Excavator oil consumption optimization method and system based on cloud computing

Abstract

The invention discloses an excavator oil consumption optimization method and system based on cloud computing. The excavator oil consumption optimization method comprises the steps that multi-dimensional operation data of an excavator are obtained and preprocessed; identifying the working condition of the excavator by using a machine learning algorithm, and dividing independent working periods; on the basis of each independent operation cycle, oil consumption of the unit operation amount is obtained through calculation, and energy efficiency evaluation is completed on the basis of the oil consumption of the unit operation amount in combination with historical multi-dimensional operation data; and based on an energy efficiency evaluation result, comparing the operation habit of the driver with a preset optimal operation model, and evaluating the matching degree of the engine power and the hydraulic load to obtain an optimization suggestion. According to the invention, refined, active and intelligent management of the oil consumption of the excavator is realized, so that the fuel cost is obviously reduced, and the operation efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of excavator fuel consumption optimization technology, and in particular to a cloud computing-based method and system for excavator fuel consumption optimization. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In the field of engineering construction, excavators, as core construction equipment, account for a dominant portion of overall operating costs due to their fuel consumption. The level of fuel consumption management directly impacts the economic efficiency of engineering construction. With the continuous expansion of infrastructure construction, the application scenarios for excavators are becoming increasingly diverse, and the intensity of their operations is constantly increasing, making the need for refined and efficient fuel consumption management ever more urgent.

[0004] Currently, the technical solutions for excavator fuel consumption management in the industry mainly revolve around three core ideas: First, relying on the driver's experience, allowing the driver to adjust the operation mode according to their own working habits to control fuel consumption; second, using simple on-board monitoring equipment to collect and display basic data such as fuel consumption and engine speed; and third, applying single-machine energy-saving technology to achieve energy saving by optimizing the equipment's own hardware parameters or local control logic.

[0005] Research has revealed significant limitations in existing technological solutions, making it difficult to meet the demands of large-scale, intelligent fuel consumption management: First, existing technologies optimize single devices or individual operational steps without considering equipment status, driver operation, work tasks, and environmental conditions as a holistic system, resulting in significant isolation and a lack of cross-device and cross-scenario collaborative analysis capabilities. Second, neither experience-based operation nor simple monitoring can proactively identify the causes of high fuel consumption and push optimization solutions; most analyses are post-event statistical analyses, exhibiting a clear passivity and hindering real-time intervention. Third, existing data collection, analysis, and feedback suffer from significant delays, failing to respond promptly to dynamic fuel consumption changes during operations, resulting in insufficient targeting of optimization measures due to non-real-time constraints. Fourth, existing solutions do not form a complete closed loop of "data collection-analysis-decision-execution-feedback" and lack a scientifically unified energy efficiency assessment standard, lacking systematic support and failing to accurately reflect the correlation between operational efficiency and fuel consumption, leading to a lack of quantitative basis and effective control measures for fuel consumption management. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a cloud computing-based method and system for optimizing excavator fuel consumption. Through real-time monitoring, cloud-based big data analysis, and intelligent decision-making, this invention achieves refined, proactive, and intelligent management of excavator fuel consumption, thereby significantly reducing fuel costs and improving operational efficiency.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a cloud computing-based method for optimizing excavator fuel consumption, comprising the following steps: Acquire multi-dimensional operational data from the excavator and preprocess the multi-dimensional operational data; Based on the preprocessed multi-dimensional operation data, machine learning algorithms are used to identify the operating conditions of the excavator and divide it into independent operation cycles. Based on each independent work cycle, the fuel consumption per unit of work is calculated. Based on the fuel consumption per unit of work and combined with historical multi-dimensional work data, an energy efficiency assessment is completed. Based on the energy efficiency assessment results, the driver's operating habits are compared with the preset optimal operating model, and the matching degree between engine power and hydraulic load is evaluated to obtain optimization suggestions.

[0008] As an optional implementation, the multi-dimensional operating data includes fuel consumption data, operating condition data, operation data, and positioning and attitude data; wherein, the fuel consumption data includes actual fuel consumption or cumulative fuel consumption; the operating condition data includes engine speed, hydraulic system pressure, main pump flow, cylinder pressure of each working device, and rotation speed; the operation data includes pilot handle signal, throttle signal, and working mode selection; and the positioning and attitude data includes excavator position, working posture, and movement trajectory.

[0009] As an optional implementation, the preprocessing includes data cleaning, filtering, alignment, and standardization of the multi-dimensional job data. The data cleaning involves setting a physically reasonable range and removing obviously erroneous data. The filtering involves eliminating noise in the multi-dimensional job data. The alignment uses nearest neighbor interpolation.

[0010] As an alternative implementation, the machine learning algorithm is a clustering analysis algorithm that identifies the working conditions of the excavator, including digging, leveling, loading, and traveling.

[0011] As an alternative implementation method, for loading operations, the calculation method for the fuel consumption per unit of work volume is as follows: ;in, This refers to the fuel consumption per unit of work done during loading operations. This refers to the total fuel consumption for a single work cycle. The number of buckets in a single work cycle. This refers to the rated capacity of the bucket.

[0012] For leveling operations, the calculation method for fuel consumption per unit of work volume is as follows: ;in, This refers to the fuel consumption per unit of work done during leveling operations. This refers to the leveled area.

[0013] As an alternative implementation, the driver's operating habits include the engine's high idling time, the frequency of violent operations, and the proportion of ineffective actions.

[0014] Secondly, the present invention provides a cloud computing-based excavator fuel consumption optimization system, comprising the following modules: The data access and preprocessing module is configured to: acquire multi-dimensional operation data of the excavator and preprocess the multi-dimensional operation data; The working condition identification and job division module is configured to: identify the working conditions of the excavator based on the preprocessed multi-dimensional job data using machine learning algorithms, and divide the work into independent work cycles; The energy efficiency assessment and fuel consumption optimization analysis module is configured to: calculate the fuel consumption per unit of work volume based on each independent work cycle; complete the energy efficiency assessment based on the fuel consumption per unit of work volume and combined with historical multi-dimensional work data; and, based on the energy efficiency assessment results, compare the driver's operating habits with the preset optimal operating model, evaluate the matching degree between engine power and hydraulic load, and obtain optimization suggestions.

[0015] Thirdly, the present invention provides an electronic device, including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the excavator fuel consumption optimization method based on cloud computing described in the first aspect.

[0016] Fourthly, the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the excavator fuel consumption optimization method based on cloud computing described in the first aspect.

[0017] Fifthly, the present invention provides a computer program product, including a computer program, which, when executed by a processor, implements the excavator fuel consumption optimization method based on cloud computing described in the first aspect.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention acquires multi-dimensional operational data not through a single device or a single process, but by incorporating elements such as equipment status, driver operation, and operational tasks into a unified analysis framework. At the same time, it combines historical multi-dimensional operational data with data from similar equipment to conduct energy efficiency assessments, breaking down the data analysis barriers of a single device and forming a global collaborative optimization system of "equipment-driver-operation task," thus completely overcoming the isolation defects of single-point optimization in existing technologies.

[0019] This invention acquires multi-dimensional operational data in real time, and after preprocessing, operating condition identification, and energy efficiency assessment, proactively compares driver operating habits with the optimal operating model, evaluates the matching degree between engine power and hydraulic load, accurately locates the causes of high fuel consumption, and generates optimization suggestions. The entire process transforms from "passive response" to "proactive prediction and intervention," solving the passive problems of existing technologies that cannot proactively identify problems and lack forward-looking optimization methods.

[0020] This invention utilizes machine learning algorithms to identify operating conditions and divide work cycles, calculate fuel consumption per unit of work volume, conduct energy efficiency assessments, and ultimately output targeted optimization suggestions in real time. This forms an efficient closed loop from data acquisition to optimization suggestion generation, enabling timely responses to dynamic fuel consumption changes during operations. It avoids the problems of delayed data feedback and insufficient targeting of optimization measures inherent in existing technologies, ensuring the timeliness and accuracy of fuel consumption management.

[0021] This invention constructs a complete technical chain of "data acquisition - preprocessing - operating condition identification - energy efficiency assessment - cause analysis - optimization suggestions", forming a closed-loop system from perception and analysis to decision-making and execution, solving the problem of the lack of a complete control system in existing technologies. At the same time, it uses fuel consumption per unit of work volume as the core energy efficiency assessment indicator, directly linking fuel consumption with actual work volume, replacing the traditional single and one-sided measurement standard, and giving fuel consumption management a clear quantitative basis. Furthermore, through detailed analysis such as comparison of driver operating habits and equipment matching degree assessment, it completely changes the current situation of fuel consumption management lacking scientific standards and effective control methods.

[0022] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0023] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0024] Figure 1 A flowchart illustrating a cloud computing-based method for optimizing excavator fuel consumption, provided in Embodiment 1 of the present invention; Figure 2 This is a framework diagram of a cloud computing-based excavator fuel consumption optimization system provided in Embodiment 2 of the present invention. Detailed Implementation

[0025] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0026] It should be noted that the following detailed description is exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0027] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0028] Example 1 The implementation scenario in this embodiment is an excavator loading operation at an earthwork engineering company, such as... Figure 1 As shown in the figure, this embodiment provides a cloud computing-based method for optimizing excavator fuel consumption, specifically including the following steps: S1: Acquire multi-dimensional operation data of the excavator and preprocess the multi-dimensional operation data.

[0029] In step S1, an onboard data acquisition terminal is installed on the excavator to acquire multi-dimensional operational data of the excavator in real time. This multi-dimensional operational data includes fuel consumption data, operating condition data, operational data, and positioning and attitude data. Specifically: Fuel consumption data includes actual or cumulative fuel consumption collected and processed by the engine's own ECU; operating condition data comes from various sensors, including engine speed, hydraulic system pressure, main pump flow, cylinder pressure of each working device, and swing speed; operation data comes from the controller area network bus, including pilot handle signal, throttle signal, working mode selection, etc.; positioning and attitude data comes from GPS / BeiDou module and inertial measurement unit, including excavator position, working attitude, and movement trajectory.

[0030] The multi-dimensional operation data is preprocessed and then packaged and uploaded to the cloud platform via wireless communication.

[0031] Preprocessing of multi-dimensional operational data includes data cleaning, filtering, alignment, and standardization. Data cleaning involves setting a physically reasonable range and removing obviously erroneous data. In this embodiment, IF(engine speed < 600 rpm or engine speed > 2200 rpm)THEN is marked as invalid. Filtering removes noise from the multi-dimensional operational data. Alignment uses nearest-neighbor interpolation.

[0032] S2: Based on the preprocessed multi-dimensional operation data, machine learning algorithms are used to identify the operating conditions of the excavator and divide it into independent operation cycles.

[0033] In step S2, based on the action sequence of "walking-digging-slewing-unloading-slewing," a machine learning algorithm is used to identify the excavator's operating conditions. In this embodiment, the machine learning algorithm is a clustering analysis algorithm, which enables intelligent understanding of unstructured operational data. The identified operating conditions of the excavator include digging, leveling, loading, and walking.

[0034] S3: Based on each independent work cycle, calculate the fuel consumption per unit of work volume. Based on the fuel consumption per unit of work volume and combined with historical multi-dimensional work data, complete the energy efficiency assessment. Based on the energy efficiency assessment results, compare the driver's operating habits with the preset optimal operating model, and evaluate the matching degree of engine power and hydraulic load to obtain optimization suggestions.

[0035] In step S3, the calculation method for fuel consumption per unit of work volume for loading operations is as follows: ;in, This refers to the fuel consumption per unit of work done during loading operations. This refers to the total fuel consumption for a single work cycle. The number of buckets in a single work cycle. This refers to the rated capacity of the bucket.

[0036] For leveling operations, the fuel consumption per unit of work volume is calculated as follows: ;in, This refers to the fuel consumption per unit of work done during leveling operations. The area has been leveled. The concept of "fuel consumption per unit of work" as the core energy efficiency evaluation indicator links fuel consumption with actual work efficiency, solving the one-sidedness of traditional fuel consumption indicators (such as liters per hour).

[0037] In this embodiment, the unit for loading operations is "liters per cubic meter"; for leveling operations, it can be "liters per square meter". This is a more scientific way to evaluate energy efficiency than simply using "liters per hour".

[0038] Energy efficiency assessment clarifies the current operational energy efficiency level and the criteria for determining whether fuel consumption is high. Based on the energy efficiency assessment results, the driver's operating habits are analyzed, such as engine high idling time, frequency of violent operations, and the proportion of ineffective actions. The driver's operating habits are compared with the preset optimal operating model to identify areas for improvement.

[0039] Simultaneously, the matching degree between engine power and hydraulic load is evaluated to determine whether there is power waste such as "over-powered engine for under-powered load". Based on the above, specific and actionable optimization suggestions are generated. A closed-loop optimization architecture integrating "driver behavior analysis", "equipment matching analysis", and "real-time guidance" forms a complete intelligent chain from perception to decision-making to execution. In this embodiment, a cloud computing-based excavator fuel consumption optimization method also provides users with an interactive interface and output results, including: The system utilizes an in-vehicle tablet or mobile app to receive real-time optimization suggestions from the cloud platform, such as "It is recommended to reduce engine speed to the economic zone" or "The current action can be smoother to reduce overflow loss." Simultaneously, it provides fleet managers with a web-based or mobile display dashboard showing overall fuel consumption reports, individual engine efficiency rankings, driver ratings, and the implementation status of optimization suggestions.

[0040] In this embodiment, the vehicle-mounted terminal continuously acquires the excavator's fuel consumption, operating conditions, operation, and location data, and uploads it to the cloud platform via a wireless network. The cloud platform preprocesses the data. Using a machine learning model, the cloud platform identifies the current operating conditions in real-time or near real-time and divides the work cycle. For each work cycle, the cloud platform calculates the fuel consumption per unit of work and performs an energy efficiency assessment by combining historical data and data from similar equipment. It also conducts in-depth analysis of the reasons behind high fuel consumption (whether it is an operational problem, an equipment setting problem, or a task planning problem). Finally, it generates optimization strategies, such as providing operational suggestions to the driver through a real-time guidance terminal and providing long-term analysis reports and management suggestions (such as providing targeted training to drivers and adjusting equipment operating mode parameters) to the administrator through the management platform.

[0041] Example 2 like Figure 2 As shown, this embodiment provides a cloud computing-based excavator fuel consumption optimization system, including the following modules: The data access and preprocessing module is configured to: acquire multi-dimensional operation data of the excavator and preprocess the multi-dimensional operation data; The working condition identification and job division module is configured to: identify the working conditions of the excavator based on the preprocessed multi-dimensional job data using machine learning algorithms, and divide the work into independent work cycles; The energy efficiency assessment and fuel consumption optimization analysis module is configured to: calculate the fuel consumption per unit of work volume based on each independent work cycle; complete the energy efficiency assessment based on the fuel consumption per unit of work volume and combined with historical multi-dimensional work data; and, based on the energy efficiency assessment results, compare the driver's operating habits with the preset optimal operating model, evaluate the matching degree between engine power and hydraulic load, and obtain optimization suggestions.

[0042] It should be noted that the above modules correspond to the steps in Embodiment 1, and the examples and application scenarios implemented by the above modules and their corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should also be noted that the above modules can be executed in a computer system as part of the system.

[0043] In further embodiments, the following is also provided: An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0044] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0045] A computer-readable storage medium for storing computer instructions that, when executed by a processor, perform the method of Embodiment 1.

[0046] The method in Example 1 can be directly executed by a hardware processor, or it can be executed by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0047] A computer program product includes a computer program that, when executed by a processor, implements the method in Embodiment 1.

[0048] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0049] The computer program code used to implement the methods of the present invention may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the computer or other programmable data processing device, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0050] In the context of this invention, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0051] Those skilled in the art will recognize that the units and algorithm steps described in conjunction with the embodiments herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0052] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A cloud computing-based method for optimizing excavator fuel consumption, characterized in that, Includes the following steps: Acquire multi-dimensional operational data from the excavator and preprocess the multi-dimensional operational data; Based on the preprocessed multi-dimensional operation data, machine learning algorithms are used to identify the operating conditions of the excavator and divide it into independent operation cycles. Based on each independent work cycle, the fuel consumption per unit of work is calculated. Based on the fuel consumption per unit of work and combined with historical multi-dimensional work data, an energy efficiency assessment is completed. Based on the energy efficiency assessment results, the driver's operating habits are compared with the preset optimal operating model, and the matching degree between engine power and hydraulic load is evaluated to obtain optimization suggestions.

2. The excavator fuel consumption optimization method based on cloud computing as described in claim 1, characterized in that, The multi-dimensional operational data includes fuel consumption data, operating condition data, operational data, and positioning and attitude data; wherein, the fuel consumption data includes actual fuel consumption or cumulative fuel consumption; the operating condition data includes engine speed, hydraulic system pressure, main pump flow, hydraulic cylinder pressure of each working device, and rotation speed; the operational data includes pilot handle signal, throttle signal, and working mode selection; and the positioning and attitude data includes excavator position, working posture, and movement trajectory.

3. The excavator fuel consumption optimization method based on cloud computing as described in claim 1, characterized in that, The preprocessing includes data cleaning, filtering, alignment, and standardization of multi-dimensional work data. Specifically, data cleaning involves setting a physically reasonable range and removing obviously erroneous data; filtering involves eliminating noise in the multi-dimensional work data; and alignment uses nearest neighbor interpolation.

4. The excavator fuel consumption optimization method based on cloud computing as described in claim 1, characterized in that, The machine learning algorithm is a clustering analysis algorithm, which identifies the working conditions of the excavator, including digging, leveling, loading, and traveling.

5. The excavator fuel consumption optimization method based on cloud computing as described in claim 1, characterized in that, For loading operations, the calculation method for fuel consumption per unit of work volume is as follows: ;in, This refers to the fuel consumption per unit of work done during loading operations. This refers to the total fuel consumption for a single work cycle. The number of buckets in a single work cycle. This refers to the rated capacity of the bucket. For leveling operations, the calculation method for fuel consumption per unit of work volume is as follows: ;in, This refers to the fuel consumption per unit of work done during leveling operations. This refers to the leveled area.

6. The excavator fuel consumption optimization method based on cloud computing as described in claim 1, characterized in that, The driver's operating habits include the time the engine idles at high speed, the frequency of violent operations, and the percentage of ineffective actions.

7. A cloud computing-based excavator fuel consumption optimization system, characterized in that, include: The data access and preprocessing module is configured to: acquire multi-dimensional operation data of the excavator and preprocess the multi-dimensional operation data; The working condition identification and job division module is configured to: identify the working conditions of the excavator based on the preprocessed multi-dimensional job data using machine learning algorithms, and divide the work into independent work cycles; The energy efficiency assessment and fuel consumption optimization analysis module is configured to: calculate the fuel consumption per unit of work volume based on each independent work cycle; complete the energy efficiency assessment based on the fuel consumption per unit of work volume and combined with historical multi-dimensional work data; and, based on the energy efficiency assessment results, compare the driver's operating habits with the preset optimal operating model, evaluate the matching degree between engine power and hydraulic load, and obtain optimization suggestions.

8. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the excavator fuel consumption optimization method based on any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, complete the excavator fuel consumption optimization method based on cloud computing as described in any one of claims 1-6.

10. A computer program product, characterized in that, The invention includes a computer program that, when executed by a processor, implements the excavator fuel consumption optimization method based on cloud computing as described in any one of claims 1-6.

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