CN117422555A - Intelligent decision analysis system for large-volume aquatic product transaction based on big data - Google Patents
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Abstract
The invention relates to the technical field of data analysis, in particular to an intelligent decision analysis system for large-volume aquatic product transaction based on big data, which comprises a database module, a data processing module and a data processing module, wherein the database module is used for storing large-volume aquatic product transaction data; the data processing module is used for establishing a mapping relation among the transaction data characteristics of the bulk aquatic products through analyzing the transaction data of the bulk aquatic products; the analysis module is used for detecting whether the transaction data characteristics of the large-volume aquatic products appear in the object to be transacted; the decision module is used for screening the quasi-trading object and calculating the duty ratio of the characteristic of the transaction data of the bulk aquatic product of the quasi-trading object to take the quasi-trading object as a standard trading object, or carrying out associated data characteristic detection on the quasi-trading object to calculate the trading index of the quasi-trading object, taking the quasi-trading object as the standard trading object according to the trading index, or calculating a correction decision value to correct the trading index to determine whether the quasi-trading object is taken as the standard trading object again. The invention improves the decision making efficiency and the transaction efficiency.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to an intelligent decision analysis system for large-volume aquatic product transactions based on big data.
Background
Bulk goods are goods with good properties for industrial and agricultural production and consumption, which can enter the circulation field but do not belong to the retail category. The method has the characteristics of industrial basic raw materials, easy standardization, easy storage and transportation and easy price fluctuation.
Chinese patent publication No.: CN116777506a discloses a bulk goods transaction decision-making method and system based on a generation type AI service, comprising: acquiring transaction information data; preprocessing the acquired transaction information data, and extracting features for predicting market trend; constructing an artificial neural network model; training and verifying the artificial neural network model by utilizing the preprocessed transaction information data; analyzing the characteristics for predicting market trend by using an artificial neural network model; and outputting a prediction result. In this scheme, the generative artificial intelligence technique is mainly applied to the presentation of analysis results. By analyzing, predicting, and extracting the market data, the system can automatically generate market analysis reports and present them to traders in a natural, consistent language.
However, the prior art does not provide real-time market data, transaction analysis and decision support tools for the trader, and the trading efficiency of the trading participants is low.
Disclosure of Invention
Therefore, the invention provides an intelligent decision analysis system for large-volume aquatic product transactions based on big data, which is used for solving the problems that real-time market data, transaction analysis and decision support tools cannot be provided for a transactor in the prior art, and transaction efficiency of transaction participants is low.
In order to achieve the above object, the present invention provides an intelligent decision analysis system for large-volume aquatic product transactions based on big data, comprising:
the database module is used for storing transaction data of a large number of aquatic products;
the data processing module is connected with the database module and is used for establishing a mapping relation among the transaction data characteristics of the bulk aquatic products by analyzing the transaction data of the bulk aquatic products;
the analysis module is connected with the data processing module and used for detecting whether the transaction data characteristics of the large-volume aquatic products appear in the object to be transacted;
the decision module is connected with the database module, the data processing module and the analysis module and is used for screening the quasi-trading object, calculating the duty ratio of the trading data characteristics of the large aquatic product of the quasi-trading object to take the quasi-trading object as a standard trading object, or carrying out associated data characteristic detection on the quasi-trading object to calculate the trading index of the quasi-trading object, taking the quasi-trading object as the standard trading object according to the trading index, or calculating a correction decision value to correct the trading index to determine whether the quasi-trading object is taken as the standard trading object again.
Further, the mapping relation of the commodity aquatic product transaction data features comprises the commodity aquatic product transaction data features, the associated data features associated with the commodity aquatic product transaction data features and the associated probability of the commodity aquatic product transaction data features and the associated data features.
Further, the decision module takes the transaction object meeting the screening condition as a quasi-transaction object, the analysis module detects the transaction data characteristics of the quasi-transaction object, counts the number of the transaction data characteristics of the bulk aquatic product, which appear in the quasi-transaction object, so as to calculate the duty ratio of the transaction data characteristics of the bulk aquatic product, and takes the quasi-transaction object as a standard transaction object or detects the associated data characteristics of the quasi-transaction object according to the duty ratio.
Further, the decision module is preset with a first preset duty ratio and a second preset duty ratio, the first preset duty ratio is smaller than the second preset duty ratio, the decision module takes the quasi-transaction object with the duty ratio larger than the second preset duty ratio as a standard transaction object, the quasi-transaction object with the duty ratio smaller than or equal to the second preset duty ratio and larger than the first preset duty ratio is subjected to associated data feature detection, and the quasi-transaction object with the duty ratio smaller than or equal to the first preset duty ratio is repeatedly subjected to transaction data feature detection.
Further, the decision module performs associated data feature detection on the quasi-transaction object with the duty ratio smaller than or equal to a second preset duty ratio and larger than a first preset duty ratio, and if associated data features exist, extracts the transaction data features, the associated data features and corresponding associated probabilities to form an associated data set.
Further, the decision module calculates a transaction index of the object to be transacted according to the data in the associated data set, and sets: f=n×m×kb;
wherein F is a transaction index, N is the number of transaction data features of a large number of aquatic products in the associated data set, M is the number of associated data features in the associated data set, and Kb is the average value of associated probability in the associated data set.
Further, the decision module is pre-provided with a first comparison index and a second comparison index, the first comparison index is smaller than the second comparison index, and the decision module takes the transaction object as a standard transaction object or calculates a correction judgment value to correct the transaction index according to the comparison result of the transaction index and the first comparison index and the second comparison index.
Further, if the transaction index is greater than the second comparison index, the decision module takes the object to be transacted as a standard transaction object; if the trading index is smaller than or equal to the second contrast index and larger than the first contrast index, the decision module calculates a correction decision value to correct the trading index; and if the transaction index is smaller than or equal to the first contrast index, the decision module repeatedly performs transaction data feature detection on the object to be transacted.
Further, the decision module calculates a correction decision value to correct the transaction index, the decision module counts the types of the associated data features in the associated data set, calculates the correction decision value according to the following formula, and sets:
wherein e is a correction determination value, Z is the type of the associated data features in the associated data set, and M is the number of the associated data features in the associated data set.
Further, a plurality of correction coefficients are preset in the decision module, the decision module selects the corresponding correction coefficient to correct the transaction index according to the correction judgment value, and if the corrected transaction index is larger than the second comparison index, the decision module takes the transaction object as a standard transaction object; otherwise, the decision module repeatedly performs transaction data feature detection on the to-be-transacted object.
Compared with the prior art, the method has the beneficial effects that the mapping relation of the transaction data characteristics of the bulk aquatic products is established through the big data, the transaction data of the bulk aquatic products are correlated, the relation among the transaction data of the bulk aquatic products is mined, and the analysis of the big data is used for carrying out auxiliary decision analysis on the transaction of the bulk aquatic products, so that the decision efficiency and the transaction efficiency are improved.
Further, when the system of the invention makes decision analysis on the bulk aquatic product transaction, firstly, the quasi-transaction object is screened, the decision accuracy of the decision standard transaction object is improved, then the quantity of the bulk aquatic product transaction data characteristics of the quasi-transaction object is detected, if the duty ratio of the bulk aquatic product transaction data characteristics of the quasi-transaction object is larger than the second preset duty ratio, the fact that the bulk aquatic product transaction data characteristics of the quasi-transaction object are more is indicated, and at the moment, the quasi-transaction object can be directly used as the standard transaction object.
Further, the decision module carries out associated data feature detection on the quasi-trading object with the duty ratio of the transaction data feature of the bulk aquatic product of the quasi-trading object being smaller than or equal to the second preset duty ratio and larger than the first preset duty ratio, at this time, the quantity of the transaction data feature of the bulk aquatic product appearing by the quasi-trading object is insufficient to directly judge the quasi-trading object as a standard trading object, whether the quasi-trading object can be used as the standard trading object is further judged by carrying out associated data feature detection, and the quasi-trading object is judged twice by the transaction data feature of the bulk aquatic product and the associated data feature, so that the decision accuracy of the standard trading object is improved, and meanwhile, the decision efficiency and the trading efficiency are improved.
Further, the decision module calculates the transaction index of the to-be-transacted object, the transaction index is a characteristic parameter of whether the to-be-transacted object can be used as a standard transaction object, the three variables related to the to-be-transacted object are jointly participated in calculation, the three variables are the number of the transaction data features of the bulk aquatic product in the association data set, the number of the association data features in the association data set and the average value of the association probability in the association data set respectively, when the number of the transaction data features of the bulk aquatic product is larger, the number of the association data features is larger, and the association probability of the transaction data features of the bulk aquatic product and the association data features is larger, the corresponding to-be-transacted object with the transaction index larger than the second comparison index is used as the standard transaction object, whether the to-be-transacted object can be used as the standard transaction object is further judged by calculating the transaction index, the decision accuracy of the standard transaction object is improved, and meanwhile, the decision efficiency and the transaction efficiency are improved.
Further, when the decision module judges that the transaction index is smaller than or equal to the second comparison index and larger than the first comparison index, the correction judgment value is calculated to correct the transaction index, and whether the planned transaction object can be used as a standard transaction object or not is further judged by calculating the correction judgment value, so that the decision accuracy of the standard transaction object is improved, and meanwhile, the decision efficiency and the transaction efficiency are improved.
Further, the decision module calculates a correction decision value, wherein the correction decision value is a ratio of the type of the associated data features in the associated data set to the number of the associated data features in the associated data set, and is used for representing the duty ratio of the repeatedly occurring associated data features in the associated data set, when the number of the repeatedly occurring associated data features is larger, the correction decision value is smaller, when the same associated data features with the mapping relation exist in the large-area aquatic product transaction data features of the transaction object, the transaction index of the transaction object is raised, the transaction index is corrected by calculating the correction decision value, and whether the transaction object can be used as the standard transaction object is judged again by the corrected transaction index, so that the decision accuracy of the standard transaction object is further improved, and meanwhile, the decision efficiency and the transaction efficiency are improved.
Drawings
FIG. 1 is a block diagram of an intelligent decision analysis system for large-volume aquatic product transactions based on big data according to an embodiment of the invention;
FIG. 2 is a flowchart illustrating a method for determining whether to perform associated data feature monitoring on a transaction object according to an embodiment of the present invention;
FIG. 3 is a flowchart of a determination process for calculating a correction determination value for a transaction object according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating a method for determining correction factor selection when correcting a transaction index according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
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 merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1, the intelligent decision analysis system for large-volume aquatic product transaction based on big data according to the present invention includes:
the database module is used for storing transaction data of a large number of aquatic products;
the data processing module is connected with the database module and is used for establishing a mapping relation among the transaction data characteristics of the bulk aquatic products by analyzing the transaction data of the bulk aquatic products;
the analysis module is connected with the data processing module and used for detecting whether the transaction data characteristics of the large-volume aquatic products appear in the object to be transacted;
the decision module is connected with the database module, the data processing module and the analysis module and is used for screening the quasi-trading object, calculating the duty ratio of the trading data characteristics of the large aquatic product of the quasi-trading object to take the quasi-trading object as a standard trading object, or carrying out associated data characteristic detection on the quasi-trading object to calculate the trading index of the quasi-trading object, taking the quasi-trading object as the standard trading object according to the trading index, or calculating a correction decision value to correct the trading index to determine whether the quasi-trading object is taken as the standard trading object again.
In this embodiment, the transaction data of the bulk aquatic product can be obtained according to actual needs, all data related to the transaction of the bulk aquatic product can be obtained, some of the data can be obtained, and the data can be set according to actual needs.
Specifically, the mapping relation of the transaction data features of the bulk aquatic products comprises the transaction data features of the bulk aquatic products, the associated data features associated with the transaction data features of the bulk aquatic products and the associated probability of the transaction data features of the bulk aquatic products and the associated data features.
According to the invention, the mapping relation of the transaction data characteristics of the bulk aquatic products is established through the big data, the transaction data of the bulk aquatic products are correlated, the relation among the transaction data of the bulk aquatic products is mined, and the analysis of the big data is used for carrying out auxiliary decision analysis on the transaction of the bulk aquatic products, so that the decision efficiency and the transaction efficiency are improved.
Referring to fig. 2, fig. 2 is a flowchart illustrating a determination of whether to perform associated data feature monitoring on a transaction object according to an embodiment of the invention.
The decision module takes a transaction object meeting screening conditions as a quasi-transaction object, the analysis module detects transaction data characteristics of the quasi-transaction object, counts the number of the transaction data characteristics of the bulk aquatic product, which appear in the quasi-transaction object, so as to calculate the duty ratio of the transaction data characteristics of the bulk aquatic product, and takes the quasi-transaction object as a standard transaction object or detects the associated data characteristics of the quasi-transaction object according to the duty ratio.
It can be understood by those skilled in the art that a plurality of screening conditions are preset in the decision module, and the transaction object meeting the screening conditions is taken as the quasi-transaction object.
In this embodiment, the method for calculating the ratio of the transaction data features of the bulk aquatic product of the to-be-transacted object is a ratio of the number of the transaction data features of the bulk aquatic product of the to-be-transacted object to the total number of the transaction data features of the bulk aquatic product obtained by the data processing module through analysis of the transaction data of the bulk aquatic product.
Specifically, the decision module presets a first preset duty ratio and a second preset duty ratio, the first preset duty ratio is smaller than the second preset duty ratio, the decision module takes the quasi-transaction object with the duty ratio larger than the second preset duty ratio as a standard transaction object, carries out associated data feature detection on the quasi-transaction object with the duty ratio smaller than or equal to the second preset duty ratio and larger than the first preset duty ratio, and repeatedly carries out transaction data feature detection on the quasi-transaction object with the duty ratio smaller than or equal to the first preset duty ratio.
When the system of the invention makes decision analysis on the transaction of the bulk aquatic product, firstly, the quasi-transaction object is screened, the decision accuracy of the decision standard transaction object is improved, then, the quantity of the transaction data characteristics of the bulk aquatic product, which appear in the quasi-transaction object, is detected, if the duty ratio of the transaction data characteristics of the bulk aquatic product, which appear in the quasi-transaction object, is larger than the second preset duty ratio, the fact that the transaction data characteristics of the bulk aquatic product, which appear in the quasi-transaction object, are more, at the moment, the quasi-transaction object can be directly used as the standard transaction object, and the decision efficiency and the transaction efficiency are improved while the decision accuracy of the standard transaction object is ensured through the technical scheme.
Specifically, the decision module performs associated data feature detection on the quasi-transaction object with the duty ratio smaller than or equal to a second preset duty ratio and larger than a first preset duty ratio, and if associated data features exist, extracts the transaction data features, the associated data features and corresponding associated probabilities to form an associated data set.
The decision module of the invention carries out the associated data feature detection on the quasi-trading object with the occupation ratio of the large-volume aquatic product trading data feature of the quasi-trading object smaller than or equal to the second preset occupation ratio and larger than the first preset occupation ratio, at the moment, the quantity of the large-volume aquatic product trading data feature of the quasi-trading object is insufficient to directly judge the quasi-trading object as the standard trading object, whether the quasi-trading object can be used as the standard trading object is further judged by carrying out the associated data feature detection, and the quasi-trading object is judged twice by the large-volume aquatic product trading data feature and the associated data feature, so that the decision accuracy of the standard trading object is improved, and meanwhile, the decision efficiency and the trading efficiency are improved.
Specifically, the decision module calculates a transaction index of the object to be transacted according to the data in the associated data set, and sets: f=n×m×kb;
wherein F is a transaction index, N is the number of transaction data features of a large number of aquatic products in the associated data set, M is the number of associated data features in the associated data set, and Kb is the average value of associated probability in the associated data set.
The decision module calculates the transaction index of the quasi-trading object, wherein the transaction index is a characteristic parameter of whether the quasi-trading object can be used as a standard trading object or not, the three variables related to the transaction index are obtained by jointly participating in calculation, the three variables are the number of the transaction data features of a large amount of aquatic products in the association data set, the number of the association data features in the association data set and the average value of association probabilities in the association data set respectively, when the number of the transaction data features of the large amount of aquatic products is larger, the number of the association data features is larger, the association probability of the transaction data features of the large amount of aquatic products and the association data features is larger, the corresponding transaction index of the quasi-trading object is larger, the quasi-trading object with the transaction index larger than the second comparison index is used as the standard trading object, whether the quasi-trading object can be used as the standard trading object or not is further judged by calculating the transaction index, and meanwhile, the decision accuracy of the standard trading object is improved, and the decision efficiency and the trading efficiency are improved.
In this embodiment, when counting the number of associated data features in the associated data set, the associated data features associated with any one of the plurality of data features of the aquatic product transaction data are counted once, for example, in the associated data set, the associated data features of the plurality of data features of the aquatic product transaction data a1 include b1 and b2, and the associated data features of the plurality of data features of the aquatic product transaction data a2 include b2 and c2, and when counting the associated data features, the associated data features b2 are counted respectively, that is, counted twice.
Referring to fig. 3, fig. 3 is a flowchart illustrating a determination process for calculating a correction determination value for a transaction object according to an embodiment of the invention.
Specifically, the decision module is preset with a first comparison index and a second comparison index, the first comparison index is smaller than the second comparison index, and the decision module takes a transaction object as a standard transaction object or calculates a correction judgment value to correct the transaction index according to the comparison result of the transaction index and the first comparison index and the second comparison index.
Specifically, if the transaction index is greater than the second comparison index, the decision module takes the object to be transacted as a standard transaction object; if the trading index is smaller than or equal to the second contrast index and larger than the first contrast index, the decision module calculates a correction decision value to correct the trading index; and if the transaction index is smaller than or equal to the first contrast index, the decision module repeatedly performs transaction data feature detection on the object to be transacted.
When the decision module judges that the trading index is smaller than or equal to the second contrast index and larger than the first contrast index, the correction judgment value is calculated to correct the trading index, and whether the quasi trading object can be used as the standard trading object or not is further judged by calculating the correction judgment value, so that the decision accuracy of the standard trading object is improved, and meanwhile, the decision efficiency and the trading efficiency are improved.
Specifically, the decision module calculates a correction decision value to correct the transaction index, the decision module counts the types of the associated data features in the associated data set, calculates the correction decision value according to the following formula, and sets:
wherein e is a correction determination value, Z is the type of the associated data features in the associated data set, and M is the number of the associated data features in the associated data set.
Specifically, a plurality of correction coefficients are preset in the decision module, the decision module selects the corresponding correction coefficient to correct the transaction index according to the correction judgment value, and if the corrected transaction index is larger than the second comparison index, the decision module takes the transaction object as a standard transaction object; otherwise, the decision module repeatedly performs transaction data feature detection on the to-be-transacted object.
Referring to fig. 4, fig. 4 is a flowchart illustrating a determination of correction factor selection when correcting a transaction index according to an embodiment of the invention.
The decision module is preset with a plurality of correction coefficients, and is preset with a first preset comparison judgment value and a second preset comparison judgment value, the first preset comparison judgment value is smaller than the second preset comparison judgment value, the decision module respectively compares the calculated correction judgment value with the first preset comparison judgment value and the second preset comparison judgment value to select the correction coefficients to correct the transaction index,
if the calculated correction judgment value is smaller than the first preset comparison judgment value, the decision module judges that the transaction index is corrected by using the first correction coefficient f1, and the corrected transaction index=transaction index multiplied by f1 is set;
if the calculated correction judgment value is larger than or equal to the first preset comparison judgment value and smaller than the second preset comparison judgment value, the decision module judges that the transaction index is corrected by using the second correction coefficient f2, and the corrected transaction index = transaction index x f2 is set;
if the calculated correction judgment value is greater than or equal to a second preset comparison judgment value, the decision module judges that the transaction index is corrected by using a third correction coefficient f3, and the corrected transaction index=transaction index×f3 is set;
where 1 < f3 < f2 < f1 < 1.3, f3=1.15, f2=1.2, f1=1.25 are preferred in this embodiment.
The decision module calculates a correction decision value, wherein the correction decision value is the ratio of the type of the associated data features in the associated data set to the number of the associated data features in the associated data set, and is used for representing the duty ratio of the repeatedly occurring associated data features in the associated data set, the smaller the correction decision value is when the number of the repeatedly occurring associated data features is larger, the higher the transaction index of the quasi-transaction object is when the same associated data features with mapping relation are all occurred in the transaction data features of the bulk aquatic product, the transaction index of the quasi-transaction object is raised, the correction decision value is calculated to correct the transaction index, and whether the quasi-transaction object can be used as a standard transaction object is judged again by the corrected transaction index, so that the decision accuracy of the standard transaction object is further improved, and meanwhile, the decision efficiency and the transaction efficiency are improved.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An intelligent decision analysis system for large-volume aquatic product transactions based on big data, which is characterized by comprising:
the database module is used for storing transaction data of a large number of aquatic products;
the data processing module is connected with the database module and is used for establishing a mapping relation among the transaction data characteristics of the bulk aquatic products by analyzing the transaction data of the bulk aquatic products;
the analysis module is connected with the data processing module and used for detecting whether the transaction data characteristics of the large-volume aquatic products appear in the object to be transacted;
the decision module is connected with the database module, the data processing module and the analysis module and is used for screening the quasi-trading object, calculating the duty ratio of the trading data characteristics of the large aquatic product of the quasi-trading object to take the quasi-trading object as a standard trading object, or carrying out associated data characteristic detection on the quasi-trading object to calculate the trading index of the quasi-trading object, taking the quasi-trading object as the standard trading object according to the trading index, or calculating a correction decision value to correct the trading index to determine whether the quasi-trading object is taken as the standard trading object again.
2. The intelligent decision analysis system for large data based bulk aquatic product transactions of claim 1, wherein the mapping relationship of the bulk aquatic product transaction data features includes bulk aquatic product transaction data features, associated data features associated with the bulk aquatic product transaction data features, and associated probabilities of the bulk aquatic product transaction data features and the associated data features.
3. The intelligent decision analysis system for large-data-based bulk aquatic product transactions according to claim 2, wherein the decision module takes a transaction object meeting screening conditions as a quasi-transaction object, the analysis module detects transaction data characteristics of the quasi-transaction object and counts the number of the large-volume aquatic product transaction data characteristics of the quasi-transaction object to calculate the duty ratio of the large-volume aquatic product transaction data characteristics of the quasi-transaction object, takes the quasi-transaction object as a standard transaction object according to the duty ratio, or detects association data characteristics of the quasi-transaction object.
4. The intelligent decision analysis system for large-scale aquatic product transactions based on big data according to claim 3, wherein the decision module is preset with a first preset duty ratio and a second preset duty ratio, the first preset duty ratio is smaller than the second preset duty ratio, the decision module takes a quasi-transaction object with the duty ratio being larger than the second preset duty ratio as a standard transaction object, carries out associated data feature detection on the quasi-transaction object with the duty ratio being smaller than or equal to the second preset duty ratio and larger than the first preset duty ratio, and repeatedly carries out transaction data feature detection on the quasi-transaction object with the duty ratio being smaller than or equal to the first preset duty ratio.
5. The intelligent decision analysis system for large-data-based bulk aquatic product transactions according to claim 4, wherein the decision module performs associated data feature detection on the quasi-transaction objects with the duty ratio less than or equal to a second preset duty ratio and greater than a first preset duty ratio, and if associated data features exist, extracts the transaction data features, the associated data features and corresponding associated probabilities to form an associated data set.
6. The intelligent decision analysis system for large-data-based bulk aquatic product transactions according to claim 5, wherein the decision module calculates a transaction index of a subject to be transacted from the data in the associated data set, sets: f=n×m×kb;
wherein F is a transaction index, N is the number of transaction data features of a large number of aquatic products in the associated data set, M is the number of associated data features in the associated data set, and Kb is the average value of associated probability in the associated data set.
7. The intelligent decision analysis system for large-scale aquatic product transactions based on big data according to claim 6, wherein the decision module is pre-provided with a first contrast index and a second contrast index, the first contrast index is smaller than the second contrast index, and the decision module takes a transaction object as a standard transaction object or calculates a correction decision value to correct the transaction index according to the comparison result of the transaction index and the first contrast index and the second contrast index.
8. The intelligent decision analysis system for large-scale aquatic product transactions based on big data according to claim 7, wherein if the transaction index is greater than the second contrast index, the decision module takes a subject to be transacted as a standard transaction subject; if the trading index is smaller than or equal to the second contrast index and larger than the first contrast index, the decision module calculates a correction decision value to correct the trading index; and if the transaction index is smaller than or equal to the first contrast index, the decision module repeatedly performs transaction data feature detection on the object to be transacted.
9. The intelligent decision analysis system for large-scale aquatic product transactions based on big data according to claim 8, wherein the decision module calculates a correction decision value to correct the transaction index, and the decision module counts the types of associated data features in the associated data set, and calculates the correction decision value according to the following formula, and sets:
wherein e is a correction determination value, Z is the type of the associated data features in the associated data set, and M is the number of the associated data features in the associated data set.
10. The intelligent decision analysis system for large-scale aquatic product transactions based on big data according to claim 9, wherein a plurality of correction coefficients are preset in the decision module, the decision module selects the corresponding correction coefficient according to the correction determination value to correct the transaction index, and if the corrected transaction index is greater than the second comparison index, the decision module takes the transaction object as a standard transaction object; otherwise, the decision module repeatedly performs transaction data feature detection on the to-be-transacted object.
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