CN117035947B - Agricultural product data analysis method and cloud platform based on big data processing - Google Patents
Agricultural product data analysis method and cloud platform based on big data processing Download PDFInfo
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Abstract
The invention discloses an agricultural product data analysis method and a cloud platform based on big data processing, which belong to the technical field of big data analysis and specifically comprise the following steps: extracting relevant data of agricultural products in the cloud platform, preprocessing the relevant data of the agricultural products, classifying the agricultural products according to the preprocessed relevant data of the agricultural products, calculating the cost performance of each type of agricultural products and the cost performance of merchants, sorting the agricultural products according to the cost performance of the merchants from large to small, acquiring historical records of browsing and purchasing of the agricultural products of the users, analyzing the historical records, calculating browsing and purchasing tendency of the users, recommending the agricultural products to the users according to the browsing and purchasing tendency of the users by the cloud platform, and sequentially arranging the agricultural products according to the cost performance of the merchants and the number of times of purchasing or browsing the agricultural products according to the order from large to small, so that the purchasing efficiency of the users is improved, and meanwhile, the agreements of the agricultural products, the users and the markets are greatly improved.
Description
Technical Field
The invention belongs to the technical field of big data analysis, and particularly relates to an agricultural product data analysis method and a cloud platform based on big data processing.
Background
In recent years, the economic and technological levels of China are rapidly improved, and a rich material condition and a technical foundation are accumulated for realizing the modernization of agriculture. Under the promotion of new generation information technologies represented by big data, internet of things, cloud computing, artificial intelligence and the like, the Internet and agriculture are becoming new power for promoting the transformation and upgrading of agricultural industry in China. At present, the data types and the data volumes in the agricultural field are continuously and drastically increased, so that great challenges are brought to the aspects of acquisition, integration, storage, processing and the like of agricultural big data. As such, how to obtain the required data information from a large amount of agricultural data information in a targeted manner and apply the data information to actual production and living is also a problem to be solved by those skilled in the art.
In the agricultural product big data cloud platform, the agricultural products with full meshes are often not selectable by users, the cost performance of the agricultural products cannot be judged, the agricultural products are difficult to be selected and disqualified by merchants, the agricultural products with high price and poor quality are easy to purchase, a recommendation system is a hot topic in the current industry and academia, the most popular is a collaborative filtering algorithm, the preference of the users is judged by mining the historical behaviors of the users, and the users are recommended according to the preference of the users, and the commonly used recommendation algorithms comprise the collaborative filtering algorithm, a knowledge-based recommendation algorithm and deep learning, so that the recommendation algorithms have a plurality of problems and cannot be recommended in a targeted way.
The Chinese patent with the application publication number of CN111104573A discloses a method for analyzing and storing agricultural product data, which comprises the following steps: and obtaining market popularity and supply-demand ratio for each product in one agricultural product classification, then statistically analyzing the market index and cost performance of the agricultural products, performing list sorting in the classification corresponding to the agricultural products, listing a plurality of parameters in the calculation process corresponding to each agricultural product, simultaneously calculating the integral classification value of the classification according to the market index and cost performance of all the agricultural products in the classification, and performing list sorting on a plurality of classifications. The invention also relates to a system for analyzing and storing agricultural product data. According to a plurality of parameters in the market and production, the market prospect of the agricultural products is comprehensively calculated, relatively accurate and visible analysis and judgment are made for the production and sales of the agricultural products, and the degree of fit between the agricultural production and the market is greatly improved.
The Chinese patent with the application publication number of CN113393277A discloses an agricultural product market data analysis system based on big data, and relates to the technical field of big data. Collecting market data of agricultural products through a data collecting unit; the support analysis unit analyzes market data according to a value analysis method to obtain regional support; the comparison unit compares the regional support degree corresponding to the agricultural products with the comprehensive regional support degree corresponding to the same type of agricultural products to obtain the value degree; the pre-adjustment unit pre-adjusts market data of the agricultural products according to the value degree, and further obtains a result according to a big data algorithm after the market data of the agricultural products are integrated, analyzed and processed, so that the traditional method that the market data of the agricultural products are collected in the analysis process is broken through, and the method is completely dependent on human brain operation and judgment.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the agricultural product data analysis method and the cloud platform based on big data processing, extracts the related data of agricultural products in the cloud platform, carries out preprocessing on the related data of the agricultural products, classifies the agricultural products according to the preprocessed related data of the agricultural products, calculates the cost performance and the cost performance of merchants of each type of agricultural products, sorts the cost performance according to the merchant's cost performance from big to small, acquires the historical records of browsing and purchasing of the agricultural products of the user, carries out analysis, calculates the browsing and purchasing tendency of the user, sorts and recommends the agricultural products to the user according to the cost performance of merchants from big to small, sequentially arranges the agricultural products according to the number of types of the agricultural products purchased or browsed according to the number of the agricultural products from big to small, breaks through the traditional method for acquiring the market data of the agricultural products, carries out the cost performance analysis on the merchants and the agricultural products in the cloud platform, reasonably looks like the browsing and purchasing the agricultural products of the user according to the browsing and purchasing tendency of the agricultural products of the user, and greatly improves the purchasing efficiency of the agricultural products.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the agricultural product data analysis method based on big data processing comprises the following steps:
step S1: extracting related data of agricultural products in the cloud platform, and preprocessing the related data of the agricultural products;
step S2: classifying the agricultural products according to the preprocessed agricultural product related data, calculating the cost performance of each type of agricultural products and the cost performance of merchants, and sorting the agricultural products according to the cost performance of the merchants from large to small;
step S3: acquiring a history record of agricultural product browsing and purchasing of a user, analyzing the history record, and calculating browsing and purchasing tendency of the user;
step S4: the cloud platform carries out sequencing recommendation to the user from big to small according to the browsing and purchasing tendency of the user and the cost performance of the merchant, and then sequentially arranges the agricultural products according to the number of times of purchasing or browsing the agricultural products from big to small.
Specifically, the relevant data in step S1 includes: agricultural product name, agricultural product type, selling price, selling merchant, agricultural product quality, and sales volume ranking.
Specifically, the preprocessing in step S1 includes: and (3) cleaning the data, removing repeated data, filling missing values and processing abnormal values.
Specifically, the specific method in step S2 is as follows:
step S201: the set of kinds of agricultural products is set to N,wherein->Represents the agricultural products of the s-th kind,
，/>represents the t agricultural product under the s agricultural product category,>representing the s-th agricultural product categoryPrice for sale of the t th agricultural product, +.>Representing sales ranking of the t-th agricultural product under the s-th agricultural product category;
step S202: calculating the cost performance of each agricultural product under each agricultural product category, wherein the calculation formula is as follows:
，
wherein,indicating the deterioration index of the agricultural product in the ith +.>Representing the quality of the jth agricultural product in the ith agricultural product category, < >>Represents the jth agricultural product price influence weight under the ith agricultural product category, +.>Representing a j-th agricultural product sales ranking impact weight under the i-th agricultural product category;
step S203: and sorting merchants selling the agricultural products according to the cost performance of each agricultural product under each agricultural product category from high cost performance to low cost performance.
Specifically, the calculation formula of the cost performance of the merchant in step S203 is as follows:
wherein->Representing the cost performance of the ith agricultural product sold by the merchant, sj represents the quantity of the ith agricultural product sold by the merchant.
Specifically, the specific steps of the step S3 are as follows:
step S301: collecting histories of agricultural product browsing and purchasing of a user, setting a set of histories as L,wherein->Indicating the number of browsing the kth merchant for the kth agricultural product,/-th agricultural product>Representing the number of purchases of the kth merchant of the kth agricultural product;
step S302: and calculating browsing and purchasing tendencies of the user, wherein the calculation formula is as follows:
，
wherein,representing the weight of the purchase impact of agricultural products,/->Represents the agricultural product browsing influence weight, and +.>，/>Indicating the number of times of purchasing the ith agricultural product of the nth merchant,/-th agricultural product>Representing a number of times the ith agricultural product of the w th merchant is browsed;
step S303: and according to browsing and purchasing tendencies of the user, real-time adjustment and recommendation are carried out on merchants and agricultural products of the user purchasing pages in the cloud platform.
Agricultural product data analysis cloud platform based on big data processing includes: the system comprises an agricultural product data acquisition module, a cost performance calculation module, a user history data acquisition module, a user browsing and purchasing tendency calculation module, a merchant and agricultural product recommendation module and a cloud platform display module;
the agricultural product data acquisition module is used for extracting agricultural product related data in the cloud platform, including agricultural product names, agricultural product types, selling prices, selling merchants, agricultural product quality and sales volume ranking;
the cost performance calculation module is used for calculating the cost performance of each type of agricultural products and the cost performance of a merchant, and sorting the agricultural products according to the cost performance of the merchant from large to small;
the user history data acquisition module is used for acquiring history browsing and purchasing data of a user;
the user browsing and purchasing tendency calculation module is used for calculating the browsing and purchasing tendency of the user;
the merchant and agricultural product recommending module is used for recommending to the user according to the browsing and purchasing tendency of the user and the cost performance of the merchant, and sequentially arranging the merchant and the agricultural products in the cloud platform according to the number of times of purchasing or browsing the agricultural product types from large to small to carry out real-time adjustment and recommendation on the merchant and the agricultural products of the user purchasing pages;
the cloud platform display module is used for displaying the agricultural product related data.
Specifically, the cost performance calculation module comprises a farm product cost performance calculation unit and a merchant cost performance calculation unit,
the agricultural product cost performance calculation unit is used for calculating the cost performance of each type of agricultural product;
the merchant cost performance calculation unit is used for calculating the cost performance of the merchant.
Specifically, the user history data acquisition module comprises a user history browsing data acquisition unit and a user history purchasing data acquisition unit,
the user history browsing data acquisition unit is used for acquiring history browsing agricultural product data of a user;
the user history purchase data acquisition unit is used for acquiring the history purchase agricultural product data of the user.
Specifically, the merchant and agricultural product recommending module comprises a merchant recommending unit and an agricultural product recommending unit,
the merchant recommending unit is used for sequencing and recommending to the user according to the browsing and purchasing tendency of the user and the cost performance of the merchant from large to small;
the agricultural product recommending unit is used for recommending the cost performance of each type of agricultural product to a user according to the cost performance of each type of agricultural product from large to small.
An electronic device comprising a memory storing a computer program and a processor implementing the steps of a method of analyzing agricultural product data based on big data processing when the computer program is executed.
A computer readable storage medium having stored thereon computer instructions which, when executed, perform the steps of a method of analyzing agricultural product data based on big data processing.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention provides an agricultural product data analysis cloud platform based on big data processing, and optimizes and improves the architecture, the operation steps and the flow, and the system has the advantages of simple flow, low investment and operation cost and low production and working costs.
2. According to the agricultural product data analysis method based on big data processing, related data of agricultural products in a cloud platform are extracted, the related data of the agricultural products are preprocessed, the agricultural products are classified according to the preprocessed related data of the agricultural products, the cost performance and the cost performance of each type of agricultural products are calculated, the historical records of browsing and purchasing of the agricultural products of users are obtained and analyzed according to the cost performance of the businesses, the browsing and purchasing tendency of the users are calculated, the cloud platform is recommended to the users according to the browsing and purchasing tendency of the users, and then the cloud platform is sequentially arranged according to the cost performance of the businesses according to the number of times of purchasing or browsing agricultural products, so that the cost performance analysis of merchants and the agricultural products in the cloud platform can be carried out according to the cost performance of the agricultural products of the traditional analysis process, and the cost performance of the agricultural products of the users are reasonably like the browsing and purchasing tendency of the users, the agricultural products of the users are improved, and the agreements of the agricultural products, the users and the markets are greatly improved.
3. According to the agricultural product data analysis method based on big data processing, the cost performance of each type of agricultural product and the cost performance of a merchant are calculated, the browsing and purchasing tendency of a user are calculated, the cost performance of the agricultural product is reasonably analyzed and calculated, and the agricultural product of interest of the user is found by mining, so that the recommendation accuracy of the agricultural product and the merchant is greatly improved.
Drawings
FIG. 1 is a flow chart of a method for analyzing agricultural product data based on big data processing;
FIG. 2 is a flow chart of agricultural product recommendation based on the agricultural product data analysis method of big data processing according to the present invention;
FIG. 3 is a diagram of a agricultural product data analysis cloud platform based on big data processing in accordance with the present invention;
FIG. 4 is a diagram of an electronic device for the method for analyzing agricultural product data based on big data processing according to the present invention.
Detailed Description
In order that the technical means, the creation characteristics, the achievement of the objects and the effects of the present invention may be easily understood, it should be noted that in the description of the present invention, the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements to be referred to 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, the terms "a", "an", "the" and "the" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The invention is further described below in conjunction with the detailed description.
Example 1
Referring to fig. 1-2, an embodiment of the present invention is provided: gait recognition correction method based on deep learning comprises the following steps:
the agricultural product data analysis method based on big data processing comprises the following steps:
step S1: extracting related data of agricultural products in the cloud platform, and preprocessing the related data of the agricultural products;
step S2: classifying the agricultural products according to the preprocessed agricultural product related data, calculating the cost performance of each type of agricultural products and the cost performance of merchants, and sorting the agricultural products according to the cost performance of the merchants from large to small;
step S3: acquiring a history record of agricultural product browsing and purchasing of a user, analyzing the history record, and calculating browsing and purchasing tendency of the user;
step S4: the cloud platform carries out sequencing recommendation to the user from big to small according to the browsing and purchasing tendency of the user and the cost performance of the merchant, and then sequentially arranges the agricultural products according to the number of times of purchasing or browsing the agricultural products from big to small.
The relevant data in step S1 includes: agricultural product name, agricultural product type, selling price, selling merchant, agricultural product quality, and sales volume ranking.
The preprocessing in step S1 includes: and (3) cleaning the data, removing repeated data, filling missing values and processing abnormal values.
The specific method of the step S2 is as follows:
step S201: the set of kinds of agricultural products is set to N,wherein->Represents the agricultural products of the s-th kind,
，/>represents the t agricultural product under the s agricultural product category,>representing the selling price of the t agricultural product under the s agricultural product category, < >>Representing sales ranking of the t-th agricultural product under the s-th agricultural product category;
step S202: calculating the cost performance of each agricultural product under each agricultural product category, wherein the calculation formula is as follows:
，
wherein,indicating the deterioration index of the agricultural product in the ith +.>Representing the quality of the jth agricultural product in the ith agricultural product category, < >>Represents the jth agricultural product price influence weight under the ith agricultural product category, +.>Representing a j-th agricultural product sales ranking impact weight under the i-th agricultural product category;
step S203: and sorting merchants selling the agricultural products according to the cost performance of each agricultural product under each agricultural product category from high cost performance to low cost performance.
In step S203, the calculation formula of the cost performance of the merchant is:
wherein->Representing the cost performance of the ith agricultural product sold by the merchant, sj represents the quantity of the ith agricultural product sold by the merchant.
The specific steps of the step S3 are as follows:
step S301: collecting histories of agricultural product browsing and purchasing of a user, setting a set of histories as L,wherein->Indicating the number of browsing the kth merchant for the kth agricultural product,/-th agricultural product>Representing the number of purchases of the kth merchant of the kth agricultural product;
step S302: and calculating browsing and purchasing tendencies of the user, wherein the calculation formula is as follows:
，
wherein,representing the weight of the purchase impact of agricultural products,/->Represents the agricultural product browsing influence weight, and +.>，/>Indicating the number of times of purchasing the ith agricultural product of the nth merchant,/-th agricultural product>Representing a number of times the ith agricultural product of the w th merchant is browsed;
step S303: and according to browsing and purchasing tendencies of the user, real-time adjustment and recommendation are carried out on merchants and agricultural products of the user purchasing pages in the cloud platform.
The recommended method comprises the following steps: 1) The collaborative filtering algorithm (UserCF) is based on searching adjacent or similar users according to the historical behavior of the users on the items, and recommending the favorite commodities of the adjacent or similar users to the users. The user's preferences for products can be known from the user's historical behavioral data and measured and scored. And calculating the relation among the users by analyzing the attitudes and the favorites of different users on the same product, and recommending the product among consumers with common favorites. In general, assuming that a user likes merchandise with similar interests to someone, someone will also like it, the key is to find similar users, user similarity measures. Here we will describe a simple example, assuming that user a likes item a and item C, and user C likes item a, item C and item D, user a would recommend item D to user a when similar to user C; 2) Item-based collaborative filtering algorithms (ItemCF) that seek similar or related items through user feedback or preferences of the items and recommend items to the user based on the user's historical feedback and the degree of item similarity. The basic principle of collaborative filtering recommendation based on items is similar to that of collaborative filtering algorithm based on users, the similarity of products is found through the preference of users to the products, and similar products are recommended to the users according to the historical preference of the users. Also briefly illustrated herein, assuming that user a likes item A and item C, and that user C likes item A, item A is similar to item C, item C would be recommended to user C.
Problems of the conventional recommendation algorithm: 1) The cold start problem comprises two layers, namely cold start of a new user and cold start of a new article. In collaborative filtering recommendation based on users, for a new user, behavior data such as browsing, collecting, adding shopping carts or purchasing is not left on commodities, so that the preference of the new user cannot be known, and recommendation cannot be made to the new user. In collaborative filtering recommendation based on articles, because new items have no behavior data of users, there is naturally no way to make recommendations by collaborative filtering. If the problem of cold start of the new project can be well solved, not only can fresh articles be provided for users, but also the economic benefit of the website can be improved. The challenges faced by the cold start problem are more severe because the inability of the system to accurately recommend users may result in significant loss of users; 2) The problem of data sparsity, which is a great factor affecting recommendation, is that a data set in a recommendation system, which has interacted with a specific or specific similar group of user inputs, has a too low ratio in the whole data set. With the continuous expansion of the electronic commerce scale of agricultural products, a large number of agricultural products emerge, and the items evaluated by users are only a small part of a large website, so that data in an evaluation matrix of the users are very rare. Therefore, when calculating the nearest neighbors of the user and the item, its accuracy will be reduced and the recommendation quality of the recommendation system will be greatly reduced. Sparsity problems can lead to increased agricultural products, non-interactive agricultural products, low-interactive agricultural products becoming increasingly unavailable for recommendation. The more the scoring is, the denser the scoring matrix is, the higher the recommended quality is, and at present, various methods for solving the sparsity problem exist, and common methods include clustering, matrix decomposition, matrix filling, collaborative filtering of combined content and the like; 3) The problem of expandability, the coming of big data age, the expansion of website scale, the increase of user volume and the rapid increase of data volume make the problem of expandability of recommendation system become a problem that needs to pay attention to. Under the condition that the number reaches millions, a large number of expansibility problems exist in a general algorithm, and if the problems are not solved well, the real-time performance and the accuracy of a recommendation system are greatly influenced, so that whether the system is willing to be accepted by a large number of users is influenced. Currently, many systems require immediate recommendation according to user needs, which requires a collaborative filtering system with high scalability.
Example 2
Referring to fig. 3, another embodiment of the present invention is provided: agricultural product data analysis cloud platform based on big data processing includes: the system comprises an agricultural product data acquisition module, a cost performance calculation module, a user history data acquisition module, a user browsing and purchasing tendency calculation module, a merchant and agricultural product recommendation module and a cloud platform display module;
the agricultural product data acquisition module is used for extracting agricultural product related data in the cloud platform, including agricultural product names, agricultural product types, selling prices, selling merchants, agricultural product quality and sales volume ranking;
the cost performance calculation module is used for calculating the cost performance of each type of agricultural products and the cost performance of a merchant, and sorting the agricultural products according to the cost performance of the merchant from large to small;
the user history data acquisition module is used for acquiring history browsing and purchasing data of a user;
the user browsing and purchasing tendency calculation module is used for calculating the browsing and purchasing tendency of the user;
the merchant and agricultural product recommending module is used for recommending to the user according to the browsing and purchasing tendency of the user and the cost performance of the merchant, and sequentially arranging the merchant and the agricultural products in the cloud platform according to the number of times of purchasing or browsing the agricultural product types from large to small to carry out real-time adjustment and recommendation on the merchant and the agricultural products of the user purchasing pages;
the cloud platform display module is used for displaying the agricultural product related data.
The cost performance calculation module comprises a farm product cost performance calculation unit and a merchant cost performance calculation unit,
the agricultural product cost performance calculation unit is used for calculating the cost performance of each type of agricultural product;
the merchant cost performance calculation unit is used for calculating the cost performance of the merchant.
The user history data acquisition module includes a user history browsing data acquisition unit and a user history purchase data acquisition unit,
the user history browsing data acquisition unit is used for acquiring history browsing agricultural product data of a user;
the user history purchase data acquisition unit is used for acquiring the history purchase agricultural product data of the user.
The merchant and agricultural product recommendation module includes a merchant recommendation unit and an agricultural product recommendation unit,
the merchant recommending unit is used for sequencing and recommending to the user according to the browsing and purchasing tendency of the user and the cost performance of the merchant from large to small;
the agricultural product recommending unit is used for recommending the cost performance of each type of agricultural product to a user according to the cost performance of each type of agricultural product from large to small.
Example 3
Referring to fig. 4, an electronic device includes a memory storing a computer program and a processor implementing steps of a method for analyzing agricultural product data based on big data processing when the processor executes the computer program.
A computer readable storage medium having stored thereon computer instructions which, when executed, perform the steps of a method of analyzing agricultural product data based on big data processing.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are all within the protection of the present invention.
Claims (10)
1. The agricultural product data analysis method based on big data processing is characterized by comprising the following steps of:
step S1: extracting related data of agricultural products in the cloud platform, and preprocessing the related data of the agricultural products;
step S2: classifying the agricultural products according to the preprocessed agricultural product related data, calculating the cost performance of each type of agricultural products and the cost performance of merchants, and sorting the agricultural products according to the cost performance of the merchants from large to small;
step S3: acquiring a history record of agricultural product browsing and purchasing of a user, analyzing the history record, and calculating browsing and purchasing tendency of the user;
step S4: the cloud platform carries out sequencing recommendation to the user from big to small according to the browsing and purchasing tendency of the user and the cost performance of the merchant, and then sequentially arranges the agricultural products according to the number of times of purchasing or browsing the agricultural products from big to small;
the specific method of the step S2 is as follows:
step S201: the set of kinds of agricultural products is set to N,wherein->Represents the agricultural products of the s-th kind,
，/>represents the t agricultural product under the s agricultural product category,>representing the selling price of the t agricultural product under the s agricultural product category, < >>Representing sales ranking of the t-th agricultural product under the s-th agricultural product category;
step S202: calculating the cost performance of each agricultural product under each agricultural product category, wherein the calculation formula is as follows:
，
wherein,representing the ith agricultural productDeterioration index (I)>Representing the quality of the jth agricultural product in the ith agricultural product category, < >>Represents the jth agricultural product price influence weight under the ith agricultural product category, +.>Representing a j-th agricultural product sales ranking impact weight under the i-th agricultural product category;
step S203: sorting merchants selling the agricultural products according to the cost performance of each agricultural product under each agricultural product category from high cost performance to low cost performance;
the calculating formula of the cost performance of the merchant in the step S203 is as follows:
wherein->Representing the cost performance of the ith agricultural product sold by the merchant, sj represents the quantity of the ith agricultural product sold by the merchant.
2. The agricultural product data analysis method based on big data processing according to claim 1, wherein the related data in the step S1 includes: agricultural product name, agricultural product type, selling price, selling merchant, agricultural product quality, and sales volume ranking.
3. The agricultural product data analysis method based on big data processing according to claim 2, wherein the preprocessing in step S1 includes: and (3) cleaning the data, removing repeated data, filling missing values and processing abnormal values.
4. The agricultural product data analysis method based on big data processing as set forth in claim 3, wherein the specific steps of step S3 are:
step S301: collecting histories of agricultural product browsing and purchasing of a user, setting a set of histories as L,wherein->Indicating the number of browsing the kth merchant for the kth agricultural product,/-th agricultural product>Representing the number of purchases of the kth merchant of the kth agricultural product;
step S302: and calculating browsing and purchasing tendencies of the user, wherein the calculation formula is as follows:
，
wherein,representing the weight of the purchase impact of agricultural products,/->Represents the agricultural product browsing influence weight, and +.>，/>Indicating the number of times of purchasing the ith agricultural product of the nth merchant,/-th agricultural product>Representing a number of times the ith agricultural product of the w th merchant is browsed;
step S303: and according to browsing and purchasing tendencies of the user, real-time adjustment and recommendation are carried out on merchants and agricultural products of the user purchasing pages in the cloud platform.
5. A big data processing based agricultural product data analysis cloud platform implemented based on the big data processing based agricultural product data analysis method of any one of claims 1-4, comprising: the system comprises an agricultural product data acquisition module, a cost performance calculation module, a user history data acquisition module, a user browsing and purchasing tendency calculation module, a merchant and agricultural product recommendation module and a cloud platform display module;
the agricultural product data acquisition module is used for extracting agricultural product related data in the cloud platform, including agricultural product names, agricultural product types, selling prices, selling merchants, agricultural product quality and sales volume ranking;
the cost performance calculation module is used for calculating the cost performance of each type of agricultural products and the cost performance of a merchant, and sorting the agricultural products according to the cost performance of the merchant from large to small;
the user history data acquisition module is used for acquiring history browsing and purchasing data of a user;
the user browsing and purchasing tendency calculation module is used for calculating the browsing and purchasing tendency of the user;
the merchant and agricultural product recommending module is used for recommending to the user according to the browsing and purchasing tendency of the user and the cost performance of the merchant, and sequentially arranging the merchant and the agricultural products in the cloud platform according to the number of times of purchasing or browsing the agricultural product types from large to small to carry out real-time adjustment and recommendation on the merchant and the agricultural products of the user purchasing pages;
the cloud platform display module is used for displaying the agricultural product related data.
6. The agricultural product data analysis cloud platform based on big data processing of claim 5, wherein the cost performance calculation module comprises an agricultural product cost performance calculation unit and a merchant cost performance calculation unit,
the agricultural product cost performance calculation unit is used for calculating the cost performance of each type of agricultural product;
the merchant cost performance calculation unit is used for calculating the cost performance of the merchant.
7. The agricultural product data analysis cloud platform based on big data processing of claim 6, wherein the user history data acquisition module includes a user history browsing data acquisition unit and a user history purchase data acquisition unit,
the user history browsing data acquisition unit is used for acquiring history browsing agricultural product data of a user; the user history purchase data acquisition unit is used for acquiring the history purchase agricultural product data of the user.
8. The big data processing based agricultural product data analysis cloud platform of claim 7, wherein the merchant and agricultural product recommendation module includes a merchant recommendation unit and an agricultural product recommendation unit,
the merchant recommending unit is used for sequencing and recommending to the user according to the browsing and purchasing tendency of the user and the cost performance of the merchant from large to small;
the agricultural product recommending unit is used for recommending the cost performance of each type of agricultural product to a user according to the cost performance of each type of agricultural product from large to small.
9. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the big data processing based agricultural product data analysis method of any of claims 1-4 when the computer program is executed.
10. A computer readable storage medium having stored thereon computer instructions which when executed perform the steps of the big data processing based agricultural product data analysis method of any of claims 1-4.
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