Credit supervision method based on bulk aquatic product transaction

Abstract

The invention relates to the technical field of data analysis, in particular to a credit supervision method based on bulk aquatic product transaction, which comprises the following steps: acquiring behavior data and commodity feature abnormal data of bulk aquatic product transaction; under the condition of determining to obtain the commodity feature abnormal data, determining a supervision strategy for bulk aquatic product transaction based on an abnormal value of the commodity feature abnormal data; under the condition of determining to obtain the behavior data, determining the supervision strength on the transaction node in the supervision process based on the abnormal association degree between the behavior data of the user side and the production side and the behavior data of the supplier; under the condition of determining to obtain the behavior data, determining whether to analyze the abnormal features based on the supplier proportion; and under the condition of determining to analyze the abnormal features, determining an adjustment strategy of credit supervision of the bulk aquatic product transaction based on the influence weight coefficient of the abnormal features on credit. According to the invention, the response speed of bulk aquatic product transaction credit supervision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a credit supervision method based on bulk aquatic product transactions. Background Art

[0002] As consumers' requirements for food safety and transaction integrity are increasing, traditional supervision methods can no longer meet market demand. Bulk aquatic product transactions involve a large amount of capital flow and goods delivery, and credit issues are particularly prominent. Fraudulent behavior by illegal merchants occurs from time to time, seriously damaging the legitimate rights and interests of consumers. Therefore, establishing an effective credit supervision mechanism has become the key to maintaining market order and protecting the rights and interests of both parties to the transaction. By establishing a scientific and reasonable credit evaluation system, strengthening credit information sharing, improving the credit supervision system, and strengthening the application of credit supervision technology, a comprehensive understanding and monitoring of both parties to the transaction can be achieved. This will not only help to promptly discover and deal with potential risks and problems, but also improve transaction efficiency and promote the healthy development of the market.

[0003] Chinese patent application publication number: CN117422555A discloses an intelligent decision-making analysis system for bulk aquatic product transactions based on big data, including a database module for storing bulk aquatic product transaction data; a data processing module for establishing a mapping relationship between bulk aquatic product transaction data features by analyzing bulk aquatic product transaction data; an analysis module for detecting whether the intended transaction object has bulk aquatic product transaction data features; and a decision module for screening the intended transaction object and calculating the proportion of the bulk aquatic product transaction data features of the intended transaction object to use the intended transaction object as a standard transaction object, or performing associated data feature detection on the intended transaction object to calculate the transaction index of the intended transaction object, using the intended transaction object as a standard transaction object according to the transaction index, or calculating a correction judgment value to correct the transaction index to determine again whether to use the intended transaction object as a standard transaction object. However, the prior art has the following problems: the prior art does not analyze the data in bulk aquatic product transactions, resulting in low accuracy of credit supervision of bulk aquatic product transactions, thereby resulting in low response speed of credit supervision of bulk aquatic product transactions and inability to effectively manage the problem. Summary of the invention

[0004] To this end, the present invention provides a credit supervision method based on bulk aquatic product transactions, which is used to overcome the low accuracy of credit supervision on bulk aquatic product transactions in the prior art, resulting in low response speed of credit supervision on bulk aquatic product transactions and inability to effectively manage.

[0005] To achieve the above object, the present invention provides a credit supervision method based on bulk aquatic product transactions, comprising: Obtain behavioral data and commodity feature anomaly data from the user, supplier, and production ends of bulk aquatic product transactions; Determine a regulatory strategy for bulk aquatic product transactions based on the abnormal values ​​of the commodity characteristic abnormal data, wherein the regulatory strategy is to regulate the behavior data of the user end, the supplier end and the production end, or to regulate the behavior data of the user end, the supplier end and the production end and the behavior data of other associated ends respectively associated with the user end, the supplier end and the production end; Determine the supervision intensity of transaction nodes in the supervision process based on the abnormal correlation between the behavior data of the user end and the production end and the behavior data of the supplier, wherein the supervision intensity includes that the number of important nodes supervised is equal to the number of non-important nodes, or the number of important nodes supervised is a multiple of the number of non-important nodes; Extracting abnormal features of the behavior data, determining the proportion of suppliers with the same abnormal features in the data pool, and determining whether to analyze the abnormal features based on the proportion of suppliers; Based on the weight coefficient of the impact of abnormal characteristics on credit, an adjustment strategy for credit supervision of bulk aquatic product transactions is determined, wherein the adjustment strategy is to eliminate the abnormal characteristics, reduce the abnormal correlation threshold of the supervision intensity with a preset correlation adjustment coefficient, or adjust the preset abnormal value of the supervision strategy with a preset abnormal value adjustment coefficient.

[0006] Furthermore, under the condition of obtaining the abnormal feature data of the commodity, the supervision strategy is determined to be to supervise the behavioral data of the user side, supplier side and production side based on the comparison result that the abnormal value of the abnormal feature data of the commodity is less than or equal to the preset abnormal value.

[0007] Furthermore, under the condition of determining to obtain the abnormal data of the commodity characteristics, the supervision strategy is determined to supervise the behavior data of the user end, supplier end and production end and the behavior data of other associated ends associated with the user end, supplier end and production end based on the comparison result that the abnormal value of the abnormal data of the commodity characteristics is greater than the preset abnormal value.

[0008] Furthermore, under the condition of determining the acquisition of behavior data, the supervision intensity is determined to be equal to the number of important nodes to be supervised and the number of non-important nodes based on the comparison result that the abnormal correlation between the behavior data of the user end and the production end and the behavior data of the supplier is less than or equal to the abnormal correlation threshold.

[0009] Furthermore, under the condition of determining the acquisition of behavioral data, the supervision intensity is determined to be a multiple of the number of non-important nodes based on the comparison result that the abnormal correlation between the behavioral data of the user end and the production end and the behavioral data of the supplier is greater than the abnormal correlation threshold.

[0010] Furthermore, under the condition of obtaining the behavior data, abnormal features of the behavior data are extracted, the proportion of suppliers with the same abnormal features in the data pool is determined, and based on the comparison result that the proportion of suppliers is greater than the preset proportion of suppliers, it is determined to analyze the abnormal features.

[0011] Further, under the condition of determining to analyze the abnormal feature, it is determined to eliminate the abnormal feature based on the comparison result that the influence weight coefficient of the abnormal feature on the credit is less than the first preset influence weight coefficient.

[0012] Furthermore, under the condition of determining to analyze the abnormal characteristics, the abnormal correlation threshold for reducing the supervision intensity by using the preset correlation adjustment coefficient is determined based on the comparison result that the impact weight coefficient of the abnormal characteristics on the credit is greater than or equal to the first preset impact weight coefficient and less than the second preset impact weight coefficient.

[0013] Further, under the condition of determining to analyze the abnormal characteristics, the preset abnormal value for adjusting the regulatory strategy with the preset abnormal value adjustment coefficient is determined based on the comparison result that the impact weight coefficient of the abnormal characteristics on the credit is greater than or equal to the second preset impact weight coefficient.

[0014] Furthermore, under the condition of determining a preset abnormal value of the regulatory strategy adjusted by a preset abnormal adjustment coefficient, several adjustment methods are set for the preset abnormal value based on the preset abnormal adjustment coefficient, and the adjustment amplitude of the preset abnormal value in each adjustment method is positively correlated with the abnormal adjustment coefficient.

[0015] Compared with the prior art, the present invention accurately identifies potential logistics risks in bulk aquatic product transactions by acquiring behavioral data and abnormal commodity characteristic data of bulk aquatic product transactions, then formulates a supervision strategy based on the abnormal commodity characteristic data, and adjusts the supervision intensity according to the abnormal correlation between suppliers and upstream and downstream behavior data, thereby achieving comprehensive coverage and effective supervision of all aspects of the transaction, and then extracts abnormal characteristics of behavioral data, and decides whether to conduct in-depth analysis based on the proportion of suppliers with abnormal characteristics. Finally, the credit supervision strategy is adjusted based on the impact of abnormal characteristics on credit. By adjusting the supervision intensity and strategy, the accuracy of supervision is improved, thereby improving the response speed of credit supervision of bulk aquatic product transactions.

[0016] Furthermore, the present invention adjusts the supervision strategy for bulk aquatic product transactions by comparing the abnormal values ​​of commodity characteristic abnormal data with preset abnormal values, thereby realizing the precision and dynamism of the supervision strategy, being able to adjust the supervision focus according to different situations, improving the effectiveness and pertinence of supervision, and thus improving the efficiency of credit supervision for bulk aquatic product transactions.

[0017] Furthermore, the present invention adjusts the supervision intensity of transaction nodes through the abnormal correlation between the behavior data of the user end and the production end and the behavior data of the supplier, and can adjust the supervision resource allocation according to the correlation of the behavior data, thereby improving the accuracy and efficiency of supervision. By setting different ratios of the number of important nodes, the security and stability of the transaction process are ensured.

[0018] Furthermore, the present invention extracts abnormal features and calculates the proportion of suppliers with the same abnormal features, and decides whether to conduct in-depth analysis based on the proportion. If the proportion is high, the impact of the abnormal features on credit is analyzed, and the credit supervision strategy is adjusted according to the impact weight coefficient. The abnormal features are screened and analyzed, thereby improving the pertinence and accuracy of credit supervision, thereby improving the efficiency of credit supervision of bulk aquatic product transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of a credit supervision method based on bulk aquatic product transactions according to an embodiment of the present invention; Figure 2 A flow chart of determining a regulatory strategy for bulk aquatic product transactions according to an embodiment of the present invention; Figure 3 A flow chart of determining the supervision intensity of a transaction node during the supervision process according to an embodiment of the present invention; Figure 4 The present invention is a flowchart for determining whether to analyze abnormal features. DETAILED DESCRIPTION

[0020] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0022] It should be pointed out that the data in this embodiment are obtained by comprehensive analysis and evaluation of the historical test data and the corresponding historical test results of the present invention in the three months before this test. It can be understood by those skilled in the art that the present invention can determine the above parameters for a single item by selecting the highest proportion of values ​​as the preset standard parameters according to the data distribution, using weighted summation to use the obtained values ​​as the preset standard parameters, substituting each historical data into a specific formula and using the values ​​obtained by the formula as the preset standard parameters or other selection methods, as long as the present invention can clearly define different specific situations in the single determination process through the obtained values.

[0023] See also Figure 1-Figure 4 As shown, Figure 1 It is a flow chart of a credit supervision method based on bulk aquatic product transactions according to an embodiment of the present invention; Figure 2 A flow chart of determining a regulatory strategy for bulk aquatic product transactions according to an embodiment of the present invention; Figure 3 A flow chart of determining the supervision intensity of a transaction node during the supervision process according to an embodiment of the present invention; Figure 4 The present invention is a flowchart for determining whether to analyze abnormal features.

[0024] The credit supervision method based on bulk aquatic product transactions in the embodiment of the present invention includes: Step S1, obtaining behavioral data and abnormal commodity characteristic data of the user side, supplier side and production side of bulk aquatic product transactions; Step S2, determining a regulatory strategy for bulk aquatic product transactions based on the abnormal values ​​of the commodity characteristic abnormal data; Step S3, determining the supervision intensity of the transaction node during the supervision process based on the abnormal correlation between the behavior data of the user end and the production end and the behavior data of the supplier; Step S4, extracting abnormal features of the behavior data, determining the proportion of suppliers with the same abnormal features in the data pool, and determining whether to analyze the abnormal features based on the proportion of suppliers; Step S5, determining the adjustment strategy for credit supervision of bulk aquatic product transactions based on the weight coefficient of the impact of abnormal characteristics on credit.

[0025] In the embodiment of the present invention, the user end is the consumer or enterprise that ultimately purchases and uses the aquatic products, the supplier end purchases the aquatic products from the production end, and carries out processes such as processing, packaging, and transportation, and finally delivers the products to the user end, and the production end is the enterprise or individual operator that engages in production activities such as aquatic product breeding, fishing, and processing.

[0026] In the embodiment of the present invention, the behavior data and the abnormal commodity feature data are acquired in a preset collection period, and the preset collection period is 1 month. Those skilled in the art may also adjust the preset collection period according to actual needs.

[0027] In the embodiment of the present invention, the behavior data includes but is not limited to transaction frequency, transaction quantity, transaction time and / or transaction price.

[0028] In the embodiment of the present invention, the abnormal commodity characteristic data includes but is not limited to abnormal weight and / or size.

[0029] In an embodiment of the present invention, the transaction nodes are the key links or participants involved in the commodity circulation, transaction activities and information exchange in the bulk aquatic product transaction process. The important nodes include but are not limited to long-distance transportation points, large storage points and / or key sales points, and non-important nodes are short-distance transportation points, small storage points and / or non-key sales points.

[0030] In the embodiment of the present invention, the data pool stores massive transaction data from the user side, supplier side, production side and various transaction nodes.

[0031] In an embodiment of the present invention, the abnormal feature is a feature whose characteristic value is greater than 2 times the average value of several numerical values ​​in the behavior data or less than 0.5 times the average value of several numerical values ​​in the behavior data, wherein the abnormal feature includes but is not limited to abnormal transaction frequency, abnormal transaction quantity, abnormal transaction time and / or abnormal transaction price. For example, the characteristic value of abnormal transaction frequency is greater than 2 times the ratio of the total transaction frequency value to the transaction frequency quantity or less than 0.5 times the ratio of the total transaction frequency value to the transaction frequency quantity.

[0032] Specifically, the present invention accurately identifies potential logistics risks in bulk aquatic product transactions by acquiring behavioral data and abnormal commodity characteristic data of bulk aquatic product transactions, and then formulates a supervision strategy based on the abnormal commodity characteristic data, and adjusts the supervision intensity according to the abnormal correlation between suppliers and upstream and downstream behavior data, thereby achieving comprehensive coverage and effective supervision of all aspects of the transaction. The abnormal characteristics of the behavioral data are then extracted, and a decision is made whether to conduct in-depth analysis based on the proportion of suppliers with abnormal characteristics. Finally, the credit supervision strategy is adjusted based on the impact of abnormal characteristics on credit. By adjusting the supervision intensity and strategy, the accuracy of supervision is improved, thereby improving the response speed of credit supervision of bulk aquatic product transactions.

[0033] Specifically, under the condition of obtaining the abnormal data of the commodity characteristics, the embodiment of the present invention determines the supervision strategy of bulk aquatic product transactions according to the comparison result of the abnormal value of the abnormal data of the commodity characteristics and the preset abnormal value 0.9; When the abnormal value is less than or equal to the preset abnormal value, the supervision strategy is determined to be to supervise the behavior data at the user end, the supplier end and the production end; When the abnormal value is greater than the preset abnormal value, the supervision strategy is determined to supervise the behavior data of the user end, the supplier end and the production end and the behavior data of other associated ends associated with the user end, the supplier end and the production end respectively.

[0034] In the embodiment of the present invention, the preset abnormal value is 0.9, and the preset abnormal value is obtained by taking the average value of the abnormal values ​​of several historical abnormal data of commodity characteristics, but the above value is not limited to this, and technical personnel in this field can also adjust the value according to actual needs.

[0035] In the embodiment of the present invention, the other associated ends are other user ends, other supplier ends and other production ends associated with the user end, the supplier end and the production end.

[0036] Specifically, the abnormal value is the product of the ratio of the maximum weight data to the average weight multiplied by the ratio of the minimum weight data to the average weight data multiplied by the ratio of the maximum size data to the average size data and the ratio of the minimum size data to the average size data.

[0037] Specifically, the present invention adjusts the supervision strategy for bulk aquatic product transactions by comparing the abnormal values ​​of commodity characteristic abnormal data with preset abnormal values, thereby realizing the precision and dynamic nature of the supervision strategy, being able to adjust the focus of supervision according to different situations, improving the effectiveness and pertinence of supervision, and thus improving the efficiency of credit supervision for bulk aquatic product transactions.

[0038] Specifically, under the condition of determining the acquisition of behavior data, the embodiment of the present invention determines the supervision intensity of the transaction node in the supervision process according to the comparison result of the abnormal correlation between the behavior data of the user end and the production end and the behavior data of the supplier and the abnormal correlation threshold value of 0.89; When the abnormal correlation is less than or equal to the abnormal correlation threshold, the supervision intensity is determined to be that the number of important nodes supervised is equal to the number of non-important nodes, wherein the value range of the important nodes is 10-16, preferably 12, and the ratio of the number of important nodes to the number of non-important nodes is 1:1; When the abnormal correlation degree is greater than the abnormal correlation degree threshold, the supervision intensity is determined as the number of important nodes supervised is a multiple of the number of non-important nodes, wherein the value range of important nodes is 16-22, preferably 18, and the ratio of the number of important nodes to the number of non-important nodes is in the range of 2 times to 6 times, preferably 4 times.

[0039] In the embodiment of the present invention, the abnormal correlation threshold value is 0.89, but the above value is not limited thereto, and those skilled in the art may also adjust the value according to actual needs.

[0040] Specifically, the abnormal correlation degree is the ratio of the behavior data of the production end to the behavior data of the supplier multiplied by the ratio of the behavior data of the user end to the behavior data of the supplier.

[0041] Specifically, the present invention adjusts the supervision intensity of transaction nodes through the abnormal correlation between the behavior data of the user end and the production end and the behavior data of the supplier, and can adjust the supervision resource allocation according to the correlation of the behavior data, thereby improving the accuracy and efficiency of supervision. By setting different ratios of the number of important nodes, the security and stability of the transaction process are ensured.

[0042] Specifically, under the condition of determining to obtain the behavior data, the embodiment of the present invention extracts abnormal features of the behavior data, determines the proportion of suppliers with the same abnormal features in the data pool, and determines whether to analyze the abnormal features according to the comparison result of the supplier proportion with the preset supplier proportion of 80%; When the supplier ratio is less than or equal to the preset supplier ratio, it is determined that the abnormal feature will not be analyzed; When the supplier proportion is greater than the preset supplier proportion, it is determined to analyze the abnormal characteristics.

[0043] In the embodiment of the present invention, the preset supplier ratio is 80%, which is obtained by taking the average value of several supplier ratios that have been analyzed for abnormal characteristics in history, but the above value is not limited to this, and technical personnel in this field can also adjust the value according to actual needs.

[0044] Specifically, the supplier ratio is the ratio of suppliers with the same abnormal characteristics in the data pool to all suppliers in the data pool.

[0045] Specifically, under the condition of determining to analyze the abnormal characteristics, the embodiment of the present invention determines the adjustment strategy of the credit supervision of bulk aquatic product transactions according to the comparison result of the influence weight coefficient of the abnormal characteristics on the credit and the preset influence weight coefficient; When the influence weight coefficient is less than the first preset influence weight coefficient of 0.75, it is determined to remove the abnormal feature; When the influence weight coefficient is greater than or equal to the first preset influence weight coefficient and less than the second preset influence weight coefficient 0.85, it is determined to increase or decrease the abnormal correlation threshold of the supervision intensity to the corresponding value by the preset correlation adjustment coefficient 0.88; When the influence weight coefficient is greater than or equal to the second preset influence weight coefficient, it is determined that the preset abnormal value of the supervision strategy is adjusted by the preset abnormal value adjustment coefficient of 0.92.

[0046] In the embodiment of the present invention, the first preset influence weight coefficient is set to 0.75, and the second preset influence weight coefficient is set to 0.85, but the above values ​​are not limited thereto, and those skilled in the art may also adjust the values ​​according to actual needs.

[0047] In the embodiment of the present invention, the reduced abnormal correlation threshold is the product of the abnormal correlation threshold and a preset correlation adjustment coefficient, and the preset ratio adjustment coefficient is 0.88.

[0048] Specifically, the influence weight coefficient is the ratio of the behavior data of the abnormal characteristics to the average behavior data.

[0049] Specifically, under the condition of determining that a preset abnormal value of the regulatory strategy is reduced by a preset abnormal adjustment coefficient, an embodiment of the present invention sets several adjustment methods for the preset abnormal value based on the preset abnormal adjustment coefficient, and the adjustment amplitude of the preset abnormal value for each adjustment method is positively correlated with the abnormal adjustment coefficient.

[0050] Specifically, the present invention extracts abnormal features and calculates the proportion of suppliers with the same abnormal features, and decides whether to conduct in-depth analysis based on the proportion. If the proportion is high, the impact of the abnormal features on credit is analyzed, and the credit supervision strategy is adjusted according to the impact weight coefficient. The abnormal features are screened and analyzed, which improves the pertinence and accuracy of credit supervision, thereby improving the efficiency of credit supervision of bulk aquatic product transactions.

[0051] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0052] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A credit supervision method based on bulk aquatic product transactions, characterized in that: include: Obtain behavioral data and commodity feature anomaly data from the user, supplier, and production ends of bulk aquatic product transactions; Determine a regulatory strategy for bulk aquatic product transactions based on the abnormal values ​​of the commodity characteristic abnormal data, wherein the regulatory strategy is to regulate the behavior data of the user end, the supplier end and the production end, or to regulate the behavior data of the user end, the supplier end and the production end and the behavior data of other associated ends respectively associated with the user end, the supplier end and the production end; Determine the supervision intensity of transaction nodes in the supervision process based on the abnormal correlation between the behavior data of the user end and the production end and the behavior data of the supplier, wherein the supervision intensity includes that the number of important nodes supervised is equal to the number of non-important nodes, or the number of important nodes supervised is a multiple of the number of non-important nodes; Extracting abnormal features of the behavior data, determining the proportion of suppliers with the same abnormal features in the data pool, and determining whether to analyze the abnormal features based on the proportion of suppliers; Based on the weight coefficient of the impact of abnormal characteristics on credit, an adjustment strategy for credit supervision of bulk aquatic product transactions is determined, wherein the adjustment strategy is to eliminate the abnormal characteristics, reduce the abnormal correlation threshold of the supervision intensity with a preset correlation adjustment coefficient, or adjust the preset abnormal value of the supervision strategy with a preset abnormal value adjustment coefficient.

2. The credit supervision method based on bulk aquatic product transactions according to claim 1 is characterized in that: Under the condition of obtaining the abnormal feature data of the commodity, a supervision strategy is determined to be supervising the behavior data of the user side, the supplier side and the production side based on the comparison result that the abnormal value of the abnormal feature data of the commodity is less than or equal to the preset abnormal value.

3. The credit supervision method based on bulk aquatic product transactions according to claim 1 is characterized in that: Under the condition of determining to obtain the abnormal data of the commodity characteristics, the supervision strategy is determined to supervise the behavior data of the user side, supplier side and production side and the behavior data of other associated sides associated with the user side, supplier side and production side based on the comparison result that the abnormal value of the abnormal data of the commodity characteristics is greater than the preset abnormal value.

4. The credit supervision method based on bulk aquatic product transactions according to claim 1 is characterized in that: Under the condition of determining the acquisition of behavior data, the supervision intensity is determined to be equal to the number of important nodes to be supervised and the number of non-important nodes based on the comparison result that the abnormal correlation between the behavior data of the user end and the production end and the behavior data of the supplier is less than or equal to the abnormal correlation threshold.

5. The credit supervision method based on bulk aquatic product transactions according to claim 1 is characterized in that: Under the condition of determining the acquisition of behavior data, the supervision intensity is determined to be a multiple of the number of non-important nodes based on the comparison result that the abnormal correlation between the behavior data of the user end and the production end and the behavior data of the supplier is greater than the abnormal correlation threshold.

6. The credit supervision method based on bulk aquatic product transactions according to claim 1 is characterized in that: Under the condition of obtaining the behavior data, the abnormal features of the behavior data are extracted, the proportion of suppliers with the same abnormal features in the data pool is determined, and the abnormal features are analyzed based on the comparison result that the proportion of suppliers is greater than the preset proportion of suppliers.

7. The credit supervision method based on bulk aquatic product transactions according to claim 6 is characterized in that: Under the condition of determining to analyze the abnormal feature, it is determined to eliminate the abnormal feature based on the comparison result that the influence weight coefficient of the abnormal feature on the credit is less than the first preset influence weight coefficient.

8. The credit supervision method based on bulk aquatic product transactions according to claim 6 is characterized in that: Under the condition of determining to analyze the abnormal characteristics, the abnormal correlation threshold for reducing the supervision intensity with the preset correlation adjustment coefficient is determined based on the comparison result that the influence weight coefficient of the abnormal characteristics on the credit is greater than or equal to the first preset influence weight coefficient and less than the second preset influence weight coefficient.

9. The credit supervision method based on bulk aquatic product transactions according to claim 6 is characterized in that: Under the condition of determining to analyze the abnormal characteristics, the preset abnormal value for adjusting the supervision strategy with the preset abnormal value adjustment coefficient is determined based on the comparison result that the influence weight coefficient of the abnormal characteristics on the credit is greater than or equal to the second preset influence weight coefficient.

10. The credit supervision method based on bulk aquatic product transactions according to claim 9 is characterized in that: Under the condition of determining a preset abnormal value of the supervision strategy adjusted by a preset abnormal adjustment coefficient, several adjustment methods are set for the preset abnormal value based on the preset abnormal adjustment coefficient, and the adjustment amplitude of the preset abnormal value by each adjustment method is positively correlated with the abnormal adjustment coefficient.

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