CN112966965A - Import and export big data analysis and decision method, device, equipment and storage medium - Google Patents
Import and export big data analysis and decision method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN112966965A CN112966965A CN202110316413.0A CN202110316413A CN112966965A CN 112966965 A CN112966965 A CN 112966965A CN 202110316413 A CN202110316413 A CN 202110316413A CN 112966965 A CN112966965 A CN 112966965A
- Authority
- CN
- China
- Prior art keywords
- information
- risk
- import
- goods
- export
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Images
Classifications
-
- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- 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
- G06Q10/0831—Overseas transactions
-
- 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
- G06Q30/00—Commerce
- G06Q30/018—Certifying business or products
Abstract
The invention discloses a method, a device, equipment and a storage medium for analyzing and deciding import and export big data, wherein the method comprises the following steps: acquiring coding information of goods at an inlet and an outlet; performing correlation processing according to the encoding information of the import and export goods to obtain declaration information of the import and export goods; extracting preset risk information in declaration information of the imported and exported goods; determining risk grade information according to the preset risk information, and determining weight information according to declaration information of the imported and exported goods; and predicting the risk grade information and the weight information through a risk prediction model to obtain a risk prediction result, and managing and controlling the goods according to the risk prediction result, so that detailed analysis is performed on declaration information of the imported and exported goods to obtain corresponding risk grade information and weight, more effective data analysis and decision are realized, and the information of the imported and exported goods is effectively managed and controlled.
Description
Technical Field
The invention relates to the technical field of data decision, in particular to an import and export big data analysis and decision method, an import and export big data analysis and decision device, import and export big data analysis equipment and a storage medium.
Background
In recent years, on the basis of the rapid development of import and export trade, as the variety of import and export goods information is various, an effective method for managing import and export goods is urgently needed, and therefore, the management and control of import and export goods are more and more emphasized, so that the method is suitable for the development of society.
However, at the present stage, the management of the information of the goods for import and export mainly depends on near-field data, corresponding barcode information is obtained by encoding the information of the goods for import and export, and the goods are controlled according to the barcode information.
Disclosure of Invention
The invention mainly aims to provide a method, a device, equipment and a storage medium for analyzing and deciding import and export big data, and aims to solve the technical problem that import and export goods information cannot be effectively controlled in the prior art.
In order to achieve the above object, the present invention provides an analysis and decision method for import and export big data, which comprises the following steps:
acquiring coding information of goods at an inlet and an outlet;
performing correlation processing according to the encoding information of the import and export goods to obtain declaration information of the import and export goods;
extracting preset risk information in declaration information of the imported and exported goods;
determining risk grade information according to the preset risk information, and determining weight information according to declaration information of the imported and exported goods;
and predicting the risk grade information and the weight information through a risk prediction model to obtain a risk prediction result, and managing and controlling the goods according to the risk prediction result.
Optionally, the performing correlation processing according to the encoded information of the import and export goods to obtain declaration information of the import and export goods includes:
analyzing the code information of the imported and exported goods to obtain region code information, manufacturer identification code information, commodity item code information and check code information of the imported and exported goods;
obtaining corresponding processing terminal information according to the region code information, the manufacturer identification code information, the commodity item code information and the check code information of the imported and exported goods;
associating with a processing terminal to obtain goods information related to the import and export goods in the processing terminal;
and obtaining declaration information of the import and export cargos according to the cargo information related to the import and export cargos.
Optionally, the extracting preset risk information in the declaration information of the import and export goods includes:
acquiring preset keyword information;
performing parameter feature identification on declaration information of the imported and exported goods according to the preset keyword information to obtain manufacturer information, domestic importer information, declaration agent information, compliance file information and logistics transportation information in the declaration information;
and taking manufacturer information, domestic importer information, reporting agent information, compliance file information and logistics transportation information in the reporting information as preset risk information.
Optionally, the determining risk level information according to the preset risk information includes:
acquiring import scale information in the manufacturer information, and determining manufacturer risk level information according to the import scale information;
acquiring management capacity information in the domestic import business information, and determining domestic import business grade information according to the management capacity information;
acquiring agent condition information in the reporting agent information, and determining reporting agent grade information according to the agent condition information;
acquiring regulation constraint information in the compliance file information, and determining compliance file grade information according to the regulation constraint information;
acquiring accident investigation information in the logistics transportation information, and determining logistics transportation grade information according to the accident investigation information;
and determining risk grade information according to the manufacturer risk grade information, the domestic importer grade information, the declaration agent grade information, the conformity file grade information and the logistics transportation grade information.
Optionally, determining weight information according to the declaration information of the import and export goods includes:
determining the types of the imported and exported cargos according to the declaration information of the imported and exported cargos;
determining regional risk information, foreign exchange risk information, contract risk information and credit risk information according to the types of the imported and exported goods;
taking the regional risk information, the foreign exchange risk information, the contract risk information and the credit card risk information as calibration indexes;
calibrating according to the preset importance level according to the calibration index to obtain a scoring result;
comparing every two according to the average value of the grading result to obtain an index discrimination matrix;
and determining weight information according to the index discrimination matrix.
Optionally, before determining the weight information according to the index discrimination matrix, the method further includes:
obtaining the calibration quantity according to the calibration index;
obtaining a maximum characteristic value of the matrix according to the index discrimination matrix and the calibration quantity;
obtaining a matrix consistency index according to the maximum characteristic value of the matrix and the calibration quantity;
obtaining an average consistency index, and obtaining a relative consistency index according to the average consistency index and the matrix consistency index;
comparing the relative consistency index with a preset threshold value;
and when the relative consistency index is less than or equal to a preset threshold value, executing a step of determining weight information according to the index discrimination matrix.
Optionally, the performing risk prediction on the risk level information and the corresponding weight information through a risk prediction model to obtain a risk prediction result includes:
acquiring risk information of a supervision relation party, product risk information and requirement information of a supervision party;
establishing a first risk prediction model between the first risk prediction model and batch risk parameters according to the risk information of the supervision relation party, the product risk information and the requirement information of the supervision party;
establishing a second risk prediction model between the risk of a monitoring relation party according to manufacturer risk grade information, importer risk grade information, agent risk grade information, conformity file risk grade information and transportation logistics risk grade information in the risk grade information;
establishing a third risk prediction model between the third risk prediction model and the batch risk parameters according to preset/early warning type control parameters in the risk grade information, whether the product is imported for the first time within a certain time period, the product category risk parameters, the basic sampling rate and the corresponding weight information;
and performing risk prediction through the first risk prediction model, the second risk prediction model and the third risk prediction model to obtain a risk prediction result.
In addition, in order to achieve the above object, the present invention further provides a method for analyzing and deciding import and export big data, where the method comprises:
the acquisition module is used for acquiring the coding information of the goods at the inlet and the outlet;
the correlation module is used for performing correlation processing according to the coding information of the import and export goods to obtain declaration information of the import and export goods;
the extraction module is used for extracting preset risk information in the declaration information of the imported and exported goods;
the acquisition module is further used for determining risk grade information according to the preset risk information and determining weight information according to declaration information of the imported and exported goods;
and the prediction module is used for predicting the risk grade information and the weight information through a risk prediction model to obtain a risk prediction result and managing and controlling the goods according to the risk prediction result.
Furthermore, to achieve the above object, the present invention also proposes an apparatus comprising: the system comprises a memory, a processor and an analysis and decision program of import and export big data, wherein the analysis and decision program of import and export big data is stored in the memory and can run on the processor, and is configured to realize the analysis and decision method of import and export big data.
In addition, in order to achieve the above object, the present invention further provides a storage medium, on which an analysis and decision program for import and export big data is stored, and when the analysis and decision program for import and export big data is executed by a processor, the analysis and decision method for import and export big data as described above is implemented.
The import and export big data analysis and decision method provided by the invention obtains the coding information of the import and export goods; performing correlation processing according to the encoding information of the import and export goods to obtain declaration information of the import and export goods; extracting preset risk information in declaration information of the imported and exported goods; determining risk grade information according to the preset risk information, and determining weight information according to declaration information of the imported and exported goods; and predicting the risk grade information and the weight information through a risk prediction model to obtain a risk prediction result, and managing and controlling the goods according to the risk prediction result, so that detailed analysis is performed on declaration information of the imported and exported goods to obtain corresponding risk grade information and weight, more effective data analysis and decision are realized, and the information of the imported and exported goods is effectively managed and controlled.
Drawings
FIG. 1 is a schematic structural diagram of an analysis and decision method for import and export big data of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of a method for analyzing and deciding import and export big data according to the present invention;
FIG. 3 is a schematic flow chart of a second embodiment of a method for analyzing and deciding import/export big data according to the present invention;
FIG. 4 is a schematic diagram of a decision matrix according to an embodiment of the import/export big data analysis and decision method of the present invention;
fig. 5 is a functional block diagram of a first embodiment of the import-export big data analysis and decision method apparatus according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the apparatus may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may comprise a Display screen (Display), an input unit such as keys, and the optional user interface 1003 may also comprise a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration of the import-export big data analysis and decision-making method apparatus illustrated in FIG. 1 does not constitute a limitation of the import-export big data analysis and decision-making method apparatus, and may include more or fewer components than those illustrated, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and an analysis and decision method program for importing and exporting big data.
In the analysis and decision method device for import and export big data shown in fig. 1, the network interface 1004 is mainly used for connecting the server and communicating data with the server; the user interface 1003 is mainly used for connecting a user terminal and performing data communication with the terminal; the device for analyzing and deciding import and export big data calls the program for analyzing and deciding import and export big data stored in the memory 1005 through the processor 1001, and executes the method for analyzing and deciding import and export big data provided by the embodiment of the invention.
Based on the hardware structure, the embodiment of the method for analyzing and deciding import and export big data is provided.
Referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the method for analyzing and deciding import/export big data according to the present invention.
In a first embodiment, the method for analyzing and deciding import and export big data comprises the following steps:
and step S10, acquiring the code information of the import and export goods.
It should be noted that the execution subject of this embodiment may be an analysis and decision device for import and export big data, the analysis and decision device for import and export big data is provided with an analysis and decision method program for import and export big data, and may also be other devices that can achieve the same or similar functions.
In this embodiment, the codes of the import and export goods may be EAC codes, and may also be UPC codes, which is not limited in this embodiment.
In concrete implementation, import and export goods information can be further arranged at the cloud end, when the import and export goods information is collected, the import and export goods information is sent to the cloud server, and management of the import and export goods is achieved through the cloud server, wherein the cloud server can collect various import and export goods through the big data platform based on the big data platform, so that analysis of the import and export goods is achieved.
And step S20, performing correlation processing according to the code information of the import and export goods to obtain declaration information of the import and export goods.
It can be understood that the corresponding area code information, manufacturer identification code information, commodity item code information and check code information can be obtained according to the code information of the imported and exported goods, that is, the corresponding data information can be obtained by querying according to the manufacturer identification code information or the commodity item code information, but in order to obtain the corresponding data information according to the agent information query, interaction with the query device corresponding to the code information is required, so that the corresponding data information can be queried according to the code information, and the accuracy of the data is improved.
In the concrete implementation, the inquiry equipment which needs to be inquired can be determined according to the code information, handshake is carried out with the inquiry equipment, so as to interact with the inquiry equipment, when the inquiry equipment interacts with the inquiry equipment, the related inquiry information on the inquiry equipment can be obtained, the coded information of the import and export goods and the inquiry information are subjected to data integration, so as to carry out correlation processing on the coded information of the import and export goods, so as to obtain the declaration information of the import and export goods, namely the declaration information of the import and export goods is all goods information related to the import and export goods inquired through the coded information of the import and export goods, for example, the vendor identification code information can be obtained according to the coded information of the import and export goods, the vendor identification code information can be communicated with a vendor database, so that other information related to the current import and export goods, which can be obtained from the vendor database, except the coded information of the, so as to expand cargo information and realize data integrity.
And step S30, extracting preset risk information in the declaration information of the import and export goods.
In this embodiment, the preset risk information may include manufacturer information of import and export goods, domestic importer information, reporting agent information, compliance document information, logistics transportation information, foreign exchange information, and may further include other information, which is not limited in this embodiment, the manufacturer information, the domestic importer information, the reporting agent information, the compliance document information, the logistics transportation information, and the foreign exchange information in the import and export goods are obtained by extracting keywords from the reporting information of the import and export goods, and the manufacturer information, the domestic importer information, the reporting agent information, the compliance document information, the logistics transportation information, and the foreign exchange information are analyzed, so as to determine the preset risk information, thereby implementing security analysis of the import and export goods.
In a specific implementation, for example, when manufacturer information of import and export goods is acquired, risk assessment of the manufacturer information can be obtained through scale, integrity and user evaluation of the manufacturer, when compliance file information of the import and export goods is acquired, risk assessment can be performed through information such as provision information of the compliance file information and whether relevant regulatory policy constraints exist, and when domestic importer information is acquired, risk assessment can be performed through whether distrusted behavior or acceptance exists, whether management capability has serious problems, whether import is not included in an assessment range due to annual batch import or operation scale failure or whether first import and credit conditions are excellent, whether product acceptance and management capability are strong, and the like, so that more accurate risk assessment is achieved through detailed analysis of declaration.
And step S40, determining risk grade information according to the preset risk information, and determining weight information according to declaration information of the import and export goods.
It should be noted that the risk level information may be divided into three levels, for example, a risk level 1 level, a risk level 2 level, and a risk level 3 level, and further, a more detailed division may be performed, for example, four levels and five levels, which is not limited in this embodiment.
It can be understood that, since the extent of the influence of the risk information existing in the declaration information of each import and export goods is different, in order to ensure the accuracy of the analysis, the analysis can be performed according to the declaration information of the import and export goods, the corresponding weight information can be determined, and the risk prediction can be performed according to the risk level information and the weight information, for example, the weight of the foreign exchange risk is a%, the weight of the contract risk is B%, the credit risk is C%, and the like, so that the comprehensive analysis can be performed by introducing the weight corresponding to the risk information and the risk level information, and the accuracy of the risk prediction can be ensured.
And step S50, predicting the risk grade information and the weight information through a risk prediction model to obtain a risk prediction result, and managing and controlling the goods according to the risk prediction result.
In this embodiment, the risk prediction model may be obtained by training through a deep learning model, for example, training based on a convolutional neural network, or training through a model in another form, which is not limited in this embodiment.
In specific implementation, sample data can be acquired, wherein the sample data comprises a defined risk level, weight information and a risk result, a risk prediction model is obtained by training the defined risk level, weight information and risk result, and risk prediction is performed through the risk prediction model.
In the embodiment, the code information of the goods in and out is obtained; performing correlation processing according to the encoding information of the import and export goods to obtain declaration information of the import and export goods; extracting preset risk information in declaration information of the imported and exported goods; determining risk grade information according to the preset risk information, and determining weight information according to declaration information of the imported and exported goods; and predicting the risk grade information and the weight information through a risk prediction model to obtain a risk prediction result, and managing and controlling the goods according to the risk prediction result, so that detailed analysis is performed on declaration information of the imported and exported goods to obtain corresponding risk grade information and weight, more effective data analysis and decision are realized, and the information of the imported and exported goods is effectively managed and controlled.
In an embodiment, as shown in fig. 3, a second embodiment of the method for analyzing and deciding import/export big data according to the present invention is proposed based on the first embodiment, and the step S20 includes:
step S201, analyzing the code information of the import and export goods to obtain the region code information, the manufacturer identification code information, the commodity item code information, and the check code information of the import and export goods.
In this embodiment, the preset encoding information in the encoding information of the import and export goods may be obtained by comparing the encoding information of the import and export goods with the preset keyword, and the code mapping table is queried according to the preset encoding information to obtain the corresponding area code information, manufacturer identification code information, commodity item code information, and check code information of the import and export goods, where the area code information may be country code information or may also be area code information, which is not limited in this embodiment.
And step S202, obtaining corresponding processing terminal information according to the region code information, the manufacturer identification code information, the commodity item code information and the check code information of the imported and exported goods.
In a specific implementation, the processing terminal information may be a server for managing data, such as a manufacturer server corresponding to manufacturer information, a server of a product manufacturer corresponding to product item code information, or other forms of processing terminal information, and a corresponding data storage terminal is obtained according to the area code information of the imported and exported goods, the manufacturer identification code information, the product item code information, and the check code information, so that other data related to the current imported and exported goods data can be obtained through the data storage terminal, thereby implementing data expansion.
Step S203, associating with a processing terminal to obtain the goods information related to the import and export goods in the processing terminal.
In this embodiment, for example, the corresponding vendor server B is obtained according to the vendor identification code information a of the import and export goods I, and the other information C related to the import and export goods I is obtained according to the vendor server B, thereby implementing data expansion.
In order to obtain corresponding processing terminal information according to the area code information, the manufacturer identification code information, the commodity item code information, and the check code information of the imported and exported goods, the server tag table may be queried according to the code information, the corresponding server tag information may be obtained through the server tag table, and the corresponding processing terminal may be located through the server tag information.
And step S204, obtaining declaration information of the import and export cargos according to the cargo information related to the import and export cargos.
In one embodiment, the step S30 includes:
in order to identify risk information, preset keyword information is acquired; performing parameter feature identification on declaration information of the imported and exported goods according to the preset keyword information to obtain manufacturer information, domestic importer information, declaration agent information, compliance file information and logistics transportation information in the declaration information; and taking manufacturer information, domestic importer information, reporting agent information, compliance file information and logistics transportation information in the reporting information as preset risk information.
In this embodiment, the preset keyword information may be "manufacturer", "importer", "file", and "transportation", so as to identify the risk information through the preset keyword information, and may further include other forms of keyword information, which is not limited in this embodiment.
In specific implementation, the manufacturer risk level information is determined according to import scale information obtained from the manufacturer information; acquiring management capacity information in the domestic import business information, and determining domestic import business grade information according to the management capacity information; acquiring agent condition information in the reporting agent information, and determining reporting agent grade information according to the agent condition information; acquiring regulation constraint information in the compliance file information, and determining compliance file grade information according to the regulation constraint information; acquiring accident investigation information in the logistics transportation information, and determining logistics transportation grade information according to the accident investigation information; and determining risk grade information according to the manufacturer risk grade information, the domestic importer grade information, the declaration agent grade information, the conformity file grade information and the logistics transportation grade information.
In one embodiment, determining the weight information according to the declaration information of the import and export goods comprises:
determining the types of the imported and exported cargos according to the declaration information of the imported and exported cargos; determining regional risk information, foreign exchange risk information, contract risk information and credit risk information according to the types of the imported and exported goods; taking the regional risk information, the foreign exchange risk information, the contract risk information and the credit card risk information as calibration indexes; calibrating according to the preset importance level according to the calibration index to obtain a scoring result; comparing every two according to the average value of the grading result to obtain an index discrimination matrix; and determining weight information according to the index discrimination matrix.
In a particular implementation, regional risk information, foreign exchange risk information, contract risk information, and letter of credit risk information are represented by B1, B2, B3, and B4, respectively. Obtaining a discriminant matrix, such as the schematic diagram of the discriminant matrix shown in FIG. 4, and obtaining the product M of each row of elements according to the matrix of each element in the discriminant matrixi：
Wherein, bijThe matrix representing the current element, n representing an elementAnd (4) the number.
According to MiObtained MiRoot of Szechwan Chinesemedicinal
To pair
Normalized to obtain wi：
I.e. wiIs the corresponding weight information.
In an embodiment, before determining the weight information according to the index discrimination matrix, the method further includes:
obtaining the calibration quantity according to the calibration index; obtaining a maximum characteristic value of the matrix according to the index discrimination matrix and the calibration quantity; obtaining a matrix consistency index according to the maximum characteristic value of the matrix and the calibration quantity; obtaining an average consistency index, and obtaining a relative consistency index according to the average consistency index and the matrix consistency index; comparing the relative consistency index with a preset threshold value; and when the relative consistency index is less than or equal to a preset threshold value, executing a step of determining weight information according to the index discrimination matrix.
In specific implementation, the matrix consistency index is CI, the average consistency index RI, and the average consistency index CR;
wherein λ ismaxRepresenting the maximum characteristic value of the matrix, wherein n represents the calibration quantity;
where BW denotes a discrimination matrix.
The relative consistency index is obtained through the above formula (i), formula (ii), and formula (iii), wherein the preset threshold may be 0.1, and other parameters may also be used.
In this embodiment, the calibration number is obtained according to the calibration index; obtaining a maximum characteristic value of the matrix according to the index discrimination matrix and the calibration quantity; obtaining a matrix consistency index according to the maximum characteristic value of the matrix and the calibration quantity; obtaining an average consistency index, and obtaining a relative consistency index according to the average consistency index and the matrix consistency index; and comparing the relative consistency index with a preset threshold value so as to verify the correctness of the weight information.
In one embodiment, the step S50 includes:
and acquiring risk information of a supervision relation party, product risk information and requirement information of a supervision party. And establishing a first risk prediction model between the first risk prediction model and the batch risk parameters according to the risk information of the supervision relation party, the product risk information and the requirement information of the supervision party.
In this embodiment, the first risk prediction model is:
R(w)＝∫L(y,f(x,ω))dF(x,y)；
where F (x, y) represents two probabilities, F (x, ω) represents a set of functions, and R (ω) represents F (x, ω) to find the expected risk for an optimal function F (x, ω) prediction.
And establishing a second risk prediction model between the risk of the monitoring relation party according to the manufacturer risk level information, the risk level information of the importer, the risk level information of the agent, the risk level information of the compliance file and the risk level information of the transportation logistics in the risk level information. And establishing a third risk prediction model between the third risk prediction model and the batch risk parameters according to preset/early warning type control parameters in the risk grade information, whether the product is imported for the first time within a certain time period, the product category risk parameters, the basic sampling rate and the corresponding weight information. And performing risk prediction through the first risk prediction model, the second risk prediction model and the third risk prediction model to obtain a risk prediction result, wherein the second risk prediction model and the third risk prediction model are different in input parameters, and the adopted models are the same.
In this embodiment, risk prediction is performed through the first risk prediction model, the second risk prediction model and the third risk prediction model to obtain a risk prediction result, so that prediction is performed by combining a plurality of models, and the prediction accuracy is improved.
The invention further provides a device for analyzing and deciding import and export big data.
Referring to fig. 5, fig. 5 is a functional block diagram of an analysis and decision device for import/export big data according to a first embodiment of the present invention.
In a first embodiment of the apparatus for analyzing and deciding import/export big data according to the present invention, the apparatus for analyzing and deciding import/export big data includes:
the acquiring module 10 is used for acquiring the coding information of the goods at the inlet and the outlet.
In this embodiment, the codes of the import and export goods may be EAC codes, and may also be UPC codes, which is not limited in this embodiment.
In concrete implementation, import and export goods information can be further arranged at the cloud end, when the import and export goods information is collected, the import and export goods information is sent to the cloud server, and management of the import and export goods is achieved through the cloud server, wherein the cloud server can collect various import and export goods through the big data platform based on the big data platform, so that analysis of the import and export goods is achieved.
And the association module 20 is configured to perform association processing according to the encoded information of the import and export goods to obtain declaration information of the import and export goods.
It can be understood that the corresponding area code information, manufacturer identification code information, commodity item code information and check code information can be obtained according to the code information of the imported and exported goods, that is, the corresponding data information can be obtained by querying according to the manufacturer identification code information or the commodity item code information, but in order to obtain the corresponding data information according to the agent information query, interaction with the query device corresponding to the code information is required, so that the corresponding data information can be queried according to the code information, and the accuracy of the data is improved.
In the concrete implementation, the inquiry equipment which needs to be inquired can be determined according to the code information, handshake is carried out with the inquiry equipment, so as to interact with the inquiry equipment, when the inquiry equipment interacts with the inquiry equipment, the related inquiry information on the inquiry equipment can be obtained, the coded information of the import and export goods and the inquiry information are subjected to data integration, so as to carry out correlation processing on the coded information of the import and export goods, so as to obtain the declaration information of the import and export goods, namely the declaration information of the import and export goods is all goods information related to the import and export goods inquired through the coded information of the import and export goods, for example, the vendor identification code information can be obtained according to the coded information of the import and export goods, the vendor identification code information can be communicated with a vendor database, so that other information related to the current import and export goods, which can be obtained from the vendor database, except the coded information of the, so as to expand cargo information and realize data integrity.
And the extracting module 30 is configured to extract preset risk information in the declaration information of the import and export goods.
In this embodiment, the preset risk information may include manufacturer information of import and export goods, domestic importer information, reporting agent information, compliance document information, logistics transportation information, foreign exchange information, and may further include other information, which is not limited in this embodiment, the manufacturer information, the domestic importer information, the reporting agent information, the compliance document information, the logistics transportation information, and the foreign exchange information in the import and export goods are obtained by extracting keywords from the reporting information of the import and export goods, and the manufacturer information, the domestic importer information, the reporting agent information, the compliance document information, the logistics transportation information, and the foreign exchange information are analyzed, so as to determine the preset risk information, thereby implementing security analysis of the import and export goods.
In a specific implementation, for example, when manufacturer information of import and export goods is acquired, risk assessment of the manufacturer information can be obtained through scale, integrity and user evaluation of the manufacturer, when compliance file information of the import and export goods is acquired, risk assessment can be performed through information such as provision information of the compliance file information and whether relevant regulatory policy constraints exist, and when domestic importer information is acquired, risk assessment can be performed through whether distrusted behavior or acceptance exists, whether management capability has serious problems, whether import is not included in an assessment range due to annual batch import or operation scale failure or whether first import and credit conditions are excellent, whether product acceptance and management capability are strong, and the like, so that more accurate risk assessment is achieved through detailed analysis of declaration.
The obtaining module 10 is further configured to determine risk level information according to the preset risk information, and determine weight information according to declaration information of the import and export goods.
It should be noted that the risk level information may be divided into three levels, for example, a risk level 1 level, a risk level 2 level, and a risk level 3 level, and further, a more detailed division may be performed, for example, four levels and five levels, which is not limited in this embodiment.
It can be understood that, since the extent of the influence of the risk information existing in the declaration information of each import and export goods is different, in order to ensure the accuracy of the analysis, the analysis can be performed according to the declaration information of the import and export goods, the corresponding weight information can be determined, and the risk prediction can be performed according to the risk level information and the weight information, for example, the weight of the foreign exchange risk is a%, the weight of the contract risk is B%, the credit risk is C%, and the like, so that the comprehensive analysis can be performed by introducing the weight corresponding to the risk information and the risk level information, and the accuracy of the risk prediction can be ensured.
And the prediction module 40 is configured to predict the risk level information and the weight information through a risk prediction model to obtain a risk prediction result, and perform cargo control according to the risk prediction result.
In this embodiment, the risk prediction model may be obtained by training through a deep learning model, for example, training based on a convolutional neural network, or training through a model in another form, which is not limited in this embodiment.
In specific implementation, sample data can be acquired, wherein the sample data comprises a defined risk level, weight information and a risk result, a risk prediction model is obtained by training the defined risk level, weight information and risk result, and risk prediction is performed through the risk prediction model.
In the embodiment, the code information of the goods in and out is obtained; performing correlation processing according to the encoding information of the import and export goods to obtain declaration information of the import and export goods; extracting preset risk information in declaration information of the imported and exported goods; determining risk grade information according to the preset risk information, and determining weight information according to declaration information of the imported and exported goods; and predicting the risk grade information and the weight information through a risk prediction model to obtain a risk prediction result, and managing and controlling the goods according to the risk prediction result, so that detailed analysis is performed on declaration information of the imported and exported goods to obtain corresponding risk grade information and weight, more effective data analysis and decision are realized, and the information of the imported and exported goods is effectively managed and controlled.
In an embodiment, the association module 20 is further configured to analyze the code information of the import and export goods to obtain area code information, manufacturer identification code information, commodity item code information, and check code information of the import and export goods;
obtaining corresponding processing terminal information according to the region code information, the manufacturer identification code information, the commodity item code information and the check code information of the imported and exported goods;
associating with a processing terminal to obtain goods information related to the import and export goods in the processing terminal;
and obtaining declaration information of the import and export cargos according to the cargo information related to the import and export cargos.
In an embodiment, the association module 30 is further configured to extract preset risk information in the declaration information of the import and export goods, including:
acquiring preset keyword information;
performing parameter feature identification on declaration information of the imported and exported goods according to the preset keyword information to obtain manufacturer information, domestic importer information, declaration agent information, compliance file information and logistics transportation information in the declaration information;
and taking manufacturer information, domestic importer information, reporting agent information, compliance file information and logistics transportation information in the reporting information as preset risk information.
In an embodiment, the obtaining module 10 is further configured to obtain import scale information in the manufacturer information, and determine manufacturer risk level information according to the import scale information;
acquiring management capacity information in the domestic import business information, and determining domestic import business grade information according to the management capacity information;
acquiring agent condition information in the reporting agent information, and determining reporting agent grade information according to the agent condition information;
acquiring regulation constraint information in the compliance file information, and determining compliance file grade information according to the regulation constraint information;
acquiring accident investigation information in the logistics transportation information, and determining logistics transportation grade information according to the accident investigation information;
and determining risk grade information according to the manufacturer risk grade information, the domestic importer grade information, the declaration agent grade information, the conformity file grade information and the logistics transportation grade information.
In an embodiment, the obtaining module 10 is further configured to determine the type of import/export goods according to the declaration information of the import/export goods;
determining regional risk information, foreign exchange risk information, contract risk information and credit risk information according to the types of the imported and exported goods;
taking the regional risk information, the foreign exchange risk information, the contract risk information and the credit card risk information as calibration indexes;
calibrating according to the preset importance level according to the calibration index to obtain a scoring result;
comparing every two according to the average value of the grading result to obtain an index discrimination matrix;
and determining weight information according to the index discrimination matrix.
In an embodiment, the import-export big data analysis and decision device further includes: the comparison module is used for obtaining the calibration quantity according to the calibration index;
obtaining a maximum characteristic value of the matrix according to the index discrimination matrix and the calibration quantity;
obtaining a matrix consistency index according to the maximum characteristic value of the matrix and the calibration quantity;
obtaining an average consistency index, and obtaining a relative consistency index according to the average consistency index and the matrix consistency index;
comparing the relative consistency index with a preset threshold value;
and when the relative consistency index is less than or equal to a preset threshold value, executing a step of determining weight information according to the index discrimination matrix.
In an embodiment, the prediction module is further configured to obtain risk information of a regulatory relation party, risk information of a product, and requirement information of a regulatory party;
establishing a first risk prediction model between the first risk prediction model and batch risk parameters according to the risk information of the supervision relation party, the product risk information and the requirement information of the supervision party;
establishing a second risk prediction model between the risk of a monitoring relation party according to manufacturer risk grade information, importer risk grade information, agent risk grade information, conformity file risk grade information and transportation logistics risk grade information in the risk grade information;
establishing a third risk prediction model between the third risk prediction model and the batch risk parameters according to preset/early warning type control parameters in the risk grade information, whether the product is imported for the first time within a certain time period, the product category risk parameters, the basic sampling rate and the corresponding weight information;
and performing risk prediction through the first risk prediction model, the second risk prediction model and the third risk prediction model to obtain a risk prediction result.
In addition, in order to achieve the above object, the present invention further provides an import and export big data analysis and decision device, including: the system comprises a memory, a processor and an analysis and decision program of import and export big data stored on the memory and capable of running on the processor, wherein the analysis and decision program of import and export big data is configured to realize the steps of the analysis and decision method of import and export big data.
In addition, an embodiment of the present invention further provides a storage medium, where an analysis and decision program for import and export big data is stored on the storage medium, and when the analysis and decision program for import and export big data is executed by a processor, the steps of the analysis and decision method for import and export big data as described above are implemented.
Since the storage medium adopts all technical solutions of all the embodiments, at least all the beneficial effects brought by the technical solutions of the embodiments are achieved, and no further description is given here.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system 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 system. 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 system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be substantially or partially embodied in the form of a software product, which is stored in a computer-readable storage medium (such as ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling an intelligent terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An analysis and decision method for import and export big data, which is characterized in that the analysis and decision method for import and export big data comprises the following steps:
acquiring coding information of goods at an inlet and an outlet;
performing correlation processing according to the encoding information of the import and export goods to obtain declaration information of the import and export goods;
extracting preset risk information in declaration information of the imported and exported goods;
determining risk grade information according to the preset risk information, and determining weight information according to declaration information of the imported and exported goods;
and predicting the risk grade information and the weight information through a risk prediction model to obtain a risk prediction result, and managing and controlling the goods according to the risk prediction result.
2. The method for analyzing and deciding import and export big data according to claim 1, wherein said correlating process according to the encoded information of import and export goods to obtain declaration information of import and export goods comprises:
analyzing the code information of the imported and exported goods to obtain region code information, manufacturer identification code information, commodity item code information and check code information of the imported and exported goods;
obtaining corresponding processing terminal information according to the region code information, the manufacturer identification code information, the commodity item code information and the check code information of the imported and exported goods;
associating with a processing terminal to obtain goods information related to the import and export goods in the processing terminal;
and obtaining declaration information of the import and export cargos according to the cargo information related to the import and export cargos.
3. The method for analyzing and deciding import/export big data according to claim 1, wherein the extracting of the preset risk information in the declaration information of the import/export goods comprises:
acquiring preset keyword information;
performing parameter feature identification on declaration information of the imported and exported goods according to the preset keyword information to obtain manufacturer information, domestic importer information, declaration agent information, compliance file information and logistics transportation information in the declaration information;
and taking manufacturer information, domestic importer information, reporting agent information, compliance file information and logistics transportation information in the reporting information as preset risk information.
4. The method for analyzing and deciding import-export big data according to claim 3, wherein the determining risk level information according to the preset risk information comprises:
acquiring import scale information in the manufacturer information, and determining manufacturer risk level information according to the import scale information;
acquiring management capacity information in the domestic import business information, and determining domestic import business grade information according to the management capacity information;
acquiring agent condition information in the reporting agent information, and determining reporting agent grade information according to the agent condition information;
acquiring regulation constraint information in the compliance file information, and determining compliance file grade information according to the regulation constraint information;
acquiring accident investigation information in the logistics transportation information, and determining logistics transportation grade information according to the accident investigation information;
and determining risk grade information according to the manufacturer risk grade information, the domestic importer grade information, the declaration agent grade information, the conformity file grade information and the logistics transportation grade information.
5. The method for analyzing and deciding import/export big data as claimed in any one of claims 1 to 4, wherein the determining weight information according to the declaration information of the import/export goods comprises:
determining the types of the imported and exported cargos according to the declaration information of the imported and exported cargos;
determining regional risk information, foreign exchange risk information, contract risk information and credit risk information according to the types of the imported and exported goods;
taking the regional risk information, the foreign exchange risk information, the contract risk information and the credit card risk information as calibration indexes;
calibrating according to the preset importance level according to the calibration index to obtain a scoring result;
comparing every two according to the average value of the grading result to obtain an index discrimination matrix;
and determining weight information according to the index discrimination matrix.
6. The method for analyzing and deciding import-export big data according to claim 5, wherein before determining the weight information according to the index decision matrix, further comprising:
obtaining the calibration quantity according to the calibration index;
obtaining a maximum characteristic value of the matrix according to the index discrimination matrix and the calibration quantity;
obtaining a matrix consistency index according to the maximum characteristic value of the matrix and the calibration quantity;
obtaining an average consistency index, and obtaining a relative consistency index according to the average consistency index and the matrix consistency index;
comparing the relative consistency index with a preset threshold value;
and when the relative consistency index is less than or equal to a preset threshold value, executing a step of determining weight information according to the index discrimination matrix.
7. The method for analyzing and deciding import and export big data according to any one of claims 1 to 4, wherein the step of performing risk prediction on the risk level information and the corresponding weight information through a risk prediction model to obtain a risk prediction result comprises:
acquiring risk information of a supervision relation party, product risk information and requirement information of a supervision party;
establishing a first risk prediction model between the first risk prediction model and batch risk parameters according to the risk information of the supervision relation party, the product risk information and the requirement information of the supervision party;
establishing a second risk prediction model between the risk of a monitoring relation party according to manufacturer risk grade information, importer risk grade information, agent risk grade information, conformity file risk grade information and transportation logistics risk grade information in the risk grade information;
establishing a third risk prediction model between the third risk prediction model and the batch risk parameters according to preset/early warning type control parameters in the risk grade information, whether the product is imported for the first time within a certain time period, the product category risk parameters, the basic sampling rate and the corresponding weight information;
and performing risk prediction through the first risk prediction model, the second risk prediction model and the third risk prediction model to obtain a risk prediction result.
8. An import-export big data analysis and decision device, comprising:
the acquisition module is used for acquiring the coding information of the goods at the inlet and the outlet;
the correlation module is used for performing correlation processing according to the coding information of the import and export goods to obtain declaration information of the import and export goods;
the extraction module is used for extracting preset risk information in the declaration information of the imported and exported goods;
the acquisition module is further used for determining risk grade information according to the preset risk information and determining weight information according to declaration information of the imported and exported goods;
and the prediction module is used for predicting the risk grade information and the weight information through a risk prediction model to obtain a risk prediction result and managing and controlling the goods according to the risk prediction result.
9. An analysis and decision method device for import and export big data, which is characterized in that the analysis and decision device for import and export big data comprises: a memory, a processor, and an analysis and decision procedure for import-export big data stored on the memory and executable on the processor, the analysis and decision procedure for import-export big data configured to implement the analysis and decision method for import-export big data according to any one of claims 1 to 7.
10. A storage medium, wherein an analysis and decision program for import and export big data is stored on the storage medium, and when the analysis and decision program for import and export big data is executed by a processor, the analysis and decision method for import and export big data according to any one of claims 1 to 7 is implemented.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110316413.0A CN112966965A (en) | 2021-03-23 | 2021-03-23 | Import and export big data analysis and decision method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110316413.0A CN112966965A (en) | 2021-03-23 | 2021-03-23 | Import and export big data analysis and decision method, device, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112966965A true CN112966965A (en) | 2021-06-15 |
Family
ID=76278386
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110316413.0A Withdrawn CN112966965A (en) | 2021-03-23 | 2021-03-23 | Import and export big data analysis and decision method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112966965A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113869562A (en) * | 2021-09-10 | 2021-12-31 | 中铁二十局集团有限公司 | Abnormal event response level determining method, device, equipment and readable storage medium |
| CN117436705A (en) * | 2023-12-11 | 2024-01-23 | 深圳市明心数智科技有限公司 | Trade risk analysis method, system and medium |
-
2021
- 2021-03-23 CN CN202110316413.0A patent/CN112966965A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113869562A (en) * | 2021-09-10 | 2021-12-31 | 中铁二十局集团有限公司 | Abnormal event response level determining method, device, equipment and readable storage medium |
| CN117436705A (en) * | 2023-12-11 | 2024-01-23 | 深圳市明心数智科技有限公司 | Trade risk analysis method, system and medium |
| CN117436705B (en) * | 2023-12-11 | 2024-04-19 | 深圳市明心数智科技有限公司 | Trade risk analysis method, system and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112383891B (en) | Equipment registration method and device based on object model automatic matching | |
| CN112966965A (en) | Import and export big data analysis and decision method, device, equipment and storage medium | |
| CN111444956B (en) | Low-load information prediction method, device, computer system and readable storage medium | |
| CN110610431A (en) | Intelligent claim settlement method and intelligent claim settlement system based on big data | |
| CN110888625B (en) | Method for controlling code quality based on demand change and project risk | |
| CN111932146A (en) | Method and device for analyzing pollution cause, computer equipment and readable storage medium | |
| CN114139931A (en) | Enterprise data evaluation method and device, computer equipment and storage medium | |
| CN114399367A (en) | Insurance product recommendation method, device, equipment and storage medium | |
| CN109947639B (en) | Automatic test method for ESB interface | |
| CN115147020B (en) | Decoration data processing method, device, equipment and storage medium | |
| CN111324594A (en) | Data fusion method, device, equipment and storage medium for grain processing industry | |
| CN115619539A (en) | Pre-loan risk evaluation method and device | |
| CN115907898A (en) | Method for recommending financial products to reinsurance client and related equipment | |
| CN109544348B (en) | Asset security screening method, device and computer readable storage medium | |
| CN114595216A (en) | Data verification method and device, storage medium and electronic equipment | |
| CN111027296A (en) | Report generation method and system based on knowledge base | |
| CN112529319A (en) | Grading method and device based on multi-dimensional features, computer equipment and storage medium | |
| CN110674129A (en) | Abnormal event processing method, system, computer equipment and storage medium | |
| CN112559000A (en) | Vehicle software updating method and device | |
| CN115906170B (en) | Security protection method and AI system applied to storage cluster | |
| CN115131138A (en) | Credit assessment method, device, equipment and medium based on enterprise financial stability | |
| CN116594833A (en) | Equipment detection and evaluation method and device, computer equipment and storage medium | |
| CN113919318A (en) | Enterprise index data processing method and device, computer equipment and storage medium | |
| CN116955071A (en) | Fault classification method, device, equipment and storage medium | |
| CN117909333A (en) | Screening method and system for realizing data based on big data combined with artificial intelligence |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20210615 |
|
| WW01 | Invention patent application withdrawn after publication |