CN113502870B - Excavator working condition judging method and device - Google Patents
Excavator working condition judging method and device Download PDFInfo
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- CN113502870B CN113502870B CN202110802807.7A CN202110802807A CN113502870B CN 113502870 B CN113502870 B CN 113502870B CN 202110802807 A CN202110802807 A CN 202110802807A CN 113502870 B CN113502870 B CN 113502870B
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- 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
Abstract
The invention provides a method and a device for judging working conditions of an excavator, which are used for acquiring real-time state parameter data of the excavator; inputting the real-time state parameter data into an excavator working condition judgment model to obtain the working condition type of the excavator output by the excavator working condition judgment model; the excavator working condition judgment model is obtained by training based on the state parameter data sample carrying the working condition type label, the data required by the method is easy to obtain, and a large amount of data is used when the excavator working condition judgment model is trained, so that the universality of the excavator working condition judgment model is strong, and the judgment of the excavator working condition is easy to realize.
Description
Technical Field
The invention relates to the technical field of engineering machinery, in particular to a method and a device for judging working conditions of an excavator.
Background
The excavator plays a very important role in the engineering machinery industry, is widely applied to the industries such as construction, traffic, military industry and the like, and the demand of various industries on the excavator is further expanded along with the continuous development of the infrastructure industry.
However, since the work contents of the excavator include excavation, crushing, leveling, and the like, the excavator work conditions are very complicated. Different working conditions of the excavator correspond to different target parameters, and if the target parameters are not matched with the actual working conditions of the excavator, the working efficiency of the excavator can be seriously influenced, so that the working condition of the excavator is very important to judge. .
At present, when working condition judgment is carried out on an excavator, the actual working flow of the excavator is generally obtained, flow detection data are generated, then the flow detection data are compared with expert database information, and the actual working condition of the excavator is determined.
Disclosure of Invention
The invention provides a method and a device for judging working conditions of an excavator, which are used for solving the defects that data required by excavator working condition judgment are difficult to obtain and the judgment model is not high in universality in the prior art, and realizing the judgment of the working conditions of the excavator by using the data which are easy to obtain and the model with high universality.
The invention provides an excavator working condition judgment method, which comprises the following steps:
acquiring real-time state parameter data of the excavator;
inputting the real-time state parameter data into an excavator working condition judgment model to obtain the working condition type of the excavator output by the excavator working condition judgment model;
the excavator working condition judgment model is obtained by training based on a state parameter data sample carrying a working condition type label.
According to the excavator working condition judgment method provided by the invention, the step of acquiring the real-time state parameter data of the excavator specifically comprises the following steps: acquiring real-time state parameter data of the excavator in each preset time period in a target time period;
correspondingly, the inputting the real-time state parameter data into an excavator working condition judgment model to obtain the working condition type of the excavator output by the excavator working condition judgment model specifically comprises:
and respectively inputting the real-time state parameter data of the excavator in each preset time period to the excavator working condition judgment model to obtain the corresponding working condition type of the excavator in each preset time period, which is output by the excavator working condition judgment model.
According to the excavator working condition judging method provided by the invention, the real-time state parameter data of the excavator in each preset time period are respectively input into the excavator working condition judging model, so as to obtain the corresponding working condition type of the excavator in each preset time period, which is output by the excavator working condition judging model, and then the method further comprises the following steps:
and determining the working condition type ratio corresponding to each preset time period based on the corresponding working condition type of the excavator in each preset time period.
According to the excavator working condition judgment method provided by the invention, the real-time state parameter data in each preset time period of the excavator are respectively input into the excavator working condition judgment model, so as to obtain the corresponding working condition type of the excavator in each preset time period, which is output by the excavator working condition judgment model, and then the method further comprises the following steps:
the working condition types corresponding to the excavator in each preset time period and the real-time state parameter data of the excavator in each preset time period are uploaded to a cloud data platform, so that the cloud data platform conducts aggregation calculation on the real-time state parameter data of the excavator in each preset time period to obtain target state parameter data of the excavator in each preset time period, and the working condition types corresponding to the excavator in each preset time period and the target state parameter data of the excavator in each preset time period are stored in a cloud data warehouse.
According to the excavator working condition judgment method provided by the invention, the working condition types corresponding to the excavator in each preset time period and the real-time state parameter data of the excavator in each preset time period are uploaded to a cloud data platform, and then the method further comprises the following steps:
performing secondary training on the excavator working condition judgment model based on the corresponding working condition type of the excavator in each preset time period and the target state parameter data of the excavator in each preset time period to obtain an excavator working condition judgment model after secondary training;
and updating the excavator working condition judgment model based on the excavator working condition judgment model after secondary training.
According to the excavator working condition judgment method provided by the invention, the real-time state parameter data comprises at least one of engine speed, pilot pressure, current, pump pressure and service life.
The invention also provides an excavator working condition judgment device, which comprises:
the parameter data acquisition module is used for acquiring real-time state parameter data of the excavator;
the excavator working condition judgment module is used for inputting the real-time state parameter data into an excavator working condition judgment model to obtain the working condition type of the excavator output by the excavator working condition judgment model;
the excavator working condition judgment model is obtained by training based on a state parameter data sample carrying a working condition type label.
The present invention also provides an excavator, comprising: the excavator working condition judgment device is used for judging the excavator working condition type.
The invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the steps of any one of the excavator working condition judging methods.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the excavator condition determination method as described in any one of the above.
According to the excavator working condition judging method and device, the working condition type of the excavator output by the excavator working condition judging model is obtained by acquiring the real-time state parameter data of the excavator and inputting the real-time state parameter data into the excavator working condition judging model.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for determining the working condition of an excavator according to the present invention;
FIG. 2 is a schematic flow chart of a method for determining the working condition of the excavator according to the present invention;
FIG. 3 is a schematic structural diagram of an excavator working condition determination device provided by the present invention;
fig. 4 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
The invention provides an excavator working condition judgment method, which is characterized in that the flow data used is not easy to obtain when the excavator working condition is identified at present, and the model constructed by the used expert database is not high in universality.
Fig. 1 is a schematic flow chart of an excavator working condition determination method provided by the present invention, and as shown in fig. 1, the method includes:
s1, acquiring real-time state parameter data of an excavator;
s2, inputting the real-time state parameter data into an excavator working condition judgment model to obtain the working condition type of the excavator output by the excavator working condition judgment model;
the excavator working condition judgment model is obtained by training based on a state parameter data sample with a working condition type label.
According to the excavator working condition judgment method provided by the embodiment of the invention, the execution main body is the server, the server can be a local server or a cloud server, the local server can be a computer, a tablet computer, a smart phone and the like, and the method is not limited in the embodiment of the invention.
Firstly, step S1 is executed, and real-time state parameter data of the excavator are obtained.
The real-time state parameter data of the excavator can comprise real-time state parameter data such as engine rotating speed, pilot pressure, current, pump pressure, service life and the like.
In the embodiment of the invention, the real-time state parameter data of the excavator CAN be acquired through the CAN bus. The CAN (Controller Area Network, CAN) bus refers to a Controller Area Network, and is a multi-host serial bus standard for connecting electronic control units. The data communication among all nodes of the network of the CAN bus has strong real-time performance, so that all real-time state parameter data of the excavator CAN be directly acquired through the CAN bus.
The real-time state parameter data of the excavator is acquired through the CAN bus, a sensor does not need to be additionally arranged on a main valve or a controller of the excavator, the cost for acquiring the state parameter data is reduced, and the input data required by the excavator working condition identification model CAN be acquired more conveniently.
And then executing the step S2, inputting the acquired real-time state parameter data to an excavator working condition judgment model, and obtaining the type of the working condition of the excavator output by the excavator working condition judgment model.
The excavator working condition determination model may be an existing open source neural network model, such as a convolutional neural network model, a residual neural network model, or a cyclic neural network model, and the embodiment of the present invention is not limited in this respect.
The excavator working condition judgment model is obtained by training based on a state parameter data sample with a working condition type label. Specifically, the excavator working condition judgment model can be obtained through training in the following way: firstly, collecting a large number of state parameter data samples of the excavator, and labeling the state parameter data samples, namely enabling the state parameter data samples to carry working condition type labels. And then, training an initial model based on the state parameter data sample carrying the working condition type label, thereby obtaining an excavator working condition judgment model.
Because different working condition types of the excavator correspond to different state parameter data, the real-time state parameter data are input into the trained excavator working condition judgment model, and the working condition type of the excavator output by the excavator working condition judgment model can be obtained. The working condition types of the excavator comprise excavating, leveling, loading, slope repairing, crushing and the like.
When the working condition type of the excavator is output by the excavator working condition judgment model, a time period corresponding to the working condition type can be generated, and the time period can respectively represent the starting time and the ending time by using two timestamps. For example, the real-time state parameter data in the time period from 30 minutes at 29 th 12 th 2021 year 06 month 29 th 35 th 2021 year is input to the excavator working condition determination model, and the working condition type in the time period output by the excavator working condition determination model is obtained, so that the starting time of the time period corresponding to the working condition type in the time period can be represented by the time stamp 2021.06.30.12.30, and the ending time can be represented by the time stamp 2021.06.30.12.35. The representation of the timestamp may be a unix timestamp. The time stamp may be used to verify that the real-time status parameter data has been tampered with, i.e. the time stamp represents a trustworthy time.
According to the excavator working condition judging method, the working condition type of the excavator output by the excavator working condition judging model is obtained by acquiring the real-time state parameter data of the excavator and inputting the real-time state parameter data into the excavator working condition judging model.
On the basis of the foregoing embodiment, the method for determining the working condition of the excavator according to the embodiment of the present invention, where the obtaining of the real-time state parameter data of the excavator includes: acquiring real-time state parameter data of the excavator in each preset time period in a target time period;
correspondingly, the inputting the real-time state parameter data into an excavator working condition judgment model to obtain the working condition type of the excavator output by the excavator working condition judgment model specifically comprises:
and respectively inputting the real-time state parameter data of the excavator in each preset time period to the excavator working condition judgment model to obtain the corresponding working condition type of the excavator in each preset time period, which is output by the excavator working condition judgment model.
Specifically, in the embodiment of the present invention, real-time state parameter data of the excavator in each preset time period in the target time period needs to be acquired. The target time period may be a working time period of the excavator, for example, if the excavator works for 8 hours a day, the target time period may be the 8 hours of the excavator working. Each preset time period may be set according to actual needs, for example, the preset time period may be set to 5 minutes.
Correspondingly, the real-time state parameter data are input into the excavator working condition judgment model, and the working condition type of the excavator output by the excavator working condition judgment model is obtained by respectively inputting the real-time state parameter data of the excavator in each preset time period into the excavator working condition judgment model, so that the working condition type of the excavator output by the excavator working condition judgment model in each preset time period is obtained.
For example, in the above example, the target time period is a working time period of the excavator, the preset time period is 5 minutes, if the working time period of the excavator is from 8 am to 6 pm, the target time period is 10 hours, and the real-time state parameter data in each 5 minutes in the 10 hours are respectively input to the excavator working condition determination model, so that the corresponding working condition type of the excavator in each preset time period, which is output by the excavator working condition determination model, can be obtained. For example, from 9 points to 9:05 inputting the real-time state parameter data in the time period into an excavator working condition judgment model, and obtaining the data from 9 points to 9 points: 05 the type of the working condition corresponding to the time period.
According to the excavator working condition judgment method, the real-time state parameter data of the excavator in each preset time period are respectively input into the excavator working condition judgment model, the corresponding working condition type of the excavator in each preset time period output by the excavator working condition judgment model is obtained, the working condition type of the excavator can be continuously judged in the working time range of the excavator, and the judgment of the working condition type of the excavator is more accurate.
On the basis of the foregoing embodiment, the method for determining the working condition of the excavator according to the embodiment of the present invention is that the real-time state parameter data of the excavator in each preset time period is respectively input to the excavator working condition determination model to obtain the corresponding working condition type of the excavator in each preset time period, where the working condition type is output by the excavator working condition determination model, and then the method further includes:
and determining the working condition type ratio corresponding to each preset time period based on the corresponding working condition type of the excavator in each preset time period.
Specifically, in the embodiment of the present invention, after the operating condition types corresponding to the excavator within each preset time period output by the excavator operating condition judgment model are obtained, the operating condition type proportion corresponding to each preset time period may be determined based on the operating condition types corresponding to each preset time period.
For example, if the target time period is 8 hours and each preset time period is set to 10 minutes, the operating condition types corresponding to each 10 minutes in 8 hours need to be acquired, that is, a total of 48 operating condition types are acquired. And calculating the working condition type proportion corresponding to each preset time period based on the 48 working condition types. For example, in the 48 condition types, 24 condition types are excavation, and the percentage of the excavation condition type is 50%.
In the embodiment of the invention, the working condition type ratio corresponding to each preset time period is determined based on the working condition type corresponding to the excavator in each preset time period, so that a user can conveniently know the working condition of the excavator in the target time period, and more data references are provided for the user.
On the basis of the foregoing embodiment, the method for determining the working condition of the excavator according to the embodiment of the present invention is that the real-time state parameter data in each preset time period of the excavator is respectively input to the excavator working condition determination model to obtain the corresponding working condition type of the excavator in each preset time period, where the working condition type is output by the excavator working condition determination model, and then the method further includes:
the working condition types corresponding to the excavator in each preset time period and the real-time state parameter data of the excavator in each preset time period are uploaded to a cloud data platform, so that the cloud data platform conducts aggregation calculation on the real-time state parameter data of the excavator in each preset time period to obtain target state parameter data of the excavator in each preset time period, and the working condition types corresponding to the excavator in each preset time period and the target state parameter data of the excavator in each preset time period are stored in a cloud data warehouse.
Specifically, in the embodiment of the present invention, after the working condition types of the excavator within the preset time periods, which are output by the excavator working condition determination model, are obtained, the corresponding working condition types of the excavator within the preset time periods and the real-time state parameter data of the excavator within the preset time periods need to be uploaded to the cloud data platform.
The working condition types corresponding to the excavator in each preset time period and the real-time state parameter data of the excavator in each preset time period can be uploaded to the cloud data platform through a transmission mode of a fourth generation mobile communication technology or a fifth generation mobile communication technology.
The fourth Generation Mobile Communication technology (4 th Generation Mobile Communication technology, 4G) is an upgrade to the third Generation Mobile Communication technology, and the rate of data transmission is faster than 3G by using Orthogonal Frequency Division Multiplexing (OFDM), multiple Input Multiple Output (MIMO), smart antenna, and other technologies.
The fifth Generation Mobile Communication technology (5 th Generation Mobile Communication technology,5 g) is a new Generation broadband Mobile Communication technology with the characteristics of high speed, low time delay and large connection, and is a network infrastructure for realizing man-machine interconnection. The 5G technology has three major application scenes, namely mobile broadband enhancement, ultrahigh-reliability low-delay communication and massive machine type communication, and the data transmission speed is faster by using the 5G technology for data transmission, and the 5G technology has higher equipment connection capacity.
After the working condition types corresponding to the excavator in each preset time period and the real-time state parameter data of the excavator in each preset time period are uploaded to the cloud data platform, the cloud data platform can also perform aggregation calculation on the real-time state parameter data of the excavator in each preset time period to obtain the target state parameter data of the excavator in each preset time period.
Because the real-time state parameter data in each preset time period are acquired through the CAN bus, each preset time period comprises a plurality of pieces of real-time state parameter data, and the real-time state parameter data in each preset time period CAN be aggregated into a strip of state parameter data through aggregation calculation, namely the target state parameter data in each preset time period.
After the target state parameter data are obtained, the corresponding working condition types of the excavator in each preset time period and the target state parameter data of the excavator in each preset time period can be stored in a cloud data warehouse. The cloud data warehouse may be a cloud database, and the type of the cloud database may be selected according to actual needs, which is not specifically limited in the embodiment of the present invention.
According to the excavator working condition judgment method in the embodiment of the invention, the working condition types corresponding to the excavator in each preset time period and the real-time state parameter data of the excavator in each preset time period are uploaded to the cloud data platform, then the real-time state parameter data of the excavator in each preset time period are subjected to aggregation calculation to obtain the target state parameter data of the excavator in each preset time period, and the working condition types corresponding to the excavator in each preset time period and the target state parameter data of the excavator in each preset time period are stored in the cloud data warehouse, so that a worker can conveniently inquire the working condition types and the real-time state parameter data of the excavator on line, a data support for analyzing the relation between the real-time state parameter data and the working condition types in the life cycle of the excavator is provided for the worker, the cloud data platform is strong in expandability, more data can be stored, and the storage cost of the data is reduced.
On the basis of the above embodiment, the method for determining the working condition of the excavator according to the embodiment of the present invention further includes:
and storing the working condition type, the real-time state parameter data and the preset time period corresponding to the working condition type to a local storage module.
Specifically, in the embodiment of the present invention, after the working condition type, the real-time state parameter data, and the preset time period corresponding to the working condition type of the excavator output by the excavator working condition determination model are obtained, the working condition type, the real-time state parameter data, and the preset time period corresponding to the working condition type of the excavator may be sent to the local storage module.
Wherein, the local storage module can be a local database. The local database may be a relational database, a non-relational database, and a key-value database, for example, the relational database may be MySQL, maridb, or the like, the non-relational database may be BigTable, cassandra, or the like, and the key-value database may be Apache Cassandra, or the like, and the storage format of the preset time period corresponding to the working condition type, the real-time state parameter data, and the working condition type is required to correspond to the database to be used. The embodiment of the present invention does not specifically limit the type of the database.
In the embodiment of the present invention, when the working condition type, the real-time state parameter data, and the preset time period corresponding to the working condition type are stored in the local storage module, the working condition type, the real-time state parameter data, and the preset time period corresponding to the working condition type may be stored according to the sequence of each preset time period, and the data in the local storage module may be overwritten according to a preset period, that is, the storage time of the data in the local storage module is a preset period. The preset period may be set according to actual needs, which is not specifically limited in the embodiment of the present invention.
For example, when the preset period is 30 days, if the working condition type, the real-time state parameter data and the preset time period corresponding to the working condition type are stored in the local storage module on the date a, after 30 days, the working condition type, the real-time state parameter data and the preset time period corresponding to the working condition type stored on the date a are covered by the newly stored working condition type, the newly stored real-time state parameter data and the preset time period corresponding to the working condition type.
In the embodiment of the invention, the working condition type, the real-time state parameter data and the preset time period corresponding to the working condition type are stored in the local storage module according to the sequence of each preset time period, the newly stored working condition type, the newly stored real-time state parameter data and the preset time period corresponding to the working condition type are used for covering the working condition type, the newly stored real-time state parameter data and the preset time period corresponding to the working condition type in the local storage module according to the preset period, so that a worker can conveniently check the working condition of the excavator locally, and when the working condition type, the newly stored real-time state parameter data and the preset time period corresponding to the working condition type are transmitted to the cloud data platform to cause problems, data support can be provided, the guarantee of the data is enhanced, the working condition type, the newly stored real-time state parameter data and the preset time period corresponding to the working condition type are covered according to the preset period, and the data maintenance difficulty and the memory requirement of the local storage module are reduced.
On the basis of the foregoing embodiment, the excavator working condition determination method provided in the embodiment of the present invention uploads the corresponding working condition types of the excavator in each preset time period and the real-time status parameter data of the excavator in each preset time period to the cloud data platform, and then further includes:
performing secondary training on the excavator working condition judgment model based on the corresponding working condition type of the excavator in each preset time period and the target state parameter data of the excavator in each preset time period to obtain an excavator working condition judgment model after secondary training;
and updating the excavator working condition judgment model based on the excavator working condition judgment model after secondary training.
Specifically, in the embodiment of the invention, after the excavator reaches a certain service time, the performance of the excavator is reduced, and at the moment, various real-time state parameter data of the excavator under the same working condition are changed, namely, the real-time state parameter data drift is caused. Due to drift of the real-time state parameter data of the excavator, the accuracy of the judgment result of the original excavator working condition judgment model is reduced, so that the excavator working condition judgment model can better meet the actual condition of the excavator, and the corresponding working condition types of the excavator in each preset time period and the target state parameter data of the excavator in each preset time period can be used for carrying out secondary training on the excavator working condition judgment model.
After the training is finished, the excavator working condition judgment model after the secondary training can be obtained. And updating the excavator working condition judgment model based on the excavator working condition judgment model after secondary training to obtain the excavator working condition judgment model which is more in line with the actual condition of the excavator. The excavator working condition determination model may be trained for the second time according to a target frequency, and the target frequency may be set according to actual needs, for example, set to 90 days, and the like.
When the working condition type corresponding to the excavator in each preset time period and the target state parameter data of the excavator in each preset time period are used for carrying out secondary training on the excavator working condition judgment model, the accumulated working condition type corresponding to the excavator in each preset time period and the target state parameter data of the excavator in each preset time period are used, for example, when the excavator working condition judgment model is subjected to secondary training every 90 days and the excavator working condition judgment model is subjected to secondary training for the first time, the working condition type corresponding to the excavator in each preset time period and the target state parameter data result of the excavator in each preset time period, which are accumulated in the cloud data warehouse in the previous 90 days, are the working condition type corresponding to the excavator in each preset time period and the target state parameter data of the excavator in each preset time period. Similarly, when the excavator working condition judgment model is updated for the second time, the corresponding working condition types of the excavator stored in the cloud storage data warehouse in each preset time period and the target state parameter data of the excavator in each preset time period are accumulated in 180 days before the excavator working condition judgment model is used.
According to the excavator working condition judgment method in the embodiment of the invention, the excavator working condition judgment model is trained and updated for the second time based on the corresponding working condition type of the excavator in each preset time period and the target state parameter data of the excavator in each preset time period, so that the instability of the excavator working condition judgment model caused by the aging or performance reduction of the excavator and the like is avoided, and the judgment accuracy of the excavator working condition judgment model is improved.
Fig. 2 is a schematic specific flow chart of the excavator working condition determination method provided by the present invention, and as shown in fig. 2, a solid line rectangular frame represents a working condition determination edge calculation device, and the working condition determination edge calculation device includes a CAN data acquisition module, an excavator working condition determination module, a data local storage module, and a data remote transmission module. The excavator working condition determination module is represented by the content in a dashed box in fig. 2.
When the working condition of the excavator is judged, the following steps are required to be executed:
1) The method comprises the following steps of acquiring real-time state parameter data of the excavator, such as engine rotating speed, pilot pressure, current, pumping pressure, use duration and the like, by using a CAN data acquisition module through a CAN bus;
2) Inputting the real-time state parameter data into a trained working condition judgment model, calculating the corresponding working condition type of the excavator in each preset time period by using the working condition judgment model, and after the corresponding working condition type of the excavator in each preset time period is obtained, determining the working condition type proportion corresponding to each preset time period, wherein the working condition type with the largest proportion is the working condition type with the longest duration of the excavator in the target time period; the model judgment working conditions, namely the types of all working conditions of the excavator in the target time period, can also be obtained;
3) Storing the real-time state parameter data acquired by the CAN data acquisition module, the corresponding working condition type of the excavator in each preset time period and each preset time period to a data local storage module; for the data in the data local storage module, the data in the data local storage module can be maintained in a mode of manually taking out the data regularly, and a working condition judgment model is trained;
4) Through a data remote transmission module, real-time state parameter data acquired by a CAN data acquisition module, corresponding working condition types of the excavator in each preset time period and each preset time period are uploaded to a cloud big data platform in a 4G or 5G mode;
5) The method comprises the steps of carrying out aggregation calculation on real-time state parameter data of the excavator within each preset time period based on a cloud big data platform to obtain target state parameter data, storing the target state parameter data and the corresponding working condition types of the excavator within each preset time period to a cloud data warehouse, training an excavator working condition judgment model based on the target state parameter data and the corresponding working condition types of the excavator within each preset time period, and continuously updating the excavator working condition judgment model based on a training result to enable the excavator working condition judgment model to better accord with the actual condition of the excavator.
Fig. 3 is a schematic structural diagram of an excavator working condition determination device provided by the invention. As shown in fig. 3, the apparatus includes:
a parameter data acquisition module 301, configured to acquire real-time state parameter data of the excavator;
the excavator working condition judgment module 302 is used for inputting the real-time state parameter data into an excavator working condition judgment model to obtain the working condition type of the excavator output by the excavator working condition judgment model;
the excavator working condition judgment model is obtained by training based on a state parameter data sample carrying a working condition type label.
On the basis of the foregoing embodiment, in the excavator working condition determination device according to the embodiment of the present invention, the parameter data acquisition module specifically includes:
the state parameter data acquisition submodule is used for acquiring real-time state parameter data of the excavator in each preset time period in a target time period;
correspondingly, the excavator operating condition judgment module specifically comprises:
and the excavator working condition judgment submodule is used for respectively inputting the real-time state parameter data of the excavator in each preset time period to the excavator working condition judgment model to obtain the corresponding working condition type of the excavator in each preset time period, which is output by the excavator working condition judgment model.
On the basis of the above embodiment, the excavator working condition determination device provided by the embodiment of the present invention further includes:
and the proportion calculation module is used for determining the proportion of the working condition types corresponding to each preset time period based on the corresponding working condition types of the excavator in each preset time period.
On the basis of the above embodiment, the excavator working condition determination device provided by the embodiment of the present invention further includes:
and the cloud module is used for uploading the working condition types corresponding to the excavator in each preset time period and the real-time state parameter data of the excavator in each preset time period to a cloud data platform, so that the cloud data platform performs aggregation calculation on the real-time state parameter data of the excavator in each preset time period to obtain the target state parameter data of the excavator in each preset time period, and storing the working condition types corresponding to the excavator in each preset time period and the target state parameter data of the excavator in each preset time period to a cloud data warehouse. On the basis of the above embodiment, the excavator working condition determination device provided by the embodiment of the present invention further includes:
the model updating module is used for carrying out secondary training on the excavator working condition judgment model based on the corresponding working condition type of the excavator in each preset time period and the target state parameter data of the excavator in each preset time period to obtain the excavator working condition judgment model after secondary training;
and updating the excavator working condition judgment model based on the excavator working condition judgment model after secondary training.
On the basis of the above embodiment, in the excavator working condition determination device provided in the embodiment of the present invention, the real-time state parameter data includes at least one of an engine speed, a pilot pressure, a current, a pump pressure, and a usage duration.
Specifically, the functions of the modules in the excavator working condition determination device provided in the embodiment of the present invention correspond to the operation flows of the steps in the above method embodiments one to one, and the achieved effects are also consistent, for which, reference is specifically made to the above embodiments, which are not described again in the embodiment of the present invention.
The invention also provides an excavator, which comprises the excavator working condition judgment device and is used for judging the type of the excavator working condition.
Fig. 4 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 4: a processor (processor) 410, a communication Interface 420, a memory (memory) 430 and a communication bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 are communicated with each other via the communication bus 440. The processor 410 may invoke logic instructions in the memory 430 to perform a method of determining an operating condition of the excavator, the method comprising: acquiring real-time state parameter data of the excavator; inputting the real-time state parameter data into an excavator working condition judgment model to obtain the working condition type of the excavator output by the excavator working condition judgment model; the excavator working condition judgment model is obtained by training based on a state parameter data sample carrying a working condition type label.
In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, and various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the method for determining the working condition of an excavator provided by the above methods, the method comprising: acquiring real-time state parameter data of the excavator; inputting the real-time state parameter data into an excavator working condition judgment model to obtain the working condition type of the excavator output by the excavator working condition judgment model; the excavator working condition judgment model is obtained by training based on a state parameter data sample carrying a working condition type label.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, is implemented to perform the above-mentioned excavator condition determination methods, the method comprising: acquiring real-time state parameter data of the excavator; inputting the real-time state parameter data into an excavator working condition judgment model to obtain the working condition type of the excavator output by the excavator working condition judgment model; the excavator working condition judgment model is obtained by training based on a state parameter data sample carrying a working condition type label.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. The excavator working condition judgment method is characterized by comprising the following steps:
acquiring real-time state parameter data of the excavator in each preset time period in a target time period;
respectively inputting the real-time state parameter data of the excavator in each preset time period to an excavator working condition judgment model to obtain a working condition type corresponding to the excavator in each preset time period and a time period corresponding to the working condition type output by the excavator working condition judgment model, wherein the starting time and the ending time of the time period are represented by two timestamps, and the timestamps are used for verifying whether the real-time state parameter data are tampered;
the excavator working condition judgment model is obtained by training based on a state parameter data sample carrying a working condition type label.
2. The method for judging the working condition of the excavator according to claim 1, wherein the step of respectively inputting the real-time state parameter data of the excavator in each preset time period to the excavator working condition judgment model to obtain the corresponding working condition type of the excavator in each preset time period, which is output by the excavator working condition judgment model, further comprises the following steps:
and determining the working condition type ratio corresponding to each preset time period based on the corresponding working condition type of the excavator in each preset time period.
3. The method for judging the working condition of the excavator according to claim 1, wherein the step of respectively inputting the real-time state parameter data of the excavator in each preset time period to the excavator working condition judgment model to obtain the corresponding working condition type of the excavator in each preset time period, which is output by the excavator working condition judgment model, further comprises the following steps:
the working condition types corresponding to the excavator in each preset time period and the real-time state parameter data of the excavator in each preset time period are uploaded to a cloud data platform, so that the cloud data platform conducts aggregation calculation on the real-time state parameter data of the excavator in each preset time period to obtain target state parameter data of the excavator in each preset time period, and the working condition types corresponding to the excavator in each preset time period and the target state parameter data of the excavator in each preset time period are stored in a cloud data warehouse.
4. The method for judging the working condition of the excavator according to claim 3, wherein the method for uploading the corresponding working condition types of the excavator in the preset time periods and the real-time state parameter data of the excavator in the preset time periods to a cloud data platform further comprises the following steps:
performing secondary training on the excavator working condition judgment model based on the corresponding working condition type of the excavator in each preset time period and the target state parameter data of the excavator in each preset time period to obtain an excavator working condition judgment model after secondary training;
and updating the excavator working condition judgment model based on the excavator working condition judgment model after secondary training.
5. The excavator condition determination method of any one of claims 1 to 4 wherein the real time condition parameter data comprises at least one of engine speed, pilot pressure, current, pump pressure and duration of use.
6. An excavator operating condition decision device, characterized by includes:
the parameter data acquisition module is used for acquiring real-time state parameter data of the excavator;
the excavator working condition judgment module is used for inputting the real-time state parameter data into an excavator working condition judgment model to obtain the working condition type of the excavator output by the excavator working condition judgment model;
the parameter data acquisition module specifically includes:
the state parameter data acquisition submodule is used for acquiring real-time state parameter data of the excavator in each preset time period in the target time period;
correspondingly, the excavator operating condition judgment module specifically comprises:
the excavator working condition judgment submodule is used for respectively inputting the real-time state parameter data of the excavator in each preset time period to the excavator working condition judgment model to obtain the working condition type of the excavator corresponding to each preset time period and the time period corresponding to the working condition type output by the excavator working condition judgment model, the starting time and the ending time of the time period are represented by two timestamps, and the timestamps are used for verifying whether the real-time state parameter data are tampered;
the excavator working condition judgment model is obtained by training based on a state parameter data sample carrying a working condition type label.
7. An excavator, comprising: the excavator working condition determination device according to claim 6, which is used for determining the type of the working condition of the excavator.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the excavator condition determination method according to any one of claims 1 to 5.
9. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the steps of the excavator condition determination method according to any one of claims 1 to 5.
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| CN202110802807.7A CN113502870B (en) | 2021-07-15 | 2021-07-15 | Excavator working condition judging method and device |
| PCT/CN2022/097336 WO2023284443A1 (en) | 2021-07-15 | 2022-06-07 | Method and apparatus for determining working condition of excavator |
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| CN113502870B (en) * | 2021-07-15 | 2023-02-07 | 上海三一重机股份有限公司 | Excavator working condition judging method and device |
| CN113774985B (en) * | 2021-10-28 | 2023-03-24 | 三一重机有限公司 | Method and device for controlling working machine, medium, and working machine |
| CN115982541B (en) * | 2023-01-16 | 2023-09-29 | 徐州徐工挖掘机械有限公司 | Big data-based excavator working condition duty ratio statistical method |
| CN116451809A (en) * | 2023-06-16 | 2023-07-18 | 北谷电子股份有限公司 | Excavator working condition identification method and system based on DAGSVM algorithm |
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| JP7083597B2 (en) * | 2017-02-24 | 2022-06-13 | 住友重機械工業株式会社 | Construction machinery and its management equipment |
| CN107908874B (en) * | 2017-11-15 | 2021-10-29 | 上海华兴数字科技有限公司 | Working condition identification method and device and engineering mechanical equipment |
| CN109359524B (en) * | 2018-09-07 | 2021-06-22 | 长安大学 | Loader condition identification model construction and identification method |
| CN109358549B (en) * | 2018-11-01 | 2020-11-03 | 三一重机有限公司 | Intelligent control method and device for excavator |
| CN109636951B (en) * | 2018-11-21 | 2021-03-05 | 中南大学 | Excavator energy consumption analysis method based on working phase recognition |
| CN110258709B (en) * | 2019-07-08 | 2021-07-30 | 山重建机有限公司 | Method for automatically matching different working conditions of excavator |
| CN112734246A (en) * | 2021-01-14 | 2021-04-30 | 上海华兴数字科技有限公司 | Excavator working condition identification method and device, storage medium and electronic equipment |
| CN113006188B (en) * | 2021-03-22 | 2022-02-18 | 大连理工大学 | Excavator staged power matching method based on LSTM neural network |
| CN113029571B (en) * | 2021-03-31 | 2023-06-27 | 徐州徐工挖掘机械有限公司 | System and method for testing pollutant emission of hydraulic excavator |
| CN113502870B (en) * | 2021-07-15 | 2023-02-07 | 上海三一重机股份有限公司 | Excavator working condition judging method and device |
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