CN114148916B - Intelligent lifting hook using method and device, electronic equipment and storage medium - Google Patents
Intelligent lifting hook using method and device, electronic equipment and storage medium Download PDFInfo
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- CN114148916B CN114148916B CN202111180746.1A CN202111180746A CN114148916B CN 114148916 B CN114148916 B CN 114148916B CN 202111180746 A CN202111180746 A CN 202111180746A CN 114148916 B CN114148916 B CN 114148916B
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- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000012544 monitoring process Methods 0.000 claims abstract description 39
- 238000001514 detection method Methods 0.000 claims description 96
- 238000012549 training Methods 0.000 claims description 58
- 238000012806 monitoring device Methods 0.000 claims description 22
- 238000012545 processing Methods 0.000 claims description 19
- 238000000605 extraction Methods 0.000 claims description 17
- 230000006870 function Effects 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 11
- 230000004913 activation Effects 0.000 claims description 9
- 238000011176 pooling Methods 0.000 claims description 8
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- 230000003213 activating effect Effects 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000013441 quality evaluation Methods 0.000 abstract description 6
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C13/00—Other constructional features or details
- B66C13/18—Control systems or devices
- B66C13/22—Control systems or devices for electric drives
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C13/00—Other constructional features or details
- B66C13/04—Auxiliary devices for controlling movements of suspended loads, or preventing cable slack
- B66C13/08—Auxiliary devices for controlling movements of suspended loads, or preventing cable slack for depositing loads in desired attitudes or positions
- B66C13/085—Auxiliary devices for controlling movements of suspended loads, or preventing cable slack for depositing loads in desired attitudes or positions electrical
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C15/00—Safety gear
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- Control And Safety Of Cranes (AREA)
Abstract
The invention relates to an artificial intelligence technology, and discloses an intelligent lifting hook using method, an intelligent lifting hook using device, electronic equipment and a storage medium, wherein the intelligent lifting hook using method comprises the following steps: starting a crane and monitoring equipment, judging whether an intelligent lifting hook is included in the crane or not by using the monitoring equipment, if the intelligent lifting hook is not included in the crane, stopping the crane, if the intelligent lifting hook is included in the crane, hanging and buckling an object to be lifted to the intelligent lifting hook, recognizing the object mass of the object to be lifted by using the monitoring equipment, judging whether the object mass is in the bearing range of the intelligent lifting hook or not, if the object mass is not in the bearing range of the intelligent lifting hook, stopping the crane, and if the object mass is in the bearing range of the intelligent lifting hook, lifting the object to be lifted by using the crane. The invention can solve the problems that the quality evaluation of the object to be lifted is not needed, so that the object to be lifted is unhooked by the lifting hook due to overweight, and safety accidents occur.
Description
Technical Field
The present invention relates to artificial intelligence technology, and in particular, to a method and apparatus for using an intelligent hook, an electronic device, and a computer readable storage medium.
Background
With the development of technology, various industries have developed rapidly, such as civil engineering industry, in which lifting hooks in a crane are often used to carry articles to be lifted up and down, so as to improve building efficiency.
The conventional lifting hook using method mainly comprises the steps of hanging an object to be lifted on the lifting hook, and hanging the object to be lifted to a specified height by utilizing the power of a crane.
Disclosure of Invention
The invention provides an intelligent lifting hook using method, an intelligent lifting hook using device, electronic equipment and a computer readable storage medium, and mainly aims to solve the problem that when lifting is carried out, quality evaluation is not carried out on an article to be lifted, so that the lifting hook is unhooked due to overweight of the article to be lifted, and safety accidents occur.
In order to achieve the above purpose, the application method of the intelligent lifting hook provided by the invention comprises the following steps:
receiving a crane starting instruction, and starting a crane and monitoring equipment according to the crane starting instruction;
judging whether the crane comprises an intelligent lifting hook or not by using the monitoring equipment, and stopping the crane if the crane does not comprise the intelligent lifting hook;
if the crane comprises an intelligent lifting hook, hanging and buckling an object to be lifted to the intelligent lifting hook, and identifying the object type and the object volume of the object to be lifted by utilizing the monitoring equipment;
calculating the mass of the object according to the object type and the object volume;
Judging whether the object mass is in the bearing range of the intelligent lifting hook, and stopping the crane if the object mass is not in the bearing range of the intelligent lifting hook;
And if the object mass is in the bearing range of the intelligent lifting hook, lifting the object to be lifted by using the crane.
Optionally, the determining, by using the monitoring device, whether the crane includes an intelligent hook includes:
Receiving a lifting hook image training set, and training a lifting hook detection model pre-built in the monitoring equipment by using the lifting hook image training set to obtain a lifting hook detection model after training;
Shooting the crane by using the monitoring equipment to obtain a crane shooting diagram;
and carrying out lifting hook detection on the crane shooting image by using the lifting hook detection model after training, and judging whether the crane comprises an intelligent lifting hook or not according to the detection result of the lifting hook detection model.
Optionally, the receiving a hook image training set, training a hook detection model pre-built in the monitoring device by using the hook image training set to obtain a trained hook detection model, including:
Executing hook detection on the hook image training set by using the hook detection model to obtain an image to be determined of the hook;
Calculating a difference value between an image to be determined of the lifting hook and a preset real lifting hook image;
Judging the size between the difference value and a preset difference threshold value;
When the difference value is greater than or equal to the difference threshold value, carrying out parameter adjustment on the lifting hook detection model and re-executing target detection operation;
And determining a hook detection model with training completion when the difference value is smaller than the difference threshold value.
Optionally, the executing hook detection on the hook image training set by using the hook detection model to obtain an image to be determined of the hook includes:
performing convolution processing on the lifting hook image training set by utilizing a convolution layer of the lifting hook detection model to obtain a lifting hook convolution image;
Activating the lifting hook convolution image by using an activation layer of the target detection model to obtain a lifting hook activation image;
And carrying out pooling treatment on the lifting hook activation image by utilizing the pooling layer of the target detection model to obtain the image to be determined of the lifting hook.
Optionally, the training the hook detection model pre-built in the monitoring device by using the hook image training set to obtain a trained hook detection model, and before the training, further includes:
carrying out random deformation and random overturning treatment on the lifting hook image training set to obtain a random lifting hook image set;
and carrying out random brightness shake, random saturation shake and random contrast shake on the random hook image set to obtain the hook image training set with the image processing completed.
Optionally, the identifying, by using the monitoring device, the object type and the object volume of the object to be lifted includes:
Constructing a lifting article detection model according to the lifting hook detection model, detecting the lifting article by using the lifting article detection model to obtain a lifting article detection frame, and calculating according to the lifting article detection frame to obtain the object volume;
Extracting image features of the object to be lifted in the lifting object detection frame by using a lifting object feature extraction model in the monitoring equipment to obtain a lifting object feature set;
And carrying out object classification on the lifting object feature set based on a pre-constructed classification function to obtain the object class.
Optionally, the extracting, by using a lifting article feature extraction model in the monitoring device, image features of the article to be lifted in the lifting article detection frame to obtain a lifting article feature set, including:
carrying out convolution processing on the image in the lifting article detection frame by utilizing a convolution layer in the lifting article feature extraction model to obtain a convolution feature set;
Performing dense conversion processing on the convolution feature set by using dense blocks in the lifting object feature extraction model to obtain a dense feature set;
And connecting the dense feature set by using a full connection layer in the lifting object feature extraction model to obtain the lifting object feature set.
In order to solve the above problems, the present invention also provides an intelligent hook using device, the device comprising:
the starting module is used for receiving a crane starting instruction and starting the crane and the monitoring equipment according to the crane starting instruction;
The intelligent lifting hook judging module is used for judging whether the crane comprises an intelligent lifting hook or not by using the monitoring equipment, and stopping the crane if the crane does not comprise the intelligent lifting hook;
The object to be lifted quality calculating module is used for hanging and buckling an object to be lifted to the intelligent lifting hook if the crane comprises the intelligent lifting hook, identifying the object type and the object volume of the object to be lifted by utilizing the monitoring equipment, and calculating the object quality according to the object type and the object volume;
and the lifting module is used for judging whether the object mass is in the bearing range of the intelligent lifting hook, stopping the crane if the object mass is not in the bearing range of the intelligent lifting hook, and lifting the object to be lifted by using the crane if the object mass is in the bearing range of the intelligent lifting hook.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to implement the machine learning-based laser abnormal power data collection method described above.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the above-mentioned machine learning-based laser abnormal power data collection method.
Compared with the background art, the method comprises the following steps: the method comprises the steps of directly hanging an object to be lifted on a lifting hook, hanging the object to be lifted to a specified height by utilizing the power of the lifting hook, and judging whether the lifting hook comprises an intelligent lifting hook or not by utilizing monitoring equipment because the object to be lifted is not subjected to quality evaluation during lifting, and judging whether the object quality of the object to be lifted is in the bearing range of the intelligent lifting hook or not by utilizing monitoring equipment. Therefore, the intelligent lifting hook using method, the intelligent lifting hook using device, the electronic equipment and the computer readable storage medium can solve the problem that the lifting hook is unhooked due to overweight of an object to be lifted and safety accidents occur because quality evaluation is not carried out on the object to be lifted during lifting.
Drawings
FIG. 1 is a flow chart illustrating a method for using an intelligent hook according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of S2 in the method for using an intelligent hook according to an embodiment of the invention;
FIG. 3 is a schematic flow chart of S4 in the method for using an intelligent hook according to an embodiment of the invention;
FIG. 4 is a schematic block diagram of an intelligent hook using device according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an internal structure of an electronic device for implementing a method for using an intelligent hook according to an embodiment of the present invention;
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides an intelligent lifting hook using method. The execution main body of the intelligent lifting hook using method comprises at least one of an electronic device, such as a server side, a terminal and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the intelligent hook usage method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: the server can be an independent server, or can be a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), basic cloud computing services such as big data and artificial intelligent platforms, and the like.
Referring to fig. 1, a flow chart of a method for using an intelligent hook according to an embodiment of the invention is shown. In an embodiment of the present invention, the method for using the intelligent hook includes:
s1, receiving a crane starting instruction, and starting a crane and monitoring equipment according to the crane starting instruction.
Illustratively, the crane operator is about to transport a bundle of bars from the ground to the building for use by building personnel at the building, thus pressing a pre-built firing order in the crane, which is also called the crane start order, a bundle of bars being called the item to be lifted.
It will be appreciated that when the crane start command is pressed, the internal circuitry of the crane is automatically triggered to complete the crane start. It should be explained that, in the embodiment of the invention, the monitoring device is connected in parallel in the internal circuit of the crane, and when the internal circuit of the crane is triggered to run, the monitoring device is activated to realize monitoring at the same time of starting the crane.
It should be emphasized that the crane is connected with the object to be lifted through the intelligent lifting hook, and the object to be lifted is locked through the intelligent lifting hook, so that the object to be lifted can be lifted to a specified height.
S2, judging whether the crane comprises an intelligent lifting hook or not by using the monitoring equipment
In detail, referring to fig. 2, the determining, by using the monitoring device, whether the crane includes an intelligent hook includes:
S21, receiving a lifting hook image training set, and training a lifting hook detection model pre-built in the monitoring equipment by using the lifting hook image training set to obtain a lifting hook detection model after training;
S22, shooting the crane by using the monitoring equipment to obtain a crane shooting diagram;
S23, carrying out lifting hook detection on the crane shooting image by using the lifting hook detection model after training, and judging whether the crane comprises an intelligent lifting hook or not according to the detection result of the lifting hook detection model.
It should be explained that the hook image training set is a picture set which is collected and arranged by a user in advance, and is mainly used for training the hook detection model. In detail, the receiving hook image training set, training a hook detection model pre-built in the monitoring device by using the hook image training set to obtain a trained hook detection model, and the method comprises the following steps:
Executing hook detection on the hook image training set by using the hook detection model to obtain an image to be determined of the hook;
Calculating a difference value between an image to be determined of the lifting hook and a preset real lifting hook image;
Judging the size between the difference value and a preset difference threshold value;
When the difference value is greater than or equal to the difference threshold value, carrying out parameter adjustment on the lifting hook detection model and re-executing target detection operation;
And determining a hook detection model with training completion when the difference value is smaller than the difference threshold value.
It should be explained that the hook detection model may be constructed based on a convolutional neural network, including a convolutional layer, a pooling layer, a fully-connected layer, and an activation layer.
Specifically, the calculating the difference value between the image to be determined of the lifting hook and the preset real lifting hook image comprises the following steps:
calculating a difference value between the image to be determined of the lifting hook and a preset real lifting hook image by using the following calculation formula:
s＝((w/(k-b)+b/(k-w))/2
Wherein s is the difference value, w is the total number of pixels of the image to be determined of the lifting hook, b is the total number of pixels of the image of the real lifting hook, and k is a preset parameter.
Further, the executing hook detection on the hook image training set by using the hook detection model to obtain an image to be determined of the hook, including:
performing convolution processing on the lifting hook image training set by utilizing a convolution layer of the lifting hook detection model to obtain a lifting hook convolution image;
Activating the lifting hook convolution image by using an activation layer of the target detection model to obtain a lifting hook activation image;
And carrying out pooling treatment on the lifting hook activation image by utilizing the pooling layer of the target detection model to obtain the image to be determined of the lifting hook.
It should be explained that the convolution processing and pooling processing can increase the receptive field of the model to the picture, so that the hook detection model can extract richer image characteristic information. The activation processing can increase nonlinearity of the lifting hook detection model, map image characteristic information to a high-dimensional nonlinearity interval, and further improve the detection effect of the model.
Specifically, the training the hook detection model pre-built in the monitoring device by using the hook image training set to obtain a trained hook detection model, and the method further comprises the following steps:
carrying out random deformation and random overturning treatment on the lifting hook image training set to obtain a random lifting hook image set;
and carrying out random brightness shake, random saturation shake and random contrast shake on the random hook image set to obtain the hook image training set with the image processing completed.
It should be explained that the random deformation is to change the hook image in the hook image training set according to any image shape, the random overturning is to overturn the hook image along a horizontal direction, a vertical direction or other directions, the random brightness jitter is an effect of causing bright-dark intersection on the hook image, the random saturation jitter is an intersection effect of generating saturation difference on the hook image, the random contrast jitter is an intersection effect of generating contrast difference on the hook image, and the like.
S3, if the crane does not comprise the intelligent lifting hook, stopping the crane
According to the method, whether the crane comprises the intelligent lifting hook or not can be identified by utilizing the lifting hook detection model in the monitoring equipment, if the crane does not comprise the intelligent lifting hook, the crane is indicated to have potential safety hazards, and the crane is required to be stopped, so that crane operators can install the intelligent lifting hook.
S4, if the crane comprises an intelligent lifting hook, hanging and buckling the object to be lifted to the intelligent lifting hook, and identifying the object type and the object volume of the object to be lifted by utilizing the monitoring equipment.
When the crane comprises the intelligent lifting hook, the intelligent lifting hook can be directly used for hooking the object to be lifted, wherein the object to be lifted is a bundle of steel bars.
Further, referring to fig. 3, the identifying the object type and the object volume of the object to be lifted by using the monitoring device includes:
S41, constructing a lifting article detection model according to the lifting hook detection model, detecting the lifting article by using the lifting article detection model to obtain a lifting article detection frame, and calculating according to the lifting article detection frame to obtain the object volume;
In the embodiment of the present invention, the hook detection model has the same model structure as the lifting article detection model, and is mainly characterized in that training pictures received in a training stage are different, wherein the training pictures of the hook detection model are hook image training sets, and the training pictures of the lifting article detection model are image training sets formed by different lifting articles, which are not described herein.
S42, extracting image features of the object to be lifted in the lifting object detection frame by using a lifting object feature extraction model in the monitoring equipment to obtain a lifting object feature set;
in detail, the extracting the image features of the object to be lifted in the lifting object detection frame by using the lifting object feature extraction model in the monitoring device to obtain a lifting object feature set, including:
carrying out convolution processing on the image in the lifting article detection frame by utilizing a convolution layer in the lifting article feature extraction model to obtain a convolution feature set;
Performing dense conversion processing on the convolution feature set by using dense blocks in the lifting object feature extraction model to obtain a dense feature set;
And connecting the dense feature set by using a full connection layer in the lifting object feature extraction model to obtain the lifting object feature set.
It should be explained that, the lifting object feature extraction model is composed of convolution layers, dense blocks and full connection layers, and as the lifting object feature extraction model, a DenseNet-121 depth convolution model may be used in the embodiment of the present invention, which includes 4 dense blocks, 1 convolution layer and 1 full connection layer. The convolution layer is provided with 2k filters, dense blocks are formed by stacking layers, the sizes of the layers are the same, and finally, the lifting article characteristic set is obtained through connection of the full connection layer.
S43, carrying out object classification on the lifting object feature set based on a pre-constructed classification function to obtain the object class.
S5, calculating the mass of the object according to the object type and the object volume
In detail, the calculating the object mass according to the object category and the object volume includes:
Connecting to the Internet, and inquiring the object density corresponding to the object category from the Internet;
And calculating the product of the object density and the object volume to obtain the object mass.
For example, the crane driver is about to transport a bundle of steel bars from the ground to the building six, when the monitoring device is started, the monitoring device recognizes that the object type of the object to be lifted is the steel bars, and detects that the volume of the steel bars is 20m 3 by using the lifting object detection model, so that the density of the steel bars is 7.85 tons/cubic meter by networking inquiry, and 157 tons are obtained by multiplying 20m 3 by 7.85 tons/cubic meter, namely, 157 tons are expressed as the object weight of the bundle of steel bars.
S6, judging whether the object mass is in the bearing range of the intelligent lifting hook
S7, if the object mass is not in the bearing range of the intelligent lifting hook, stopping the crane
S8, if the object mass is in the bearing range of the intelligent lifting hook, lifting the object to be lifted by using the crane
For example, if the bearing range of the intelligent lifting hook is preset to be [0,200] tons, a bundle of 157 tons of steel bars is in the bearing range of the intelligent lifting hook, so that the object to be lifted can be directly lifted by using the crane, if the bearing range of the intelligent lifting hook is [0,150] tons, the crane needs to be stopped because the bearing range of the intelligent lifting hook is exceeded by a bundle of 157 tons of steel bars, and safety problems are considered.
Compared with the background art, the method comprises the following steps: the method comprises the steps of directly hanging an object to be lifted on a lifting hook, hanging the object to be lifted to a specified height by utilizing the power of the lifting hook, and judging whether the lifting hook comprises an intelligent lifting hook or not by utilizing monitoring equipment because the object to be lifted is not subjected to quality evaluation during lifting, and judging whether the object quality of the object to be lifted is in the bearing range of the intelligent lifting hook or not by utilizing monitoring equipment. Therefore, the intelligent lifting hook using method, the intelligent lifting hook using device, the electronic equipment and the computer readable storage medium can solve the problem that the lifting hook is unhooked due to overweight of an object to be lifted and safety accidents occur because quality evaluation is not carried out on the object to be lifted during lifting.
As shown in fig. 4, a functional block diagram of the intelligent hook using apparatus of the present invention is shown.
The intelligent hook using apparatus 100 of the present invention may be installed in an electronic device. The intelligent hook using device may include a starting module 101, an intelligent hook judging module 102, an object mass calculating module 103 to be lifted and a lifting module 104 according to the implemented functions. The module of the present invention may also be referred to as a unit, meaning a series of computer program segments capable of being executed by a processor of an electronic device and of performing a fixed function, stored in a memory of the electronic device.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the starting module 101 is configured to receive a crane starting instruction, and start a crane and a monitoring device according to the crane starting instruction;
The intelligent hook judging module 102 is configured to judge whether the crane includes an intelligent hook by using the monitoring device, and if the crane does not include the intelligent hook, stop the crane;
The object quality calculating module 103 is configured to, if the crane includes an intelligent hook, hook the object to be lifted to the intelligent hook, identify an object type and an object volume of the object to be lifted by using the monitoring device, and calculate an object quality according to the object type and the object volume;
The lifting module 104 is configured to determine whether the object mass is within a bearing range of the intelligent lifting hook, stop the crane if the object mass is not within the bearing range of the intelligent lifting hook, and utilize the crane to lift the object to be lifted if the object mass is within the bearing range of the intelligent lifting hook.
In detail, the modules in the intelligent hook using device 100 in the embodiment of the present invention use the same technical means as the intelligent hook using method described in fig. 1, and can produce the same technical effects, which are not described herein.
Fig. 5 is a schematic structural diagram of an electronic device 1 for implementing the intelligent hook using method according to the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a smart hook use program, stored in the memory 11 and executable on the processor 10.
The processor 10 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device 1, connects the respective components of the entire electronic device 1 using various interfaces and lines, executes or executes programs or modules (for example, executes a smart hook use program or the like) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process data.
The memory 11 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of smart hook use programs, etc., but also for temporarily storing data that has been output or is to be output.
The communication bus 12 may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
The communication interface 13 is used for communication between the electronic device 1 and other devices, including a network interface and a user interface. Optionally, the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device 1 and other electronic devices 1. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
Fig. 5 shows only an electronic device 1 with components, it being understood by a person skilled in the art that the structure shown in fig. 5 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The smart hook use program stored in the memory 11 of the electronic device 1 is a combination of a plurality of computer programs, which when run in the processor 10, can implement:
receiving a crane starting instruction, and starting a crane and monitoring equipment according to the crane starting instruction;
judging whether the crane comprises an intelligent lifting hook or not by using the monitoring equipment;
If the crane does not comprise the intelligent lifting hook, stopping the crane;
if the crane comprises an intelligent lifting hook, hanging and buckling an object to be lifted to the intelligent lifting hook, and identifying the object type and the object volume of the object to be lifted by utilizing the monitoring equipment;
calculating the mass of the object according to the object type and the object volume;
Judging whether the object mass is in the bearing range of the intelligent lifting hook, and stopping the crane if the object mass is not in the bearing range of the intelligent lifting hook;
And if the object mass is in the bearing range of the intelligent lifting hook, lifting the object to be lifted by using the crane.
In particular, the specific implementation method of the processor 10 on the computer program may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
Further, the integrated modules/units of the electronic device 1 may be stored in a non-volatile computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device 1, may implement:
receiving a crane starting instruction, and starting a crane and monitoring equipment according to the crane starting instruction;
judging whether the crane comprises an intelligent lifting hook or not by using the monitoring equipment;
If the crane does not comprise the intelligent lifting hook, stopping the crane;
if the crane comprises an intelligent lifting hook, hanging and buckling an object to be lifted to the intelligent lifting hook, and identifying the object type and the object volume of the object to be lifted by utilizing the monitoring equipment;
calculating the mass of the object according to the object type and the object volume;
Judging whether the object mass is in the bearing range of the intelligent lifting hook, and stopping the crane if the object mass is not in the bearing range of the intelligent lifting hook;
And if the object mass is in the bearing range of the intelligent lifting hook, lifting the object to be lifted by using the crane.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Wherein artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A method of using an intelligent hook, the method comprising:
receiving a crane starting instruction, and starting a crane and monitoring equipment according to the crane starting instruction;
judging whether the crane comprises an intelligent lifting hook or not by using the monitoring equipment;
If the crane does not comprise the intelligent lifting hook, stopping the crane;
if the crane comprises an intelligent lifting hook, hanging and buckling an object to be lifted to the intelligent lifting hook, and identifying the object type and the object volume of the object to be lifted by utilizing the monitoring equipment;
calculating the mass of the object according to the object type and the object volume;
Judging whether the object mass is in the bearing range of the intelligent lifting hook, and stopping the crane if the object mass is not in the bearing range of the intelligent lifting hook;
And if the object mass is in the bearing range of the intelligent lifting hook, lifting the object to be lifted by using the crane.
2. The method for using the intelligent hook according to claim 1, wherein the step of determining whether the crane includes the intelligent hook by using the monitoring device includes:
Receiving a lifting hook image training set, and training a lifting hook detection model pre-built in the monitoring equipment by using the lifting hook image training set to obtain a lifting hook detection model after training;
Shooting the crane by using the monitoring equipment to obtain a crane shooting diagram;
and carrying out lifting hook detection on the crane shooting image by using the lifting hook detection model after training, and judging whether the crane comprises an intelligent lifting hook or not according to the detection result of the lifting hook detection model.
3. The method of claim 2, wherein receiving the training set of hook images, training a hook detection model pre-built in the monitoring device using the training set of hook images, to obtain a trained hook detection model, comprises:
Executing hook detection on the hook image training set by using the hook detection model to obtain an image to be determined of the hook;
Calculating a difference value between an image to be determined of the lifting hook and a preset real lifting hook image;
Judging the size between the difference value and a preset difference threshold value;
When the difference value is greater than or equal to the difference threshold value, carrying out parameter adjustment on the lifting hook detection model and re-executing target detection operation;
And determining a hook detection model with training completion when the difference value is smaller than the difference threshold value.
4. The method of claim 3, wherein performing hook detection on the training set of hook images using the hook detection model to obtain an image to be determined of the hook comprises:
performing convolution processing on the lifting hook image training set by utilizing a convolution layer of the lifting hook detection model to obtain a lifting hook convolution image;
activating the lifting hook convolution image by using an activating layer of the lifting hook detection model to obtain a lifting hook activating image;
And carrying out pooling treatment on the lifting hook activation image by utilizing the pooling layer of the lifting hook detection model to obtain the image to be determined of the lifting hook.
5. The method for using the intelligent hook according to claim 2 or 3, wherein training the hook detection model pre-built in the monitoring device by using the hook image training set to obtain a trained hook detection model, and further comprising:
carrying out random deformation and random overturning treatment on the lifting hook image training set to obtain a random lifting hook image set;
and carrying out random brightness shake, random saturation shake and random contrast shake on the random hook image set to obtain the hook image training set with the image processing completed.
6. The method of using the intelligent hook as set forth in claim 5, wherein the identifying the object type and the object volume of the object to be lifted by the monitoring device includes:
Constructing a lifting article detection model according to the lifting hook detection model, detecting the lifting article by using the lifting article detection model to obtain a lifting article detection frame, and calculating according to the lifting article detection frame to obtain the object volume;
Extracting image features of the object to be lifted in the lifting object detection frame by using a lifting object feature extraction model in the monitoring equipment to obtain a lifting object feature set;
And carrying out object classification on the lifting object feature set based on a pre-constructed classification function to obtain the object class.
7. The method for using the intelligent hook according to claim 6, wherein extracting the image feature of the object to be lifted in the lifting object detection frame by using the lifting object feature extraction model in the monitoring device to obtain a lifting object feature set comprises:
carrying out convolution processing on the image in the lifting article detection frame by utilizing a convolution layer in the lifting article feature extraction model to obtain a convolution feature set;
Performing dense conversion processing on the convolution feature set by using dense blocks in the lifting object feature extraction model to obtain a dense feature set;
And connecting the dense feature set by using a full connection layer in the lifting object feature extraction model to obtain the lifting object feature set.
8. An intelligent hook using device, the device comprising:
the starting module is used for receiving a crane starting instruction and starting the crane and the monitoring equipment according to the crane starting instruction;
The intelligent lifting hook judging module is used for judging whether the crane comprises an intelligent lifting hook or not by using the monitoring equipment, and stopping the crane if the crane does not comprise the intelligent lifting hook;
The object to be lifted quality calculating module is used for hanging and buckling an object to be lifted to the intelligent lifting hook if the crane comprises the intelligent lifting hook, identifying the object type and the object volume of the object to be lifted by utilizing the monitoring equipment, and calculating the object quality according to the object type and the object volume;
and the lifting module is used for judging whether the object mass is in the bearing range of the intelligent lifting hook, stopping the crane if the object mass is not in the bearing range of the intelligent lifting hook, and lifting the object to be lifted by using the crane if the object mass is in the bearing range of the intelligent lifting hook.
9. An electronic device, the electronic device comprising:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the smart hook usage method of any one of claims 1 to 7.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the intelligent hook use method according to any one of claims 1 to 7.
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| CN114148916A (en) | 2022-03-08 |
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