CN112215885A - Container position identification method and device based on autonomous learning - Google Patents
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Abstract
The embodiment of the invention discloses a container position identification method and a device based on autonomous learning, which are characterized in that the initial coordinates of a container to be operated on a cabin are obtained; determining a bin position identifier corresponding to the initial coordinate of the container according to the initial coordinate of the container and a pre-established identification model, wherein the pre-established identification model comprises a corresponding relation between the coordinate and the bin position identifier, and the corresponding relation between the coordinate and the bin position identifier is obtained according to an empirical value; the container to be operated is moved to the corresponding position on the cabin according to the box position identification, so that the influence of external factors can be avoided, the placement accuracy of the container is improved, and the adaptability is improved.
Description
Technical Field
The invention relates to the technical field of containers, in particular to a container position identification method and device based on autonomous learning.
Background
Containers are the global standard shipping containers in global material transportation activities and are the most widely used containers. The application standard radiates transfer bases such as global ports, docks, railway stations, land and road stations, and the affected carriers comprise loading and unloading carriers such as trains, ships, automobiles, shore bridges, door machines and the like.
In the present day of the intelligent development trend of port and pier, the container is still mostly depending on the manual work outside the open air place or the steamer on the dock place and the steamer loading and unloading tally, rely on the mode operation of naked eye inspection handwriting Bay position when the container is loaded and unloaded. The problems of low efficiency, severe outdoor environment, outdoor personal safety and the like exist. Two solutions exist in the current field, the first method is input after being checked by naked eyes, the reliability is relatively high, but people need to stare at the operation outdoors, the working strength is high, the safety risk is high, and the labor cost is high. And the second computer calculates the site position, and calculates the bay position of the current crane according to the coordinate value of the previous crane after the first crane is calibrated manually. This kind of mode is along with external factors such as the driving habit, the hoist of morning and evening tides influence and driver operation rock, and Bay position is calculated according to original hoist coordinate and is inevitable to be influenced all the time, leads to Bay position to calculate there is the upper limit bottleneck, and adaptability is relatively poor.
Disclosure of Invention
In view of the above, embodiments of the present invention have been made to provide a container slot identifier recognition method and apparatus based on autonomous learning, which overcome or at least partially solve the above-mentioned problems.
In a first aspect, an embodiment of the present invention provides a container slot identifier identification method based on autonomous learning, where the method includes:
acquiring initial coordinates of a container to be operated on a cabin;
determining a bin position identifier corresponding to the initial coordinate of the container according to the initial coordinate of the container and a pre-established identification model, wherein the pre-established identification model comprises a corresponding relation between the coordinate and the bin position identifier, and the corresponding relation between the coordinate and the bin position identifier is obtained according to an empirical value;
and moving the container to be operated to a corresponding position on the cabin according to the box position identification.
Optionally, the pre-established recognition model is obtained by:
according to the cabin structure, an initial two-dimensional model of a digital structure corresponding to the cabin structure is created, and the two-dimensional model is stored in a memory space;
and according to the habit of a driver, the tidal condition and the state of a lifting appliance, matching and training the initial two-dimensional model to obtain the identification model of the digital structure.
Optionally, obtaining the recognition model of the digital structure after performing matching training on the initial two-dimensional model according to the driver habit, the tidal condition and the sling state includes:
during each lifting operation, inputting the acquired initial information of the container into the initial two-dimensional model, and calculating a container position identifier;
and inputting the habit of the driver, the tidal condition and the state of the lifting appliance into the initial two-dimensional model again, and performing matching training on the initial two-dimensional model to obtain the identification model of the digital structure.
Optionally, the method further comprises:
when a first container is loaded into the cabin, calculating a first coordinate of a first container position in the initial two-dimensional model based on a single point;
when a second container is loaded into the cabin, calculating to obtain a second coordinate corresponding to the second container;
and superposing the first coordinate and the second coordinate to obtain a coordinate of the empirical value weight corresponding to the box position.
Optionally, the initial two-dimensional model includes a bin identifier and coordinates of a container, the coordinates of the container include an abscissa and an ordinate, and an intersection position of the abscissa and the ordinate is the bin identifier.
In a second aspect, an embodiment of the present invention provides an autonomous learning-based container slot identifier identifying apparatus, where the apparatus includes:
the acquisition module is used for acquiring initial coordinates of a container to be operated on a cabin;
the determining module is used for determining a bin position identifier corresponding to the initial coordinate of the container according to the initial coordinate of the container and a pre-established identification model, wherein the pre-established identification model comprises a corresponding relation between the coordinate and the bin position identifier, and the corresponding relation between the coordinate and the bin position identifier is obtained according to an empirical value;
and the moving module is used for moving the container to be operated to a corresponding position on the cabin according to the box position identification.
Optionally, the apparatus further comprises a model building module, the model building module is configured to:
according to the cabin structure, an initial two-dimensional model of a digital structure corresponding to the cabin structure is created, and the two-dimensional model is stored in a memory space;
and according to the habit of a driver, the tidal condition and the state of a lifting appliance, matching and training the initial two-dimensional model to obtain the identification model of the digital structure.
Optionally, the model building module is configured to:
during each lifting operation, inputting the acquired initial information of the container into the initial two-dimensional model, and calculating a container position identifier;
and inputting the habit of the driver, the tidal condition and the state of the lifting appliance into the initial two-dimensional model again, and performing matching training on the initial two-dimensional model to obtain the identification model of the digital structure.
Optionally, the model building module is further configured to:
when a first container is loaded into the cabin, calculating a first coordinate of a first container position in the initial two-dimensional model based on a single point;
when a second container is loaded into the cabin, calculating to obtain a second coordinate corresponding to the second container;
and superposing the first coordinate and the second coordinate to obtain a coordinate of the empirical value weight corresponding to the box position.
Optionally, the initial two-dimensional model includes a bin identifier and coordinates of a container, the coordinates of the container include an abscissa and an ordinate, and an intersection position of the abscissa and the ordinate is the bin identifier.
In a third aspect, an embodiment of the present invention provides a terminal device, including: at least one processor and memory;
the memory stores a computer program; the at least one processor executes the computer program stored in the memory to implement the autonomous learning based container slot identification recognition method provided in the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed, implements the autonomous learning based container slot identifier identifying method provided in the first aspect.
The embodiment of the invention has the following advantages:
according to the container position identification method and device based on autonomous learning, provided by the embodiment of the invention, the initial coordinates of the container to be operated on the cabin are obtained; determining a bin position identifier corresponding to the initial coordinate of the container according to the initial coordinate of the container and a pre-established identification model, wherein the pre-established identification model comprises a corresponding relation between the coordinate and the bin position identifier, and the corresponding relation between the coordinate and the bin position identifier is obtained according to an empirical value; the container to be operated is moved to the corresponding position on the cabin according to the box position identification, so that the influence of external factors can be avoided, the placement accuracy of the container is improved, and the adaptability is improved.
Drawings
FIG. 1 is a flow chart of steps of an embodiment of a container slot identifier identification method based on autonomous learning of the present invention;
FIG. 2 is a schematic illustration of the creation of a recognition model of the present invention;
FIG. 3 is a schematic diagram of the creation of yet another recognition model of the present invention;
FIG. 4 is a block diagram of an embodiment of an autonomous learning based container slot identifier recognition apparatus according to the present invention;
fig. 5 is a schematic structural diagram of a terminal device of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
An embodiment of the invention provides a container position identification method based on autonomous learning, which is used for lifting a container to a ship container from land by shore bridge equipment each time. The execution main body of the embodiment is a container position identification device based on autonomous learning, and is arranged on computer equipment.
Referring to fig. 1, a flowchart illustrating steps of an embodiment of a container slot identifier identification method based on autonomous learning according to the present invention is shown, where the method may specifically include the following steps:
s101, acquiring initial coordinates of a container to be operated on a cabin;
in particular, the embodiment of the invention is applied to the ship loading process from land container hoisting to ship hoisting of the shore bridge equipment each time.
The computer acquires initial coordinates of a container to be operated on a cabin through the shore bridge equipment, wherein the initial coordinates comprise a horizontal coordinate and a vertical coordinate.
S102, determining a bin position identifier corresponding to the initial coordinate of the container according to the initial coordinate of the container and a pre-established identification model, wherein the pre-established identification model comprises a corresponding relation between the coordinate and the bin position identifier, and the corresponding relation between the coordinate and the bin position identifier is obtained according to an empirical value;
specifically, the computer device needs to establish a recognition model in advance, where the recognition model is to add parameters subjected to external factors into the recognition model in advance, obtain different empirical values according to the corresponding relationship between the bin identifier and the initial coordinate, and obtain the pre-established recognition model according to the weight of the empirical values.
The computer equipment inputs the initial coordinates of the container into a pre-established recognition model to obtain a bin position identification, namely a Bay position, corresponding to the initial coordinates.
S103, moving the container to be operated to a corresponding position on the cabin according to the box position identification.
Specifically, the computer controls the shore bridge equipment to move the container to be operated to the corresponding position on the cabin according to the calculated container position identification, and adaptability is improved.
The container position identification method based on the autonomous learning, provided by the embodiment of the invention, comprises the steps of acquiring the initial coordinates of a container to be operated on a cabin; determining a bin position identifier corresponding to the initial coordinate of the container according to the initial coordinate of the container and a pre-established identification model, wherein the pre-established identification model comprises a corresponding relation between the coordinate and the bin position identifier, and the corresponding relation between the coordinate and the bin position identifier is obtained according to an empirical value; the container to be operated is moved to the corresponding position on the cabin according to the box position identification, so that the influence of external factors can be avoided, the placement accuracy of the container is improved, and the adaptability is improved.
The present invention further provides a supplementary explanation of the container slot identifier recognition method based on autonomous learning provided in the above embodiments.
Optionally, the pre-established recognition model is obtained by:
according to the cabin structure, an initial two-dimensional model of a digital structure corresponding to the cabin structure is created, and the two-dimensional model is stored in a memory space;
and according to the habit of a driver, the tidal condition and the state of a lifting appliance, matching and training the initial two-dimensional model to obtain the identification model of the digital structure.
Specifically, the identification model provided by the embodiment of the invention is based on the fact that time is an abscissa and a packing experience value is an ordinate, the binding of coordinates and Bay bit is realized by manually calibrating a first crane, and a digital model structure of a stockpiling space of a complete field and coordinate data is deduced through the crane data.
And each subsequent hoisting operation can deduce to generate a new mode, matching superposition calculation is carried out on all previous operation modes, and finally, a special Bay structure digital model formed by combining a plurality of factors such as matching driver habits, tides, states of the lifting appliance and the like is obtained.
When a container is loaded into the cabin for the first time, the positions are determined manually and the obtained coordinates are mapped into the digital model, and the coordinates of each position of the whole two-dimensional model are automatically calculated by taking the width and the height of the container as units. The coordinates of all the positions of the model are generated through a single point, and when the next container is loaded into the cabin, the system can automatically identify the position of the loaded container based on the coordinates of the digital model position.
Optionally, obtaining the recognition model of the digital structure after performing matching training on the initial two-dimensional model according to the driver habit, the tidal condition and the sling state includes:
during each lifting operation, inputting the acquired initial information of the container into the initial two-dimensional model, and calculating a container position identifier;
and inputting the habit of the driver, the tidal condition and the state of the lifting appliance into the initial two-dimensional model again, and performing matching training on the initial two-dimensional model to obtain the identification model of the digital structure.
And inputting the acquired information into the digital model in each hoisting operation, calculating to obtain a result Bay bit by the digital model, and feeding back new operation data serving as input materials to the digital model for secondary strengthening training. Even if the lifting appliance shakes occasionally, data impurities can be removed through the data model, the more abundant the empirical model is along with the accumulation of the workload, and the more accurate the Bay recognition rate is, as shown in FIG. 2, the establishment schematic diagram of the recognition model of the invention is shown.
The coordinates of each box position in the whole two-dimensional model can be calculated based on a single point every time the container is loaded, a new box position coordinate calculation result can be obtained for each box position when the container is continuously loaded, and the new coordinate calculation result can be superposed with a box position coordinate result generated before, so that the more containers are loaded, the more experience values obtained by the coordinates of each box position are, and finally, the coordinates with the highest experience weight value can be obtained for each box position.
Optionally, the method further comprises:
when a first container is loaded into the cabin, calculating a first coordinate of a first container position in the initial two-dimensional model based on a single point;
when a second container is loaded into the cabin, calculating to obtain a second coordinate corresponding to the second container;
and superposing the first coordinate and the second coordinate to obtain a coordinate of the empirical value weight corresponding to the box position.
Optionally, the initial two-dimensional model includes a bin identifier and coordinates of a container, the coordinates of the container include an abscissa and an ordinate, and an intersection position of the abscissa and the ordinate is the bin identifier.
In particular, each Bay can be understood as a two-dimensional table, each cell being a bin, each bin having its own unique number. The Bay binning process is the process of filling up a two-dimensional table.
Fig. 3 is a schematic diagram illustrating the establishment of another recognition model according to the present invention, in which the models in the memory are graphically displayed, each model has the abscissa and the ordinate, and each cell is a bin. The position where the abscissa crosses the ordinate is the unique code belonging to this slot, and the abscissa + the ordinate is used, for example 0992, to indicate the first container on the first floor of the following figure. The number in each bin is the corresponding coordinate value, e.g., the coordinates of 0992 are 1032, 2465.
The operation habits of each driver are different, so that the coordinates of the box position are floated during shipment, and after the embodiment of the invention is adopted, the corresponding relation between the box position and the coordinates of the driver is automatically calculated and summarized by the computer along with the shipment process of the driver, so that the box position recognition rate is higher.
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the illustrated order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments of the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
The container position identification method based on the autonomous learning, provided by the embodiment of the invention, comprises the steps of acquiring the initial coordinates of a container to be operated on a cabin; determining a bin position identifier corresponding to the initial coordinate of the container according to the initial coordinate of the container and a pre-established identification model, wherein the pre-established identification model comprises a corresponding relation between the coordinate and the bin position identifier, and the corresponding relation between the coordinate and the bin position identifier is obtained according to an empirical value; the container to be operated is moved to the corresponding position on the cabin according to the box position identification, so that the influence of external factors can be avoided, the placement accuracy of the container is improved, and the adaptability is improved.
Another embodiment of the present invention provides an autonomous learning-based container slot identifier recognition apparatus, which is used to execute the autonomous learning-based container slot identifier recognition method provided in the foregoing embodiments.
Referring to fig. 4, a block diagram of an embodiment of the autonomous learning-based container slot identifier recognition apparatus according to the present invention is shown, and the apparatus may specifically include the following modules: an obtaining module 401, a determining module 402 and a moving module 403, wherein:
the acquisition module 401 is configured to acquire initial coordinates of a container to be operated in a cabin;
the determining module 402 is configured to determine, according to the initial coordinates of the container and a pre-established identification model, a slot identifier corresponding to the initial coordinates of the container, where the pre-established identification model includes a correspondence between coordinates and the slot identifier, and the correspondence between the coordinates and the slot identifier is obtained according to an empirical value;
the moving module 403 is configured to move the container to be operated to a corresponding position on the cabin according to the slot identifier.
The container position identification recognition device based on the autonomous learning provided by the embodiment of the invention obtains the initial coordinates of the container to be operated on the cabin; determining a bin position identifier corresponding to the initial coordinate of the container according to the initial coordinate of the container and a pre-established identification model, wherein the pre-established identification model comprises a corresponding relation between the coordinate and the bin position identifier, and the corresponding relation between the coordinate and the bin position identifier is obtained according to an empirical value; the container to be operated is moved to the corresponding position on the cabin according to the box position identification, so that the influence of external factors can be avoided, the placement accuracy of the container is improved, and the adaptability is improved.
The present invention further provides a supplementary explanation of the container slot identifier recognition apparatus based on autonomous learning provided in the above embodiments.
Optionally, the apparatus further comprises a model building module, the model building module is configured to:
according to the cabin structure, an initial two-dimensional model of a digital structure corresponding to the cabin structure is created, and the two-dimensional model is stored in a memory space;
and according to the habit of a driver, the tidal condition and the state of a lifting appliance, matching and training the initial two-dimensional model to obtain the identification model of the digital structure.
Optionally, the model building module is configured to:
during each lifting operation, inputting the acquired initial information of the container into the initial two-dimensional model, and calculating a container position identifier;
and inputting the habit of the driver, the tidal condition and the state of the lifting appliance into the initial two-dimensional model again, and performing matching training on the initial two-dimensional model to obtain the identification model of the digital structure.
Optionally, the model building module is further configured to:
when a first container is loaded into the cabin, calculating a first coordinate of a first container position in the initial two-dimensional model based on a single point;
when a second container is loaded into the cabin, calculating to obtain a second coordinate corresponding to the second container;
and superposing the first coordinate and the second coordinate to obtain a coordinate of the empirical value weight corresponding to the box position.
Optionally, the initial two-dimensional model includes a bin identifier and coordinates of a container, the coordinates of the container include an abscissa and an ordinate, and an intersection position of the abscissa and the ordinate is the bin identifier.
It should be noted that the respective implementable modes in the present embodiment may be implemented individually, or may be implemented in combination in any combination without conflict, and the present application is not limited thereto.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The container position identification recognition device based on the autonomous learning provided by the embodiment of the invention obtains the initial coordinates of the container to be operated on the cabin; determining a bin position identifier corresponding to the initial coordinate of the container according to the initial coordinate of the container and a pre-established identification model, wherein the pre-established identification model comprises a corresponding relation between the coordinate and the bin position identifier, and the corresponding relation between the coordinate and the bin position identifier is obtained according to an empirical value; the container to be operated is moved to the corresponding position on the cabin according to the box position identification, so that the influence of external factors can be avoided, the placement accuracy of the container is improved, and the adaptability is improved.
Still another embodiment of the present invention provides a terminal device, configured to execute the container slot identifier identification method based on autonomous learning provided in the foregoing embodiment.
Fig. 5 is a schematic structural diagram of a terminal device of the present invention, and as shown in fig. 5, the terminal device includes: at least one processor 501 and memory 502;
the memory stores a computer program; the at least one processor executes the computer program stored in the memory to implement the autonomous learning based container slot identification recognition method provided by the above embodiments.
The terminal device provided by the embodiment acquires the initial coordinates of the container to be operated on the cabin; determining a bin position identifier corresponding to the initial coordinate of the container according to the initial coordinate of the container and a pre-established identification model, wherein the pre-established identification model comprises a corresponding relation between the coordinate and the bin position identifier, and the corresponding relation between the coordinate and the bin position identifier is obtained according to an empirical value; the container to be operated is moved to the corresponding position on the cabin according to the box position identification, so that the influence of external factors can be avoided, the placement accuracy of the container is improved, and the adaptability is improved.
Yet another embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed, implements the container slot identifier identification method based on autonomous learning provided in any of the above embodiments.
According to the computer-readable storage medium of the embodiment, by acquiring initial coordinates of a container to be operated on a cabin; determining a bin position identifier corresponding to the initial coordinate of the container according to the initial coordinate of the container and a pre-established identification model, wherein the pre-established identification model comprises a corresponding relation between the coordinate and the bin position identifier, and the corresponding relation between the coordinate and the bin position identifier is obtained according to an empirical value; the container to be operated is moved to the corresponding position on the cabin according to the box position identification, so that the influence of external factors can be avoided, the placement accuracy of the container is improved, and the adaptability is improved.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, electronic devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing electronic device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing electronic device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or electronic device that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or electronic device. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or electronic device that comprises the element.
Claims (10)
1. A container slot identification recognition method based on autonomous learning is characterized by comprising the following steps:
acquiring initial coordinates of a container to be operated on a cabin;
determining a bin position identifier corresponding to the initial coordinate of the container according to the initial coordinate of the container and a pre-established identification model, wherein the pre-established identification model comprises a corresponding relation between the coordinate and the bin position identifier, and the corresponding relation between the coordinate and the bin position identifier is obtained according to an empirical value;
and moving the container to be operated to a corresponding position on the cabin according to the box position identification.
2. The method of claim 1, wherein the pre-established recognition model is obtained by:
according to the cabin structure, an initial two-dimensional model of a digital structure corresponding to the cabin structure is created, and the two-dimensional model is stored in a memory space;
and according to the habit of a driver, the tidal condition and the state of a lifting appliance, matching and training the initial two-dimensional model to obtain the identification model of the digital structure.
3. The method of claim 2, wherein obtaining the recognition model of the digital structure after the match training of the initial two-dimensional model according to the driver's habits, tidal conditions, and spreader states comprises:
during each lifting operation, inputting the acquired initial information of the container into the initial two-dimensional model, and calculating a container position identifier;
and inputting the habit of the driver, the tidal condition and the state of the lifting appliance into the initial two-dimensional model again, and performing matching training on the initial two-dimensional model to obtain the identification model of the digital structure.
4. The method of claim 2, further comprising:
when a first container is loaded into the cabin, calculating a first coordinate of a first container position in the initial two-dimensional model based on a single point;
when a second container is loaded into the cabin, calculating to obtain a second coordinate corresponding to the second container;
and superposing the first coordinate and the second coordinate to obtain a coordinate of the empirical value weight corresponding to the box position.
5. The method of claim 2, wherein the initial two-dimensional model includes a bin identification and coordinates of a container, the coordinates of the container including an abscissa and an ordinate, the intersection location of the abscissa and the ordinate being the bin identification.
6. An autonomous learning-based container slot identifier recognition device, the device comprising:
the acquisition module is used for acquiring initial coordinates of a container to be operated on a cabin;
the determining module is used for determining a bin position identifier corresponding to the initial coordinate of the container according to the initial coordinate of the container and a pre-established identification model, wherein the pre-established identification model comprises a corresponding relation between the coordinate and the bin position identifier, and the corresponding relation between the coordinate and the bin position identifier is obtained according to an empirical value;
and the moving module is used for moving the container to be operated to a corresponding position on the cabin according to the box position identification.
7. The apparatus of claim 6, further comprising a model building module to:
according to the cabin structure, an initial two-dimensional model of a digital structure corresponding to the cabin structure is created, and the two-dimensional model is stored in a memory space;
and according to the habit of a driver, the tidal condition and the state of a lifting appliance, matching and training the initial two-dimensional model to obtain the identification model of the digital structure.
8. The apparatus of claim 7, wherein the model building module is configured to:
during each lifting operation, inputting the acquired initial information of the container into the initial two-dimensional model, and calculating a container position identifier;
and inputting the habit of the driver, the tidal condition and the state of the lifting appliance into the initial two-dimensional model again, and performing matching training on the initial two-dimensional model to obtain the identification model of the digital structure.
9. A terminal device, comprising: at least one processor and memory;
the memory stores a computer program; the at least one processor executes the memory-stored computer program to implement the autonomous learning based container slot identification method of any of claims 1-5.
10. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which when executed implements the autonomous learning based container slot identification method of any of claims 1-5.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115676410A (en) * | 2022-11-29 | 2023-02-03 | 深圳市平方科技股份有限公司 | Container slot identification method, device, equipment and medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106516990A (en) * | 2017-01-06 | 2017-03-22 | 北京国泰星云科技有限公司 | Container wharf field bridge anti-collision control system and method based on object contour tracing |
| CN106599885A (en) * | 2016-08-30 | 2017-04-26 | 中海网络科技股份有限公司 | Monitoring system and method for container Bay |
| CN108182552A (en) * | 2018-02-02 | 2018-06-19 | 上海西井信息科技有限公司 | Loading-unloading method, system, equipment and the storage medium of container ship |
| CN110182621A (en) * | 2019-05-10 | 2019-08-30 | 大连华锐重工集团股份有限公司 | A kind of digitization system and control method of unmanned bucket continuous ship unloader |
| WO2020124247A1 (en) * | 2018-12-21 | 2020-06-25 | Canscan Softwares And Technologies Inc. | Automated inspection system and associated method for assessing the condition of shipping containers |
-
2020
- 2020-09-16 CN CN202010970681.XA patent/CN112215885A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106599885A (en) * | 2016-08-30 | 2017-04-26 | 中海网络科技股份有限公司 | Monitoring system and method for container Bay |
| CN106516990A (en) * | 2017-01-06 | 2017-03-22 | 北京国泰星云科技有限公司 | Container wharf field bridge anti-collision control system and method based on object contour tracing |
| CN108182552A (en) * | 2018-02-02 | 2018-06-19 | 上海西井信息科技有限公司 | Loading-unloading method, system, equipment and the storage medium of container ship |
| WO2020124247A1 (en) * | 2018-12-21 | 2020-06-25 | Canscan Softwares And Technologies Inc. | Automated inspection system and associated method for assessing the condition of shipping containers |
| CN110182621A (en) * | 2019-05-10 | 2019-08-30 | 大连华锐重工集团股份有限公司 | A kind of digitization system and control method of unmanned bucket continuous ship unloader |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115676410A (en) * | 2022-11-29 | 2023-02-03 | 深圳市平方科技股份有限公司 | Container slot identification method, device, equipment and medium |
| CN115676410B (en) * | 2022-11-29 | 2023-09-05 | 深圳市平方科技股份有限公司 | Container position identification method, device, equipment and medium |
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