Intelligent farm product market supervision system based on big data analysis

Abstract

The invention discloses a big data analysis-based farmer market intelligent supervision system, and relates to the field of data analysis, and the system comprises a farmer market supervision center which comprises a market data collection module, an agricultural trade data management module, an agricultural trade data traceability module, a transaction information analysis module and a data enabling and sharing module; the market data acquisition module is used for acquiring historical market data and real-time market data; the agricultural trade data management module is used for constructing a business architecture model and a corresponding supervision analysis model according to historical market data; the agricultural trade data traceability module is used for storing the real-time market data into the business architecture model and setting a traceability sharing scheme; the transaction information analysis module is used for acquiring transaction evaluation data and order evaluation data according to the supervision analysis model; the data enabling and sharing module is used for acquiring comprehensive evaluation data and performing enabling and sharing processing; according to the invention, the information source accuracy and the traceability data transparency degree are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and particularly to an intelligent supervision system for farmers' markets based on big data analysis. Background Art

[0002] Traditional farmers' markets usually adopt an artificial management mode to check and record commodity prices, sanitation conditions, etc. one by one. Moreover, the commodities operated in farmers' markets are diverse in types and wide in sources, which brings great challenges to food safety supervision. The traditional supervision method mainly relies on irregular spot checks and cannot achieve the whole-process supervision of food from the source to the sales.

[0003] However, with the upgrading of urban management and civilization construction, as a major part of urban construction, farmers' markets under the artificial management mode may have many problems. Moreover, with the continuous development of big data technology and Internet of Things technology, it provides modern conditions for further promoting the intelligent supervision of farmers' markets. Therefore, it is a problem that we need to solve to scientifically manage information such as market transaction behaviors and food safety in farmers' markets through big data technology.

[0004] After retrieval, the invention patent with the Chinese patent number CN112819329A discloses an urban-level intelligent farmers' market platform management system based on food safety and traceability, including a big data supervision and early warning platform, a farmers' market management platform, and a food safety public platform. The big data supervision and early warning platform, the farmers' market management platform, and the food safety public platform are all connected to the system background. The farmers' market management platform includes a traceability detection system, a smart payment system, an information publicity system, an O2O online vegetable purchasing system, a merchant assessment management system, a large property management system, and a membership card management system. Through the integration of the three platforms and seven systems, the present invention forms a comprehensive management solution for farmers' markets, communicates all hardware devices and network interconnections. Through the big data supervision and early warning platform, the problems of supervision Internet of Things and no early warning of crises are solved, and potential hazards are discovered and predicted in a timely manner by using big data. Through the large property management system, the property management of farmers' markets is realized, reducing artificial management and facilitating the operation of merchants.

[0005] Compared with the prior art, the invention patent with the Chinese patent number CN112819329A can be connected through multiple platforms to comprehensively manage farmers' markets, improving the management efficiency of farmers' markets.

[0006] However, in the actual use process of the above system, the integration process of each platform and system is not described in detail, resulting in problems such as possible tampering of transaction data and inability to guarantee data authenticity during the integration process of the platform and system. Therefore, how to ensure the accuracy of information sources and the transparency of traceable data is a problem that we need to solve. Summary of the Invention

[0007] The object of the present invention is to solve the disadvantages of low accuracy of information sources and insufficient transparency of traceable data in the prior art, and to propose an intelligent supervision system for farmers' markets based on big data analysis.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] An intelligent supervision system for farmers' markets based on big data analysis, including a farmers' market supervision center, the farmers' market supervision center includes a market data collection module, a farmers' market data management module, a farmers' market data traceability module, a transaction information analysis module, and a data empowerment and sharing module;

[0010] The market data collection module is used to collect the corresponding historical market data and real-time market data in the corresponding farmers' market, including market food information, market transaction information, and market order information;

[0011] The farmers' market data management module is used to analyze and process the corresponding historical market data in the farmers' market respectively, construct the corresponding business architecture model, and set the corresponding supervision analysis model based on the business architecture model;

[0012] The farmers' market data traceability module is used to store the obtained real-time market data according to the corresponding business architecture model, and set the corresponding traceability sharing scheme according to the storage result;

[0013] The transaction information analysis module is used to evaluate and process the obtained real-time market data through the corresponding monitoring and analysis model in the business architecture model, obtain the corresponding transaction evaluation data and order evaluation data, and update the corresponding traceability sharing scheme;

[0014] The data empowerment and sharing module is used to comprehensively evaluate the obtained transaction evaluation data and order evaluation data, obtain the corresponding comprehensive evaluation data corresponding to the corresponding business types in the corresponding farmers' market, and perform empowerment and sharing processing on the comprehensive evaluation data.

[0015] The above technical solution further includes: The process of collecting the corresponding historical market data and real-time market data in the farmers' market includes:

[0016] Set a historical data collection unit and a real-time data collection unit;

[0017] Collect the corresponding historical market data through the historical data collection unit, and perform marking processing on the collected historical market data;

[0018] The real-time data acquisition unit is connected to the corresponding data acquisition terminals, and the data acquisition terminals include intelligent traceability scales, image acquisition terminals, and transaction acquisition terminals. The corresponding market food information, market transaction information, and market order information are collected through the corresponding data acquisition terminals in the farmers' market, and the collected information is marked as real-time market data.

[0019] Further, the process of constructing the business architecture model in the farmers' market includes:

[0020] Obtain historical market data, set classification standard information for the data types corresponding to the corresponding market food information, market transaction information, and market order information in the historical market data, and set a large-scale classification basic layer according to the classification standard information;

[0021] Extract features from the market food information, market transaction information, and market order information respectively in the corresponding large-scale classification basic layer, obtain the corresponding market feature data respectively, set a classification standard system according to the corresponding market feature data, and correlate the set classification standard system with the corresponding market feature data;

[0022] Obtain the classification standard systems corresponding to each large-scale classification basic layer, set multiple basic layers in sequence according to the corresponding classification standard systems, and set data analysis nodes according to the classification results in each basic layer;

[0023] Integrate and connect the corresponding large-scale basic layer, the basic layer corresponding to the classification standard system, and the corresponding data analysis nodes to construct the corresponding business architecture model in the farmers' market.

[0024] Further, the process of setting the corresponding supervision analysis model based on the business architecture model includes:

[0025] Obtain the market feature data of the data analysis nodes corresponding to each basic layer in the business architecture model; the data analysis nodes obtain the corresponding data information in the historical market data according to the corresponding market feature data, and set a business feature data set according to the obtained data information;

[0026] Divide the obtained business feature data set into a data training set and a data validation set, analyze and process the corresponding data training set based on the deep learning algorithm, construct the supervision analysis model corresponding to the corresponding data analysis node according to the analysis and processing results, and perform verification analysis on the constructed supervision analysis model through the corresponding data validation set until the corresponding loss function tends to be stable, and obtain the supervision analysis model corresponding to the corresponding data analysis node;

[0027] Store the obtained supervision analysis model according to the corresponding data analysis node.

[0028] Further, the process of storing real-time market data into the business architecture model and setting up the traceability sharing scheme includes:

[0029] Obtain real-time market data, extract features from the real-time market data to obtain the market feature data corresponding to the real-time market data, input the obtained market feature data into the business architecture model, and perform comparative analysis through the classification standard system corresponding to each data analysis node in the business architecture model to obtain the corresponding comparison results;

[0030] Store according to each data analysis node to which the real-time market data belongs into the corresponding business architecture model, and set the corresponding data storage link;

[0031] Set a collection code for the collected real-time market data, and sequentially set the traceability encryption password corresponding to the data analysis node according to the collection code and the corresponding data storage link according to the corresponding operation process;

[0032] Share the traceability encryption password corresponding to each data analysis node of the data storage link among nodes, generate a traceability sharing scheme according to the shared storage result, and send the obtained traceability sharing scheme to the transaction information analysis module.

[0033] Further, the process of obtaining transaction evaluation data includes:

[0034] Input the corresponding market transaction information into the corresponding monitoring and analysis model respectively, output the corresponding process evaluation data by the monitoring and analysis model, and horizontally connect the data analysis nodes corresponding to the corresponding market food information according to the positions of each data analysis node in the business architecture model;

[0035] Obtain the corresponding food evaluation data according to the monitoring and analysis model of the data analysis nodes involved in the market food information;

[0036] Conduct transaction evaluation according to the obtained process evaluation data and the corresponding food evaluation data;

[0037] Obtain the user account corresponding to the corresponding transaction evaluation data, and obtain the corresponding user portrait according to the historical market transaction information corresponding to the user account;

[0038] Conduct comparative analysis on the process evaluation data, the food evaluation data and the corresponding user portrait to obtain evaluation deviation data, and integrate the evaluation deviation data corresponding to each data analysis node to obtain the transaction evaluation data corresponding to the corresponding user account.

[0039] Further, the process of obtaining order evaluation data includes:

[0040] Obtain the market order information corresponding to the real-time market data stored in the business architecture model, and obtain the monitoring and analysis model corresponding to the corresponding data analysis node according to the obtained market order information based on the corresponding data storage link;

[0041] Input the corresponding market order information into the corresponding monitoring and analysis model respectively. The corresponding monitoring and analysis model performs analysis and processing based on the market characteristic data corresponding to the corresponding market order information, and outputs the corresponding behavior evaluation data xp i , where i is the data analysis node involved;

[0042] Analyze and process the behavior evaluation data obtained by each data analysis node to obtain the behavior standard data xb of the corresponding data analysis node i Transaction impact factor β i , and substitute the obtained data information into the formula Obtain the corresponding order evaluation data ZP, where n represents the number of data analysis nodes involved in the corresponding market order information.

[0043] Furthermore, the process of obtaining the comprehensive evaluation data and performing the empowerment and sharing process on the comprehensive evaluation data includes:

[0044] Preset the three-level warning evaluation criteria corresponding to the farmers' market;

[0045] Obtain the transaction evaluation data and order evaluation data corresponding to the user account, compare and analyze the corresponding evaluation data with the corresponding three-level warning evaluation criteria in turn to obtain the corresponding comparison results, integrate the obtained comparison results, generate the comprehensive evaluation data corresponding to the corresponding business type according to the integration results, and perform the empowerment and sharing process on the obtained comprehensive evaluation data. The corresponding management personnel in the farmers' market perform closed-loop disposal according to the corresponding comprehensive evaluation data.

[0046] The present invention has the following beneficial effects:

[0047] 1. In the present invention, by constructing a business architecture model to integrate the real-time market data obtained in the farmers' market, and setting up the monitoring and analysis model corresponding to the corresponding data analysis node, the monitoring and analysis model analyzes and processes each process corresponding to the market transaction information and market order information in the farmers' market, and to a certain extent realizes the whole-process supervision of the corresponding market transaction behavior, business order and food safety in the farmers' market, and improves the management efficiency in the farmers' market;

[0048] 2. In the present invention, the obtained real-time market data is stored according to the distribution of corresponding data analysis nodes within the business architecture model, and corresponding data storage links are set. Corresponding traceability encryption codes are set through the data storage links and the stored data information, and corresponding traceability sharing schemes are set according to the corresponding traceability encryption codes. To a certain extent, the transparency process of tracing data of real-time market data within the data storage links is improved, and abnormal situations such as tampering and loss of corresponding data in the farmers' markets are avoided, thereby improving the accuracy of information sources.

[0049] 3. In the present invention, during the process of analyzing and processing the corresponding market transaction information in the real-time market data, the whole process involved in the corresponding market food information is obtained, and the corresponding market transaction information is jointly analyzed according to the whole process involved in the market food information to obtain whether there are abnormalities in the corresponding transaction process and behavior, thereby improving the accuracy in the supervision process of farmers' markets. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic structural diagram of an intelligent supervision system for farmers' markets based on big data analysis proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0052] Embodiment 1

[0053] As Figure 1 shown, an intelligent supervision system for farmers' markets based on big data analysis proposed by the present invention includes a farmers' market supervision center, and the farmers' market supervision center is communicatively linked with a market data collection module, a farmers' market data management module, a farmers' market data traceability module, a transaction information analysis module, and a data empowerment sharing module;

[0054] In this embodiment, the farmers' market supervision center is used to provide an integrated application solution combining software and hardware through self-developed intelligent traceability scales and new generation information technologies such as the Internet of Things, big data, artificial intelligence, and cloud computing, to intelligently manage food safety and transaction safety during the food transaction process, and to fully share the traceability information of the whole process of food information in the farmers' market, thereby constructing a linkage traceability system that can be traced forward, traced backward, the responsibility can be investigated, and the risks can be controlled, and realizing the intelligent supervision of farmers' markets. The specific implementation process includes:

[0055] The market data collection module is used to collect corresponding historical market data information and real-time market data information based on the data collection terminals corresponding to the Internet of Things, including market food information, market transaction information, and market order information. The specific implementation process includes:

[0056] Set up a historical data collection unit and a real-time data collection unit;

[0057] Collect the historical market data information in the corresponding farmers' market through the historical data collection unit, and perform marking processing on the historical market data information;

[0058] Corresponding data collection terminals are set in the real-time data collection unit. The data collection terminals include a variety of terminal devices such as self-developed intelligent traceability scales, image collection terminals, and transaction collection terminals;

[0059] Collect the corresponding market food information, market transaction information, and market order information through the data collection terminals in the farmers' market, where:

[0060] Market food information includes food types, food origin information, food quality inspection standard information, and food circulation information in the farmers' market, etc.;

[0061] Market transaction information includes various transaction information such as customer transaction information and operator transaction information in the farmers' market;

[0062] Market order information includes various business behavior specification information such as personnel behavior data, business price information, trademark specification information, and food quality information of relevant personnel in the farmers' market;

[0063] It should be further noted that in the specific implementation process, the market data collection module obtains the corresponding historical market data information and real-time market data information through corresponding legal channels. During the process of collecting the corresponding market data information, relevant personnel are informed in writing and a written notice is issued.

[0064] The farmers' market data management module is used to analyze and process the corresponding historical market data in the farmers' market, construct a corresponding business architecture model, and set up a corresponding supervision analysis model based on the business architecture model. The specific implementation process includes:

[0065] Obtain the historical market data information, process the historical market data information of the corresponding data type, and construct a business architecture model corresponding to the corresponding data type. The specific implementation process includes:

[0066] Analyze and process the corresponding market food information in the historical market data information obtained recently in the corresponding farmers' market, obtain the food types corresponding to the market food information, and obtain the packaging image data, food image data, and key food description information during the sales process of the corresponding food types in the farmers' market;

[0067] Analyze and process the corresponding market transaction information in the historical market data information obtained recently in the corresponding farmers' market, obtain the transaction types corresponding to the market transaction information, and obtain various types such as transaction amounts, transaction methods, and transaction processes during the sales process of the corresponding transaction types in the farmers' market;

[0068] Analyze and process the corresponding market order information in the historical market data information obtained recently in the corresponding farmers' market, obtain the order types corresponding to the market order information, and obtain various types such as product quality order information, product price order information, product label order information, and product operation order information during the sales process of the corresponding order types in the farmers' market;

[0069] Set the corresponding classification standard information according to the food types, transaction types, and order types corresponding to the historical market data information, and set a large-scale classification basic layer according to the classification standard information;

[0070] Extract features from the market food information, market transaction information, and market order information respectively in the corresponding large-scale classification basic layer, obtain the corresponding market feature data respectively, set a classification standard system according to the corresponding market feature data, and correlate the set classification standard system with the corresponding market feature data;

[0071] The classification standard system includes the classification standards of multiple classification levels corresponding to the corresponding data types;

[0072] Obtain the classification standard systems corresponding to each large-scale classification basic layer, set multiple basic layers in sequence according to the corresponding classification standard systems, and set data analysis nodes according to the classification results in each basic layer;

[0073] The data classification node includes the classification evaluation indicators corresponding to the corresponding classification standard system and is used to analyze and process the corresponding market data information;

[0074] Integrate and link the corresponding large-scale basic layer, the basic layer corresponding to the classification standard system, and the corresponding data analysis nodes, perform correlation analysis on each data analysis node between different large-scale basic layers according to the corresponding classification evaluation indicators, and construct a corresponding business architecture model in the farmers' market according to the correlation analysis results;

[0075] It should be further noted that in the specific implementation process, within the corresponding classification basic layer, a refined division is set according to the food types of market food information, and corresponding product links are set. Through the product links, traceability management is carried out on food origin information, food quality inspection standard information, and food circulation information.

[0076] Obtain the market characteristic data of each data analysis node corresponding to the basic layer within the business architecture model; the data analysis node obtains the corresponding data information in the historical market data according to the corresponding market characteristic data, and sets the business characteristic data set according to the obtained data information.

[0077] Divide the obtained business characteristic data set into a data training set and a data verification set, perform analysis and processing on the corresponding data training set based on the deep learning algorithm, construct the regulatory analysis model corresponding to the corresponding data analysis node according to the analysis and processing results, verify and analyze the constructed regulatory analysis model through the corresponding data verification set, obtain the number of samples m in the data verification set, and obtain the corresponding true value y j and the predicted value output by the regulatory analysis model

[0078] Obtain the corresponding loss function difference value L, where:

[0079]

[0080] Preset the loss function difference threshold CY, compare and analyze the loss function difference value L with the loss function difference threshold CY, until the corresponding loss function difference value is less than the loss function difference threshold, and obtain the regulatory analysis model corresponding to the corresponding data analysis node;

[0081] Store the obtained regulatory analysis model according to the corresponding data analysis node.

[0082] The agricultural trade data traceability module is used to store the obtained real-time market data according to the corresponding business architecture model, and set the corresponding traceability sharing scheme according to the storage result. Its specific implementation process includes:

[0083] Obtain the real-time market data, extract the characteristics of the real-time market data, obtain the market characteristic data corresponding to the real-time market data, input the obtained market characteristic data into the business architecture model, and perform comparative analysis through the classification standard system corresponding to each data analysis node within the business architecture model to obtain the corresponding comparison result;

[0084] Store the real-time market data belonging to each data analysis node into the corresponding business architecture model, and set the corresponding data storage link.

[0085] Obtain real-time market data and set corresponding acquisition codes for the corresponding real-time market data. The acquisition codes include characteristic information of the corresponding real-time market data, including various characteristic information such as acquisition time characteristics, image characteristics, numerical characteristics, and character characteristics;

[0086] Preset a coding character library, obtain the corresponding acquisition codes, perform scrambling processing on the coding character library according to the obtained data storage link, and obtain the link coding character library corresponding to the corresponding data information;

[0087] Obtain the corresponding acquisition codes, segment the acquisition codes according to the data storage link, and obtain the operation function corresponding to the data storage link;

[0088] Perform arithmetic processing on the segmented processing results of the acquisition codes corresponding to the data storage link through the corresponding arithmetic function, obtain the corresponding extraction character positions according to the arithmetic results, sequentially obtain the corresponding character data in the link coding character library according to the corresponding extraction character positions, and integrate the obtained character data to generate the traceability encryption password corresponding to the corresponding data analysis node;

[0089] Share the traceability encryption passwords corresponding to each data analysis node of the data storage link among the nodes, generate a traceability sharing scheme according to the shared storage results, and send the obtained traceability sharing scheme to the transaction information analysis module.

[0090] The transaction information analysis module is used to evaluate the obtained real-time market data through the corresponding monitoring and analysis models in the business architecture model, and obtain the corresponding transaction evaluation data and order evaluation data. Its specific implementation process includes:

[0091] Input the corresponding market transaction information into the corresponding monitoring and analysis models respectively. The monitoring and analysis models output the corresponding process evaluation data, and horizontally connect the data analysis nodes corresponding to the corresponding market food information according to the positions of each data analysis node in the business architecture model;

[0092] Obtain the corresponding food evaluation data according to the monitoring and analysis models of the data analysis nodes involved in the market food information;

[0093] Conduct transaction evaluation based on the obtained process evaluation data and the corresponding food evaluation data;

[0094] Obtain the user accounts corresponding to the corresponding transaction evaluation data, and obtain the corresponding user portraits according to the historical market transaction information corresponding to the user accounts;

[0095] Compare and analyze the process evaluation data and food evaluation data with the corresponding user portraits to obtain evaluation deviation data, and integrate the evaluation deviation data corresponding to each data analysis node to obtain the transaction evaluation data corresponding to the corresponding user account;

[0096] Obtain the market order information corresponding to the real-time market data stored in the business architecture model, and obtain the monitoring and analysis model corresponding to the corresponding data analysis node according to the corresponding data storage link for the obtained market order information;

[0097] Input the corresponding market order information into the corresponding monitoring and analysis model respectively. The corresponding monitoring and analysis model performs analysis and processing according to the market characteristic data corresponding to the corresponding market order information, and outputs the corresponding behavior evaluation data xp i , where i is the data analysis node involved;

[0098] Analyze and process the behavior evaluation data obtained for each data analysis node to obtain the behavior standard data xb of the corresponding data analysis node i Transaction impact factor β i , and substitute the obtained data information into the formula Obtain the corresponding order evaluation data ZP, where n represents the number of data analysis nodes involved in the corresponding market order information.

[0099] The data empowerment and sharing module is used to comprehensively evaluate the obtained transaction evaluation data and order evaluation data, obtain the comprehensive evaluation data corresponding to the corresponding business type in the corresponding farmers' market, and perform empowerment and sharing processing on the comprehensive evaluation data. The specific implementation process includes:

[0100] Preset the three-level warning evaluation criteria corresponding to the farmers' market. The three-level warning evaluation criteria are respectively three-level warning, second-level warning, and first-level three-level warning, where:

[0101] The three-level warning is the evaluation criteria for relatively minor illegal business operations such as price fraud and non-standard commodity labels;

[0102] The second-level warning is the evaluation criteria for illegal business operations that have a greater impact on the local market order and the rights and interests of some consumers;

[0103] The first-level warning is the evaluation criteria for illegal business operations that pose a serious threat to public safety, major livelihoods, and market order;

[0104] Obtain the transaction evaluation data and order evaluation data corresponding to the user account, compare and analyze the corresponding evaluation data with the corresponding third-level alarm evaluation criteria in sequence, obtain the corresponding comparison results, integrate the obtained comparison results, generate the comprehensive evaluation data corresponding to the corresponding business type according to the integration results, and perform empowerment sharing processing on the obtained comprehensive evaluation data. The corresponding management personnel in the farmers' market perform closed-loop disposal according to the corresponding comprehensive evaluation data.

[0105] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart supervision system for farmers' markets based on big data analysis, including a farmers' market supervision center, characterized in that: The agricultural market supervision center includes a market data collection module, an agricultural market data management module, an agricultural market data traceability module, a transaction information analysis module, and a data empowerment sharing module; The market data collection module is used to collect historical market data and real-time market data corresponding to the corresponding farmers' market, including market food information, market transaction information and market order information; The agricultural trade data management module is used to analyze and process the historical market data corresponding to the agricultural trade market, build a corresponding business architecture model, and set a corresponding supervision analysis model based on the business architecture model; The agricultural trade data traceability module is used to store the obtained real-time market data according to the corresponding business architecture model, and set up a corresponding traceability sharing plan according to the storage results; The transaction information analysis module is used to evaluate and process the obtained real-time market data through the corresponding monitoring and analysis model in the business architecture model to obtain corresponding transaction evaluation data and order evaluation data; The data empowerment sharing module is used to perform a comprehensive evaluation on the acquired transaction evaluation data and order evaluation data, obtain the comprehensive evaluation data corresponding to the corresponding business type in the corresponding farmers' market, and perform empowerment sharing processing on the comprehensive evaluation data.

2. According to claim 1, a smart supervision system for farmers' markets based on big data analysis is characterized in that: The process of collecting relevant historical market data and real-time market data in farmers' markets includes: Setting up historical data collection unit and real-time data collection unit; Collecting corresponding historical market data through the historical data collection unit, and marking the collected historical market data; The real-time data acquisition unit is connected to the corresponding data acquisition terminal, which includes a smart traceability scale, an image acquisition terminal and a transaction acquisition terminal. The corresponding market food information, market transaction information and market order information are collected through the corresponding data acquisition terminals in the farmers' market, and the collected information is marked as real-time market data.

3. According to claim 2, a smart supervision system for farmers' markets based on big data analysis is characterized in that: The process of building a business architecture model within a farmers market includes: Obtain historical market data, set classification standard information for the data types corresponding to the corresponding market food information, market transaction information and market order information in the historical market data, and set a large-scale classification base layer according to the classification standard information; Extracting features of market food information, market transaction information and market order information at the corresponding large-scale classification base layer, respectively, obtaining corresponding market feature data, setting a classification standard system according to the corresponding market feature data, and correlating the set classification standard system with the corresponding market feature data; Obtain the classification standard system corresponding to each large-scale classification base layer, set multiple base layers in sequence according to the corresponding classification standard system, and set data analysis nodes according to the classification results in each base layer; Integrate and connect the corresponding large-scale basic layer, the basic layer corresponding to the classification standard system, and the corresponding data analysis nodes to build the corresponding business architecture model within the farmers' market.

4. According to claim 3, a smart supervision system for farmers' markets based on big data analysis is characterized in that: The process of setting up a corresponding regulatory analysis model based on the business architecture model includes: Obtaining market characteristic data of data analysis nodes corresponding to each basic layer in the business architecture model; the data analysis nodes obtain corresponding data information in historical market data according to the corresponding market characteristic data, and setting a business characteristic data set according to the obtained data information; The obtained business feature data set is divided into a data training set and a data verification set. The corresponding data training set is analyzed and processed based on the deep learning algorithm. The supervision analysis model corresponding to the corresponding data analysis node is constructed according to the analysis and processing results. The constructed supervision analysis model is verified and analyzed through the corresponding data verification set until the corresponding loss function tends to be stable, and the supervision analysis model corresponding to the corresponding data analysis node is obtained. The obtained supervision analysis model is stored according to the corresponding data analysis node.

5. According to claim 4, a smart supervision system for farmers' markets based on big data analysis is characterized in that: The process of storing real-time market data into the business architecture model and setting up a traceability sharing solution includes: Acquire real-time market data, extract features from the real-time market data, obtain market feature data corresponding to the real-time market data, input the obtained market feature data into the business architecture model, perform comparative analysis through the classification standard system corresponding to each data analysis node in the business architecture model, and obtain corresponding comparative results; Store the real-time market data in the corresponding business architecture model according to the data analysis nodes to which it belongs, and set up corresponding data storage links; Set the collection code for the collected real-time market data, and set the traceability encryption code corresponding to the data analysis node in sequence according to the corresponding calculation process for the collection code and the corresponding data storage link; The traceability encryption codes corresponding to the data storage links of each data analysis node are shared among the nodes, a traceability sharing plan is generated based on the shared storage results, and the obtained traceability sharing plan is sent to the transaction information analysis module.

6. According to claim 5, a smart supervision system for farmers' markets based on big data analysis is characterized in that: The process of obtaining transaction evaluation data includes: The corresponding market transaction information is input into the corresponding monitoring and analysis model respectively, and the monitoring and analysis model outputs the corresponding process evaluation data. The data analysis nodes corresponding to the corresponding market food information are horizontally connected in series according to the position of each data analysis node in the business architecture model; Obtain corresponding food evaluation data based on the monitoring and analysis model of the data analysis nodes involved in the market food information; Conduct transaction evaluation based on the obtained process evaluation data and corresponding food evaluation data; Obtain the user account corresponding to the corresponding transaction evaluation data, and obtain the corresponding user portrait based on the historical market transaction information corresponding to the user account; Compare and analyze the process evaluation data and food evaluation data with the corresponding user portraits to obtain the evaluation deviation data, integrate the evaluation deviation data corresponding to each data analysis node, and obtain the transaction evaluation data corresponding to the corresponding user account.

7. The smart supervision system for farmers' markets based on big data analysis according to claim 6 is characterized in that: The process of obtaining order assessment data includes: Obtain the market order information corresponding to the real-time market data stored in the business architecture model, and obtain the monitoring and analysis model corresponding to the corresponding data analysis node based on the corresponding data storage link for the obtained market order information; The corresponding market order information is input into the corresponding monitoring and analysis model, and the corresponding monitoring and analysis model analyzes and processes the market characteristic data corresponding to the corresponding market order information, and outputs the corresponding behavior evaluation data xp i , i is the data analysis node involved; Analyze and process the behavior evaluation data obtained by each data analysis node to obtain the behavior standard data xb of the corresponding data analysis node i Transaction Impact Factor β i , substitute the obtained data information into the formula Obtain the corresponding order evaluation data ZP, where n represents the number of data analysis nodes involved in the corresponding market order information.

8. The smart supervision system for farmers' markets based on big data analysis according to claim 7 is characterized in that: The process of obtaining comprehensive assessment data and enabling sharing of the comprehensive assessment data includes: Preset the three-level alarm assessment standards corresponding to farmers' markets; Obtain the transaction evaluation data and order evaluation data corresponding to the user account, compare and analyze the corresponding evaluation data with the corresponding three-level alarm evaluation standards in turn, obtain the corresponding comparison results, integrate the obtained comparison results, generate comprehensive evaluation data corresponding to the corresponding business type according to the integration results, and enable sharing of the obtained comprehensive evaluation data, and the corresponding management personnel in the farmers' market will perform closed-loop disposal according to the corresponding comprehensive evaluation data.

Images