Agricultural material platform account system based on multi-source data

Abstract

The application relates to the technical field of data processing, in particular to an agricultural material platform account system based on multi-source data, which comprises an account database, a key generation module used for generating corresponding account keys based on historical account data, a data fusion module used for acquiring data of each data source of the agricultural material platform, classifying the data in each data source according to attribute labels of the data to generate a plurality of data sets, and determining the sensitive degrees of the data sets, a key matching module used for determining data keywords corresponding to each data set based on the data in each data set, determining encryption keys corresponding to each data set based on comparison results of the data keywords and target keywords and the account keys, and an encryption control module used for determining encryption modes corresponding to each data set based on the sensitive degrees of the data sets. The application can improve the data security of the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a farm material platform account system based on multi-source data. BACKGROUND

[0002] In modern agricultural production, the application of agricultural materials (agricultural production materials) plays a crucial role in improving crop yield and quality. Agricultural materials include fertilizers, pesticides, seeds, soil conditioners, etc. However, problems such as counterfeit and substandard products, lack of transparency in information, etc. in the agricultural material market have seriously affected the safety and efficiency of agricultural production. In addition, the correct use and management of agricultural materials are also key to improving agricultural production efficiency and ensuring crop quality.

[0003] With the development of information technology, the agricultural material platform account system plays a key role in the management of agricultural production materials. Through the agricultural material platform account system, the transparency of agricultural material information can be achieved, the efficiency of the agricultural material supply chain can be improved, and technical support can be provided for the correct use and management of agricultural materials. The agricultural material platform account system integrates data from multiple channels, including agricultural material production enterprises, distributors, retailers, logistics companies, and meteorological and soil testing agencies. By centrally managing these multi-source data, the account system can achieve full traceability and supervision of agricultural product production, inventory, sales, transportation, etc. to improve the efficiency and accuracy of agricultural material management and ensure smooth agricultural production. However, the agricultural material platform account system involves enterprise information, personal information of farmers, transaction records, geographic location, and other private data, and there is a risk of data leakage or data tampering, such as attackers hacking into the system to access data or improper operation or malicious behavior by internal personnel, making it difficult to ensure the data security of the agricultural material platform account system.

[0004] Chinese patent application publication No. CN112612842A discloses a method and device for entering account information, an electronic device and a storage medium, relating to the technical field of information entry. The method for entering account information includes: obtaining account data; verifying the account data; if the verification is qualified, entering the account information according to the account data; verifying the account information; if the verification is qualified, encrypting the account information and sending it to the power grid system for entry.

[0005] Therefore, the prior art has the following problems: different data sensitivity is not considered, and a single encryption method cannot guarantee data security. SUMMARY

[0006] Therefore, the present application provides a farm material platform account system based on multi-source data to overcome the problem that the prior art does not consider the sensitivity of different data and a single encryption method cannot guarantee data security.

[0007] To achieve the above object, the application provides a platform account system for agricultural materials based on multi-source data, comprising:

[0008] An account database is used to store historical account data of the agricultural material platform.

[0009] A key generation module is connected with the account database and used to determine a target keyword based on the historical account data and generate a corresponding account key based on the target keyword.

[0010] A data fusion module is used to obtain data of each data source of the agricultural material platform, classify the data in each data source according to attribute tags to generate a plurality of data sets, determine data features corresponding to each data set, and determine the sensitivity of each data set based on the data features corresponding to each data set.

[0011] A key matching module is connected with the key generation module and the data fusion module respectively and used to determine data keywords corresponding to each data set based on the data in each data set, determine encryption keys corresponding to each data set based on the comparison results of each data keyword and the target keyword and the account key.

[0012] An encryption control module is connected with the data fusion module and the key matching module respectively and used to determine encryption methods corresponding to each data set based on the sensitivity of each data set, comprising,

[0013] encrypting the encryption keys corresponding to each data set based on the sensitivity of each data set and encrypting the data in each data set based on the encrypted encryption keys.

[0014] Or, encrypting the data in each data set based on the encryption keys corresponding to each data set.

[0015] Further, the key generation module comprises:

[0016] A data analysis submodule is connected with the account database and used to determine a plurality of candidate fields based on the historical account data and extract a plurality of keywords based on the field contents of each candidate field.

[0017] A keyword matching submodule is connected with the data analysis submodule and used to analyze each keyword to generate corresponding fuzzy words and cluster each fuzzy word to determine a plurality of account keywords.

[0018] An account key generation submodule is connected with the keyword matching submodule and used to determine a target keyword based on each account keyword and generate a corresponding account key based on the target keyword.

[0019] Further, the data fusion module comprises:

[0020] a data acquisition sub-module configured to acquire data of each data source of the agricultural platform;

[0021] a data classification sub-module connected with the data acquisition sub-module and configured to classify the data according to attribute labels of the data in each data source to generate a plurality of data sets, wherein the attribute labels of the data in each data set are the same;

[0022] a data feature extraction sub-module connected with the data classification sub-module and configured to determine data features corresponding to each data set based on the data in each data set and a feature evaluation model;

[0023] a data evaluation sub-module connected with the data feature extraction sub-module and configured to determine a sensitive index corresponding to each data set based on the data features corresponding to each data set, and determine a sensitive degree of each data set based on the sensitive index and a sensitive degree evaluation index system.

[0024] Further, the key matching module comprises:

[0025] a data keyword determination sub-module connected with the data fusion module and configured to determine a plurality of candidate keywords corresponding to each data set based on the data in each data set;

[0026] a keyword comparison sub-module connected with the data keyword determination sub-module and the key generation module respectively and configured to determine a data keyword corresponding to each data set based on each candidate keyword, and determine a keyword matching degree corresponding to each data set based on a comparison result of each data keyword and the target keyword;

[0027] an encryption key generation sub-module connected with the keyword comparison sub-module and configured to determine an encryption key corresponding to each data set based on the keyword matching degree corresponding to each data set and the account key.

[0028] Further, the encryption control module determines, based on a comparison result that the sensitive degree of each data set meets a preset standard, that an encryption mode corresponding to each data set is to encrypt the encryption key corresponding to each data set based on the sensitive degree of each data set, and encrypt the data in each data set based on the encrypted encryption key.

[0029] Further, the encryption control module determines, based on a comparison result that the sensitive degree of each data set does not meet the preset standard, that the encryption mode corresponding to each data set is to encrypt the data in each data set based on the encryption key corresponding to each data set.

[0030] Further, the encryption control module determines an encryption coefficient corresponding to each data set based on the sensitivity of each data set, and encrypts an encryption key corresponding to each data set based on the encryption coefficient corresponding to each data set.

[0031] Further, the data classification submodule determines a corresponding attribute label according to a data attribute of data in each data source.

[0032] The data attribute includes data source, data security level, and data transmission feature.

[0033] Further, the data evaluation submodule includes:

[0034] a system construction unit configured to construct a sensitivity evaluation index system;

[0035] a sensitive index determination unit connected to the data feature extraction submodule, configured to determine a sensitive index corresponding to each data set based on the data feature corresponding to each data set;

[0036] a sensitivity determination unit connected to the system construction unit and the sensitive index determination unit, respectively, configured to determine the sensitivity of each data set based on the sensitivity evaluation index system and the sensitive index.

[0037] Further, the encryption key generation submodule determines a matching feature value based on a comparison result of a keyword matching degree corresponding to each data set and a preset matching degree, and determines an encryption key corresponding to each data set based on the matching feature value and the account key.

[0038] Compared with the prior art, the application has the beneficial effects that the key generation module in the application determines the target keyword based on the historical account data and generates the corresponding account key, so that the key is closely related to the data content, improving the pertinence and security of key generation. By setting the data fusion module, the data of each data source of the agricultural platform can be integrated, and multiple data sets can be generated according to the attribute label classification, which can reduce the data processing amount and improve the data processing efficiency. By determining the data characteristics of each data set and evaluating its sensitivity, a basis is provided for the security management of data, which helps to implement differentiated data protection strategies and improve data security. By setting the key matching module, the data key in the data set is determined based on the data, and the encryption key is determined by comparing with the target keyword and combining the account key, which increases the flexibility and accuracy of key matching. The content of the data set is closely combined with the key generation and matching process, ensuring the relevance and security of the data encryption key and the data. By setting the encryption control module, different encryption methods are determined according to the sensitivity of the data set, which can encrypt the encryption key before encrypting the data or directly encrypt the data, providing a variety of encryption strategy selection. Through reasonable encryption control, it ensures that data of different sensitivity levels is properly encrypted, which can improve data security while avoiding over-encryption, and can improve data quality.

[0039] Further, the key generation module in the application determines a plurality of candidate fields based on the historical account data through the data analysis submodule, and extracts a plurality of keywords based on the field content of each candidate field, providing accurate and key information basis for subsequent key generation, ensuring that the generated key is closely related to the data content, and improving data processing efficiency. By setting the keyword matching submodule, the extracted keywords are analyzed to generate corresponding fuzzy words, and by clustering the fuzzy words, the accuracy and coverage of the account keywords can be improved, and the security and flexibility of the subsequent generated account key can be improved. By setting the account key generation submodule, the corresponding account key is generated based on the target keyword, which can ensure that the account key is related to the data content and has high unpredictability, which can improve the security of the key and further improve the data security.

[0040] Further, the data fusion module of the present application can ensure the timeliness and comprehensiveness of the data by setting the data acquisition submodule to acquire the data of each data source of the agricultural platform. The data can be classified according to the attribute label of the data by setting the data classification submodule, the attribute label of the data in each data set is the same, the data is ordered, and the subsequent data processing efficiency can be improved. The data features corresponding to each data set can be accurately determined based on the attribute label of the data in each data set and the feature evaluation model by setting the data feature extraction submodule. The sensitive index can be determined based on the data features corresponding to each data set by setting the data evaluation submodule, and the sensitive degree is further determined, the different data protection strategies can be implemented according to the different sensitive levels of the data, and the data security is improved.

[0041] Further, the key matching module of the present application can accurately determine a plurality of candidate keywords from each data set by setting the data keyword determination submodule, which provides an accurate basis for subsequent screening of data keywords, helps to fully reflect the characteristics of the data set, and provides diversified input for subsequent generation of data encryption key. The data keywords corresponding to each data set are determined by analyzing and comparing the candidate keywords by setting the keyword comparison submodule, the matching degree of the data keywords corresponding to each data set is determined based on the comparison result of the data keywords and the target keywords, and the matching degree is presented in a quantitative manner, which provides an objective and measurable reference index for the generation of encryption key. The encryption key matching the data set can be flexibly generated according to the keyword matching degree of each data set and the account key by setting the encryption key generation submodule, the correlation between the data set and the account key can be fully considered, the generated encryption key has stronger relevance with the data set, the pertinence and effectiveness of the key are improved, the encryption key is more difficult to be cracked, and thus the data security is improved. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 The structure block diagram of the agricultural platform account system based on multi-source data of the embodiment of the present application is shown in the figure;

[0043] Figure 2 The structure block diagram of the key generation module of the embodiment of the present application is shown in the figure;

[0044] Figure 3 The structure block diagram of the data fusion module of the embodiment of the present application is shown in the figure;

[0045] Figure 4 The structure block diagram of the data evaluation submodule of the embodiment of the present application is shown in the figure;

[0046] Figure 5 The structure block diagram of the key matching module of the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0047] In order to make the objects, technical schemes and advantages of the present application clearer, the following further describes the present application with reference to the embodiments; it should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0048] The preferred embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and not to limit the protection scope of the present application.

[0049] It should be noted that, in the description of the present application, the terms indicating the direction or positional relationship such as "upper", "lower", "left", "right", "inner", "outer" and the like are based on the direction or positional relationship shown in the drawings, which is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application.

[0050] In addition, it should also be noted that, in the description of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connection" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be direct connection, or indirect connection through intermediate medium, or internal communication of two elements. Those skilled in the art can understand the specific meaning of the above terms in the present application according to the specific circumstances.

[0051] Please refer to Figure 1 The structure block diagram of the agricultural material platform account system based on multi-source data according to the embodiment of the present application is shown in the figure; the embodiment of the present application provides an agricultural material platform account system based on multi-source data, which comprises:

[0052] An account database is used to store the historical account data of the agricultural material platform;

[0053] In implementation, the historical account data includes p pieces of account data, each piece of account data includes q fields, that is, the historical account data includes p*q field contents, each piece of account data can include customer information (the name, contact information, address, planting or business scope, credit level, credit limit, historical credit performance of customers such as farmers and dealers), supplier information (the name, contact information, address, product category provided by the supplier, execution of the purchase order, product quality feedback, timeliness of supply, etc.), transaction records (detailed information of farmers or dealers purchasing agricultural products, such as transaction date, product name, specification, quantity, unit price, total price and supplier information, detailed information of agricultural products sold to downstream customers, such as sales date, customer name, product details, sales amount, and payment method), inventory data (agricultural product in and out of the warehouse, including purchase into the warehouse, sales out of the warehouse, inventory count quantity change, and agricultural product inventory balance, inventory location, inventory value and inventory shelf life at different time points), logistics information (detailed information of agricultural products in the transportation process, including delivery date, delivery location, pickup location, transportation method, carrier information, and logistics distribution arrangement for different customers or warehouses, such as distribution time, distribution route, and estimated delivery time), production information (agricultural product production arrangement, including production date, production batch, production quantity, production process, and various cost information involved in the production process, such as raw material cost, labor cost, and manufacturing cost) and the like corresponding to the fields and field contents.

[0054] a key generation module connected with the account database, configured to determine a target keyword based on the historical account data, and generate a corresponding account key based on the target keyword;

[0055] Referring to Figure 2 The key generation module includes:

[0056] a data analysis submodule connected with the account database, configured to determine a plurality of candidate fields based on the historical account data, and extract a plurality of keywords based on the field contents of each candidate field;

[0057] In implementation, statistical analysis is performed on each field, and if the statistical frequency (a statistical frequency is counted when the field content is not empty) of any field is greater than a first preset frequency, it is determined as a candidate field. The first preset frequency can be set by the actual implementer based on the actual situation, and is preferably set to 2-4.

[0058] It can be understood that the field contents of each candidate field are subjected to cluster analysis to obtain a plurality of cluster groups, each cluster group including a plurality of field contents, the keywords corresponding to the field contents in the same cluster group being the same, each field content in the cluster group is subjected to entity recognition, and the entity with a frequency greater than a second preset frequency in the same cluster group is determined as the keyword corresponding to the field content of the cluster group to obtain a plurality of keywords. The actual implementer can set the second preset frequency based on the actual situation or based on the number of field contents in the cluster group. Preferably, the second preset frequency is set to 1 / 2-2 / 3 of the number of field contents in the cluster group.

[0059] It can be understood that any method capable of performing entity recognition in the prior art falls within the protection scope of the present application, and will not be described here.

[0060] A keyword matching sub-module connected to the data analysis sub-module is used to analyze each keyword to generate corresponding fuzzy words, and each fuzzy word is clustered to determine a plurality of account keywords.

[0061] In implementation, each keyword can be set with a corresponding fuzzy word, and the fuzzy word has a hyponym relationship with the keyword. The actual implementer can set it according to the actual situation. Generally, the fuzzy word is a broad definition (hyponym) covering the specific keyword.

[0062] It can be understood that each fuzzy word is clustered to obtain a plurality of cluster groups, each cluster group including at least one fuzzy word. If there is one fuzzy word in the cluster group, it is determined as an account keyword. If the fuzzy words in the cluster group are not unique, each fuzzy word is subjected to broad definition to determine a broad fuzzy word, which is determined as an account keyword.

[0063] An account key generation sub-module connected to the keyword matching sub-module is used to determine a target keyword based on each account keyword, and generate a corresponding account key based on the target keyword.

[0064] In implementation, each account keyword is subjected to deduplication, and the target keyword is obtained by sorting and merging based on the initial letters of the pinyin of each account keyword in English alphabetical order. For example, the account keywords are: pesticide (nongyao), fertilizer (huafei), seed (zhongzi), and pesticide (nongyao). After removing the duplicate word "pesticide", the target keyword obtained is: fertilizer pesticide seed (sorted according to the order of h, n, and z).

[0065] It can be understood that the target keyword can be processed based on a preset key generation algorithm to obtain a corresponding account key. Those skilled in the art know that any algorithm in the prior art that can process a target keyword to obtain an account key, such as a hash algorithm, falls within the protection scope of the present application, and will not be described here.

[0066] The key generation module of the present application determines a plurality of candidate fields based on the historical account data through the setting of the data analysis submodule, and extracts a plurality of keywords based on the field content of each candidate field, providing accurate and key information basis for subsequent key generation, ensuring that the generated key is closely related to the data content, and improving the data processing efficiency. By setting the keyword matching submodule to analyze the extracted keywords, corresponding fuzzy words are generated, and by clustering the fuzzy words, the accuracy and coverage of the account keywords can be improved, and the security and flexibility of the subsequent generated account keys can be improved. By setting the account key generation submodule to generate a corresponding account key based on the target keyword, it can be ensured that the account key is related to the data content and has high unpredictability, which can improve the security of the key and further improve the data security.

[0067] The data fusion module is used to obtain data of each data source of the agricultural platform, classify the data in each data source according to the attribute label of the data to generate a plurality of data sets, determine the data features corresponding to each data set, and determine the sensitivity of each data set based on the data features corresponding to each data set;

[0068] Please refer to Figure 3 The data fusion module includes:

[0069] The data acquisition submodule is used to obtain data of each data source of the agricultural platform;

[0070] In implementation, the data sources of the agricultural platform include databases of agricultural production enterprises, agricultural distributors, agricultural retailers, logistics and transportation enterprises, agricultural meteorological departments, agricultural research institutions, and agricultural supervision agencies, etc. that are open or shared.

[0071] The data classification submodule is connected with the data acquisition submodule and is used to classify the data in each data source according to the attribute label of the data to generate a plurality of data sets, wherein the attribute label of the data in each data set is the same;

[0072] Specifically, the data classification submodule determines the corresponding attribute label according to the data attribute of the data in each data source;

[0073] The data attribute includes data source, data security level, and data transmission feature, and also includes data quality, data type, and public level, etc.

[0074] In implementation, the training data set can be constructed based on the data attributes of the historical data to train the neural network model to obtain the attribute label model, the data in each data source is input into the attribute label model to output the attribute label corresponding to the data in each data source, and a data set is formed according to the data with the same attribute label.

[0075] The data feature extraction submodule is connected with the data classification submodule, and is used to determine the data features corresponding to each data set based on the data in each data set and the feature evaluation model;

[0076] In implementation, the actual implementer can train the feature evaluation model based on the historical data, the feature evaluation model can evaluate the features of the data in the same data set to obtain a representative data feature as the data feature corresponding to the data set, and each data feature can reflect the comprehensive security attribute of the data in each data set. Those skilled in the art know that any machine learning model capable of evaluating the features of the data in the data set in the prior art falls within the protection scope of the present application, and will not be described here.

[0077] The data evaluation submodule is connected with the data feature extraction submodule, and is used to determine the sensitive index corresponding to each data set based on the data features corresponding to each data set, and determine the sensitive degree of each data set based on the sensitive index and the sensitive degree evaluation index system.

[0078] Please refer to Figure 4 The data evaluation submodule is connected with the data feature extraction submodule, and is used to determine the sensitive index corresponding to each data set based on the data features corresponding to each data set, and determine the sensitive degree of each data set based on the sensitive index and the sensitive degree evaluation index system.

[0079] The system construction unit is used to construct the sensitive degree evaluation index system.

[0080] In implementation, the sensitivity evaluation index system includes multiple indexes, such as data content sensitivity, data use sensitivity, data propagation range, and data compliance requirement, and each index is quantitatively scored according to a standard. For example, for data content sensitivity, the data is scored according to whether it contains personal privacy or business secrets, such as scoring high for containing personal privacy information and scoring low for only containing basic information such as product specifications; for data use sensitivity, the data is scored according to its importance in business decision-making and fund transaction, such as scoring high for data used in key decision-making and fund transaction and scoring low for data with narrow use; for data propagation range, the data is scored according to the breadth and depth of data sharing and propagation, such as scoring high for data widely propagated among multiple departments and external partners and scoring low for data limited to internal use; and for data compliance requirement, the data is scored according to whether it is strictly protected by laws and regulations, such as scoring high for data strictly regulated by data protection laws and scoring low for data without clear constraints. Actual implementers can set the scoring rules based on actual conditions, for example, the score is from 0 to 10, with 0 being low and 10 being high.

[0081] a sensitivity index determination unit connected to the data feature extraction submodule, configured to determine the sensitivity index of each data set based on the data features corresponding to each data set;

[0082] In implementation, the data features corresponding to each data set can reflect the comprehensive security level of the data in each data set, and the comprehensive security level is positively correlated with the sensitivity index, that is, the higher the comprehensive security level, the higher the sensitivity index.

[0083] a sensitivity degree determination unit connected to the system construction unit and the sensitivity index determination unit, configured to determine the sensitivity degree of each data set based on the sensitivity degree evaluation index system and the sensitivity index.

[0084] In implementation, each data set is comprehensively evaluated based on the sensitivity degree evaluation index system and the sensitivity index. If the score of the data set based on the sensitivity degree evaluation index system is less than a preset score and the sensitivity index is greater than a preset sensitivity index, the corresponding sensitivity degree is determined to be high; if the score of the data set based on the sensitivity degree evaluation index system is greater than or equal to the preset score and the sensitivity index is greater than the preset sensitivity index, the corresponding sensitivity degree is determined to be medium; and if the score of the data set based on the sensitivity degree evaluation index system is greater than or equal to the preset score and the sensitivity index is less than or equal to the preset sensitivity index, the corresponding sensitivity degree is determined to be low. It can be understood that actual implementers can set the preset score and the preset sensitivity index based on actual conditions.

[0085] The data fusion module of the application can guarantee the timeliness and comprehensiveness of data by setting a data acquisition submodule to acquire data of each data source of the agricultural platform. The data can be classified according to the attribute label of the data by setting a data classification submodule, the attribute label of the data in each data set is the same, the data is ordered, and the subsequent data processing efficiency can be improved. The data features of each data set can be accurately determined based on the attribute label of the data in each data set and the feature evaluation model by setting a data feature extraction submodule. The sensitive index of each data set can be determined based on the data features corresponding to each data set, and the sensitive degree can be further determined by setting a data evaluation submodule. Different data protection strategies can be implemented according to different sensitive levels of data, and the data security is improved.

[0086] A key matching module is connected with the key generation module and the data fusion module, respectively, to determine the data keywords corresponding to each data set based on the data in each data set, and to determine the encryption key corresponding to each data set based on the comparison result of each data keyword and the target keyword and the account key;

[0087] Referring to Figure 5 The key matching module includes:

[0088] A data keyword determination submodule is connected with the data fusion module to determine a plurality of candidate keywords corresponding to each data set based on the data in each data set.

[0089] In the implementation, entity recognition is performed on the data in each data set, the occurrence frequency of each entity in the data set is counted, and the entity with an occurrence frequency greater than a third preset frequency is determined as a candidate keyword.

[0090] It can be understood that the actual implementer can set the third preset frequency based on the actual situation, and preferably, the third preset frequency is set to a value range of 8-15.

[0091] A keyword comparison submodule is connected with the data keyword determination submodule and the key generation module, respectively, to determine the data keywords corresponding to each data set based on each candidate keyword, and to determine the keyword matching degree corresponding to each data set based on the comparison result of each data keyword and the target keyword.

[0092] In the implementation, each candidate keyword corresponding to each data set is combined to obtain the data keyword corresponding to the data set, and the combination method is to sort the candidate keywords according to the first letter of the pinyin, if the first letter of the candidate keywords is the same, the first letter of the second word is sorted, and so on.

[0093] It can be understood that the calculation of the similarity between the data keywords and the target keywords can be converted into the calculation of the cosine similarity between the word vectors, and the cosine similarity can be taken as the corresponding keyword matching degree.

[0094] The encryption key generation submodule is connected with the keyword comparison submodule, and is used to determine the encryption key corresponding to each data set based on the keyword matching degree corresponding to each data set and the account key.

[0095] Specifically, the encryption key generation submodule determines a matching feature value based on the comparison result of the keyword matching degree corresponding to each data set and a preset matching degree, and determines the encryption key corresponding to each data set based on the matching feature value and the account key.

[0096] In implementation, the matching feature value is determined according to the ratio of the keyword matching degree to the preset matching degree, if the matching feature value corresponding to any data set is greater than or equal to 1, the account key is determined as the encryption key of the data set, if the matching feature value corresponding to any data set is less than 1, the account key is transformed, for example, XOR operation, hash algorithm, etc., and the transformed account key is determined as the encryption key of the data set.

[0097] It can be understood that the actual implementer can set the preset matching degree based on the actual situation, and preferably, the preset matching degree is set to 0.7-0.8.

[0098] The key matching module of the application accurately determines a plurality of candidate keywords from each data set through the setting of the data keyword determination submodule, provides an accurate basis for subsequent screening of data keywords, helps to fully reflect the characteristics of the data set, and provides diversified input for subsequent data encryption key generation. The data keyword corresponding to each data set is determined through the analysis and comparison of the candidate keywords by the keyword comparison submodule, the keyword matching degree corresponding to each data set is determined based on the comparison result of the data keyword and the target keyword, the matching degree is presented in a quantitative manner, and an objective and measurable reference index is provided for the generation of the encryption key. The encryption key generation submodule is set to flexibly generate the encryption key matched with the data set according to the keyword matching degree of each data set and the account key, which can fully consider the correlation between the data set and the account key, the generated encryption key has stronger relevance with the data set, improves the pertinence and effectiveness of the key, makes the encryption key more difficult to be cracked, and thus improves the data security.

[0099] The encryption control module is connected with the data fusion module and the key matching module respectively, and is used to determine the encryption mode corresponding to each data set based on the sensitivity of each data set, including,

[0100] encrypt the encryption key corresponding to each of the data sets based on the sensitivity of each of the data sets, and encrypt the data in each of the data sets based on the encrypted encryption key;

[0101] Alternatively, encrypt the data in each of the data sets based on the encryption key corresponding to each of the data sets.

[0102] Specifically, the encryption control module determines, based on the comparison result of whether the sensitivity of the data set meets the preset standard, that the encryption method corresponding to the data set is encrypting the encryption key corresponding to each of the data sets based on the sensitivity of each of the data sets, and encrypting the data in each of the data sets based on the encrypted encryption key.

[0103] In implementation, the preset standard is high sensitivity or medium sensitivity, i.e., the sensitivity of the data set is high sensitivity or medium sensitivity, then the encryption key corresponding to each of the data sets is encrypted based on the sensitivity of each of the data sets, and the data in each of the data sets is encrypted based on the encrypted encryption key.

[0104] It can be understood that if the sensitivity of the data set is high sensitivity, the encryption key corresponding to the data set is encrypted based on a strong encryption algorithm (for example, asymmetric encryption algorithm RSA-2048, asymmetric encryption algorithm RSA-4096, symmetric algorithm AES-256, etc.), and if the sensitivity of the data set is medium sensitivity, the encryption key corresponding to the data set is encrypted based on a general encryption algorithm (for example, symmetric algorithm AES-128, symmetric algorithm AES-192, etc.).

[0105] Specifically, the encryption control module determines, based on the comparison result of whether the sensitivity of the data set meets the preset standard, that the encryption method corresponding to the data set is encrypting the data in each of the data sets based on the encryption key corresponding to each of the data sets.

[0106] In implementation, the sensitivity of the data set does not meet the preset standard, i.e., the sensitivity of the data set is low sensitivity.

[0107] It can be understood that the method of encrypting the data in each of the data sets is not specifically limited, which is prior art and will not be described here.

[0108] The application sets the key generation module to determine the target keyword based on the historical account data and generate the corresponding account key, so that the key is closely related to the data content, and the pertinence and security of key generation are improved. By setting the data fusion module, the data of each data source of the agricultural platform can be integrated, and multiple data sets can be generated according to the attribute label classification, which can reduce the data processing amount and improve the data processing efficiency. By determining the data characteristics of each data set and evaluating its sensitivity, a basis is provided for the security management of data, which helps to implement differentiated data protection strategies and improve data security. By setting the key matching module, the data key in the data set is determined based on the data, and the encryption key is determined by comparing with the target keyword and combining the account key, which increases the flexibility and accuracy of key matching. The content of the data set is closely combined with the key generation and matching process, ensuring the relevance and security of the data encryption key and data. By setting the encryption control module, different encryption methods are determined according to the sensitivity of the data set, which can encrypt the encryption key and then encrypt the data, or directly encrypt the data, providing a variety of encryption strategy selection. Through reasonable encryption control, it ensures that data of different sensitivity levels is properly encrypted, which can improve data security while avoiding over-encryption, and can improve data quality.

[0109] So far, the technical solutions of the application have been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to related technical features without departing from the principles of the application, and the technical solutions after the changes or replacements will fall within the protection scope of the application.

Claims

1. An agricultural input platform ledger system based on multi-source data, characterized in that, include: The ledger database is used to store historical ledger data of the agricultural input platform; A key generation module, which is connected to the ledger database, is used to determine target keywords based on the historical ledger data and generate corresponding ledger keys based on the target keywords. The data fusion module is used to acquire data from various data sources of the agricultural input platform, classify the data according to the attribute tags of the data in each data source to generate several datasets, determine the data characteristics of each dataset, and determine the sensitivity of each dataset based on the data characteristics of each dataset. A key matching module, which is connected to the key generation module and the data fusion module respectively, is used to determine the data keywords corresponding to each dataset based on the data in each dataset, and to determine the encryption key corresponding to each dataset based on the comparison results of each data keyword and the target keyword and the ledger key. An encryption control module, connected to both the data fusion module and the key matching module, is used to determine the encryption method for each dataset based on its sensitivity. include, The encryption control module determines the encryption method corresponding to each dataset based on the comparison results of the sensitivity of the datasets meeting the preset standard. The encryption method is to encrypt the encryption key corresponding to each dataset based on the sensitivity of each dataset, and then encrypt the data in each dataset based on the encrypted encryption key. The encryption control module determines the encryption method for each dataset based on the comparison results showing that the sensitivity of the dataset does not meet the preset standard. The encryption method is to encrypt the data in each dataset based on the encryption key corresponding to each dataset. The key generation module includes: The data analysis submodule is connected to the ledger database and is used to determine several candidate fields based on the historical ledger data, and extract several keywords based on the field content of each candidate field. In this submodule, cluster analysis is performed on the field content of each candidate field to obtain several cluster groups, and entity recognition is performed on the field content of each cluster group to determine several keywords. The keyword matching submodule, which is connected to the data analysis submodule, is used to analyze each keyword to generate corresponding fuzzy words, and to cluster each fuzzy word to determine a number of ledger keywords; The ledger key generation submodule is connected to the keyword matching submodule and is used to determine the target keyword based on each of the ledger keywords and generate the corresponding ledger key based on the target keyword.

2. The agricultural input platform ledger system based on multi-source data according to claim 1, characterized in that, The data fusion module includes: The data acquisition submodule is used to acquire data from various data sources on the agricultural input platform; The data classification submodule, which is connected to the data acquisition submodule, is used to classify data according to the attribute labels of data from each data source to generate several datasets, wherein the attribute labels of data in each dataset are the same. A data feature extraction submodule, which is connected to the data classification submodule, is used to determine the data features corresponding to each dataset based on the data in each dataset and the feature evaluation model. The data evaluation submodule, which is connected to the data feature extraction submodule, is used to determine the sensitivity index corresponding to each dataset based on the data features corresponding to each dataset, and to determine the sensitivity of each dataset based on the sensitivity index and the sensitivity evaluation index system.

3. The agricultural input platform ledger system based on multi-source data according to claim 2, characterized in that, The key matching module includes: The data keyword determination submodule is connected to the data fusion module and is used to determine several candidate keywords corresponding to each dataset based on the data in each dataset. The keyword comparison submodule is connected to the data keyword determination submodule and the key generation module, respectively, to determine the data keywords corresponding to each dataset based on each candidate keyword, and to determine the keyword matching degree corresponding to each dataset based on the comparison results of each data keyword and the target keyword; An encryption key generation submodule, which is connected to the keyword comparison submodule, is used to determine the encryption key corresponding to each dataset based on the keyword matching degree of each dataset and the ledger key.

4. The agricultural input platform ledger system based on multi-source data according to claim 3, characterized in that, The encryption control module determines the encryption coefficient corresponding to each dataset based on the sensitivity of each dataset, and encrypts the encryption key corresponding to each dataset based on the encryption coefficient corresponding to each dataset.

5. The agricultural input platform ledger system based on multi-source data according to claim 4, characterized in that, The data classification submodule determines the corresponding attribute labels based on the data attributes of the data in each data source; The data attributes include data source, data security level, and data transmission characteristics.

6. The agricultural input platform ledger system based on multi-source data according to claim 5, characterized in that, The data evaluation submodule includes: System construction unit, which is used to construct a sensitivity assessment index system; A sensitivity index determination unit, which is connected to the data feature extraction submodule, is used to determine the sensitivity index corresponding to each dataset based on the data features corresponding to each dataset. A sensitivity determination unit, which is connected to the system construction unit and the sensitivity index determination unit respectively, is used to determine the sensitivity of each dataset based on the sensitivity evaluation index system and the sensitivity index.

7. The agricultural input platform ledger system based on multi-source data according to claim 6, characterized in that, The encryption key generation submodule determines the matching feature value based on the comparison result of the keyword matching degree corresponding to each dataset and the preset matching degree, and determines the encryption key corresponding to each dataset based on the matching feature value and the ledger key.

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