CN113759917A - Wisdom commodity circulation letter sorting system based on cloud calculates - Google Patents
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Abstract
The invention belongs to the technical field of intelligent logistics, and particularly relates to an intelligent logistics sorting system based on cloud computing, which comprises the following components: the system comprises an information acquisition module, a dynamic label module, a visualization module, an early warning feedback module and a cloud computing server; generating initial transportation route information based on the information of the objects to be sorted, acquiring characteristic information of the objects to be sorted through an information sensor, generating label information of the objects to be sorted according to the initial transportation route information and the characteristic information, monitoring dynamic change of the label information, judging and generating early warning feedback information; the invention solves the technical problems that the traditional sorting system can not check and trace package damage in the transportation process and has low sorting efficiency.
Description
Technical Field
The invention relates to the field of intelligent logistics, in particular to an intelligent logistics sorting system based on cloud computing.
Background
At present, in the express industry, timeliness and accuracy are the targets pursued by various large express companies, and the speed and accuracy of goods sorting become the most main limiting factors for improving timeliness of various large logistics companies. In the mainstream logistics industry in China, sorting lines with different scales are built in a regional sorting center respectively, a semi-automatic sorting system is mostly adopted, namely, a sorting device and manual mode is adopted, and the used sorting devices comprise an inclined guide wheel type sorting machine, a sliding block type sorting machine, a cross belt sorting machine, a turnover plate type sorting machine, an AGV robot and the like; these semi-automatic letter sorting systems see that intelligent equipment coverage is low on the whole, and degree of automation is not high, and most letter sorting centers still all rely on manual sorting.
With the rapid development of computer and internet technologies, the application of big data technology is becoming more and more extensive, and in the face of massive logistics data, the current logistics sorting system can not strictly check and trace items such as package damage, internal article loss and the like in the transportation process, and the presented problems are low sorting efficiency, high error rate and high labor cost, and how to establish an intelligent logistics sorting system with more perfect functions is still a technical problem to be solved.
Disclosure of Invention
The invention provides a cloud computing-based intelligent logistics sorting system, and aims to provide the intelligent logistics sorting system which is more complete in function and capable of checking and tracing package damage, reduce the package sorting error rate and solve the technical problem that the existing logistics sorting system is not high in intelligent degree.
The basic scheme provided by the invention is as follows: a wisdom commodity circulation letter sorting system based on cloud calculates, wisdom commodity circulation letter sorting system includes: the system comprises an information acquisition module, a dynamic label module, a visualization module, an early warning feedback module and a cloud computing server.
And the cloud computing platform server generates initial transportation route information of the objects to be sorted based on the received sending address information and the receiving address information.
The information acquisition module acquires characteristic information of the objects to be sorted through an information sensor, the information sensor at least comprises one of an image sensor, a weight sensor and a radio frequency sensor, and the generated characteristic information at least comprises one of order information, image information, historical logistics information and article identification information.
The dynamic label module receives initial transportation route information generated by the cloud computing platform server and characteristic information generated by the information acquisition module, generates label information of an object to be sorted according to the initial transportation route information and the characteristic information, and transmits the label information to a historical information database for storage.
The early warning feedback module monitors the dynamic change of the label information based on the upstream label information in the historical information database and the label information generated by the dynamic label module, and judges and generates early warning feedback information.
The visualization module is used for displaying the initial transportation route information, the characteristic information of the objects to be sorted and the early warning feedback information.
Preferably, the information acquisition module acquires upstream label information of the object to be sorted in the intelligent logistics sorting system through historical information data, and adds the upstream label information into the information sensor to acquire characteristic information of the object to be sorted.
Preferably, the initial transportation route information of the objects to be sorted is the optimal route information generated based on a neural network model, and the neural network model is obtained based on the transportation mode, the classification information of the objects to be sorted, the sending address information and the receiving address information through training.
Preferably, the early warning feedback module receives the upstream label information in the historical information database and extracts upstream image information, and the early warning feedback module receives the current image information acquired by the information acquisition module through the information sensor; processing upstream image information and current image information, and generating similarity parameters of the upstream image information and the current image information according to an image comparison algorithm; and judging whether to generate early warning information or not according to a preset threshold and the similarity parameter, and if the similarity parameter is greater than the preset threshold, generating the early warning information and feeding the early warning information back to a visualization module.
Preferably, the early warning feedback module receives the upstream label information in the historical information database and extracts upstream weight information, and the early warning feedback module receives the current weight information acquired by the information acquisition module through the information sensor; and judging whether the upstream weight information and the current weight information exceed a preset acceptable variation range, if so, generating early warning information and feeding the early warning information back to a visualization module.
Preferably, the image comparison algorithm uses a perceptual hash algorithm.
Preferably, the preset acceptable variation range is determined according to the classification information of the objects to be sorted.
Compared with the prior art, the invention has the beneficial effects that: compared with the prior art, the system has more perfect functions, can check and trace package damage, reduce the package sorting error rate and improve the intellectualization degree and sorting efficiency of the logistics sorting system.
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Fig. 1 is a schematic structural diagram of an intelligent logistics sorting system based on cloud computing according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments will be described clearly and completely with reference to the accompanying drawings 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.
Referring to fig. 1, in an embodiment of the present invention, an intelligent logistics sorting system based on cloud computing includes: the system comprises an information acquisition module, a dynamic label module, a visualization module, an early warning feedback module and a cloud computing server.
And the cloud computing platform server generates initial transportation route information of the objects to be sorted based on the received sending address information and the receiving address information.
The information acquisition module acquires characteristic information of the objects to be sorted through an information sensor, the information sensor at least comprises one of an image sensor, a weight sensor and a radio frequency sensor, and the generated characteristic information at least comprises one of order information, image information, historical logistics information and article identification information.
The image information of the object to be processed is obtained through the image sensor, the image information is identified, corresponding appearance information and/or order information are obtained, and a high-definition three-dimensional scanning camera module (with the model being TD-BD-SCAM) can be set.
The weight sensor is used for acquiring the weight information of the object to be processed, and a precise weight meter (model: AUX160) can be adopted.
And reading radio frequency information corresponding to a radio frequency device on the object to be processed through a radio frequency sensor, and searching order information and historical information corresponding to the radio frequency information in a preset logistics database according to the radio frequency information.
The dynamic label module receives initial transportation route information generated by the cloud computing platform server and characteristic information generated by the information acquisition module, generates label information of an object to be sorted according to the initial transportation route information and the characteristic information, and transmits the label information to a historical information database for storage.
The early warning feedback module monitors the dynamic change of the label information based on the upstream label information in the historical information database and the label information generated by the dynamic label module, and judges and generates early warning feedback information.
The visualization module is used for displaying the initial transportation route information, the characteristic information of the objects to be sorted and the early warning feedback information.
The information acquisition module acquires upstream label information of the objects to be sorted in the intelligent logistics sorting system through historical information data, and adds the upstream label information into the characteristic information of the objects to be sorted acquired through the information sensor.
The initial transportation route information of the objects to be sorted is the optimal route information generated based on a neural network model, and the neural network model is obtained based on the transportation mode, the classification information of the objects to be sorted, the sending address information and the receiving address information through training.
The early warning feedback module receives the upstream label information in the historical information database and extracts upstream image information, and the early warning feedback module receives the current image information acquired by the information acquisition module through the information sensor.
In the process of acquiring circulation, images acquired by an upstream image sensor and a current image sensor are respectively acquired, and upstream image information and current image information are generated; the upstream image sensor can be arranged at the starting point of logistics or at the transit point.
Similarly, the current image sensor is arranged at the subsequent logistics point, so that the current image of the cargo can be acquired once again to obtain two images of the cargo in transit.
The placing postures of the cargos can be specified as follows: the side on which the express bill is stuck is right above; not only can avoid the goods to cause the system misjudgment in the different gesture in different nodes, can also be convenient for follow-up commodity circulation information that obtains the goods through commodity circulation information scanning device.
Processing upstream image information and current image information, and generating similarity parameters of the upstream image information and the current image information according to an image comparison algorithm; and judging whether to generate early warning information or not according to a preset threshold and the similarity parameter, and if the similarity parameter is greater than the preset threshold, generating the early warning information and feeding the early warning information back to a visualization module.
The image comparison algorithm adopts a perceptual hash algorithm.
The perceptual hashing algorithm specifically comprises the following steps: respectively calculating the dHash values of the two pictures; the hamming distance of the two pictures is calculated by the dHash value.
Judging the similarity degree of the two pictures according to the Hamming distance; generally, the hamming distance is less than 6, which is basically the same picture.
The embodiment also comprises a preprocessing submodule used for carrying out scaling processing on the upstream image information and the current image information to generate upstream scaling image information and current scaling image information; the difficulty of system operation can be reduced, and the processing speed can be accelerated.
In addition to the judgment of the image information, the early warning feedback module can receive the upstream label information in the historical information database and extract the upstream weight information, and the early warning feedback module receives the current weight information acquired by the information acquisition module through the information sensor; and judging whether the upstream weight information and the current weight information exceed a preset acceptable variation range, if so, generating early warning information and feeding the early warning information back to a visualization module.
The preset acceptable variation range is determined according to the classification information of the objects to be sorted, for example, the classification information of the objects to be sorted can be divided into: fresh food, living goods, documents, etc.
Assuming that the classification information of a package is fresh food, the acceptable variation range can be set to be that the weight fluctuation does not exceed 5% according to the characteristic that the weight of the fresh food can be naturally lost in the logistics process. Assuming that the classification information of another package is a file, the acceptable variation range can be set such that the weight does not fluctuate more than 0.5%.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others.
Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application.
Claims (7)
1. A wisdom commodity circulation letter sorting system based on cloud calculates, wisdom commodity circulation letter sorting system includes: the system comprises an information acquisition module, a dynamic label module, a visualization module, an early warning feedback module and a cloud computing server:
the cloud computing platform server generates initial transportation route information of the objects to be sorted based on the received sending address information and the receiving address information;
the information acquisition module acquires characteristic information of an object to be sorted through an information sensor, the information sensor at least comprises one of an image sensor, a weight sensor and a radio frequency sensor, and the generated characteristic information at least comprises one of order information, image information, historical logistics information and article identification information;
the dynamic label module receives initial transportation route information generated by the cloud computing platform server and characteristic information generated by the information acquisition module, generates label information of an object to be sorted according to the initial transportation route information and the characteristic information, and transmits the label information to a historical information database for storage;
the early warning feedback module monitors the dynamic change of the tag information based on the upstream tag information in a historical information database and the tag information generated by the dynamic tag module, judges and generates early warning feedback information;
the visualization module is used for displaying the initial transportation route information, the characteristic information of the objects to be sorted and the early warning feedback information.
2. The intelligent logistics sorting system based on cloud computing of claim 1, wherein the information acquisition module acquires the upstream tag information of the object to be sorted in the intelligent logistics sorting system through a historical information database, and adds the upstream tag information to the characteristic information of the object to be sorted acquired through the information sensor.
3. The cloud-computing-based intelligent logistics sorting system of claim 1, wherein the initial transportation route information of the objects to be sorted is optimal route information generated based on a neural network model, and the neural network model is obtained based on transportation mode, classification information of the objects to be sorted, mailing address information and receiving address information training.
4. The cloud computing-based intelligent logistics sorting system according to any one of claims 2-3, wherein the early warning feedback module receives the upstream label information in the historical information database and extracts upstream image information, and the early warning feedback module receives the current image information acquired by the information acquisition module through an information sensor; processing upstream image information and current image information, and generating similarity parameters of the upstream image information and the current image information according to an image comparison algorithm; and judging whether to generate early warning information or not according to a preset threshold and the similarity parameter, and if the similarity parameter is greater than the preset threshold, generating the early warning information and feeding the early warning information back to a visualization module.
5. The cloud-computing-based intelligent logistics sorting system according to any one of claims 2-3, wherein the early warning feedback module receives the upstream label information in the historical information database and extracts upstream weight information, and the early warning feedback module receives the current weight information obtained by the information obtaining module through an information sensor; and judging whether the upstream weight information and the current weight information exceed a preset acceptable variation range, if so, generating early warning information and feeding the early warning information back to a visualization module.
6. The cloud computing-based intelligent logistics sorting system of claim 4, the image comparison algorithm employing a perceptual hashing algorithm.
7. The cloud-computing-based intelligent logistics sorting system of claim 5, wherein the preset acceptable variation range is determined according to the classification information of the objects to be sorted.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114418489A (en) * | 2022-01-04 | 2022-04-29 | 常州首信智能制造有限公司 | Intelligent monitoring and early warning method, device, equipment and medium for logistics sorting robot |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114418489A (en) * | 2022-01-04 | 2022-04-29 | 常州首信智能制造有限公司 | Intelligent monitoring and early warning method, device, equipment and medium for logistics sorting robot |
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