, when the target excavator receives the recommended working mode and throttle gear information pushed by the big data platform and displays it on the instrument, an operating interface is displayed on the instrument for the operator to switch the working mode and throttle gear with one click. The target excavator controls the working mode and throttle gear according to the input instructions on the operating interface.

[0031] When the recommended working mode and throttle gear pushed by the big data platform correspond to the current working mode and throttle gear of the target excavator, the abnormal fuel consumption prompt information of the target excavator is pushed to the big data platform monitoring management terminal. The big data platform managers are notified to conduct further analysis to find out other reasons for the excessively high average fuel consumption of the machine.

[0032] If the big data platform analyzes in step S2 that the average fuel consumption of a certain excavator is less than the preset fuel consumption value of the corresponding model, the excavator displays on its instrument the prompt information pushed by the big data platform to the excavator to maintain the current working mode and throttle gear.

Claims

1. A method for recommending gear positions for excavator modes, characterized in that Here are the steps: S1: Collect excavator operation data at a predetermined frequency and upload it to the Internet of Vehicles dynamic big data platform; S2: The big data platform analyzes the average fuel consumption and the main working mode and throttle gear of each excavator within a predetermined time according to a preset analysis frequency; the main working mode and throttle gear refer to the working mode and throttle gear with the longest working time in various modes and various throttle gears within an analysis cycle; S3: The big data platform selects excavators whose average fuel consumption is greater than the preset fuel consumption value of the corresponding model as target excavators, and uses the model and operating condition of the target excavator as screening conditions to select excavators with the same model and operating condition and average fuel consumption less than the preset fuel consumption value of the model as reference excavators; Sending the working mode and throttle gear position information mainly used by the reference excavator to each target excavator; S4: Each target excavator displays the recommended working mode and throttle gear position pushed by the big data platform on the instrument panel; The operating data includes machine model information, throttle gear information, working mode information, and working condition information and fuel consumption information used to determine the working condition.

2. The recommendation method according to claim 1, characterized in that In step S4, when the current working mode and throttle gear of the target excavator are different from the recommended working mode and throttle gear pushed by the big data platform, the target excavator sends a prompt message to remind the user to switch the working mode and throttle gear.

3. The recommendation method according to claim 1, characterized in that In step S3, the reference excavator is selected based on the fact that the difference between the altitude of the reference excavator and the altitude of the target excavator is within a predetermined range as a screening condition.

4. The recommendation method according to claim 3, characterized in that: The operation data also includes positioning information of the longitude and latitude coordinates of the target excavator. In step S3, the reference excavator is selected based on the longitude and latitude coordinates of the reference excavator and the longitude and latitude coordinates of the target excavator being in the same area as the screening condition.

5. The recommendation method according to any one of claims 1 to 4, characterized in that In step S3, the working mode and throttle gear information mainly used by the reference excavator with the lowest fuel consumption are sent to each target excavator.

6. The recommendation method according to claim 1 or 2, characterized in that In step S4, an operation interface for the operator to switch the working mode and the throttle gear position with one key is displayed on the instrument, and the target excavator controls the working mode and the throttle gear position according to the input instructions on the operation interface.

7. The recommendation method according to claim 1 or 2, characterized in that When the recommended working mode and throttle gear pushed by the big data platform correspond to the current working mode and throttle gear of the target excavator, a prompt message of abnormal fuel consumption of the target excavator is pushed on the big data platform monitoring and management terminal.

8. The recommendation method according to claim 1 or 2, characterized in that When the average fuel consumption is less than or equal to the preset fuel consumption value of the corresponding model, the excavator displays on its instrument the big data platform pushes a prompt message to the excavator to maintain the current working mode and throttle gear.

9. The recommendation method according to claim 1, characterized in that The preset fuel consumption value of the model is the comprehensive average fuel consumption of the excavator of this model under various working conditions.

10. The recommendation method according to claim 1, characterized in that The preset fuel consumption value of the model is the average fuel consumption of the excavator of this model under a specific working condition; when screening the target excavator in step S3, the working condition of each excavator is first determined, and the big data platform screens out excavators with the same model as the excavator and an average fuel consumption greater than the preset fuel consumption value of the model as the target excavator.

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