CN111275386A - Method and system for intelligent transportation, electronic device, and computer storage medium - Google Patents
Method and system for intelligent transportation, electronic device, and computer storage medium Download PDFInfo
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- CN111275386A CN111275386A CN202010073171.2A CN202010073171A CN111275386A CN 111275386 A CN111275386 A CN 111275386A CN 202010073171 A CN202010073171 A CN 202010073171A CN 111275386 A CN111275386 A CN 111275386A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K17/00—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations
- G06K17/0022—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations arrangements or provisious for transferring data to distant stations, e.g. from a sensing device
Abstract
The application discloses a method and a system for intelligent transportation, electronic equipment and a computer storage medium, wherein a storage model representing the corresponding relation between independent variables and dependent variables is established by taking the size of a storage space, the type of an express box and the size and the number of each type of the express box as independent variables and the storage area of each type of the express box as dependent variables; input data of each independent variable is acquired, and output data of the dependent variable corresponding to a combination of the input data of the independent variable is acquired by using the storage model. When in actual use, input data corresponding to the transportation are input, the corresponding storage region division result can be output by utilizing the storage model, and the storage region of each model express box is obtained, so that the express boxes of the same model can be stacked together, jolting and collision among the express boxes are reduced, and the storage space is saved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and system for intelligent transportation, an electronic device, and a computer storage medium.
Background
The express delivery has made things convenient for everybody's life, nevertheless in the express delivery transportation, also has the problem of stacking at will, has the interval between the express delivery case of different models, causes jolting and colliding in the transportation, and the interval between the on the other hand express delivery case is extravagant parking space also.
The prior art discloses a storage device, which adopts a grid type conveying device, and articles in grids can be freely moved by the device, and the positions and the sequences of the articles can be adjusted; a plurality of inlets and outlets are arranged, so that articles with different sizes and weights can be stored and conveyed; the commodity information can be pre-recorded and shared by being provided with various sensing, scanning and measuring devices, measuring the volume, the size and the weight and reading various identification codes. The defects are that a large number of grid-type hardware structures need to be arranged in advance, the cost is high, and the maintenance is inconvenient.
Disclosure of Invention
The application aims to provide a method and a system for intelligent transportation, electronic equipment and a computer storage medium, which are used for carrying out virtual region division on the existing storage space, planning out corresponding storage regions for express boxes of each model, stacking the express boxes of the same model together, reducing jolt and collision among the express boxes and saving the storage space.
The purpose of the application is realized by adopting the following technical scheme:
a method for intelligent transportation, comprising: establishing a storage model representing the corresponding relation between independent variables and dependent variables by taking the size of a storage space, the types of the express boxes and the size and the number of the express boxes of each type as independent variables and the storage area of the express boxes of each type as dependent variables; and acquiring input data of each independent variable, and acquiring output data of the dependent variable corresponding to the combination of the input data of the independent variable by using the storage model.
Therefore, according to the size of the storage space used in the transportation process, the type, the size and the quantity of the express boxes and the storage area planning result of each type of corresponding express box, a storage model representing the corresponding relation between independent variables and dependent variables is established, when the express box is actually used, various input data corresponding to the transportation are input, the corresponding storage area division result can be output by using the storage model, the storage area of each type of express box is obtained, therefore, the express boxes of the same type can be stacked together, jolting and collision among the express boxes are reduced, and the storage space is saved.
Optionally, the establishing a storage model representing a correspondence between the independent variable and the dependent variable by using the size of the storage space, the model of the express delivery box, and each model as the independent variable and the storage area of the express delivery box as the dependent variable includes: acquiring a training data set of an independent variable and a dependent variable by taking the size of a storage space, the type of an express box and the size and the number of the express boxes of each type as the independent variable and the storage area of the express box of each type as the dependent variable; and establishing a storage model representing the corresponding relation between the independent variable and the dependent variable by utilizing deep learning for the training data sets of the independent variable and the dependent variable.
Therefore, the training data set can be trained in a deep learning mode to obtain the storage model.
Optionally, the obtaining input data of each independent variable and obtaining output data of the dependent variable corresponding to a combination of the input data of the independent variable by using the storage model includes: classifying all express boxes transported this time; for each type of the express delivery box, input data of each independent variable is acquired, and output data of the dependent variable corresponding to a combination of the input data of the independent variable is acquired by using the storage model.
Like this, can classify this express delivery case of transportation in advance, to the express delivery case of single article type after the classification, recycle and deposit the region of depositing of various express delivery casees of model planning.
Optionally, classifying all the express delivery boxes in the transportation includes: classifying all the express boxes transported at this time according to the types of the objects; and/or classifying all the express boxes transported this time according to the insurance price type.
Therefore, the express boxes transported this time can be classified in advance according to the article types and/or the insurance price types, and the storage areas of various express boxes can be planned by using the storage model for the classified single type of express boxes. For example, the article types can include fresh types and non-fresh types, the express boxes are classified according to the article types, the express boxes of the fresh types and the non-fresh types are divided into regions respectively, the fresh type express boxes can be further subdivided, the regions are divided according to the types of the express boxes, risks that the qualities are damaged due to the fact that the fresh types of the express boxes are stored together are reduced, and for example, the express boxes of meat and fruits can be stored separately. For example, the insurance price types can include high-value and common-value insurance price types of express boxes, the express boxes are classified firstly, then storage areas of the classified express boxes are divided respectively, the express boxes of the same insurance price type are stored together, different levels of protection are provided for the express boxes of different insurance price types, and the insurance cost is saved.
Optionally, the method further comprises: setting an electronic tag for each express box; and according to the classification result of the express box, inputting the electronic tag information of the express box into a card reader of a storage area corresponding to the classification result of the express box.
Therefore, one or more card readers can be arranged in each storage area, and the electronic tag information of the express box of a certain classification corresponding to the storage area is recorded into the card readers, so that the express box is guaranteed to be thrown into the storage area with the correct classification. For example, the electronic tag information of the fresh type express box is input into the card reader of the fresh type storage area, when the fresh type express box is stored, the identity verification of the card reader can be carried out, and when the non-fresh type express box is stored, the identity verification cannot be carried out, so that the storage area of the fresh type express box is prevented from being stored by a worker.
Optionally, the method further comprises: responding to unidentified electronic tag information sent by the card reader, inquiring a classification result of the express box corresponding to the unidentified electronic tag information, recording the classification result as a first classification, and inquiring a storage area of the express box of the first classification; and displaying the inquired storage area on the card reader, or controlling the card reader to send out a prompt voice containing the inquired storage area.
Like this, when the staff puts in the express delivery case wrong storage area, can automatic suggestion this express delivery case belonged to categorised correct storage area, improve the efficiency of depositing of express delivery case.
A system for smart transportation, comprising a device for smart transportation, the device for smart transportation comprising: the model module is used for establishing a storage model representing the corresponding relation between the independent variable and the dependent variable by taking the size of a storage space, the type of the express box and the size and the number of the express boxes of each type as independent variables and the storage area of the express box of each type as dependent variables; and the planning module is used for acquiring input data of each independent variable and acquiring output data of the dependent variable corresponding to the combination of the input data of the independent variable by using the storage model.
Optionally, the model module comprises: the data unit is used for acquiring a training data set of the independent variable and the dependent variable by taking the size of a storage space, the type of the express boxes and the size and the number of the express boxes of each type as independent variables and taking the storage area of the express boxes of each type as a dependent variable; and the model unit is used for establishing a storage model representing the corresponding relation between the independent variable and the dependent variable by utilizing deep learning for the training data sets of the independent variable and the dependent variable.
Optionally, the planning module comprises: the classification unit is used for classifying all the express boxes transported at the time; and the planning unit is used for acquiring input data of each independent variable for each type of express box and acquiring output data of the dependent variable corresponding to the combination of the input data of the independent variable by using the storage model.
Optionally, the classification unit is configured to: classifying all the express boxes transported at this time according to the types of the objects; and/or classifying all the express boxes transported this time according to the insurance price type.
Optionally, the apparatus for smart transportation further comprises a label module comprising: the label unit is used for setting an electronic label for each express box; and the input unit is used for inputting the electronic tag information of the express box into the card reader of the storage area corresponding to the classification result of the express box according to the classification result of the express box.
Optionally, the apparatus for intelligent transportation further comprises a prompt module, the prompt module comprising: the query unit is used for responding to unidentified electronic tag information sent by the card reader, querying a classification result of the express box corresponding to the unidentified electronic tag information, recording the classification result as a first classification, and querying a storage area of the express box of the first classification; and the prompting unit is used for displaying the inquired storage area on the card reader or controlling the card reader to send a prompting voice containing the inquired storage area.
Optionally, the system further comprises a card reader, configured to send the unidentified electronic tag information to the device for intelligent transportation when the unidentified electronic tag information is read.
Therefore, the system further comprises a card reader, and the card reader is used for reading electronic tag information of the express box and judging whether the express box is placed in a storage area with correct classification.
An electronic device comprising a processor and a memory, the processor executing computer instructions stored by the memory to cause the electronic device to perform any of the above methods for intelligent transportation.
A computer storage medium comprising computer instructions which, when run on an electronic device, cause the electronic device to perform any of the above methods for intelligent transportation.
Compared with the prior art, the technical effects of the application include:
the application discloses a method and a system for intelligent transportation, electronic equipment and a computer storage medium, wherein a storage model for representing the corresponding relation between an independent variable and a dependent variable is established according to the size of a storage space used in the transportation process, the type, the size and the quantity of express boxes and the storage area planning result of each type of express box corresponding to the type, when the storage model is actually used, various input data corresponding to the transportation are input, the corresponding storage area division result can be output by using the storage model, the storage area of each type of express box is obtained, therefore, the express boxes of the same type can be stacked together, the jolt and the collision among the express boxes are reduced, and the storage space is saved.
Drawings
The present application is further described below with reference to the drawings and examples.
Fig. 1 is a schematic flow chart diagram of a method for intelligent transportation provided by an embodiment of the present application;
FIG. 2 is a schematic flow chart of step S11 in FIG. 1;
FIG. 3 is a schematic flow chart of step S12 in FIG. 1;
fig. 4 is a schematic structural diagram of a system for intelligent transportation according to an embodiment of the present application;
FIG. 5 is a schematic diagram of one configuration of the mold module 410 of FIG. 4;
FIG. 6 is a schematic diagram of an architecture of planning module 420 of FIG. 4;
FIG. 7 is a schematic diagram of one configuration of the labeling module 430 of FIG. 4;
fig. 8 is a schematic structural diagram of the prompt module 440 in fig. 4.
In the figure: 400. means for intelligent transportation; 410. a model module; 411. a data unit; 412. a model unit; 420. a planning module; 421. a classification unit; 422. a planning unit; 430. a label module; 431. a label unit; 432. a recording unit; 440. a prompt module; 441. a query unit; 442. a presentation unit; 500. a card reader.
Detailed Description
The present application is further described with reference to the accompanying drawings and the detailed description, and it should be noted that, in the present application, the embodiments or technical features described below may be arbitrarily combined to form a new embodiment without conflict.
Referring to fig. 1, an embodiment of the present application provides a method for intelligent transportation, including steps S11 to S12.
The express transportation in this embodiment includes the logistics transportation that the parcel of smallclothes is the main express transportation and the parcel of major possession is the main.
The storage area may be located on a vehicle used for transportation. The vehicle used for transportation may be a tricycle, car, train, airplane, or other vehicle.
Step S11: and establishing a storage model representing the corresponding relation between the independent variable and the dependent variable by taking the size of the storage space, the type of the express boxes and the size and the number of the express boxes of each type as independent variables and the storage area of the express boxes of each type as dependent variables.
The storage space is a storage space on a vehicle used for transportation. The storage space may be divided into a number of storage areas. The size of the storage space may be expressed in terms of length, width, height, diameter, etc.
The express delivery case is the container that holds the express delivery, and it can be the box, also can be bag, net. The materials of the express box can comprise paper, plastic, foam, metal and the like.
The express delivery case can have multiple model, for example can have extra large size, medium size, small size, special small size etc.. The dimensions of the courier box may be expressed in terms of length, width, height, etc.
Referring to FIG. 2, step S11 may include steps S21-S22.
Step S21: and acquiring training data sets of independent variables and dependent variables by taking the size of the storage space, the type of the express boxes and the size and the number of each type of express box as independent variables and taking the storage area of each type of express box as a dependent variable.
The training data set may use historical data of each parameter, or may be standard data of each parameter. In the training dataset, each set of independent and dependent variables is corresponding.
Step S22: and establishing a storage model representing the corresponding relation between the independent variable and the dependent variable by utilizing deep learning for the training data sets of the independent variable and the dependent variable.
Therefore, the training data set can be trained in a deep learning mode to obtain the storage model.
Step S12: input data of each independent variable is acquired, and output data of the dependent variable corresponding to a combination of the input data of the independent variable is acquired by using the storage model.
In the transportation process, according to the model and the quantity of the express boxes, the storage area of each model of express box is planned in advance. The express box of the same model can be placed at any position of the express box region of the model, and the position of each express box can be planned during planning.
Therefore, according to the size of the storage space used in the transportation process, the type, the size and the quantity of the express boxes and the storage area planning result of each type of corresponding express box, a storage model representing the corresponding relation between independent variables and dependent variables is established, when the express box is actually used, various input data corresponding to the transportation are input, the corresponding storage area division result can be output by using the storage model, the storage area of each type of express box is obtained, therefore, the express boxes of the same type can be stacked together, jolting and collision among the express boxes are reduced, and the storage space is saved.
Referring to FIG. 3, step S12 may include steps S31-S32.
Step S31: all the express boxes in the transportation are classified.
Wherein, the step S31 may include: classifying all the express boxes transported at this time according to the types of the objects; and/or classifying all the express boxes transported this time according to the insurance price type.
Therefore, the express boxes transported this time can be classified in advance according to the article types and/or the insurance price types, and the storage areas of various express boxes can be planned by using the storage model for the classified single type of express boxes.
For example, the article types can include fresh types and non-fresh types, the express boxes are classified according to the article types, the express boxes of the fresh types and the non-fresh types are divided into regions respectively, the fresh type express boxes can be further subdivided, the regions are divided according to the types of the express boxes, risks that the qualities are damaged due to the fact that the fresh types of the express boxes are stored together are reduced, and for example, the express boxes of meat and fruits can be stored separately.
For example, the insurance price types can include high value and common value, the express boxes are classified firstly, then the classified express boxes are divided into storage areas, the express boxes with the same insurance price type are stored together, different levels of protection are provided for the express boxes with different insurance price types, and the insurance cost is saved.
Step S32: for each type of express box, input data of each independent variable is acquired, and output data of a dependent variable corresponding to a combination of the input data of the independent variable is acquired by using a storage model.
Like this, can classify this express delivery case of transportation in advance, to the express delivery case of single article type after the classification, recycle and deposit the region of depositing of various express delivery casees of model planning.
With continued reference to FIG. 1, the method may further include steps S13-S14.
Step S13: and setting an electronic tag for each express box.
The electronic tag is an RFID (Radio Frequency Identification) tag, and correspondingly, the card reader may be an RFID card reader.
The step establishes a corresponding relationship between each express box and the electronic tag.
Step S14: and according to the classification result of the express box, inputting the electronic tag information of the express box into a card reader of a storage area corresponding to the classification result of the express box.
For example, when the classification is performed according to the types of the articles, the electronic tag information of the fresh food type express box can be input into the card reader of the corresponding storage area of the fresh food type express box.
Therefore, one or more card readers can be arranged in each storage area, and the electronic tag information of the express box of a certain classification corresponding to the storage area is recorded into the card readers, so that the express box is guaranteed to be thrown into the storage area with the correct classification. For example, the electronic tag information of the fresh type express box is input into the card reader of the fresh type storage area, when the fresh type express box is stored, the identity verification of the card reader can be carried out, and when the non-fresh type express box is stored, the identity verification cannot be carried out, so that the storage area of the fresh type express box is prevented from being stored by a worker.
With continued reference to FIG. 1, the method may further include steps S15-S16.
Step S15: and responding to the unidentified electronic tag information sent by the card reader, inquiring the classification result of the express box corresponding to the unidentified electronic tag information, recording the classification result as a first classification, and inquiring the storage area of the express box of the first classification.
For example, if the classification result of the express box corresponding to the unidentified electronic tag information is a fresh class, the storage area of the express box of the fresh class is inquired.
Step S16: and displaying the inquired storage area on the card reader, or controlling the card reader to send out a prompt voice containing the inquired storage area.
Like this, when the staff puts in the express delivery case wrong storage area, can automatic suggestion this express delivery case belonged to categorised correct storage area, improve the efficiency of depositing of express delivery case.
Referring to fig. 4, the present application further provides a system for intelligent transportation, including a device 400 for intelligent transportation.
The apparatus 400 for intelligent transportation includes a model module 410 and a planning module 420, and the model module 410 and the planning module 420 can perform data interaction.
The model module 410 is configured to establish a storage model representing a correspondence between independent variables and dependent variables, with the size of the storage space, the type of the express delivery box, and the size and number of each type of the express delivery box as independent variables, and the storage area of each type of the express delivery box as dependent variables.
The planning module 420 is configured to obtain input data for each independent variable, and obtain output data of the dependent variable corresponding to a combination of the input data of the independent variable using the storage model.
Referring to fig. 5, the model module 410 may include a data unit 411 and a model unit 412, and the data unit 411 and the model unit 412 may perform data interaction.
The data unit 411 is configured to obtain a training data set of independent variables and dependent variables by using the size of the storage space, the type of the express box, and the size and number of each type of the express box as independent variables and using the storage area of each type of the express box as a dependent variable.
The model unit 412 is configured to build a storage model representing a correspondence between independent variables and dependent variables by deep learning for training data sets of the independent variables and the dependent variables.
Referring to fig. 6, the planning module 420 may include a classification unit 421 and a planning unit 422, and the classification unit 421 and the planning unit 422 may perform data interaction.
The sorting unit 421 is used for sorting all the express delivery boxes transported at this time.
The planning unit 422 is configured to acquire input data of each independent variable for each type of express box, and acquire output data of a dependent variable corresponding to a combination of the input data of the independent variable using the storage model.
Specifically, the sorting unit 421 may be configured to sort all the express boxes transported at this time according to the types of the items; and/or classifying all the express boxes transported this time according to the insurance price type.
With continued reference to fig. 4, the apparatus 400 for smart transportation may also include a tag module 430. The tag module 430 interacts with the planning module 420 for data.
Referring to fig. 7, the label module 430 may include a label unit 431 and an entry unit 432, and the label unit 431 and the entry unit 432 may perform data interaction.
The label unit 431 is used for setting an electronic label for each express box.
The entry unit 432 is configured to enter the electronic tag information of the express box into the card reader 500 in the storage area corresponding to the classification result of the express box according to the classification result of the express box.
With continued reference to fig. 4, the apparatus 400 for intelligent transportation may also include a prompt module 440. Prompt module 440 interacts data with tag module 430.
Referring to fig. 8, the prompt module 440 may include a query unit 441 and a prompt unit 442, and the query unit 441 and the prompt unit 442 may perform data interaction.
The query unit 441 is configured to query, in response to the unidentified electronic tag information sent by the card reader 500, a classification result of the express delivery box corresponding to the unidentified electronic tag information, which is recorded as a first classification, and query a storage area of the express delivery box of the first classification.
The prompting unit 442 is used to display the queried storage area on the card reader 500 or control the card reader 500 to send a prompting voice containing the queried storage area.
With continued reference to fig. 1, the system may further include a card reader 500, the card reader 500 for transmitting the unidentified electronic tag information to the device 400 for intelligent transportation when the unidentified electronic tag information is read. The card reader 500 performs data interaction with the apparatus for smart transportation 400.
Therefore, the system further comprises a card reader 500, and the card reader 500 is used for reading electronic tag information of the express box and judging whether the express box is placed in a storage area with correct classification.
An embodiment of the present application further provides an electronic device, where the electronic device includes a processor and a memory, and the processor executes computer instructions stored in the memory, so that the electronic device executes any one of the above methods for intelligent transportation.
Embodiments of the present application further provide a computer storage medium, which includes computer instructions, and when the computer instructions are executed on an electronic device, the electronic device is caused to execute any one of the methods for intelligent transportation.
The foregoing description and drawings are only for purposes of illustrating the preferred embodiments of the present application and are not intended to limit the present application, which is, therefore, to the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present application.
Claims (12)
1. A method for intelligent transportation, comprising:
establishing a storage model representing the corresponding relation between independent variables and dependent variables by taking the size of a storage space, the types of the express boxes and the size and the number of the express boxes of each type as independent variables and the storage area of the express boxes of each type as dependent variables;
and acquiring input data of each independent variable, and acquiring output data of the dependent variable corresponding to the combination of the input data of the independent variable by using the storage model.
2. The method for intelligent transportation according to claim 1, wherein the building of the storage model representing the corresponding relationship between the independent variable and the dependent variable by taking the size of the storage space, the model number of the express boxes and the size and number of the express boxes of each model as independent variables and the storage area of the express boxes of each model as dependent variables comprises the following steps:
acquiring a training data set of an independent variable and a dependent variable by taking the size of a storage space, the type of an express box and the size and the number of the express boxes of each type as the independent variable and the storage area of the express box of each type as the dependent variable;
and establishing a storage model representing the corresponding relation between the independent variable and the dependent variable by utilizing deep learning for the training data sets of the independent variable and the dependent variable.
3. The method for intelligent transportation of claim 1, wherein the obtaining input data for each of the independent variables and using the deposit model to obtain output data for the dependent variable corresponding to a combination of the input data for the independent variable comprises:
classifying all express boxes transported this time;
for each type of the express delivery box, input data of each independent variable is acquired, and output data of the dependent variable corresponding to a combination of the input data of the independent variable is acquired by using the storage model.
4. The method for intelligent transportation of claim 3, further comprising:
setting an electronic tag for each express box;
and according to the classification result of the express box, inputting the electronic tag information of the express box into a card reader of a storage area corresponding to the classification result of the express box.
5. A system for intelligent transportation, comprising a device for intelligent transportation, the device for intelligent transportation comprising:
the model module is used for establishing a storage model representing the corresponding relation between the independent variable and the dependent variable by taking the size of a storage space, the type of the express box and the size and the number of the express boxes of each type as independent variables and the storage area of the express box of each type as dependent variables;
and the planning module is used for acquiring input data of each independent variable and acquiring output data of the dependent variable corresponding to the combination of the input data of the independent variable by using the storage model.
6. The system for intelligent transportation of claim 5, wherein the model module comprises:
the data unit is used for acquiring a training data set of the independent variable and the dependent variable by taking the size of a storage space, the type of the express boxes and the size and the number of the express boxes of each type as independent variables and taking the storage area of the express boxes of each type as a dependent variable;
and the model unit is used for establishing a storage model representing the corresponding relation between the independent variable and the dependent variable by utilizing deep learning for the training data sets of the independent variable and the dependent variable.
7. The system for intelligent transportation of claim 5, wherein the planning module comprises:
the classification unit is used for classifying all the express boxes transported at the time;
and the planning unit is used for acquiring input data of each independent variable for each type of express box and acquiring output data of the dependent variable corresponding to the combination of the input data of the independent variable by using the storage model.
8. The system for smart transportation of claim 7, wherein the means for smart transportation further comprises a tag module comprising:
the label unit is used for setting an electronic label for each express box;
and the input unit is used for inputting the electronic tag information of the express box into the card reader of the storage area corresponding to the classification result of the express box according to the classification result of the express box.
9. The system for intelligent transportation of claim 8, wherein the means for intelligent transportation further comprises a prompting module, the prompting module comprising:
the query unit is used for responding to unidentified electronic tag information sent by the card reader, querying a classification result of the express box corresponding to the unidentified electronic tag information, recording the classification result as a first classification, and querying a storage area of the express box of the first classification;
and the prompting unit is used for displaying the inquired storage area on the card reader or controlling the card reader to send a prompting voice containing the inquired storage area.
10. The system for intelligent transportation of claim 9, further comprising a card reader for sending the unidentified electronic tag information to the means for intelligent transportation when the unidentified electronic tag information is read.
11. An electronic device comprising a processor and a memory, the processor executing computer instructions stored by the memory to cause the electronic device to perform the method for intelligent transportation of any of claims 1-4.
12. A computer storage medium comprising computer instructions that, when executed on an electronic device, cause the electronic device to perform the method for intelligent transportation of any of claims 1-4.
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| CN202010073171.2A CN111275386A (en) | 2020-01-22 | 2020-01-22 | Method and system for intelligent transportation, electronic device, and computer storage medium |
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| CN202010073171.2A CN111275386A (en) | 2020-01-22 | 2020-01-22 | Method and system for intelligent transportation, electronic device, and computer storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112904865A (en) * | 2021-01-28 | 2021-06-04 | 广东职业技术学院 | Method and system for controlling transportation of ceramic material and computer readable storage medium |
| CN117235533A (en) * | 2023-11-10 | 2023-12-15 | 腾讯科技（深圳）有限公司 | Object variable analysis method, device, computer equipment and storage medium |
-
2020
- 2020-01-22 CN CN202010073171.2A patent/CN111275386A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112904865A (en) * | 2021-01-28 | 2021-06-04 | 广东职业技术学院 | Method and system for controlling transportation of ceramic material and computer readable storage medium |
| CN117235533A (en) * | 2023-11-10 | 2023-12-15 | 腾讯科技（深圳）有限公司 | Object variable analysis method, device, computer equipment and storage medium |
| CN117235533B (en) * | 2023-11-10 | 2024-03-01 | 腾讯科技（深圳）有限公司 | Object variable analysis method, device, computer equipment and storage medium |
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