Interface standardization information management system

Abstract

The invention relates to the field of information management, in particular to an interface standardization information management system. The method comprises the steps that a security management unit defines user roles and permissions based on a permission management module and verifies user identity information through an authentication module; the data management unit is used for storing information data through a data storage module and encrypting sensitive data by using a data encryption module; the user roles perform data retrieval through keywords and screening conditions based on the data search module; the report and analysis unit analyzes the data by using a data analysis module based on the exported data, and generates a statistical report from the analyzed data based on a json generation module; the interaction and display unit is used for displaying system notifications, system overviews and common function entrances; through integration of the safety management unit, the data management unit, the report and analysis unit and the interaction and display unit, all-around management of information is realized.

Description

Technical Field

[0001] This invention relates to the field of information management, and more specifically, to an information management system with a standardized interface. Background Technology

[0002] With the rapid development of information technology, enterprises have increasingly higher demands for information management systems. They are seeking systems that can provide a unified interface, ensure data security, and offer efficient data processing capabilities.

[0003] In today's information age, enterprises and organizations have an ever-growing demand for efficient and secure information management systems. Traditional information management systems often suffer from inconsistent interfaces, complex operations, and low security. These problems not only affect the user experience but may also lead to security risks such as data breaches. Therefore, it is necessary to design an information management system with a standardized interface. Summary of the Invention

[0004] The purpose of this invention is to provide a standardized information management system to solve the problems mentioned in the background art, such as inconsistent interfaces, complex operation, and low security of traditional information management systems. These problems not only affect the user experience but may also cause security risks such as data leakage.

[0005] To achieve the above objectives, the present invention aims to provide an interface-standardized information management system, including a security management unit, wherein the security management unit defines user roles and permissions based on a permission management module and verifies user identity information through an authentication module;

[0006] The data management unit stores information data through a data storage module and encrypts sensitive data using a data encryption module. User roles retrieve data based on keywords and filtering conditions using a data search module and export data to CSV format using a data export module.

[0007] The reporting and analysis unit uses a data analysis module to analyze the exported data and generates statistical reports based on the analyzed data using a JSON generation module.

[0008] The interaction and display unit is used to display system notifications, system overviews, and frequently used function entries; the interaction and display unit includes a message notification module and an interface display module.

[0009] As a further improvement to this technical solution, the permission management module is divided into system administrators and ordinary users; the permissions of the system administrator include user management, permission allocation, data submission, data access, and report viewing; the permissions of the user include data access, report viewing, and personal settings.

[0010] As a further improvement to this technical solution, the authentication module verifies the username and password. The system searches for the corresponding hash value S in the database based on the username. The system applies the same hash function H to the user-input password P to generate a hash value V(H(P)). The system compares whether V(H(P)) and S are the same, specifically:

[0011] V(H(P))==S;

[0012] In the formula, H represents converting the password into a fixed-length hash value; P represents the original password provided by the user when logging in; S represents the hash value generated by the hash function when the user registers; V(H(P)) represents the result after applying the hash function H to the password P entered by the user.

[0013] As a further improvement to this technical solution, the data encryption module uses the AES symmetric encryption algorithm to encrypt sensitive data, specifically:

[0014] C = E(K, P);

[0015] In the formula, C represents the ciphertext; K represents the key; P represents the plaintext; and E represents the encryption function.

[0016] As a further improvement to this technical solution, the data search module is based on user role rules, allowing different user roles to access different data. These rules include that administrators can search for both normal and sensitive information, while ordinary users can only search for normal information. The specific steps involved in the data search module are as follows:

[0017] The administrator decrypts the encrypted content using the AES symmetric encryption algorithm key, specifically:

[0018] D(K, C) = P;

[0019] In the formula, D represents the decryption function; K represents the key; C represents the ciphertext; and P represents the decrypted plaintext.

[0020] As a further improvement to this technical solution, the administrator searches for normal and sensitive information in the filtered strings using the KMP algorithm. The specific steps involved are as follows:

[0021] Generate a string matching table, specifically:

[0022] PMT[i]=length X∈P[0...i-1]which[0...i-1];

[0023] In the formula, X represents the main string; PMT represents the string matching table; and P represents the decrypted plaintext.

[0024] The string matching table is searched, specifically as follows:

[0025] FD = while(i <n and j<m)if(X∈P[i]==Y∈P[j])i++,j++else j=PMT∈P[j];

[0026] In the formula, X represents the main string; Y represents the pattern string; n represents the length of the main string; m represents the length of the pattern string; i represents the current position pointer of the main string; j represents the current position pointer of the pattern string; PMT represents the string matching table; P represents the decrypted plaintext; FD represents the found string;

[0027] Ordinary users can search for normal and sensitive information using the KMP algorithm based on the filtered strings. The specific steps involved are as follows:

[0028] Generate a string matching table, specifically:

[0029] PMT[i]=length X[0...i-1]which[0...i-1];

[0030] In the formula, X represents the main string; PMT represents the string matching table;

[0031] The string matching table is searched, specifically as follows:

[0032] FD = while(i <n and j<m)if(X[i]==Y[j])i++,j++else j=PMT[j];

[0033] In the formula, X represents the main string; Y represents the pattern string; n represents the length of the main string; m represents the length of the pattern string; i represents the current position pointer of the main string; j represents the current position pointer of the pattern string; PMT represents the string matching table; and FD represents the found string.

[0034] As a further improvement to this technical solution, the specific steps involved in exporting the data to CSV format based on the found string through the data export module are as follows:

[0035] Define a function `find_pattern(FD, P)` that returns the position of pattern P in the text sequence `FD`, specifically:

[0036] D′=find_pattern(FD,P)={k|0<=k<=|FD|-|P|, FD[k:k+|P|]==P};

[0037] In the formula, FD represents the found string; P represents the substring; k represents the current text position being traversed; |FD|-|P| represents the possible range of traversal; FD[k:k+|P|] means starting from position i; FD[k:k+|P|]==P means checking whether the current substring matches the pattern; find_pattern represents the integration function; D′ means returning the matching position and integrating it into a CSV file.

[0038] As a further improvement to this technical solution, the data analysis module, based on a text analysis algorithm, analyzes the word frequency, inverse document frequency, and text similarity of the text. The specific steps involved are as follows:

[0039] First, we analyze the word frequency in the document, specifically:

[0040] tf(word, D′)=frequency(word, D′) / total_words(D′);

[0041] In the formula, word represents a word; D′ represents the position of the returned match and integrates it into a CSV file; total_words represents the total number of words in the document; frequency represents the number of times a word appears in the document; tf(word, D′) represents the word frequency in the document;

[0042] Term frequency analysis is used to calculate the frequency of words appearing in a specific document. This metric helps in understanding the document's theme and content. In the data analysis module, term frequency analysis can help identify which words are most important in a particular document, thereby revealing the document's core information;

[0043] Next, the inverse document frequency of words in the document collection is analyzed, specifically:

[0044] idf(word, collection)=log(total_D′ / D′_with_word(word));

[0045] In the formula, total_D′ represents the total number of documents in the document collection; D′_with_word represents the number of documents containing the word; idf(word, collection) represents the inverse document frequency of the word in the document collection;

[0046] Inverse document frequency (IVF) analysis is used to assess the rarity of words in a document collection. If a word appears in most documents in a document collection, its IVF frequency is low. Conversely, if a word appears in only a few documents, its IVF frequency is high. In the data analysis module, IVF analysis can help identify which words are more unique in a document collection, thus revealing the uniqueness and rarity of documents.

[0047] Finally, the text similarity was analyzed, specifically as follows:

[0048] tf-idf(word, D′)=tf(word, D′)*idf(word, collection);

[0049] In the formula, tf(word, D′) represents the word frequency in the document; idf(word, collection) represents the inverse document frequency of the word in the document collection; tf-idf(word, D′) represents text similarity analysis;

[0050] Text similarity analysis calculates the TF-IDF value of a word by combining term frequency analysis and inverse document frequency analysis. This value can be used to assess the importance of a word in a specific document, while also taking into account the rarity of the word in the entire document set. In the data analysis module, text similarity analysis can help identify which words are most distinctive in a particular document, thereby revealing the similarities and differences between the document and other documents.

[0051] As a further improvement to this technical solution, the JSON generation module converts all analyzed data into JSON format, and the specific steps involved are as follows:

[0052] First, the `create_json_object()` function initializes a JSON object, returning an empty dictionary to store the analysis results.

[0053] Secondly, the analyzed data is added to the JSON object by using add_analysis_to_json(json_object, analysis_data);

[0054] Finally, the JSON object is converted into a string using the serialize_json(json_object) function.

[0055] As a further improvement to this technical solution, the message notification module is used to notify users and administrators of messages in real time, and the interface display module parses the serialized JSON object into a chart interface for users and administrators to view based on a JSON parsing algorithm; the JSON parsing algorithm is specifically as follows:

[0056] First, convert the JSON string into a Python object using `parse_json(json_string)`; second, use `create`... _ The `chart(data_structure)` function generates a chart based on the data structure. Finally, the `display_chart(chart)` function displays the chart on the interface.

[0057] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0058] 1. This standardized information management system integrates security management, data management, reporting and analysis, and interaction and display units to achieve comprehensive information management. The system employs access control, data encryption, text analysis, and JSON format conversion technologies to improve the security, efficiency, and convenience of information management. Furthermore, the system features real-time message notifications and chart display functions, enabling users to understand data more intuitively and providing strong support for enterprise decision-making. Attached Figure Description

[0059] Figure 1 This is an overall flowchart of the present invention;

[0060] The meanings of the labels in the diagram are as follows:

[0061] 1. Security Management Unit; 2. Data Management Unit; 3. Reporting and Analysis Unit; 4. Interaction and Display Unit. Detailed Implementation

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] Example

[0064] Please see Figure 1 As shown, a standardized information management system with a defined interface is provided, including...

[0065] Security management unit 1 defines user roles and permissions based on the access control module and verifies user identity information through the authentication module. The authentication module verifies the username and password. The system searches for the corresponding hash value S in the database based on the username. The system applies the same hash function H to the user-input password P to generate a hash value V(H(P)). The system compares V(H(P)) with S to see if they are the same. Specifically:

[0066] V(H(P))==S;

[0067] In the formula, H represents converting the password into a fixed-length hash value; P represents the original password provided by the user when logging in; S represents the hash value generated by the hash function when the user registers; V(H(P)) represents the result after applying the hash function H to the password P entered by the user.

[0068] During the user login process, users first enter their username and password. The system then looks up the corresponding hash value S in the database based on the username. This hash value is stored after the user processed the original password using a hash function H during registration. The system then processes the user-entered password P using the same hash function H, generating a hash value V(H(P)). Finally, the system compares V(H(P)) with S. If they match, password verification is successful, and the user can log in; otherwise, password verification fails, and the user cannot log in. This entire process ensures password security while protecting user data.

[0069] Data management unit 2 stores information data through a data storage module and encrypts sensitive data using a data encryption module. User roles retrieve data based on keywords and filtering conditions using a data search module and export data to CSV format using a data export module. The data encryption module uses the AES symmetric encryption algorithm to encrypt sensitive data, specifically:

[0070] C = E(K, P);

[0071] In the formula, C represents the ciphertext; K represents the key; P represents the plaintext; and E represents the encryption function.

[0072] The data search module is based on user role rules, granting different user roles access to different data. These rules include administrators being able to search for both normal and sensitive information, while ordinary users can only search for normal information. The specific steps involved in the data search module are as follows:

[0073] The administrator decrypts the encrypted content using the AES symmetric encryption algorithm key, specifically:

[0074] D(K, C) = P;

[0075] In the formula, D represents the decryption function; K represents the key; C represents the ciphertext; and P represents the decrypted plaintext.

[0076] The administrator searches for normal and sensitive information in the filtered strings using the KMP algorithm. The specific steps involved are as follows:

[0077] Generate a string matching table, specifically:

[0078] PMT[i]=length X∈P[0...i-1]which[0...i-1];

[0079] In the formula, X represents the main string; PMT represents the string matching table; and P represents the decrypted plaintext.

[0080] The string matching table is searched, specifically as follows:

[0081] FD = while(i <n and j<m)if(X∈P[i]==Y∈P[j])i++,j++else j=PMT∈P[j];

[0082] In the formula, X represents the main string; Y represents the pattern string; n represents the length of the main string; m represents the length of the pattern string; i represents the current position pointer of the main string; j represents the current position pointer of the pattern string; PMT represents the string matching table; P represents the decrypted plaintext; FD represents the found string;

[0083] Ordinary users can search for normal and sensitive information using the KMP algorithm based on the filtered strings. The specific steps involved are as follows:

[0084] Generate a string matching table, specifically:

[0085] PMT[i]=length X[0...i-1]which[0...i-1];

[0086] In the formula, X represents the main string; PMT represents the string matching table;

[0087] The string matching table is searched, specifically as follows:

[0088] FD = while(i <n and j<m)if(X[i]==Y[j])i++,j++else j=PMT[j];

[0089] In the formula, X represents the main string; Y represents the pattern string; n represents the length of the main string; m represents the length of the pattern string; i represents the current position pointer of the main string; j represents the current position pointer of the pattern string; PMT represents the string matching table; and FD represents the found string.

[0090] Based on the found string, the data is exported to CSV format using the data export module. The specific steps involved are as follows:

[0091] Define a function `find_pattern(FD, P)` that returns the position of pattern P in the text sequence `FD`, specifically:

[0092] D′=find_pattern(FD,P)={k|0<=k<=|FD|-|P|, FD[k:k+|P|]==P};

[0093] In the formula, FD represents the found string; P represents the substring; k represents the current text position being traversed; |FD|-|P| represents the possible range of traversal; FD[k:k+|P|] means starting from position i; FD[k:k+|P|]==P means checking whether the current substring matches the pattern; find_pattern represents the integration function; D′ means returning the matching position and integrating it into a CSV file.

[0094] Reporting and Analysis Unit 3, based on the exported data, uses a data analysis module to analyze the data and generates statistical reports from the analyzed data using a JSON generation module. The data analysis module uses text analysis algorithms to analyze word frequency, inverse document frequency, and text similarity of the text. The specific steps involved are as follows:

[0095] First, we analyze the word frequency in the document, specifically:

[0096] tf(word, D′)=frequency(word, D′) / total_words(D′);

[0097] In the formula, word represents a word; D′ represents the position of the returned match and integrates it into a CSV file; total_words represents the total number of words in the document; frequency represents the number of times a word appears in the document; tf(word, D′) represents the word frequency in the document;

[0098] Term frequency analysis is used to calculate the frequency of words appearing in a specific document. This metric helps in understanding the document's theme and content. In the data analysis module, term frequency analysis can help identify which words are most important in a particular document, thereby revealing the document's core information;

[0099] Next, the inverse document frequency of words in the document collection is analyzed, specifically:

[0100] idf(word, collection)=log(total_D′ / D′_with_word(word));

[0101] In the formula, total_D′ represents the total number of documents in the document collection; D′_with_word represents the number of documents containing the word; idf(word, collection) represents the inverse document frequency of the word in the document collection;

[0102] Inverse document frequency (IVF) analysis is used to assess the rarity of words in a document collection. If a word appears in most documents in a document collection, its IVF frequency is low. Conversely, if a word appears in only a few documents, its IVF frequency is high. In the data analysis module, IVF analysis can help identify which words are more unique in a document collection, thus revealing the uniqueness and rarity of documents.

[0103] Finally, the text similarity was analyzed, specifically as follows:

[0104] tf-idf(word, D′)=tf(word, D′)*idf(word, collection);

[0105] In the formula, tf(word, D′) represents the word frequency in the document; idf(word, collection) represents the inverse document frequency of the word in the document collection; and tf-idf(word, D′) represents text similarity analysis.

[0106] Text similarity analysis calculates the TF-IDF value of a word by combining term frequency analysis and inverse document frequency analysis. This value can be used to assess the importance of a word in a specific document, while also taking into account the rarity of the word in the entire document set. In the data analysis module, text similarity analysis can help identify which words are most distinctive in a particular document, thereby revealing the similarities and differences between the document and other documents.

[0107] The JSON generation module converts all analyzed data into JSON format. The specific steps involved are as follows:

[0108] First, the `create_json_object()` function initializes a JSON object, returning an empty dictionary to store the analysis results; specifically:

[0109] # Initialize JSON object

[0110] def create_json_object():

[0111] return{}

[0112] Secondly, the analyzed data is added to the JSON object using `add_analysis_to_json(json_object, analysis_data)`; specifically:

[0113]

[0114] Finally, the JSON object is converted to a string using the `serialize_json(json_object)` function; specifically:

[0115] # Serialize JSON objects

[0116] def serialize_json(json_object):

[0117] return json.dumps(json_object)

[0118] The interaction and display unit 4 is used to display system notifications, system overviews, and frequently used function entries; the interaction and display unit 4 includes a message notification module and an interface display module.

[0119] The access control module is divided into system administrators and regular users. System administrators have permissions including user management, permission allocation, data submission, data access, and report viewing. Regular users have permissions including data access, report viewing, and personal settings.

[0120] The message notification module is used to notify users and administrators of messages in real time. The interface display module parses the serialized JSON object into a chart interface for users and administrators to view based on a JSON parsing algorithm. The specific JSON parsing algorithm is as follows:

[0121]

[0122]

[0123] First, the JSON string is converted into a Python object using parse_json(json_string); second, a chart is generated based on the data structure using create_chart(data_structure); finally, the chart is displayed on the interface using display_chart(chart).

[0124] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A standardized interface information management system, characterized in that: include Security management unit (1), wherein the security management unit (1) defines user roles and permissions based on the permission management module and verifies user identity information through the authentication module; The data management unit (2) stores information data through a data storage module and encrypts sensitive data using a data encryption module. User roles can retrieve data using keywords and filtering criteria based on the data search module; Then use the data export module to export the data as a CSV format; The reporting and analysis unit (3) analyzes the data based on the exported data using the data analysis module, and generates statistical reports based on the analyzed data using the json generation module. The interaction and display unit (4) is used to display system notifications, system overviews and frequently used function entries; the interaction and display unit (4) includes a message notification module and an interface display module.

2. The interface standardization information management system according to claim 1, characterized in that: The permission management module is divided into system administrators and ordinary users; the permissions of system administrators include user management, permission allocation, data submission, data access, and report viewing; the permissions of users include data access, report viewing, and personal settings.

3. The interface standardization information management system according to claim 1, characterized in that: The authentication module verifies the username and password. The system searches for the corresponding hash value S in the database based on the username. The system applies the same hash function H to the user-input password P to generate a hash value V(H(P)). The system then compares V(H(P)) with S to see if they are the same. Specifically: V(H(P))==S; In the formula, H represents converting the password into a fixed-length hash value; P represents the original password provided by the user during login; and S represents the hash value generated by the hash function during user registration. V(H(P)) represents the result of applying the hash function H to the password P entered by the user.

4. The interface standardization information management system according to claim 1, characterized in that: The data encryption module uses the AES symmetric encryption algorithm to encrypt sensitive data, specifically: C = E(K, P); In the formula, C represents the ciphertext; K represents the key; P represents the plaintext; and E represents the encryption function.

5. The interface standardization information management system according to claim 1, characterized in that: The data search module is based on user role rules, allowing different user roles to access different data. These rules include administrators being able to search for both normal and sensitive information, while ordinary users can only search for normal information. The specific steps involved in the data search module are as follows: The administrator decrypts the encrypted content using the AES symmetric encryption algorithm key, specifically: D(K, C) = P; In the formula, D represents the decryption function; K represents the key; C represents the ciphertext; and P represents the decrypted plaintext.

6. The interface standardization information management system according to claim 1, characterized in that: The administrator searches for normal and sensitive information in the filtered strings using the KMP algorithm. The specific steps involved are as follows: Generate a string matching table, specifically: PMT[i]=length X∈P[0…i-1]which[0…i-1]; In the formula, X represents the main string; PMT represents the string matching table; and P represents the decrypted plaintext. The string matching table is searched, specifically as follows: FD=while(i<n and j<m)if(X∈P[i]==Y∈P[j])i++, j++else j=PMT∈P[j]; In the formula, X represents the main string; Y represents the pattern string; n represents the length of the main string; m represents the length of the pattern string; i represents the current position pointer of the main string; j represents the current position pointer of the pattern string; PMT represents the string matching table; P represents the decrypted plaintext; FD represents the found string; Ordinary users can search for normal and sensitive information using the KMP algorithm based on the filtered strings. The specific steps involved are as follows: Generate a string matching table, specifically: PMT[i]=length X[0...i-1]which[0...i-1]; In the formula, X represents the main string; PMT represents the string matching table; The string matching table is searched, specifically as follows: FD=while(i<n and j<m)if(X[i]==Y[j])i++, j++else j=PMT[j]; In the formula, X represents the main string; Y represents the pattern string; n represents the length of the main string; m represents the length of the pattern string; i represents the current position pointer of the main string; j represents the current position pointer of the pattern string; PMT represents the string matching table; and FD represents the found string.

7. The interface standardization information management system according to claim 6, characterized in that: The specific steps involved in exporting the data to CSV format using the data export module based on the found string are as follows: Define a function `find_pattern(FD, P)` that returns the position of pattern P in the text sequence `FD`, specifically: D′=find_pattern(FD,P)={k|0<=k<=|FD|-|P|, FD[k:k+|P|]==P}; In the formula, FD represents the found string; P represents the substring; k represents the current text position being traversed; |FD|-|P| represents the possible range of traversal; FD[k:k+|P|] means starting from position i; FD[k:k+|P|]==P means checking whether the current substring matches the pattern; find_pattern represents the integration function; D′ means returning the matching position and integrating it into a CSV file.

8. The interface standardization information management system according to claim 1, characterized in that: The The data analysis module, based on text analysis algorithms, analyzes word frequency, inverse document frequency, and text similarity. The specific steps involved are as follows: First, we analyze the word frequency in the document, specifically: tf(word,D′)=frequency(word,D′) / total_words(D′); In the formula, word represents a word; D′ represents the position of the returned match and integrates it into a CSV file; total_words represents the total number of words in the document; frequency represents the number of times a word appears in the document; tf(word, D′) represents the word frequency in the document; Next, the inverse document frequency of words in the document collection is analyzed, specifically: idf(word,collection)=log(total_D′ / D′-with_word(word)); In the formula, total_D′ represents the total number of documents in the document collection; D′with_word represents the number of documents containing the word; idf(word, collection) represents the inverse document frequency of the word in the document collection; Finally, the text similarity was analyzed, specifically as follows: tf-idf(word,D′)=tf(word,D′)*idf(word,collection); In the formula, tf(word, D′) represents the word frequency in the document; idf(word, collection) represents the inverse document frequency of the word in the document collection; and tf-idf(word, D′) represents text similarity analysis.

9. The interface standardization information management system according to claim 1, characterized in that: The JSON generation module converts all analyzed data into JSON format. The specific steps involved are as follows: First, the `create_json_object()` function initializes a JSON object, returning an empty dictionary to store the analysis results. Secondly, the analyzed data is added to the JS0N object by adding_analysis_to_json(json_object, analysis_data); Finally, the JSON object is converted into a string using the serialize_json(json_object) function.

10. The interface standardization information management system according to claim 1, characterized in that: The message notification module is used to notify users and administrators of messages in real time, and the interface display module parses the serialized JSON object into a chart interface for users and administrators to view based on a JSON parsing algorithm; the JSON parsing algorithm is specifically as follows: First, the JSON string is converted into a Python object using parse_json(json_string); second, a chart is generated based on the data structure using create_chart(data_structure); finally, the chart is displayed on the interface using display_chart(chart).

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