WO2024027284A1 - 行走跑偏预测方法、装置及作业机械 - Google Patents

行走跑偏预测方法、装置及作业机械 Download PDF

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Publication number
WO2024027284A1
WO2024027284A1 PCT/CN2023/095525 CN2023095525W WO2024027284A1 WO 2024027284 A1 WO2024027284 A1 WO 2024027284A1 CN 2023095525 W CN2023095525 W CN 2023095525W WO 2024027284 A1 WO2024027284 A1 WO 2024027284A1
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Prior art keywords
deviation
walking
parameters
influencing
prediction
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PCT/CN2023/095525
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English (en)
French (fr)
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余江平
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上海三一重机股份有限公司
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Publication of WO2024027284A1 publication Critical patent/WO2024027284A1/zh

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/08—Interaction between the driver and the control system
    • B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
    • E—FIXED CONSTRUCTIONS
    • E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
    • E02F—DREDGING; SOIL-SHIFTING
    • E02F9/00—Component parts of dredgers or soil-shifting machines, not restricted to one of the kinds covered by groups E02F3/00 - E02F7/00
    • E02F9/20—Drives; Control devices
    • E02F9/2058—Electric or electro-mechanical or mechanical control devices of vehicle sub-units
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/08—Interaction between the driver and the control system
    • B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
    • B60W2050/143—Alarm means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2300/00—Indexing codes relating to the type of vehicle
    • B60W2300/17—Construction vehicles, e.g. graders, excavators

Definitions

  • the present invention relates to the technical field of engineering machinery, and in particular to a walking deviation prediction method, device and operating machinery.
  • Walking deviation is an important indicator for evaluating the walking performance of working machinery. On the one hand, it has a great impact on the control accuracy and work efficiency of working operations. On the other hand, it also brings potential hidden dangers to the working environment and personnel.
  • Traditional methods usually rely on the "experience + trial and error" method to predict the walking deviation of working machines. It is poor in science and prone to chance, and cannot guarantee the prediction results of walking deviation. effectiveness.
  • the present invention provides a walking deviation prediction method, device and working machine.
  • Embodiments of the present invention provide a walking deviation prediction method, which includes:
  • the deviation prediction model is constructed based on a proxy model; the deviation prediction model includes a plurality of parameters to be solved, and the multiple parameters to be solved are solved based on historical walking data.
  • the method further includes:
  • the prediction result of the walking deviation amount is input into the pre-constructed fault detection model to obtain the deviation fault detection result of the working machine.
  • the deviation influencing parameters include a plurality of deviation influencing parameters
  • obtaining the deviation detection result of the working machine includes:
  • the deviation fault detection result of the working machine is determined based on the sensitivity coefficient of each deviation influencing parameter.
  • the prediction result based on the walking deviation amount determines the sensitivity coefficient of each of the multiple deviation influencing parameters, including:
  • the sensitivity coefficient of the deviation influencing parameter is determined based on the derivation result of the deviation influencing parameter.
  • the prediction results based on the walking deviation amount are derived for each of the deviation influencing parameters, and the derivation results of the deviation influencing parameters are obtained.
  • the derivation of each deviation influencing parameter is performed at the moment when the walking deviation amount is maximum, to obtain the derivation of each deviation influencing parameter. Lead results.
  • determining the deviation fault detection result of the working machine based on the sensitivity coefficient of each deviation influencing parameter includes:
  • the deviation fault detection result of the working machine is determined.
  • determining the deviation fault detection result of the working machine based on the positive, negative and absolute value of the sensitivity coefficient of each deviation influencing parameter includes:
  • the deviation influencing parameter ranked first with the first preset threshold is used as the key influencing parameter
  • the deviation fault detection result is determined based on the positive and negative values of the sensitivity coefficients of the key influencing parameters and the preset mapping relationship between the magnitude of the absolute value and the cause of the fault.
  • determining the deviation fault detection result of the working machine based on the positive, negative and absolute value of the sensitivity coefficient of each deviation influencing parameter includes:
  • the deviation fault detection result is determined based on the positive and negative values of the sensitivity coefficients of the key influencing parameters and the preset mapping relationship between the magnitude of the absolute value and the cause of the fault.
  • an alarm instruction is sent to the alarm device to issue an alarm through the warning device.
  • the multiple parameters to be solved are adjusted and optimized according to the test results to obtain the deviation prediction model.
  • the proxy model includes a second-order response surface proxy model or a neural network model.
  • the parameter data of the deviation influencing parameters includes a time-varying curve corresponding to each deviation influencing parameter.
  • An embodiment of the present invention also provides a walking deviation prediction device, which includes:
  • a data acquisition module used to obtain parameter data of parameters affecting parameters of deviation during the walking process of the working machine, wherein the parameters affecting parameters of deviation are parameters used to indicate whether the working machine is walking deviation;
  • a calculation module for inputting the parameter data of the deviation influencing parameters into a pre-trained deviation prediction model to obtain the prediction result of the walking deviation amount of the working machine;
  • the deviation prediction model is constructed based on a proxy model; the deviation prediction model includes a plurality of parameters to be solved, and the multiple parameters to be solved are solved based on historical walking data.
  • An embodiment of the present invention also provides a working machine, including: a walking device and a control device.
  • the control device is used to execute any of the above walking deviation prediction methods.
  • An embodiment of the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor.
  • the processor executes the program, any one of the above-mentioned methods is implemented. Walking deviation prediction method.
  • Embodiments of the present invention also provide a non-transitory computer-readable storage medium on which a computer program is stored.
  • the computer program is executed by a processor, the walking deviation prediction method as described in any of the above is implemented.
  • the walking deviation prediction method, device and working machine obtained by the embodiments of the present invention obtain the parameter data of the deviation influencing parameters during the walking process of the working machine, and input the parameter data of the deviation influencing parameters into the pre-trained deviation prediction. model, the prediction result of the walking deviation of the working machine can be obtained.
  • the deviation prediction model is obtained by solving the parameters to be solved of the agent model through historical walking data, so that the walking deviation of the working machine can be quickly and accurately predicted. Prediction based on the amount of walking deviation ensures the reliability of the prediction result of walking deviation amount.
  • Figure 1 is a schematic flow chart of a walking deviation prediction method provided by an embodiment of the present invention.
  • Figure 2 is a schematic structural diagram of a fault detection model provided by an embodiment of the present invention.
  • Figure 3 is a schematic flow chart of a walking deviation prediction method provided by an embodiment of the present invention.
  • Figure 4 is a schematic structural diagram of a walking deviation prediction device provided by an embodiment of the present invention.
  • FIG. 5 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention.
  • the routine deviation prediction method implemented in the present invention is executed by electronic equipment such as a controller or the hardware and/or software thereof.
  • the controller can be the controller of the working machine itself or a newly added controller. Specifically, it can be based on Set according to actual needs.
  • the present invention implements a routine deviation prediction method including:
  • the deviation influencing parameter is a parameter used to indicate whether the working machine is running deviation.
  • the working machinery is such as an excavator, a crane and other engineering machinery.
  • the engineering machinery may be a crawler type engineering machinery or a wheel type engineering machinery.
  • the parameters affecting the deviation during the walking process of the working machine can be determined according to the structure of the working machine itself.
  • the parameters affecting the deviation can include the outlet pressure of the main pump, the walking pilot pressure, the walking motor pressure, the left One or more of the current value of the traveling proportional solenoid valve, the current value of the right traveling proportional solenoid valve, the left crawler speed and the right crawler speed.
  • the parameter data of the misalignment influencing parameters may be the time-varying curve corresponding to each misalignment influencing parameter.
  • the deviation prediction model is constructed based on a proxy model; the deviation prediction model includes a plurality of parameters to be solved, and the multiple parameters to be solved are solved based on historical walking data.
  • the deviation prediction model is built based on the proxy model.
  • the specific category of the proxy model can be set according to actual needs. For example, it can be a response surface proxy model or a machine learning proxy model.
  • the deviation prediction model is used to characterize the relationship between each deviation influencing parameter and the amount of walking deviation. By inputting the parameter data of the deviation influencing parameters into the deviation prediction model, the prediction result of the walking deviation amount of the working machine can be obtained quickly and accurately.
  • the machine learning agent model can be a neural network model
  • the response surface agent model can be a multi-order response surface agent model.
  • the specific order can be set according to actual needs. For example, it can be set as a second-order response surface agent model.
  • the second-order response surface agent model is shown in Equation (1):
  • x i ,i ⁇ [1,n] is the parameter data of the i-th deviation influencing parameter
  • n is the number of deviation influencing parameters
  • t i1 , t i2 are the lower limit and upper limit of the i-th deviation influencing parameter respectively
  • y (x 1 , x 2 ,..., x n ) is the prediction result of the walking deviation amount
  • min() is calculated by taking the minimum value
  • b k0 , b ki , b kii and b kij are the regression coefficients of the k-th order response surface
  • x j ,j ⁇ (1,n] are the parameter data of the j-th deviation influencing parameter.
  • the deviation prediction model includes multiple parameters to be solved.
  • the parameters to be solved include w k , b k0 , b ki , b kii and b kij ;
  • the parameters to be solved are Including weight parameters and bias parameters.
  • the parameters to be solved can be solved through historical walking data.
  • the historical walking data refers to the walking data of the operating machinery at historical moments.
  • the historical walking data includes the parameter data of each deviation-affecting parameter at historical moments, as well as the corresponding deviation amount detection results.
  • Historical walking data can be walking data at multiple different historical moments, or continuous walking data in one or more historical time periods, that is, each deviation influencing parameter and deviation amount detection result are one or more historical time periods. process curve within.
  • Historical walking data can be obtained from the big data platform and stored to a local computing server or PC through API (Application Program Interface) or CAN (Controller Area Network) calls.
  • the specific method of solving the parameters to be solved based on historical walking data can be set according to actual needs.
  • the historical walking data can be split into a training set and a test set, and the agent model is trained through the training set to obtain the parameters to be solved.
  • the initial deviation prediction model is obtained; the initial deviation prediction is obtained
  • the initial deviation prediction model is further tested through the test set.
  • the parameters to be solved are adjusted and optimized so that the accuracy of the deviation prediction model reaches the preset value (for example, 0.9 or above), thus being well-trained. deviation prediction model.
  • the trained deviation prediction model can be stored in the walking deviation database of the working machine.
  • the division ratio of the training set and the test set can be set according to actual needs. For example, the training set and the test set can be divided according to the ratio of 9:1.
  • the historical walking data Before solving the parameters to be solved through historical walking data, the historical walking data can also be pre-processed; the specific method of data pre-processing can be set according to actual needs, for example, it can include: data cleaning and feature processing; data cleaning uses To remove dirty data from historical walking data, it may include one or more of validity checking, logical judgment, classification, merging, and smoothing and noise reduction processing.
  • Feature processing can be normalization processing, which can normalize the parameter data of each deviation-influencing parameter in the historical walking data and the corresponding deviation amount detection results.
  • the walking deviation amount of the work machine can be obtained.
  • the deviation prediction model is obtained by solving the parameters to be solved of the agent model through historical walking data. It does not require complex mechanism expressions and overcomes the shortcomings of traditional methods that rely on human experience, so that the operation can be quickly and accurately predicted.
  • the machine's walking deviation is predicted to ensure the reliability of the prediction results of the walking deviation.
  • the embodiment of the present invention does not require additional sensors during the walking deviation prediction process, effectively reducing the cost of the entire vehicle.
  • the method further includes:
  • the prediction result of the walking deviation amount is input into the pre-constructed fault detection model to obtain the deviation fault detection result of the working machine.
  • the specific method of determining whether there is walking deviation based on the prediction result of the walking deviation amount can be set according to actual needs. For example, the prediction result of the walking deviation amount can be compared with a preset limit value. If the prediction result of the quantity is less than or equal to the preset limit, it means that walking is normal, that is, there is no walking deviation. Otherwise, it indicates that there is walking deviation.
  • the prediction result of the walking deviation amount can also be input into the pre-built fault detection model to obtain the deviation fault detection result of the working machine.
  • the specific type of fault detection model can be set according to actual needs. For example, it can be a computing model or a machine learning model.
  • the deviation detection result can be the deviation influencing parameter that has a key influence on the deviation; in addition, the deviation can also be determined based on the deviation influencing parameter that has a key influence on the deviation and the value of the deviation influencing parameter. Cause of failure.
  • the prediction result of the walking deviation amount is further input into a pre-constructed fault detection model.
  • the fault detection model Through the fault detection model, the working machine can be quickly and accurately obtained. Deviation fault detection results, so that when the working machine has a deviation in walking, troubleshooting can be carried out in a timely manner based on the deviation detection results, effectively avoiding the impact of deviation on the control accuracy and work efficiency of the operation, and ensuring the operation Mechanical driving safety.
  • the deviation influencing parameters include multiple deviation influencing parameters, wherein the obtaining the deviation fault detection result of the working machine includes:
  • the deviation fault detection result of the working machine is determined based on the sensitivity coefficient of each deviation influencing parameter.
  • the sensitivity coefficient of the deviation influencing parameter is used to reflect the sensitivity of the deviation influencing parameter to the amount of walking deviation.
  • the specific method of determining the sensitivity coefficient of the deviation-influencing parameter based on the prediction result of the walking deviation amount can be set according to actual needs. For example, it can be determined based on the ratio of the change rate of the walking deviation amount and the change rate of the deviation-influencing parameter. This deviation affects the sensitivity coefficient of the parameter.
  • the deviation fault detection result of the working machine can be further determined based on the sensitivity coefficient of each deviation-influencing parameter.
  • the deviation influencing parameters that have a key impact on the amount of walking deviation can be determined based on the positive and negative sensitivity coefficients and the absolute values of each deviation influencing parameter.
  • the fault detection model in the embodiment of the present invention can also be implemented through a hardware structure.
  • the fault detection model includes a fault cause determination unit 202 and multiple sensitivity coefficient calculation units 201.
  • Each sensitivity coefficient calculation unit 201 is connected to the fault cause determination unit 202, and the sensitivity coefficient calculation unit 201 is connected to the deviation influence parameter.
  • the sensitivity coefficient calculation unit 201 is connected to the deviation influence parameter.
  • Sensitivity coefficient There is a one-to-one correspondence between the numbers, that is, each deviation influence parameter corresponds to a sensitivity coefficient calculation unit 201, so that the corresponding deviation influence parameter is determined through the sensitivity coefficient calculation unit 201.
  • Sensitivity coefficient By inputting the prediction results of the walking deviation amount to each sensitivity coefficient calculation unit 201, the sensitivity coefficient of each deviation influencing parameter can be obtained.
  • the misalignment fault detection result of the working machine can be obtained.
  • the embodiment of the present invention determines the sensitivity coefficient of each deviation influencing parameter based on the prediction result of the walking deviation amount, and determines the deviation fault detection result of the working machine based on the sensitivity coefficient of each deviation influencing parameter, so that according to the sensitivity coefficient of each deviation influencing parameter
  • the sensitivity coefficient can effectively ensure the efficiency of deviation detection and the reliability of deviation detection results.
  • the prediction result based on the walking deviation amount determines the sensitivity coefficient of each deviation influencing parameter in the plurality of deviation influencing parameters, including:
  • the sensitivity coefficient of the deviation influencing parameter is determined based on the derivation result of the deviation influencing parameter.
  • each deviation influencing parameter can be derived based on the prediction results of the walking deviation amount, and based on the derivation results, Determine the sensitivity coefficient of the corresponding deviation affecting parameters. For example, based on the history curve corresponding to the prediction result of the walking deviation, the derivation of each deviation influencing parameter can be performed at the moment when the walking deviation is the largest, and the sensitivity coefficient of the corresponding deviation influencing parameter can be determined based on the derivation results.
  • equation (3) the specific process of derivation is shown in equation (3):
  • the specific method of determining the sensitivity coefficient of the deviation influence parameter based on the derivation result of the deviation influence parameter can also be set according to actual needs.
  • the derivation result can be directly used as the sensitivity coefficient of the corresponding deviation influence parameter, or
  • the derivation result can be corrected, and the correction result of the derivation result can be used as the sensitivity coefficient of the corresponding deviation influencing parameter.
  • the embodiment of the present invention derives the derivation of each deviation influencing parameter based on the prediction results of the walking deviation amount, and determines the sensitivity coefficient of the corresponding deviation influencing parameter based on the derivation results.
  • the calculation process is simple and efficient, and effectively ensures the sensitivity coefficient.
  • the accuracy of the calculation results further improves the reliability of the deviation fault detection results.
  • the determination of the deviation fault detection result of the working machine based on the sensitivity coefficient of each of the deviation influencing parameters includes:
  • the deviation fault detection result of the working machine is determined.
  • the positive, negative and absolute values of the sensitivity coefficients of each deviation influencing parameter are first determined; the deviation can be determined based on the positive and negative values of the sensitivity coefficients.
  • the direction of influence of the influencing parameter on the deviation For example, if the sensitivity coefficient is positive, it indicates that the deviation influencing parameter causes the walking deviation to increase; if the sensitivity coefficient is negative, it indicates that the deviation influencing parameter causes the walking deviation to decrease; according to the sensitivity
  • the absolute value of the coefficient can determine the influence of the deviation influencing parameter on walking deviation. The greater the absolute value of the sensitivity coefficient, the greater the influence of the deviation influencing parameter on the amount of walking deviation, and the smaller the absolute value of the sensitivity coefficient. , indicating that the impact of this deviation influencing parameter on the walking deviation amount is smaller.
  • the detection results of the deviation fault of the working machine can be determined based on the positive, negative and absolute values of the sensitivity coefficients of each deviation-influencing parameter.
  • the key influencing parameters can be determined from each deviation influencing parameter based on the absolute value of the sensitivity coefficient of each deviation influencing parameter; the specific method of determining the key influencing parameters can be set according to actual needs.
  • the absolute value can be The deviation influencing parameters whose value exceeds the preset threshold are regarded as key influencing parameters.
  • the absolute values of the sensitivity coefficients of each deviation influencing parameter can also be sorted according to their sizes, and the key influencing parameters can be determined based on the sorting results.
  • the sensitivity coefficient can be used to determine the key influencing parameters.
  • the absolute values of are in order from large to small, and the first N (N ⁇ 1) deviation influencing parameters are regarded as key influencing parameters.
  • the cause of the fault can be further determined based on the positive and negative sensitivity coefficients and the absolute value of the key influencing parameters.
  • the positive and negative sensitivity coefficients and the absolute value of each deviation influencing parameter can be used to determine the cause of the fault.
  • the preset mapping relationship of causes is used to determine the cause of the fault.
  • the traditional method usually adjusts the outlet pressure of the front and rear pumps to drive the working machine to drive in a straight line.
  • this method only considers the influence of a single factor and fails to consider the synergistic effect of multiple factors, thus failing to ensure the effectiveness of the deviation correction results.
  • the embodiment of the present invention determines the positive, negative and absolute value of the sensitivity coefficient of each deviation-influencing parameter, and determines the deviation fault detection result of the working machine based on the positive, negative and absolute value of the sensitivity coefficient of each deviation-influencing parameter. It can quickly and accurately determine the key influencing parameters of walking deviation and the specific cause of the fault under the synergy of multiple deviation-affecting parameters, so as to facilitate the operators or managers of the operating machinery to troubleshoot in time and correct the deviation of the operating machinery. The correction ensures that the working machine travels in a straight line, effectively avoids the impact of walking deviation on the control accuracy and work efficiency of the working operation, and ensures the driving safety of the working machine.
  • an alarm instruction is sent to the alarm device to issue an alarm through the warning device.
  • the prediction result of the walking deviation amount and/or the deviation fault detection result can be sent to the display device, so that the prediction result of the walking deviation amount and/or the deviation fault detection result can be displayed in real time through the display device.
  • the display device may be a display screen on the working machine, or may be a display screen on the management side of the working machine, so that the operator or manager of the working machine can monitor the working machine in real time based on the predicted result of the walking deviation amount on the display device.
  • corresponding measures can be taken in time to correct the deviation of the working machine based on the deviation fault detection results, thereby effectively avoiding the impact of walking deviation on the control accuracy and work efficiency of the operation, and ensuring the safety of the working machine. driving safety.
  • an alarm command can be sent to the alarm device to issue an alarm through the alarm device to remind the operator or manager of the working machine that there is a deviation fault in the working machine.
  • Alarm devices can be buzzer alarms, voice alarms, light alarms, etc.
  • the prediction results of the misalignment amount and/or the misalignment fault detection results can also be sent to the remote monitoring terminal through the communication module.
  • the embodiment of the present invention can effectively avoid the problem by sending the prediction results of the walking deviation amount and/or the deviation fault detection results to the display device for display, and/or sending an alarm instruction to the alarm device for alarm when it is determined that there is walking deviation.
  • the impact of walking deviation on the control accuracy and work efficiency of operating operations ensures the driving safety of operating machinery.
  • the deviation prediction model in the embodiment of the present invention can also be constructed by the following method. As shown in Figure 3, the implementation of the routine deviation prediction method of the present invention also includes:
  • steps S301 to S304 please refer to the description of the above embodiment for details. To avoid repetition, they will not be described again.
  • the walking deviation prediction device provided by the embodiment of the present invention.
  • the walking deviation prediction device described below and the walking deviation prediction method described above can be referenced correspondingly.
  • the walking deviation prediction device according to the embodiment of the present invention includes:
  • the data acquisition module 401 is used to obtain the parameter data of the deviation influencing parameters during the walking process of the working machine, wherein the deviation influencing parameters are parameters used to indicate whether the working machine is walking deviation;
  • the calculation module 402 is used to input the parameter data of the deviation influencing parameters into the pre-trained deviation prediction model to obtain the prediction result of the walking deviation amount of the working machine;
  • the deviation prediction model is constructed based on a proxy model; the deviation prediction model includes a plurality of parameters to be solved, and the multiple parameters to be solved are solved based on historical walking data.
  • a fault detection module is also included, and the fault detection module is used for:
  • the prediction result of the walking deviation amount is input into the pre-constructed fault detection model to obtain the deviation fault detection result of the working machine.
  • the deviation influencing parameters include multiple deviation influencing parameters
  • the fault detection module is specifically used to:
  • the deviation fault detection result of the working machine is determined based on the sensitivity coefficient of each deviation influencing parameter.
  • the fault detection module is specifically used to:
  • the sensitivity coefficient of the deviation influencing parameter is determined based on the derivation result of the deviation influencing parameter.
  • the fault detection module is specifically used to:
  • the derivation of each deviation influencing parameter is performed at the moment when the walking deviation amount is maximum, to obtain the derivation of each deviation influencing parameter. Lead results.
  • the fault detection module is specifically used to:
  • the deviation fault detection result of the working machine is determined.
  • the fault detection module is specifically used to:
  • the deviation influencing parameter ranked first with the first preset threshold is used as the key influencing parameter
  • the deviation fault detection result is determined based on the positive and negative values of the sensitivity coefficients of the key influencing parameters and the preset mapping relationship between the magnitude of the absolute value and the cause of the fault.
  • the fault detection module is specifically used to:
  • the deviation fault detection result is determined based on the positive and negative values of the sensitivity coefficients of the key influencing parameters and the preset mapping relationship between the magnitude of the absolute value and the cause of the fault.
  • a reminder module is also included, and the reminder module is used for:
  • an alarm instruction is sent to the alarm device to issue an alarm through the warning device.
  • the computing module is specifically used to:
  • the multiple parameters to be solved are adjusted and optimized according to the test results to obtain the deviation prediction model.
  • the proxy model includes a second-order response surface proxy model or a neural network model.
  • the parameter data of the deviation influencing parameters includes a time-varying curve corresponding to each deviation influencing parameter.
  • An embodiment of the present invention also provides a working machine, including: a walking device and a control device.
  • the control device is used to execute the walking deviation prediction method as described in any of the above embodiments.
  • the working machinery is such as an excavator, a crane and other engineering machinery.
  • the engineering machinery may be a crawler type engineering machinery or a wheel type engineering machinery.
  • Figure 5 illustrates a schematic diagram of the physical structure of an electronic device.
  • the electronic device may include: a processor (processor) 501, a communications interface (Communications Interface) 502, a memory (memory) 503 and a communication bus 504.
  • the processor 501, the communication interface 502, and the memory 503 complete communication with each other through the communication bus 504.
  • the processor 501 can call the logic instructions in the memory 503 to execute the walking deviation prediction method.
  • the method includes: obtaining parameter data of the deviation influencing parameters during the walking process of the work machine, wherein the deviation influencing parameters are used to indicate the deviation. Describes the parameters of whether the working machine wanders;
  • the deviation prediction model is constructed based on a proxy model; the deviation prediction model includes a plurality of parameters to be solved, and the multiple parameters to be solved are solved based on historical walking data.
  • the above-mentioned logical instructions in the memory 503 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.
  • the technical solution of the present invention essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product.
  • the computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code. .
  • inventions of the present invention also provide a computer program product.
  • the computer program product includes a computer program stored on a non-transitory computer-readable storage medium.
  • the computer program includes program instructions. When the program instructions When executed by the computer, the computer can execute the walking deviation prediction method provided by each of the above methods.
  • the method includes: obtaining parameter data of the deviation influencing parameters during the walking process of the working machine, wherein the deviation influencing parameters are used to indicate the deviation. Describes the parameters of whether the working machine wanders;
  • the deviation prediction model is constructed based on a proxy model; the deviation prediction model includes a plurality of parameters to be solved, and the multiple parameters to be solved are solved based on historical walking data.
  • embodiments of the present invention also provide a non-transitory computer-readable storage medium on which a computer program is stored.
  • the computer program is implemented when executed by a processor to perform the walking deviation prediction methods provided above.
  • the method includes: obtaining parameter data of a deviation affecting parameter during the walking process of the working machine, wherein the deviation influencing parameter is a parameter used to indicate whether the working machine is walking deviation;
  • the deviation prediction model is constructed based on a proxy model; the deviation prediction model includes a plurality of parameters to be solved, and the multiple parameters to be solved are solved based on historical walking data.
  • the device embodiments described above are only illustrative.
  • the units described as separate components may or may not be physically separated.
  • the components shown as units may or may not be physical units, that is, they may be located in One location, or it can be distributed across multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Persons of ordinary skill in the art can understand and implement the method without any creative effort.
  • each embodiment can be implemented by software plus a necessary general hardware platform, and of course, it can also be implemented by hardware.
  • the computer software product can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., including a number of instructions to cause a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments.

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Abstract

提供一种行走跑偏预测方法、装置及作业机械，方法包括：获取作业机械行走过程中跑偏影响参数的参数数据(S101)，其中跑偏影响参数为用于指示作业机械是否行走跑偏的参数；将跑偏影响参数的参数数据输入至预先训练好的跑偏预测模型，得到作业机械的行走跑偏量的预测结果(S102)；其中，跑偏预测模型是基于代理模型构建得到的；跑偏预测模型包括多个待求解参数，多个待求解参数基于历史行走数据求解得到。能够快速准确地对作业机械的行走跑偏量进行预测，保证了行走跑偏量的预测结果的可靠性。

Description

行走跑偏预测方法、装置及作业机械 技术领域
本发明涉及工程机械技术领域，尤其涉及一种行走跑偏预测方法、装置及作业机械。
发明背景
随着国内外对作业机械需求的急剧增加，促使作业机械的性能要求不断升高。行走跑偏量作为作业机械行走性能评价的重要指标，一方面对作业操作的控制精度和工作效率具有较大影响，另一方面还会给作业环境和人员带来潜在的隐患。然而作业机械行走跑偏量的影响参数众多，传统方法通常依靠“经验+试错”的方式对作业机械的行走跑偏量进行预测，科学性差、偶然性大，无法保证行走跑偏量预测结果的有效性。
发明内容
针对现有技术中存在的问题，本发明提供一种行走跑偏预测方法、装置及作业机械。
本发明实施例提供一种行走跑偏预测方法，包括：
获取作业机械行走过程中跑偏影响参数的参数数据，其中所述跑偏影响参数为用于指示所述作业机械是否行走跑偏的参数；
将所述跑偏影响参数的参数数据输入至预先训练好的跑偏预测模型，得到所述作业机械的行走跑偏量的预测结果；
其中，所述跑偏预测模型是基于代理模型构建得到的；所述跑偏预测模型包括多个待求解参数，所述多个待求解参数基于历史行走数据求解得到。
根据本发明实施例提供的行走跑偏预测方法，在所述得到所述作业机械的行走跑偏量的预测结果之后，还包括：
将所述行走跑偏量的预测结果输入至预先构建好的故障检测模型，得到所述作业机械的跑偏故障检测结果。
根据本发明实施例提供的行走跑偏预测方法，所述跑偏影响参数包括多个跑偏影响参数，
其中，所述得到所述作业机械的跑偏故障检测结果，包括：
基于所述行走跑偏量的预测结果确定所述多个跑偏影响参数中每个跑偏影响参数的敏感系数；
基于所述每个跑偏影响参数的敏感系数确定所述作业机械的跑偏故障检测结果。
根据本发明实施例提供的行走跑偏预测方法，所述基于所述行走跑偏量的预测结果确定所述多个跑偏影响参数中每个跑偏影响参数的敏感系数，包括：
基于所述行走跑偏量的预测结果分别对所述每个跑偏影响参数进行求导，获取所述跑偏影响参数的求导结果；
基于所述跑偏影响参数的求导结果确定所述跑偏影响参数的敏感系数。
根据本发明实施例提供的行走跑偏预测方法，所述基于所述行走跑偏量的预测结果分别对所述每个跑偏影响参数进行求导，获取所述跑偏影响参数的求导结果，包括：
根据所述行走跑偏量的预测结果对应的历程曲线，在所述行走跑偏量最大的时刻对所述每个跑偏影响参数进行求导，以获取所述每个跑偏影响参数的求导结果。
根据本发明实施例提供的行走跑偏预测方法，所述基于所述每个跑偏影响参数的敏感系数确定所述作业机械的跑偏故障检测结果，包括：
确定所述每个跑偏影响参数的敏感系数的正负和绝对值；
基于所述每个跑偏影响参数的敏感系数的正负和绝对值，确定所述作业机械的跑偏故障检测结果。
根据本发明实施例提供的行走跑偏预测方法，所述基于所述每个跑偏影响参数的敏感系数的正负和绝对值，确定所述作业机械的跑偏故障检测结果，包括：
将所述每个跑偏影响参数的敏感系数的绝对值按照大小进行排序，得到排序结果；
根据排序结果将排列在前第一预设阈值的所述跑偏影响参数作为关键影响参数；
根据所述关键影响参数的敏感系数的正负以及所述绝对值的大小与故障原因的预设映射关系，确定所述跑偏故障检测结果。
根据本发明实施例提供的行走跑偏预测方法，所述基于所述每个跑偏影响参数的敏感系数的正负和绝对值，确定所述作业机械的跑偏故障检测结果，包括：
将所述每个跑偏影响参数的敏感系数的绝对值与第二预设阈值进行对比，并将大于所述第二预设阈值的所述跑偏影响参数作为关键影响参数；
根据所述关键影响参数的敏感系数的正负以及所述绝对值的大小与故障原因的预设映射关系，确定所述跑偏故障检测结果。
根据本发明实施例提供的行走跑偏预测方法，还包括：
将所述行走跑偏量的预测结果和/或所述跑偏故障检测结果发送至显示装置，以显示所述行走跑偏量的预测结果和/或所述跑偏故障检测结果；和/或
确定存在行走跑偏时，发送告警指令至告警装置，以通过所述警告装置进行告警。
根据本发明实施例提供的行走跑偏预测方法，还包括：
将所述历史行走数据拆分为训练集和测试集；
通过所述训练集对所述代理模型进行训练，得到所述多个待求解参数以及初始跑偏预测模型；
通过所述测试集对所述初始跑偏预测模型进行测试，得到测试结果；
根据所述测试结果对所述多个待求解参数进行调整优化，得到所述跑偏预测模型。
根据本发明实施例提供的行走跑偏预测方法，所述代理模型包括二阶响应面代理模型或神经网络模型。
根据本发明实施例提供的行走跑偏预测方法，所述跑偏影响参数的参数数据包括每个跑偏影响参数对应的随时间变化的曲线。
本发明实施例还提供一种行走跑偏预测装置，包括：
数据获取模块，用于获取作业机械行走过程中跑偏影响参数的参数数据，其中所述跑偏影响参数为用于指示所述作业机械是否行走跑偏的参数；
计算模块，用于将所述跑偏影响参数的参数数据输入至预先训练好的跑偏预测模型，得到所述作业机械的行走跑偏量的预测结果；
其中，所述跑偏预测模型是基于代理模型构建得到的；所述跑偏预测模型包括多个待求解参数，所述多个待求解参数基于历史行走数据求解得到。
本发明实施例还提供一种作业机械，包括：行走装置和控制装置，所述控制装置用于执行如上述任一种所述的行走跑偏预测方法。
本发明实施例还提供一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如上述任一种所述的行走跑偏预测方法。
本发明实施例还提供一种非暂态计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现如上述任一种所述的行走跑偏预测方法。
本发明实施例提供的行走跑偏预测方法、装置及作业机械，通过获取作业机械行走过程中跑偏影响参数的参数数据，并将跑偏影响参数的参数数据输入至预先训练好的跑偏预测模型，即可得到作业机械的行走跑偏量的预测结果，其中，跑偏预测模型是通过历史行走数据对代理模型的待求解参数进行求解得到，从而能够快速准确地对作业机械的行走跑偏量进行预测，保证了行走跑偏量的预测结果的可靠性。
附图简要说明
为了更清楚地说明本发明或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本发明实施例提供的行走跑偏预测方法的流程示意图；
图2是本发明实施例提供的故障检测模型的结构示意图；
图3是本发明实施例提供的行走跑偏预测方法的流程示意图；
图4是本发明实施例提供的行走跑偏预测装置的结构示意图；
图5是本发明实施例提供的电子设备的结构示意图。
实施本发明的方式
为使本发明的目的、技术方案和优点更加清楚，下面将结合本发明中的附图，对本发明中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
下面结合图1至图3描述本发明实施例的行走跑偏预测方法。本发明实施例行走跑偏预测方法由控制器等电子设备或其中的硬件和/或软件执行，其中，控制器可以为作业机械自身的控制器，也可以为新增加的控制器，具体可以根据实际需求进行设定。如图1所示，本发明实施例行走跑偏预测方法包括：
S101、获取作业机械行走过程中跑偏影响参数的参数数据。
其中，所述跑偏影响参数为用于指示所述作业机械是否行走跑偏的参数。
具体地，作业机械诸如挖掘机、起重机等工程机械，工程机械可以为履带式工程机械，也可以为轮式工程机械。作业机械行走过程中的跑偏影响参数可以根据作业机械自身的结构进行确定，例如，对于液压式履带挖掘机，跑偏影响参数可以包括主泵的出口压力、行走先导压力、行走马达压力、左行走比例电磁阀的电流值、右行走比例电磁阀的电流值、左侧履带速度以及右侧履带速度中的一种或多种。跑偏影响参数的参数数据可以为各跑偏影响参数对应的随时间变化的曲线。
S102、将所述跑偏影响参数的参数数据输入至预先训练好的跑偏预测模型，得到所述作业机械的行走跑偏量的预测结果；
其中，所述跑偏预测模型是基于代理模型构建得到的；所述跑偏预测模型包括多个待求解参数，所述多个待求解参数基于历史行走数据求解得到。
具体地，跑偏预测模型是基于代理模型构建得到的，代理模型的具体类别可以根据实际需求进行设定，例如，可以为响应面代理模型，还可以为机器学习代理模型。跑偏预测模型用于表征各跑偏影响参数与行走跑偏量之间的关系，将跑 偏影响参数的参数数据输入至跑偏预测模型，即可快速准确地得到作业机械的行走跑偏量的预测结果。机器学习代理模型可以为神经网络模型，响应面代理模型可以为多阶响应面代理模型，具体阶数可以根据实际需求进行设定，例如，可以设定为二阶响应面代理模型。二阶响应面代理模型如式(1)所示：
式中，xi,i∈[1,n]为第i个跑偏影响参数的参数数据，xi∈[ti1,ti2]，n为跑偏影响参数的数量，ti1、ti2分别为第i个跑偏影响参数的下限值和上限值；y(x1,x2,...,xn)为行走跑偏量的预测结果；min()为取最小值计算；wk为第k阶响应面的权重，k＝1,2；为第k阶响应面的行走跑偏量的预测值；yk(x1,x2,...,xn)的表达式如式(2)所示：
式中，bk0、bki、bkii和bkij为第k阶响应面的回归系数，xj,j∈(1,n]为第j个跑偏影响参数的参数数据。
跑偏预测模型包括多个待求解的参数，例如，对于响应面代理模型，待求解的参数包括wk、bk0、bki、bkii和bkij；对于机器学习代理模型，待求解的参数包括权重参数和偏置参数。待求解的参数可以通过历史行走数据进行求解。历史行走数据即历史时刻作业机械的行走数据，历史行走数据包括历史时刻各跑偏影响参数的参数数据，以及对应的跑偏量检测结果。历史行走数据可以为多个不同历史时刻的行走数据，也可以为一个或多个历史时间段内连续的行走数据，即各跑偏影响参数以及跑偏量检测结果是一个或多个历史时间段内的历程曲线。历史行走数据可以从大数据平台中获取，通过API(Application Program Interface，应用程序接口)或CAN(Controller Area Network，控制器局域网)调用将历史行走数据存储至本地计算服务器或PC。
基于历史行走数据对待求解参数进行求解的具体方式可以根据实际需求进行设定，例如，可以将历史行走数据拆分为训练集和测试集，通过训练集对代理模型进行训练，得到待求解参数，从而得到初始跑偏预测模型；得到初始跑偏预测 模型后，进一步通过测试集对初始跑偏预测模型进行测试，根据测试结果，对待求解参数进行调整优化，使得跑偏预测模型的准确率达到预设值(如，0.9以上)，从而得到训练好的跑偏预测模型。训练好的跑偏预测模型可以存储在作业机械的行走跑偏数据库中。其中，训练集和测试集的划分比例以根据实际需求进行设定，例如，可以按照训练集和测试集9:1的比例进行划分。
通过历史行走数据对待求解参数进行求解之前，还可以对历史行走数据进行数据预处理；数据预处理的具体方式可以根据实际需求进行设定，例如，可以包括：数据清洗和特征处理；数据清洗用于去除历史行走数据中的脏数据，可以包括有效性检查、逻辑判断、分类、合并以及平滑降噪处理中的一种或多种。特征处理可以为归一化处理，可以对历史行走数据中各跑偏影响参数的参数数据以及相应的跑偏量检测结果均进行归一化处理。
传统方法通常依靠“经验+试错”的方式对作业机械的行走跑偏量进行预测，例如，通过设置泵压力及泵比例阀电流值来调节作业机械低速行驶时双边跑偏问题，科学性差、偶然性大，无法保证跑偏预测结果的有效性，不适合推广。
本发明实施例通过获取作业机械行走过程中跑偏影响参数的参数数据，并将跑偏影响参数的参数数据输入至预先训练好的跑偏预测模型，即可得到作业机械的行走跑偏量的预测结果，其中，跑偏预测模型是通过历史行走数据对代理模型的待求解参数进行求解得到，无需复杂的机理表达式，且克服了传统方法依赖人员经验的弊端，从而能够快速准确地对作业机械的行走跑偏量进行预测，保证了行走跑偏量的预测结果的可靠性。
同时，本发明实施例在行走跑偏预测过程中无需额外增加传感器，有效降低了整车成本。
基于上述实施例，在所述得到所述作业机械的行走跑偏量的预测结果之后，还包括：
将所述行走跑偏量的预测结果输入至预先构建好的故障检测模型，得到所述作业机械的跑偏故障检测结果。
具体地，基于行走跑偏量的预测结果确定是否存在行走跑偏的具体方式可以根据实际需求进行设定，例如，可以将行走跑偏量的预测结果与预设限值进行比较，行走跑偏量的预测结果小于或等于预设限值表示行走正常，即，不存在行走跑偏，否则，表明存在行走跑偏。
确定存在行走跑偏时，还可以将行走跑偏量的预测结果输入至预先构建好的故障检测模型，以得到作业机械的跑偏故障检测结果。故障检测模型的具体类型可以根据实际需求进行设定，例如，可以为计算模型，也可以为机器学习模型。跑偏故障检测结果可以是对跑偏故障起关键影响的跑偏影响参数；另外，还可以根据对跑偏故障起关键影响的跑偏影响参数以及该跑偏影响参数的取值来确定跑偏故障的原因。
本发明实施例基于行走跑偏量的预测结果确定存在行走跑偏时，进一步将行走跑偏量的预测结果输入至预先构建好的故障检测模型，通过故障检测模型能够快速准确地得到作业机械的跑偏故障检测结果，从而在作业机械发生行走跑偏时，能够根据跑偏故障检测结果及时进行故障排除，有效避免了行走跑偏对作业操作的控制精度以及工作效率的影响，且保证了作业机械的行车安全性。
基于上述任一实施例，所述跑偏影响参数包括多个跑偏影响参数，其中所述得到所述作业机械的跑偏故障检测结果，包括：
基于所述行走跑偏量的预测结果确定所述多个跑偏影响参数中每个跑偏影响参数的敏感系数；
基于所述每个跑偏影响参数的敏感系数确定所述作业机械的跑偏故障检测结果。
具体地，跑偏影响参数的敏感系数用于反映该跑偏影响参数对行走跑偏量的敏感程度。基于行走跑偏量的预测结果确定跑偏影响参数的敏感系数的具体方式可以根据实际需求进行设定，例如，可以根据行走跑偏量的变化率与该跑偏影响参数的变化率的比值确定该跑偏影响参数的敏感系数。
确定各跑偏影响参数(即，每个跑偏影响参数)的敏感系数后，可以进一步基于各跑偏影响参数的敏感系数确定作业机械的跑偏故障检测结果。例如，可以根据各跑偏影响参数的敏感系数的正负以及绝对值大小，来确定对行走跑偏量起关键影响的跑偏影响参数。
可以理解的是，本发明实施例中故障检测模型还可以通过硬件结构来实现。如图2所示，故障检测模型包括故障原因确定单元202以及多个敏感系数计算单元201，各敏感系数计算单元201均与故障原因确定单元202连接，且敏感系数计算单元201与跑偏影响参数一一对应，即，每个跑偏影响参数均对应有一个敏感系数计算单元201，以通过敏感系数计算单元201确定对应的跑偏影响参数的 敏感系数。将行走跑偏量的预测结果分别输入至各敏感系数计算单元201，即可得到每一个跑偏影响参数的敏感系数。
将各跑偏影响参数的敏感系数均输入至故障原因确定单元202，即可得到作业机械的跑偏故障检测结果。
本发明实施例基于行走跑偏量的预测结果确定各跑偏影响参数的敏感系数，并基于各跑偏影响参数的敏感系数确定作业机械的跑偏故障检测结果，从而根据各跑偏影响参数的敏感系数，能够有效保证跑偏故障检测效率以及跑偏故障检测结果的可靠性。
基于上述任一实施例，所述基于所述行走跑偏量的预测结果确定所述多个跑偏影响参数中每个跑偏影响参数的敏感系数，包括：
基于所述行走跑偏量的预测结果分别对所述每个跑偏影响参数进行求导，获取所述跑偏影响参数的求导结果；
基于所述跑偏影响参数的求导结果确定所述跑偏影响参数的敏感系数。
具体地，基于行走跑偏量的预测结果确定相应的跑偏影响参数的敏感系数的过程中，可以基于行走跑偏量的预测结果对各跑偏影响参数进行求导，并根据求导结果来确定相应的跑偏影响参数的敏感系数。例如，可以根据行走跑偏量的预测结果对应的历程曲线，在行走跑偏量最大的时刻对各跑偏影响参数进行求导，以根据求导结果来确定相应的跑偏影响参数的敏感系数，求导的具体过程如式(3)所示：
式中，为行走跑偏量的预测结果对应的历程曲线在t时刻对第i个跑偏影响参数的求导结果；为第i个跑偏影响参数在t时刻的参数数据；为行走跑偏量的预测结果对应的历程曲线在t时刻的变化量；为第i个跑偏影响参数的变化量，趋于零。
基于跑偏影响参数的求导结果确定跑偏影响参数的敏感系数的具体方式也可以根据实际需求进行设定，例如，可以直接将该求导结果作为相应的跑偏影响参数的敏感系数，也可以对该求导结果进行修正，将求导结果的修正结果作为相应的跑偏影响参数的敏感系数。
本发明实施例基于行走跑偏量的预测结果分别对各跑偏影响参数进行求导，并根据求导结果确定相应的跑偏影响参数的敏感系数，计算过程简单高效，且有效保证了敏感系数计算结果的准确性，进而提高了跑偏故障检测结果的可靠性。
基于上述任一实施例，所述基于各所述跑偏影响参数的敏感系数确定所述作业机械的跑偏故障检测结果，包括：
确定所述每个跑偏影响参数的敏感系数的正负和绝对值；
基于所述每个跑偏影响参数的敏感系数的正负和绝对值，确定所述作业机械的跑偏故障检测结果。
具体地，基于各跑偏影响参数的敏感系数确定跑偏故障检测结果的过程中，首先确定各跑偏影响参数的敏感系数的正负和绝对值；根据敏感系数的正负能够确定该跑偏影响参数对跑偏的影响方向，例如，敏感系数为正，表明该跑偏影响参数造成行走跑偏量增加，敏感系数为负，表明该跑偏影响参数造成行走跑偏量减小；根据敏感系数的绝对值大小能够确定该跑偏影响参数对跑偏的影响大小，敏感系数的绝对值越大，表明该跑偏影响参数对行走跑偏量的影响越大，敏感系数的绝对值越小，表明该跑偏影响参数对行走跑偏量的影响越小。
确定各跑偏影响参数的敏感系数的正负和绝对值后，可以根据各跑偏影响参数的敏感系数的正负和绝对值来确定作业机械的跑偏故障检测结果。例如，可以根据各跑偏影响参数的敏感系数的绝对值的大小，从各跑偏影响参数中确定关键影响参数；确定关键影响参数的具体方式可以根据实际需求进行设定，例如，可以将绝对值的大小超过预设阈值的跑偏影响参数作为关键影响参数，还可以对各跑偏影响参数的敏感系数的绝对值按照大小进行排序，根据排序结果确定关键影响参数，例如，可以根据敏感系数的绝对值由大到小的顺序，将前N(N≥1)个跑偏影响参数作为关键影响参数。确定关键影响参数后，可以进一步根据关键影响参数的敏感系数的正负以及绝对值的大小来确定故障原因，例如，可以根据各跑偏影响参数的敏感系数的正负以及绝对值的大小与故障原因的预设映射关系来确定故障原因。
传统方法通常通过调节前后泵的出口压力促使作业机械直线行驶，然而该方法仅考虑了单一因素的影响，未能考虑多因素协同作用的结果，从而无法保证跑偏修正结果的有效性。
本发明实施例通过确定各跑偏影响参数的敏感系数的正负和绝对值，并基于各跑偏影响参数的敏感系数的正负和绝对值确定作业机械的跑偏故障检测结果， 能够在多跑偏影响参数的协同作用下，快速准确地确定行走跑偏的关键影响参数以及具体的故障原因，从而便于作业机械的操作人员或管理人员及时排故，以对作业机械进行跑偏修正，保证作业机械直线行驶，有效避免了行走跑偏对作业操作的控制精度以及工作效率的影响，且保证了作业机械的行车安全性。
基于上述任一实施例，还包括：
将所述行走跑偏量的预测结果和/或所述跑偏故障检测结果发送至显示装置，以显示所述行走跑偏量的预测结果和/或所述跑偏故障检测结果；和/或
确定存在行走跑偏时，发送告警指令至告警装置，以通过所述警告装置进行告警。
具体地，可以将行走跑偏量的预测结果和/或跑偏故障检测结果发送至显示装置，以通过显示装置对行走跑偏量的预测结果和/或跑偏故障检测结果进行实时显示。其中，显示装置可以为作业机械上的显示屏，也可以为作业机械管理端的显示屏，从而作业机械的操作人员或管理人员根据显示装置上的行走跑偏量的预测结果能够实时监测作业机械的行走跑偏状态，还可以根据跑偏故障检测结果及时采取相应的措施对作业机械进行跑偏修正，从而能够有效避免行走跑偏对作业操作的控制精度以及工作效率的影响，且保证了作业机械的行车安全性。
另外，还可以根据行走跑偏量的预测结果确定存在行走跑偏时，发送告警指令至告警装置，以通过告警装置进行告警，提醒作业机械的操作人员或管理人员作业机械存在跑偏故障。告警装置可以为蜂鸣报警器、语音报警器、灯光报警器等。
可以理解的是，还可以通过通讯模块将跑偏量的预测结果和/或跑偏故障检测结果发送至远程监控终端。
本发明实施例通过将行走跑偏量的预测结果和/或跑偏故障检测结果发送至显示装置进行显示，和/或确定存在行走跑偏时，发送告警指令至告警装置进行告警，能够有效避免行走跑偏对作业操作的控制精度以及工作效率的影响，且保证了作业机械的行车安全性。
本发明实施例中跑偏预测模型还可以通过如下方法来构建。如图3所示，本发明实施例行走跑偏预测方法还包括：
S301、将所述历史行走数据拆分为训练集和测试集；
S302、通过所述训练集对所述代理模型进行训练，得到所述多个待求解参数以及初始跑偏预测模型；
S303、通过所述测试集对所述初始跑偏预测模型进行测试，得到测试结果；
S304、根据所述测试结果对所述多个待求解参数进行调整优化，得到所述跑偏预测模型。
需要说明的是，关于步骤S301至S304的具体描述，详情请参见上述实施例的记载，为避免重复，再次不再赘述。
下面对本发明实施例提供的行走跑偏预测装置进行描述，下文描述的行走跑偏预测装置与上文描述的行走跑偏预测方法可相互对应参照。如图4所示，本发明实施例的行走跑偏预测装置包括：
数据获取模块401，用于获取作业机械行走过程中跑偏影响参数的参数数据，其中所述跑偏影响参数为用于指示所述作业机械是否行走跑偏的参数；
计算模块402，用于将所述跑偏影响参数的参数数据输入至预先训练好的跑偏预测模型，得到所述作业机械的行走跑偏量的预测结果；
其中，所述跑偏预测模型是基于代理模型构建得到的；所述跑偏预测模型包括多个待求解参数，所述多个待求解参数基于历史行走数据求解得到。
基于上述实施例，还包括故障检测模块，所述故障检测模块用于：
将所述行走跑偏量的预测结果输入至预先构建好的故障检测模型，得到所述作业机械的跑偏故障检测结果。
基于上述任一实施例，所述跑偏影响参数包括多个跑偏影响参数，所述故障检测模块具体用于：
基于所述行走跑偏量的预测结果确定所述多个跑偏影响参数中每个跑偏影响参数的敏感系数；
基于所述每个跑偏影响参数的敏感系数确定所述作业机械的跑偏故障检测结果。
基于上述任一实施例，所述故障检测模块具体用于：
基于所述行走跑偏量的预测结果分别对所述每个跑偏影响参数进行求导，获取所述跑偏影响参数的求导结果；
基于所述跑偏影响参数的求导结果确定所述跑偏影响参数的敏感系数。
基于上述任一实施例，所述故障检测模块具体用于：
根据所述行走跑偏量的预测结果对应的历程曲线，在所述行走跑偏量最大的时刻对所述每个跑偏影响参数进行求导，以获取所述每个跑偏影响参数的求导结果。
基于上述任一实施例，所述故障检测模块具体用于：
确定所述每个跑偏影响参数的敏感系数的正负和绝对值；
基于所述每个跑偏影响参数的敏感系数的正负和绝对值，确定所述作业机械的跑偏故障检测结果。
基于上述任一实施例，所述故障检测模块具体用于：
将所述每个跑偏影响参数的敏感系数的绝对值按照大小进行排序，得到排序结果；
根据排序结果将排列在前第一预设阈值的所述跑偏影响参数作为关键影响参数；
根据所述关键影响参数的敏感系数的正负以及所述绝对值的大小与故障原因的预设映射关系，确定所述跑偏故障检测结果。
基于上述任一实施例，所述故障检测模块具体用于：
将所述每个跑偏影响参数的敏感系数的绝对值与第二预设阈值进行对比，并将大于所述第二预设阈值的所述跑偏影响参数作为关键影响参数；
根据所述关键影响参数的敏感系数的正负以及所述绝对值的大小与故障原因的预设映射关系，确定所述跑偏故障检测结果。
基于上述任一实施例，还包括提醒模块，所述提醒模块用于：
将所述行走跑偏量的预测结果和/或所述跑偏故障检测结果发送至显示装置，以显示所述行走跑偏量的预测结果和/或所述跑偏故障检测结果；
和/或确定存在行走跑偏时，发送告警指令至告警装置，以通过所述警告装置进行告警。
基于上述任一实施例，所述计算模块具体用于：
将所述历史行走数据拆分为训练集和测试集；
通过所述训练集对所述代理模型进行训练，得到所述多个待求解参数以及初始跑偏预测模型；
通过所述测试集对所述初始跑偏预测模型进行测试，得到测试结果；
根据所述测试结果对所述多个待求解参数进行调整优化，得到所述跑偏预测模型。
基于上述任一实施例，所述代理模型包括二阶响应面代理模型或神经网络模型。
基于上述任一实施例，所述跑偏影响参数的参数数据包括每个跑偏影响参数对应的随时间变化的曲线。
本发明实施例还提供一种作业机械，包括：行走装置和控制装置，所述控制装置用于执行如上任一实施例所述的行走跑偏预测方法。
具体地，作业机械诸如挖掘机、起重机等工程机械，工程机械可以为履带式工程机械，也可以为轮式工程机械。
图5示例了一种电子设备的实体结构示意图，如图5所示，该电子设备可以包括：处理器(processor)501、通信接口(Communications Interface)502、存储器(memory)503和通信总线504，其中，处理器501，通信接口502，存储器503通过通信总线504完成相互间的通信。处理器501可以调用存储器503中的逻辑指令，以执行行走跑偏预测方法，该方法包括：获取作业机械行走过程中跑偏影响参数的参数数据，其中所述跑偏影响参数为用于指示所述作业机械是否行走跑偏的参数；
将所述跑偏影响参数的参数数据输入至预先训练好的跑偏预测模型，得到所述作业机械的行走跑偏量的预测结果；
其中，所述跑偏预测模型是基于代理模型构建得到的；所述跑偏预测模型包括多个待求解参数，所述多个待求解参数基于历史行走数据求解得到。
此外，上述的存储器503中的逻辑指令可以通过软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
另一方面，本发明实施例还提供一种计算机程序产品，所述计算机程序产品包括存储在非暂态计算机可读存储介质上的计算机程序，所述计算机程序包括程序指令，当所述程序指令被计算机执行时，计算机能够执行上述各方法所提供的行走跑偏预测方法，该方法包括：获取作业机械行走过程中跑偏影响参数的参数数据，其中所述跑偏影响参数为用于指示所述作业机械是否行走跑偏的参数；
将所述跑偏影响参数的参数数据输入至预先训练好的跑偏预测模型，得到所述作业机械的行走跑偏量的预测结果；
其中，所述跑偏预测模型是基于代理模型构建得到的；所述跑偏预测模型包括多个待求解参数，所述多个待求解参数基于历史行走数据求解得到。
又一方面，本发明实施例还提供一种非暂态计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现以执行上述各提供的行走跑偏预测方法，该方法包括：获取作业机械行走过程中跑偏影响参数的参数数据，其中所述跑偏影响参数为用于指示所述作业机械是否行走跑偏的参数；
将所述跑偏影响参数的参数数据输入至预先训练好的跑偏预测模型，得到所述作业机械的行走跑偏量的预测结果；
其中，所述跑偏预测模型是基于代理模型构建得到的；所述跑偏预测模型包括多个待求解参数，所述多个待求解参数基于历史行走数据求解得到。
以上所描述的装置实施例仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。本领域普通技术人员在不付出创造性的劳动的情况下，即可以理解并实施。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到各实施方式可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件。基于这样的理解，上述技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在计算机可读存储介质中，如ROM/RAM、磁碟、光盘等，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行各个实施例或者实施例的某些部分所述的方法。
最后应说明的是：以上实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围。

Claims (16)

  1. 一种行走跑偏预测方法，其特征在于，包括：
    获取作业机械行走过程中跑偏影响参数的参数数据，其中所述跑偏影响参数为用于指示所述作业机械是否行走跑偏的参数；
    将所述跑偏影响参数的参数数据输入至预先训练好的跑偏预测模型，得到所述作业机械的行走跑偏量的预测结果；
    其中，所述跑偏预测模型是基于代理模型构建得到的；所述跑偏预测模型包括多个待求解参数，所述多个待求解参数基于历史行走数据求解得到。
  2. 根据权利要求1所述的行走跑偏预测方法，其特征在于，在所述得到所述作业机械的行走跑偏量的预测结果之后，还包括：
    将所述行走跑偏量的预测结果输入至预先构建好的故障检测模型，得到所述作业机械的跑偏故障检测结果。
  3. 根据权利要求2所述的行走跑偏预测方法，其特征在于，所述跑偏影响参数包括多个跑偏影响参数，
    其中，所述得到所述作业机械的跑偏故障检测结果，包括：
    基于所述行走跑偏量的预测结果确定所述多个跑偏影响参数中每个跑偏影响参数的敏感系数；
    基于所述每个跑偏影响参数的敏感系数确定所述作业机械的跑偏故障检测结果。
  4. 根据权利要求3所述的行走跑偏预测方法，其特征在于，所述基于所述行走跑偏量的预测结果确定所述多个跑偏影响参数中每个跑偏影响参数的敏感系数，包括：
    基于所述行走跑偏量的预测结果分别对所述每个跑偏影响参数进行求导，获取所述跑偏影响参数的求导结果；
    基于所述跑偏影响参数的求导结果确定所述跑偏影响参数的敏感系数。
  5. 根据权利要求4所述的行走跑偏预测方法，其特征在于，所述基于所述行走跑偏量的预测结果分别对所述每个跑偏影响参数进行求导，获取所述跑偏影响参数的求导结果，包括：
    根据所述行走跑偏量的预测结果对应的历程曲线，在所述行走跑偏量最大的时刻对所述每个跑偏影响参数进行求导，以获取所述每个跑偏影响参数的求导结果。
  6. 根据权利要求3所述的行走跑偏预测方法，其特征在于，所述基于所述每个跑偏影响参数的敏感系数确定所述作业机械的跑偏故障检测结果，包括：
    确定所述每个跑偏影响参数的敏感系数的正负和绝对值；
    基于所述每个跑偏影响参数的敏感系数的正负和绝对值，确定所述作业机械的跑偏故障检测结果。
  7. 根据权利要求6所述的行走跑偏预测方法，其特征在于，所述基于所述每个跑偏影响参数的敏感系数的正负和绝对值，确定所述作业机械的跑偏故障检测结果，包括：
    将所述每个跑偏影响参数的敏感系数的绝对值按照大小进行排序，得到排序结果；
    根据排序结果将排列在前第一预设阈值的所述跑偏影响参数作为关键影响参数；
    根据所述关键影响参数的敏感系数的正负以及所述绝对值的大小与故障原因的预设映射关系，确定所述跑偏故障检测结果。
  8. 根据权利要求6所述的行走跑偏预测方法，其特征在于，所述基于所述每个跑偏影响参数的敏感系数的正负和绝对值，确定所述作业机械的跑偏故障检测结果，包括：
    将所述每个跑偏影响参数的敏感系数的绝对值与第二预设阈值进行对比，并将大于所述第二预设阈值的所述跑偏影响参数作为关键影响参数；
    根据所述关键影响参数的敏感系数的正负以及所述绝对值的大小与故障原因的预设映射关系，确定所述跑偏故障检测结果。
  9. 根据权利要求2所述的行走跑偏预测方法，其特征在于，还包括：
    将所述行走跑偏量的预测结果和/或所述跑偏故障检测结果发送至显示装置，以显示所述行走跑偏量的预测结果和/或所述跑偏故障检测结果；和/或
    确定存在行走跑偏时，发送告警指令至告警装置，以通过所述警告装置进行告警。
  10. 根据权利要求1所述的行走跑偏预测方法，其特征在于，还包括：
    将所述历史行走数据拆分为训练集和测试集；
    通过所述训练集对所述代理模型进行训练，得到所述多个待求解参数以及初始跑偏预测模型；
    通过所述测试集对所述初始跑偏预测模型进行测试，得到测试结果；
    根据所述测试结果对所述多个待求解参数进行调整优化，得到所述跑偏预测模型。
  11. 根据权利要求1至10中任一项所述的行走跑偏预测方法，其特征在于，所述代理模型包括二阶响应面代理模型或神经网络模型。
  12. 根据权利要求1至10中任一项所述的行走跑偏预测方法，其特征在于，所述跑偏影响参数的参数数据包括每个跑偏影响参数对应的随时间变化的曲线。
  13. 一种行走跑偏预测装置，其特征在于，包括：
    数据获取模块，用于获取作业机械行走过程中跑偏影响参数的参数数据，其中所述跑偏影响参数为用于指示所述作业机械是否行走跑偏的参数；
    计算模块，用于将所述跑偏影响参数的参数数据输入至预先训练好的跑偏预测模型，得到所述作业机械的行走跑偏量的预测结果；
    其中，所述跑偏预测模型是基于代理模型构建得到的；所述跑偏预测模型包括多个待求解参数，所述多个待求解参数基于历史行走数据求解得到。
  14. 一种作业机械，其特征在于，包括：行走装置和控制装置，所述控制装置用于执行如权利要求1至12任一项所述的行走跑偏预测方法。
  15. 一种电子设备，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述程序时实现如权利要求1至12任一项所述的行走跑偏预测方法。
  16. 一种非暂态计算机可读存储介质，其上存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1至12任一项所述的行走跑偏预测方法。
PCT/CN2023/095525 2022-08-02 2023-05-22 行走跑偏预测方法、装置及作业机械 WO2024027284A1 (zh)

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