CN116776716A - Method and device for determining dynamic stress of arm support, electronic equipment and readable storage medium - Google Patents
Method and device for determining dynamic stress of arm support, electronic equipment and readable storage medium Download PDFInfo
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Abstract
The application relates to the technical field of engineering machinery monitoring, and discloses a method and a device for determining dynamic stress of an arm support, electronic equipment and a readable storage medium. The method comprises the following steps: acquiring target parameters, wherein the target parameters comprise attitude information of the arm support and operation information of engineering machinery; inputting target parameters into a prediction model to obtain boom quality dynamic response, wherein the boom quality dynamic response is used for representing real-time stress of the boom under different dynamic loads, and the prediction model is obtained by training according to a preset training set; and determining the dynamic stress of the arm support according to the dynamic response of the arm support quality. According to the method for determining the dynamic stress of the arm support, provided by the embodiment of the application, the dynamic response of the arm support mass is predicted by utilizing the prediction model based on the target parameter, and the dynamic stress of the arm support is determined according to the dynamic response of the arm support mass, so that the technical problem that the calculated dynamic stress of the arm support has larger error in the prior art is solved, and the accuracy of the prediction of the dynamic stress of the arm support is improved.
Description
Technical Field
The application relates to engineering machinery monitoring technology, in particular to a method and a device for determining dynamic stress of an arm support, electronic equipment and a readable storage medium.
Background
The dynamic stress of the arm support, which is calculated through mechanical analysis, can be used for health monitoring management of the arm support, dangerous operation is predicted in advance, and the safety performance of products is greatly improved. The calculation of the dynamic stress of the arm support is to obtain an arm support dynamic stress calculation equation through arm support stress analysis, and the result is mainly related to the arm support inclination angle, the arm support mass and the dynamic load coefficient.
However, because the load of the arm support is changed in real time under the influence of the inclination angle and the gear of the arm support under different working states of the engineering machinery, the quality of the arm support is also changed in real time; meanwhile, the vibration acceleration of the arm support changes at all times, and a single dynamic load coefficient is insufficient for accurately representing the dynamic load acting on the arm support. Therefore, the dynamic stress of the arm support calculated by the traditional technical scheme has larger error.
Disclosure of Invention
The embodiment of the application aims to solve the problem that the boom dynamic stress calculated in the prior art has larger error, and provides a method and a device for determining the boom dynamic stress, electronic equipment and a readable storage medium.
In a first aspect, an embodiment of the present application provides a method for determining a dynamic stress of an arm support, including:
acquiring target parameters, wherein the target parameters comprise attitude information of the arm support and operation information of engineering machinery;
inputting target parameters into a prediction model to obtain boom quality dynamic response, wherein the boom quality dynamic response is used for representing real-time stress of the boom under different dynamic loads, and the prediction model is obtained by training according to a preset training set;
and determining the dynamic stress of the arm support according to the dynamic response of the arm support quality.
According to one embodiment of the present disclosure, the method further comprises:
under the condition that the target parameters do not belong to a preset training set, the dynamic response of the boom quality is checked to obtain a checking result;
determining boom dynamic stress according to boom quality dynamic response, including:
and under the condition that the test result meets the requirement, determining the dynamic stress of the arm support according to the dynamic response of the arm support quality.
According to a specific embodiment of the present disclosure, the method for testing the dynamic response of the boom quality to obtain the test result includes:
determining the current working condition of the corresponding engineering machinery according to the target parameters;
determining reference values of all parameters of a preset distribution function of the corresponding boom quality dynamic response according to the current working condition;
performing generalized extremum distribution fitting on the arm support quality dynamic response, and determining the current value of each parameter of a distribution function of the arm support quality dynamic response;
under the condition that the error between the reference value and the current value of any parameter is not in a preset range, obtaining an unsatisfactory test result;
and under the condition that errors of the reference value and the current value of each parameter are in a preset range, obtaining a qualified test result.
According to one embodiment of the present disclosure, the method further comprises:
and under the condition that the test result is not in accordance with the requirement, correcting the dynamic response of the boom quality so that the errors of the current value and the reference value of each parameter of the distribution function obtained by fitting the dynamic response of the modified boom quality are within a preset range, and determining the dynamic stress of the boom according to the dynamic response of the corrected boom quality.
According to one embodiment of the disclosure, the operating information of the work machine includes a pumping gear, a pumping pressure, and a cylinder pressure.
According to one embodiment of the present disclosure, the training process of the predictive model includes:
obtaining a strain data sample of each section of arm support by using a strain sensor, and obtaining a corresponding target parameter sample, wherein the target parameter sample comprises an attitude information sample of the arm support and an operation information sample of engineering machinery;
determining the boom quality dynamic response of each section of boom by using the strain data sample and the corresponding gesture information sample of each section of boom;
constructing a preset training set by utilizing the target parameter sample and the corresponding arm support quality dynamic response;
and inputting the preset training set into a preset neural network for iterative training to obtain a prediction model.
According to a specific embodiment of the present disclosure, determining boom dynamic stress according to boom quality dynamic response includes:
determining the dynamic stress of the arm support according to a preset formula, wherein the preset formula comprises:
in sigma it Is the stress of the ith section arm frame at the moment t, m it a it For dynamic response of arm support quality, L i For the distance theta between the centroid of the ith section of arm support and the arm support stress measuring point it The inclination angle of the ith section of arm support at the moment t is F (i+1)t The force transmitted to the ith section arm frame by the hinge point at the moment t is L fit Is F (i+1)t And the corresponding moment arm distance W is the bending resistance section modulus.
In a second aspect, an embodiment of the present application provides a device for determining a dynamic stress of an arm support, including:
the acquisition device is used for acquiring target parameters, wherein the target parameters comprise attitude information of the arm support and operation information of the engineering machinery;
the predicting device is used for inputting target parameters into a predicting model to obtain arm support quality dynamic response, wherein the arm support quality dynamic response is used for representing real-time stress of the arm support under different dynamic loads, and the predicting model is obtained through training according to a preset training set;
and the determining device is used for determining the dynamic stress of the arm support according to the dynamic response of the arm support quality.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor and a memory, where the memory stores machine executable instructions executable by the processor, where the processor may execute the machine executable instructions to implement the method for determining boom dynamic stress in the first aspect.
In a fourth aspect, an embodiment of the present application provides a machine-readable storage medium, where instructions are stored, where the instructions, when executed by a processor, cause the processor to implement a method for determining a boom dynamic stress according to the first aspect.
According to the technical scheme, the method for determining the dynamic stress of the arm support provided by the embodiment of the application predicts the dynamic response of the arm support mass by using the prediction model based on the target parameter, and determines the dynamic stress of the arm support according to the dynamic response of the arm support mass.
Additional features and advantages of embodiments of the application will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain, without limitation, the embodiments of the application. In the drawings:
fig. 1 is a schematic flow chart of a method for determining boom dynamic stress according to an embodiment of the present application;
FIG. 2a illustrates a schematic diagram of one operating mode provided in accordance with an embodiment of the present application;
FIG. 2b illustrates a schematic diagram of another operating mode provided in accordance with an embodiment of the present application;
FIG. 2c illustrates a schematic diagram of yet another operating mode provided in accordance with an embodiment of the present application;
fig. 3 is a schematic structural diagram of a device for determining dynamic stress of an arm support according to an embodiment of the present application.
Detailed Description
The following describes specific embodiments of the present application in detail with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the application, are not intended to limit the application.
It should be noted that, if directional indications (such as up, down, left, right, front, and rear … …) are included in the embodiments of the present application, the directional indications are merely used to explain the relative positional relationship between the components, the direction change condition, and the like in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indications are correspondingly changed.
In addition, if there is a description of "first", "second", etc. in the embodiments of the present application, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present application.
At present, the dynamic stress of the arm support has three main types of acquisition modes. Firstly, a strain gauge sensor is attached to a target position by a direct measurement method, and the strain of the arm support is collected in real time, so that the stress of the arm support is calculated. Secondly, indirectly obtaining the arm support stress by establishing an arm support stress numerical model based on the data of the inclination sensor through mechanical analysis. Thirdly, predicting the arm support stress through a neural network.
The existing boom dynamic stress prediction method is mainly limited in that: firstly, when the stress of the arm support is measured by adopting a direct method, the strain sensor is easy to damage due to complex working environment of the arm support, difficult to install and maintain, high in cost and cannot be applied to products; secondly, when the arm support stress is calculated by adopting an indirect method, the arm support stress precision obtained by mechanical calculation cannot be ensured because of the large difference between the arm support quality and the actual working state; thirdly, the dynamic load coefficient is determined only by experience, and the numerical value is single, so that the dynamic load coefficient cannot meet various working conditions; fourthly, when the dynamic stress of the arm support is predicted based on the BP neural network, the number of required samples is large, and the prediction accuracy of working conditions except training data is low.
In order to solve the above technical problems, referring to fig. 1, fig. 1 is a flow chart illustrating a method for determining boom dynamic stress according to an embodiment of the present application, as shown in fig. 1, in an embodiment of the present application, a method for determining boom dynamic stress is provided, and the method may include steps 100 to 300:
step 100: and obtaining target parameters, wherein the target parameters comprise attitude information of the arm support and operation information of the engineering machinery.
In general, some types of engineering machinery include a plurality of sections of arm frames, for example, arm frames of engineering machinery such as an excavator, a pump truck, a crane, an overhead working vehicle and the like, and the posture information of the arm frames can include the bending angle of the arm frames and the included angle between adjacent arm frames, and can be obtained by directly measuring through an inclination angle sensor or can be obtained by calculating through an inertia sensor. The operation information of the engineering machine can comprise pumping gear, pumping pressure and oil cylinder pressure, and can be directly obtained through a controller of the engineering machine. In the embodiment of the application, the operation information of the engineering machinery is adopted, so that the attitude information of the arm support can be assisted, the working condition of the arm support can be more accurately determined, the prediction of the dynamic response of the attitude information of the arm support to the quality of the arm support can be better assisted, and the accuracy of the prediction result can be improved.
Step 200: inputting target parameters into a prediction model to obtain boom quality dynamic response, wherein the boom quality dynamic response is used for representing real-time stress of the boom under different dynamic loads, and the prediction model is obtained through training according to a preset training set.
The dynamic response of the arm support quality is a key parameter for accurately calculating the dynamic stress of the arm support in the embodiment of the application, and is used for representing the real-time stress of the arm support under different dynamic loads. Exemplary boom mass dynamic response can be represented by m it a it Representation, where m it Representing arm support quality of an ith arm support at a t moment, a it And the acceleration of the ith arm frame at the t moment is shown. It can be appreciated that the boom quality dynamic response can also be m i ga it Representation, where m i g is the mass of the ith section arm frame, a it The dynamic load coefficient of the ith arm frame which changes along with time t. Embodiments of the application use m it a it For example.
In the prior art, the boom quality only has two qualities of full load and no load, and simultaneously, the boom dynamic stress is calculated by utilizing a single dynamic load coefficient, the physical state and the actual difference are larger, and the precision is lower. The embodiment of the application obtains the dynamic response of the arm support quality under different working conditions based on the prediction model, and can be suitable for the real-time prediction of the arm support dynamic stress of the multi-joint arm engineering machinery under any working condition and posture.
In an alternative embodiment, the training process of the prediction model includes:
obtaining a strain data sample of each section of arm support by using a strain sensor, and obtaining a corresponding target parameter sample, wherein the target parameter sample comprises an attitude information sample of the arm support and an operation information sample of engineering machinery;
determining the boom quality dynamic response of each section of boom by using the strain data sample and the corresponding gesture information sample of each section of boom;
constructing a preset training set by utilizing the target parameter sample and the corresponding arm support quality dynamic response;
and inputting the preset training set into a preset neural network for iterative training to obtain a prediction model.
Specifically, arm support mass m it The arm support with larger change is mainly a tail end arm support, and the filling degree of fluid in the arm support conveying pipe is different under different arm support postures and different working conditions; for hoisting equipment, the equivalent mass of the hoisting weight to the end arm support is different under different inclination angles, so that the mass change of the end arm support is obviously different compared with other arm supports. Boom acceleration a it The arm support acceleration can be obtained by deriving the change time of the inclination angle according to the inclination angle of the arm support and calculating the acceleration of the arm support according to the centroid distance.
The method can carry out theoretical solution. However, in actual engineering, the fluid viscosity and the friction factor in the pipe are difficult to determine, and meanwhile, the boom acceleration obtained by the inclination angle change tends to have a large error, so that the theoretical method for solving the boom quality and the acceleration is difficult to implement in actual engineering.
The embodiment of the application provides a method for solving the dynamic response of the boom quality by actually measuring the dynamic stress according to the motion characteristics and the structural characteristics of the boom. A strain sensor is arranged on each section of arm frame to measure strain data; and then solving the dynamic response of the boom quality according to the measured strain data by utilizing the mapping relation between the dynamic response of the boom quality and the dynamic stress of the boom. It can be understood that the strain sensor is only needed when a sample is established, and the strain sensor is not needed in actual application, so that the technical scheme provided by the embodiment of the application is convenient to use and low in cost.
The strain sensor is a sensor based on measuring the strain produced by the deformation of an object under force. Strain data of each section of arm support can be acquired through a strain sensor, and a strain data sample is constructed. Meanwhile, a target parameter sample is constructed by utilizing the corresponding target parameter. The method for obtaining the target parameter is the same as the foregoing, and will not be described in detail here. In addition, in the embodiment of the application, in order to improve the accuracy of the acquired strain data of the arm support, the strain sensor is connected in a 1/4 bridge manner, and the distance between the strain sensor and the upper web plate and the lower web plate is not less than 20mm.
Further, according to the arm support stress analysis, the arm support stress calculation formula is as follows:
in sigma it Is the stress of the ith section arm frame at the moment t, m it a it For dynamic response of arm support quality, L i For the distance theta between the centroid of the ith section of arm support and the arm support stress measuring point it The inclination angle of the ith section of arm support at the moment t is F (i+1)t The force transmitted to the ith section arm frame by the hinge point at the moment t is L fit Is F (i+1)t And the corresponding moment arm distance W is the bending resistance section modulus.
The calculation formula of the boom quality dynamic response can be obtained by transforming the formula (1), and the calculation formula can be specifically expressed as the following formula:
and (3) calculating the boom quality dynamic response of each section of boom under various working conditions based on the formula (2) by using the strain data sample and the corresponding attitude information sample of each section of boom.
In order to obtain the posture information of the arm support and the mapping relation between the posture information and the arm support quality dynamic response, the embodiment of the application utilizes the target parameter sample and the corresponding arm support quality dynamic response to construct a preset training set, and inputs the preset training set into a preset neural network for iterative training so as to obtain a prediction model of the arm support quality dynamic response. The type of the preset neural network may be set according to actual requirements, for example, a BP (Back Propagation) neural network, which is not limited in the embodiment of the present application.
It can be understood that the training samples are derived from actual work of the engineering machine, that is, strain data samples and corresponding target parameter samples of the engineering machine under different working conditions need to be obtained. In the embodiment of the application, the working conditions can be selected from the most common typical working conditions in actual working, such as arched working conditions, M-shaped working conditions and horizontal working conditions. Taking 6 sections of arm frames as an example, fig. 2a, 2b and 2c respectively show schematic diagrams of one working condition provided according to an embodiment of the present application, that is, three working conditions in total, and according to alphabetical sequences, fig. 2a, 2b and 2c sequentially show an arch working condition, an M-shaped working condition and a horizontal working condition.
Further, in the embodiment of the application, for the obtained strain data sample and the target parameter sample, cleaning can be performed first to remove the problem that environmental noise, zero drift, abnormal value and the like may affect subsequent calculation, and then the boom quality dynamic response of each section of boom under various working conditions is obtained based on the formula (2), so as to improve the accuracy of the result.
According to the embodiment of the application, the arm support quality dynamic response prediction model is obtained based on the training of the preset neural network, and the arm support quality dynamic response can be accurately predicted within the preset training set range corresponding to the trained working condition.
Step 300: and determining the dynamic stress of the arm support according to the dynamic response of the arm support quality.
It will be appreciated that the boom dynamic stress can be determined by calculating the boom quality dynamic response predicted by the prediction model based on the formula (1), that is, in an alternative embodiment, step 300 includes:
determining the dynamic stress of the arm support according to a preset formula, wherein the preset formula comprises:
in sigma it Is the stress of the ith section arm frame at the moment t, m it a it For dynamic response of arm support quality, L i For the distance theta between the centroid of the ith section of arm support and the arm support stress measuring point it The inclination angle of the ith section of arm support at the moment t is F (i+1)t The force transmitted to the ith section arm frame by the hinge point at the moment t is L fit Is F (i+1)t And the corresponding moment arm distance W is the bending resistance section modulus.
In an alternative embodiment, the method further comprises:
under the condition that the target parameters do not belong to a preset training set, the dynamic response of the boom quality is checked to obtain a checking result;
determining boom dynamic stress according to boom quality dynamic response, including:
and under the condition that the test result meets the requirement, determining the dynamic stress of the arm support according to the dynamic response of the arm support quality.
It can be understood that under the condition that the target parameter belongs to a preset training set, the accuracy of the prediction result of the dynamic response of the boom quality corresponding to the target parameter by the prediction model is higher; in the embodiment of the application, a method for checking the dynamic response of the boom quality is provided under the condition that the target parameter does not belong to the preset training set, so that the accuracy of the prediction result of the prediction model under the condition is improved, and further, the dynamic stress of the boom can be determined directly according to the prediction result of the prediction model, namely the dynamic response of the boom quality predicted by the prediction model under the condition that the checking result meets the requirement.
In an alternative embodiment, the dynamic response of the boom quality is tested to obtain a test result, including:
determining the current working condition of the corresponding engineering machinery according to the target parameters;
determining reference values of all parameters of a preset distribution function of the corresponding boom quality dynamic response according to the current working condition;
performing generalized extremum distribution fitting on the arm support quality dynamic response, and determining the current value of each parameter of a distribution function of the arm support quality dynamic response;
under the condition that the error between the reference value and the current value of any parameter is not in a preset range, obtaining an unsatisfactory test result;
and under the condition that errors of the reference value and the current value of each parameter are in a preset range, obtaining a qualified test result.
Although many studies have been made on neural network generalization methods, the problem of poor generalization ability of the neural network is not completely solved. Aiming at the problems, the embodiment of the application provides a method for improving the accuracy of a prediction result of boom quality dynamic response which does not belong to a preset training set.
Exemplary, the strain data sample and the corresponding gesture information sample of each section of arm support are counted, and the arm support quality dynamic response of each section of arm support under various working conditions is calculated based on a formula (2), in the embodiment of the application, the result shows that the arm support quality dynamic response accords with generalized extremum distribution, and the preset distribution function can be expressed as the following formula:
where μ, α, k are the position, scale and shape parameters, respectively.
For different typical conditions, there are more obvious differences among the three parameters of the corresponding distribution function, while for the same typical conditions, for example, the differences among the three parameters in multiple arched conditions are smaller. Therefore, after the current working condition of the corresponding engineering machinery is determined according to the target parameter, the reference value of each parameter of the preset distribution function of the corresponding boom quality dynamic response is determined according to the current working condition and is used as the reference standard.
And performing generalized extremum distribution fitting on the boom quality dynamic response, determining the current value of each parameter of a distribution function of the boom quality dynamic response, and comparing the obtained current values of the three parameters with corresponding reference values respectively, namely comparing the current value of mu with the reference value of mu, comparing the current value of alpha with the reference value of alpha, and comparing the current value of k with the reference value of k. Under the condition that the error between the reference value and the current value of any one of the three parameters mu, alpha and k is not in a preset range, obtaining an unsatisfactory test result, namely, the test is failed; and under the condition that the errors of the reference values and the current values of the three parameters mu, alpha and k are within a preset range, obtaining a qualified test result, namely passing the test.
It will be appreciated that the preset range of the error may be set according to the actual requirement, which is not limited in the embodiment of the present application.
In the embodiment of the application, a plurality of parameters in the distribution function corresponding to the boom quality dynamic response are adopted for inspection, so that the accuracy of the prediction result of the boom quality dynamic response which does not belong to a preset training set is improved.
Meanwhile, the problem of poor generalization effect of the neural network is avoided by a method for correcting the statistical parameters. Further, in an alternative embodiment, the method further comprises:
and under the condition that the test result is not in accordance with the requirement, correcting the dynamic response of the boom quality so that the errors of the current value and the reference value of each parameter of the distribution function obtained by fitting the dynamic response of the modified boom quality are within a preset range, and determining the dynamic stress of the boom according to the dynamic response of the corrected boom quality.
The method and the device for correcting the dynamic response of the boom quality are capable of ensuring that the error of the current value and the reference value of each parameter of the distribution function obtained by fitting the dynamic response of the boom quality after the correction is within a preset range, namely ensuring that the dynamic response of the boom quality after the correction can pass through the inspection, thereby improving the accuracy of the prediction result of the dynamic response of the boom quality which does not belong to a preset training set, determining the dynamic stress of the boom according to the dynamic response of the boom quality after the correction, and further improving the accuracy of the prediction of the dynamic stress of the boom.
Specifically, the method for correcting the dynamic response of the boom quality can be as follows: and calculating the frequency MF of the boom quality dynamic response discrete interval according to the distribution function after the parameters are corrected, and calculating the difference delta d=PF-MF between the two. When Δd is positive, it means that the frequency of the predicted data in the interval is too low, and needs to be increased; when Δd is negative, this means that the predicted data is too frequent in this interval and needs to be reduced. Because the total frequencies are consistent, the positive value cumulative value and the negative value cumulative value of the frequency difference value in delta d are basically the same, so that the time domain data point value of the interval section needing to be reduced can be changed into the time domain data point value of the interval section needing to be increased in the predicted arm support quality dynamic response time domain data PT, and the corrected arm support quality dynamic response is obtained.
According to the method for determining the dynamic stress of the arm support, provided by the embodiment of the application, based on the target parameters, the dynamic response of the arm support quality is predicted by using the prediction model, and the dynamic stress of the arm support is determined according to the dynamic response of the arm support quality.
In addition, in actual work, a strain sensor is not required to be arranged on engineering machinery to collect strain data, so that the cost is reduced; the determined dynamic stress of the arm support represents the real-time stress of the arm support under different dynamic loads, the characteristic of real-time change of the load of the arm support is considered, and the precision is obviously improved; any working condition can be calculated, and the application range is wide; meanwhile, compared with the method for predicting the arm support dynamic stress by directly adopting the neural network, the method has the advantages of less required sample size, high iteration speed and high prediction precision.
Corresponding to the above method embodiment, please refer to fig. 3, fig. 3 shows a schematic structural diagram of a device for determining boom dynamic stress according to an embodiment of the present application, and as shown in fig. 3, a device 1000 for determining boom dynamic stress includes:
the obtaining module 1001 is configured to obtain target parameters, where the target parameters include attitude information of the boom and operation information of the engineering machinery;
the prediction module 1002 is configured to input a target parameter to a prediction model, and obtain a boom quality dynamic response, where the boom quality dynamic response is used to characterize real-time stress of the boom under different dynamic loads, and the prediction model is obtained by training according to a preset training set;
and the determining module 1003 is used for determining the dynamic stress of the arm support according to the dynamic response of the arm support quality.
Optionally, the device 1000 for determining the dynamic stress of the boom further includes:
the testing module is used for testing the dynamic response of the arm support quality under the condition that the target parameter does not belong to the preset training set, so as to obtain a testing result;
the determining module 1003 is specifically configured to determine boom dynamic stress according to the boom quality dynamic response when the test result is that the boom dynamic stress meets the requirement.
Optionally, the inspection module includes:
the working condition determining unit is used for determining the current working condition of the corresponding engineering machinery according to the target parameters;
the reference value determining unit is used for determining the reference value of each parameter of the preset distribution function of the dynamic response of the corresponding arm support quality according to the current working condition;
the current value determining unit is used for performing generalized extremum distribution fitting on the arm support quality dynamic response and determining the current value of each parameter of the distribution function of the arm support quality dynamic response;
the first test result unit is used for obtaining a test result which does not meet the requirements under the condition that the error between the reference value and the current value of any parameter is not in a preset range;
and the second test result unit is used for obtaining a test result meeting the requirements under the condition that the errors of the reference value and the current value of each parameter are within a preset range.
Optionally, the device 1000 for determining the dynamic stress of the boom further includes:
and the correction module is used for correcting the dynamic response of the boom quality under the condition that the inspection result is not in accordance with the requirement, so that the errors of the current value and the reference value of each parameter of the distribution function obtained by fitting the corrected dynamic response of the boom quality are all within a preset range, and the dynamic stress of the boom is determined according to the corrected dynamic response of the boom quality.
Optionally, the determining module 1003 is specifically configured to:
determining the dynamic stress of the arm support according to a preset formula, wherein the preset formula comprises:
in sigma it Is the stress of the ith section arm frame at the moment t, m it a it For dynamic response of arm support quality, L i For the distance theta between the centroid of the ith section of arm support and the arm support stress measuring point it The inclination angle of the ith section of arm support at the moment t is F (i+1)t The force transmitted to the ith section arm frame by the hinge point at the moment t is L fit Is F (i+1)t And the corresponding moment arm distance W is the bending resistance section modulus.
The device for determining the dynamic stress of the arm support provided by the embodiment of the application can realize each process from step 100 to step 300 in the method for determining the dynamic stress of the arm support in the method embodiment, and can achieve the same technical effect, and for avoiding repetition, the description is omitted.
Optionally, the embodiment of the present application further provides an electronic device, including a processor and a memory, where the memory stores machine executable instructions capable of being executed by the processor, and the processor may execute the machine executable instructions to implement the method for determining the arm support dynamic stress in the embodiment of the method.
Optionally, the embodiment of the present application further provides a machine-readable storage medium, where instructions are stored, where the instructions when executed by a processor cause the processor to implement the method for determining the dynamic stress of the boom in the embodiment of the method.
In an embodiment of the application, there is also provided a computer program product comprising a computer program which, when executed by a processor, implements the method of determining boom dynamic stress in the above method embodiment.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by the computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (10)
1. The method for determining the dynamic stress of the arm support is characterized by comprising the following steps of:
acquiring target parameters, wherein the target parameters comprise attitude information of an arm support and operation information of engineering machinery;
inputting the target parameters into a prediction model to obtain boom quality dynamic response, wherein the boom quality dynamic response is used for representing real-time stress of the boom under different dynamic loads, and the prediction model is obtained by training according to a preset training set;
and determining the dynamic stress of the arm support according to the dynamic response of the arm support quality.
2. The method of claim 1, further comprising:
under the condition that the target parameters do not belong to a preset training set, the dynamic response of the arm support quality is checked to obtain a checking result;
the step of determining the dynamic stress of the arm support according to the dynamic response of the arm support quality comprises the following steps:
and under the condition that the test result meets the requirement, determining the dynamic stress of the arm support according to the dynamic response of the arm support quality.
3. The method for determining dynamic stress of arm support according to claim 2, wherein the step of testing the dynamic response of arm support quality to obtain a test result comprises:
determining the current working condition of the corresponding engineering machinery according to the target parameters;
determining reference values of all parameters of a preset distribution function of the dynamic response of the corresponding arm support quality according to the current working condition;
performing generalized extremum distribution fitting on the boom quality dynamic response, and determining the current value of each parameter of the boom quality dynamic response distribution function;
under the condition that the error between the reference value of any one parameter and the current value is not in a preset range, obtaining an unsatisfactory test result;
and under the condition that errors of the reference value and the current value of each parameter are within a preset range, obtaining a qualified test result.
4. A method of determining boom dynamic stress according to claim 3, further comprising:
and under the condition that the test result is not in accordance with the requirement, correcting the boom quality dynamic response so that errors of the current value and the reference value of each parameter of the distribution function obtained by fitting the corrected boom quality dynamic response are within a preset range, and determining the boom dynamic stress according to the corrected boom quality dynamic response.
5. The method for determining dynamic stress of an arm support according to claim 1, wherein the operation information of the construction machine includes a pumping gear, pumping pressure and cylinder pressure.
6. The method for determining the dynamic stress of the arm support according to claim 1, wherein the training process of the prediction model comprises:
obtaining a strain data sample of each section of arm support by using a strain sensor, and obtaining a corresponding target parameter sample, wherein the target parameter sample comprises an attitude information sample of the arm support and an operation information sample of engineering machinery;
determining the boom quality dynamic response of each section of boom by using the strain data sample and the corresponding attitude information sample of each section of boom;
constructing the preset training set by using the target parameter sample and the corresponding arm support quality dynamic response;
and inputting the preset training set into a preset neural network for iterative training to obtain the prediction model.
7. The method for determining the dynamic stress of the boom according to claim 1, wherein the determining the dynamic stress of the boom according to the dynamic response of the boom mass comprises:
determining the dynamic stress of the arm support according to a preset formula, wherein the preset formula comprises:
in sigma it Is the stress of the ith section arm frame at the moment t, m it a it For dynamic response of arm support quality, L i For the distance theta between the centroid of the ith section of arm support and the arm support stress measuring point it The inclination angle of the ith section of arm support at the moment t is F (i+1)t The force transmitted to the ith section arm frame by the hinge point at the moment t is L fit Is F (i+1)t And the corresponding moment arm distance W is the bending resistance section modulus.
8. The device for determining the dynamic stress of the arm support is characterized by comprising the following components:
the device comprises an acquisition device, a control device and a control device, wherein the acquisition device is used for acquiring target parameters, and the target parameters comprise attitude information of an arm support and operation information of engineering machinery;
the predicting device is used for inputting the target parameters into a predicting model to obtain boom quality dynamic response, wherein the boom quality dynamic response is used for representing real-time stress of the boom under different dynamic loads, and the predicting model is obtained by training according to a preset training set;
and the determining device is used for determining the dynamic stress of the arm support according to the dynamic response of the arm support quality.
9. An electronic device comprising a processor and a memory, the memory storing machine executable instructions executable by the processor to implement the method of determining boom dynamic stress of any of claims 1-7.
10. A machine-readable storage medium having instructions stored thereon, which when executed by a processor cause the processor to implement a method of determining boom dynamic stress according to any of claims 1-7.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202310524661.3A CN116776716A (en) | 2023-05-10 | 2023-05-10 | Method and device for determining dynamic stress of arm support, electronic equipment and readable storage medium |
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| CN202310524661.3A CN116776716A (en) | 2023-05-10 | 2023-05-10 | Method and device for determining dynamic stress of arm support, electronic equipment and readable storage medium |
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| CN116776716A true CN116776716A (en) | 2023-09-19 |
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| CN202310524661.3A Pending CN116776716A (en) | 2023-05-10 | 2023-05-10 | Method and device for determining dynamic stress of arm support, electronic equipment and readable storage medium |
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