CN212515885U - Bulk resource type mineral product release risk prediction system based on deep learning - Google Patents
Bulk resource type mineral product release risk prediction system based on deep learning Download PDFInfo
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- CN212515885U CN212515885U CN202021539943.9U CN202021539943U CN212515885U CN 212515885 U CN212515885 U CN 212515885U CN 202021539943 U CN202021539943 U CN 202021539943U CN 212515885 U CN212515885 U CN 212515885U
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Abstract
The utility model discloses a large amount of resources class mineral products risk prediction system that lets go based on degree of depth study, a serial communication port, include: the data operation server is connected with the front-end acquisition devices and the customs system server through a network; and the customs system server is connected with the terminal processing equipment. The system can avoid one-sidedness caused by artificial consideration of different factors, search potential factors causing risks, and has more comprehensive and stronger analysis and evaluation capability compared with manual examination.
Description
Technical Field
The utility model relates to an article detect and discernment technical field, especially relate to a large amount of resources class mineral products risk prediction system that lets go based on degree of depth study.
Background
At present, the customs clearance risk is predicted and analyzed by depending on subjective evaluation and analysis of personnel at the front line of business, however, with the rapid development of the customs business informatization, the customs business database collects and stores massive customs management data. The business data covers various fields of customs management, presents the characteristics of large data volume, rapid growth, complex relationship and the like, and has the characteristics of huge change of international economic situation and great increase of various risks faced by customs. Human processing mechanisms have been significantly fatigued.
In order to meet the requirement of continuously increasing supervision business volume, if only the manual examination of business personnel is relied, a large amount of human input is required, the cost consumption is huge, and the problems of large randomness and difficulty in discovering potential risks exist due to the lack of information of auxiliary decision making caused by artificial subjective evaluation and analysis.
Disclosure of Invention
The utility model aims at: the massive resource mineral product release risk prediction system and the storage medium based on deep learning are provided, one-sidedness generated when different factors are considered manually can be avoided, potential factors causing risks can be searched, and the system has more comprehensive and stronger analysis and evaluation capability compared with manual examination.
In order to achieve the above object, the present invention provides a system for predicting the release risk of a large amount of resource type mineral products based on deep learning, the system comprising: the data operation server is connected with the front-end acquisition devices and the customs system server through a network; and the customs system server is connected with the terminal processing equipment.
Further, the data operation server comprises a historical data server, an application server and a data processing server; the historical data server, the application server and the data processing server are sequentially connected to form a ring; the historical data server is connected with the customs system server through a data interface of an internal local area network; and the application server is connected with the terminal processing equipment through an internal local area network.
Furthermore, the front-end acquisition equipment comprises a switch, an acquisition device client, a terminal acquisition box, a risk assessment system, a display screen, a camera and a recorder; the switch is connected with the terminal acquisition box through a network, the terminal acquisition box is respectively connected with the camera, the recorder and the collector client, and the risk assessment system is respectively connected with the collector client and the display screen.
Further, the customs system server is a data source server of the customs data.
Further, the terminal processing equipment is a computer or a workstation.
Further, the recorder comprises an LCD recorder and a mini recorder.
Compared with the prior art, the system for predicting the release risk of the bulk resource type mineral products based on deep learning provided by the embodiment of the invention at least has the following beneficial effects:
the utility model discloses a large amount of resources class mineral products risk prediction system that lets go based on degree of depth study, a serial communication port, include: the system comprises a plurality of front-end acquisition devices, a terminal processing device, a data operation server and a customs system server, wherein the data operation server is connected with the front-end acquisition devices and the customs system server through a network; and the customs system server is connected with the terminal processing equipment. The system can avoid one-sidedness caused by artificial consideration of different factors, search potential factors causing risks, and has more comprehensive and stronger analysis and evaluation capability compared with manual examination.
Drawings
Fig. 1 is a schematic structural diagram of a system for predicting risk of release of a large amount of resource mineral products based on deep learning according to a first embodiment 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.
It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
The first embodiment of the present invention:
referring to fig. 1, a system 100 for predicting release risk of a large amount of resource type mineral products based on deep learning according to an embodiment of the present invention includes a plurality of front-end acquisition devices 101, a data operation server 102, a customs system server 103, and a terminal processing device 104; the data operation server 102 is connected with a plurality of front-end acquisition devices 101 and a customs system server 103 through a network; the customs system server is connected to the terminal processing device 104.
In an embodiment of the present invention, the data operation server includes a history data server, an application server, and a data processing server; the historical data server, the application server and the data processing server are sequentially connected to form a ring; the historical data server is connected with the customs system server through a data interface of an internal local area network; and the application server is connected with the terminal processing equipment through an internal local area network.
In an embodiment of the present invention, the front-end collecting device includes a switch, a collector client, a terminal collecting box, a risk assessment system, a display screen, a camera, and a recorder; the switch is connected with the terminal acquisition box through a network, the terminal acquisition box is respectively connected with the camera, the recorder and the collector client, and the risk assessment system is respectively connected with the collector client and the display screen.
In an embodiment of the present invention, the customs system server is a data source server of customs data.
In an embodiment of the present invention, the terminal processing device is a computer or a workstation.
In an embodiment of the present invention, the recorder includes an LCD recorder and a mini recorder.
The utility model discloses a large amount of resources class mineral products risk prediction system that lets go based on degree of depth study, a serial communication port, include: the system comprises a plurality of front-end acquisition devices, a terminal processing device, a data operation server and a customs system server, wherein the data operation server is connected with the front-end acquisition devices and the customs system server through a network; and the customs system server is connected with the terminal processing equipment. The system can avoid one-sidedness caused by artificial consideration of different factors, search potential factors causing risks, and has more comprehensive and stronger analysis and evaluation capability compared with manual examination.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within 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 represent some embodiments of the present invention, and the description thereof is specific and detailed, but not to be construed as limiting the scope of the present invention. It should be noted that, for those skilled in the art, without departing from the spirit of the present invention, several variations and modifications can be made, which are within the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.
Claims (6)
1. A large resource type mineral product release risk prediction system based on deep learning is characterized by comprising the following components: the data operation server is connected with the front-end acquisition devices and the customs system server through a network; and the customs system server is connected with the terminal processing equipment.
2. The deep learning based large resource class mineral product release risk prediction system of claim 1, wherein the data operation server comprises a historical data server, an application server and a data processing server; the historical data server, the application server and the data processing server are sequentially connected to form a ring; the historical data server is connected with the customs system server through a data interface of an internal local area network; and the application server is connected with the terminal processing equipment through an internal local area network.
3. The deep learning-based large-resource mineral product release risk prediction system according to claim 1, wherein the front-end acquisition device comprises a switch, an acquisition client, a terminal acquisition box, a risk assessment system, a display screen, a camera and a recorder; the switch is connected with the terminal acquisition box through a network, the terminal acquisition box is respectively connected with the camera, the recorder and the collector client, and the risk assessment system is respectively connected with the collector client and the display screen.
4. The deep learning-based large-resource mineral product release risk prediction system according to claim 1, wherein the customs system server is a data source server for customs data.
5. The deep learning-based large resource class mineral product release risk prediction system according to claim 1, wherein the terminal processing device is a computer or a workstation.
6. The deep learning-based large resource class mineral product release risk prediction system according to claim 3, wherein the recorder comprises an LCD recorder and a mini recorder.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202021539943.9U CN212515885U (en) | 2020-07-29 | 2020-07-29 | Bulk resource type mineral product release risk prediction system based on deep learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202021539943.9U CN212515885U (en) | 2020-07-29 | 2020-07-29 | Bulk resource type mineral product release risk prediction system based on deep learning |
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| Publication Number | Publication Date |
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| CN212515885U true CN212515885U (en) | 2021-02-09 |
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| CN202021539943.9U Active CN212515885U (en) | 2020-07-29 | 2020-07-29 | Bulk resource type mineral product release risk prediction system based on deep learning |
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| CN (1) | CN212515885U (en) |
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