Information management method of information management system

Abstract

The invention discloses an information management method of an information management system, and the method comprises the following steps: determining an information type according to the existing database information, determining an information type label according to the information type, and finally dividing the information importance level according to the information type. The collected information is input, and the system automatically carries out information data preprocessing and quality verification; and after the quality verification is completed, the system automatically identifies the information type and classifies the information according to the known category label. According to the method, the mathematical model is established according to the known database, and the mathematical model is used for automatically classifying the information, so that a large amount of information data can be conveniently and quickly processed, and meanwhile, the method also has the functions of information data quality inspection, repeated data elimination and encryption of different levels of information, so that the information management is more efficient, accurate and safe.

Description

Technical Field

[0001] The present invention relates to the technical field of information management, and in particular to an information management method of an information management system. Background Art

[0002] With the rapid development of information technology, the amount of information faced by enterprises and organizations has exploded. In various fields, the collection, storage, processing and management of information have become increasingly complex. Traditional information management methods often rely on manual operations, which are inefficient and prone to errors.

[0003] The influx of a large amount of different types of information and the lack of an effective classification and organization mechanism make information retrieval difficult and it is difficult to find the required information quickly and accurately. At the same time, the repeated storage and management of information not only wastes resources, but may also lead to problems such as data inconsistency. In addition, in terms of information security, there is a lack of targeted protection measures, and important information may face the risk of leakage. Therefore, an information management method of an information management system is proposed.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In view of the problems in the related art, the present invention proposes an information management method of an information management system to overcome the above technical problems existing in the existing related art.

[0006] To this end, the specific technical solution adopted by the present invention is as follows:

[0007] An information management method for an information management system, the method comprising the following steps:

[0008] S1. Based on the existing database information, clarify the information type, determine the information category label according to the information type, and finally divide the information importance level according to the information type;

[0009] S2. Input the collected information, and the system automatically pre-processes the information data and verifies its quality;

[0010] S3. After the quality verification is completed, the system will automatically identify the information type and classify it according to the known category labels;

[0011] S4, automatically matching the information with the category label with the category information in the database, and automatically eliminating the information when the matching degree is exactly the same, and encrypting the information according to the importance level of the category when the matching degree is different;

[0012] S5. Input the encrypted information into a database for storage.

[0013] As a preferred implementation, the method of clarifying the information type based on the existing database information, determining the information category label based on the information type, and finally classifying the information importance level based on the information type includes the following steps:

[0014] S11. Extract key features of existing database information, including information type and information category labels. Information category labels include customer contract information, financial information, employee file information, marketing and sales, and personal performance information;

[0015] By clarifying the information type and category labels, it is helpful to classify and manage information effectively, making the database more orderly, facilitating subsequent data maintenance and updating, and also making it easier to quickly process subsequent information.

[0016] S12. Use supervised learning method to train with existing labeled data, and use independent test set to verify model performance after training.

[0017] S13. Define the importance level of information based on business needs and information characteristics, and divide the information into three levels: high, medium, and low.

[0018] As a preferred implementation, the steps of defining the importance level of information according to business requirements and information characteristics and classifying the information into three levels: high, medium and low include the following steps:

[0019] S131. Information categories include customer contract information, financial information, employee file information, marketing and sales, and personal performance information. Customer contract information and financial information are classified as high-level information, marketing information and personal performance information are classified as medium-level information, and employee file information is classified as low-level information.

[0020] By dividing information according to different levels of importance, enterprises can manage information more efficiently, optimize resource allocation and reduce risks.

[0021] As a preferred embodiment, the collected information is input, and the system automatically pre-processes the information data and verifies the quality, including the following steps:

[0022] S21. Clean the information data, including detection of duplicate data and removal of missing values;

[0023] S22, verify the data format and convert the information data into a unified format;

[0024] S23, performing range check, consistency check and integrity verification on the information data. The range check is to verify the reasonable range of the numerical data to ensure that the information value is within a reasonable range; the consistency check is to check the consistency between data records to ensure that there is no logical conflict; the integrity check is to check the values ​​of the required fields to ensure that the records are complete;

[0025] S24. Based on the data quality inspection, a data quality report is automatically generated, including the problems and solutions in the cleaning process, and the specific abnormal information is transmitted to the management personnel for processing.

[0026] Clear problems and solutions help to quickly resolve data quality issues and avoid the accumulation and expansion of problems. At the same time, specific abnormal information is transmitted to managers so that managers can understand the data status in a timely manner, making it easier to take targeted measures to deal with it.

[0027] As a preferred implementation, after the quality verification is completed, the system will automatically identify the information type and classify it according to the known category labels. The specific steps are:

[0028] S31. Based on known model requirements, extract features from the input information, input the extracted features into the model, and run a recognition algorithm to identify the information type;

[0029] S32. Obtain the recognition result of each piece of information, and match it with a known category label according to the recognized information type.

[0030] As a preferred implementation, the automatic matching of the information with the category label with the category information in the database, when the matching degree is exactly the same, automatically eliminating the information, and when the matching degree is different, encrypting the information according to the importance level of the category includes the following steps:

[0031] S41. Use a matching algorithm to match the newly collected information with the information in the database one by one, and calculate the matching degree between each new information and the corresponding category information in the database. When the matching degree exceeds 95%, it means that the information is exactly the same as the information in the database. When the matching degree is less than 95%, it means that the information data is different.

[0032] S42. For completely identical data information, the system automatically removes it from the list of information to be processed and records the removal operation;

[0033] Recording the rejection operation helps to ensure the accuracy and traceability of data management and facilitates subsequent review and analysis of the data processing process.

[0034] S43. Encrypt different information data according to the importance level of the information category.

[0035] As a preferred implementation method, the matching algorithm is used to match the newly collected information with the information in the database one by one, and the matching degree of each new information with the corresponding category information in the database is calculated. When the matching degree value exceeds 95, it means that the information is exactly the same as the information in the database. When the matching degree value is less than 95, it means that the information data is different from the database. The steps include:

[0036] S411, split each string into words, construct a word frequency vector for each string, representing the number of times each word appears in the string, count the word frequency of each string, obtain the word frequency vector of the new information: A = [f1, f2, ..., fn] and the word frequency vector of the database information: B = [g1, g2, ..., gn], calculate the cosine similarity, the specific formula is:

[0037]

[0038] The dot product A·B represents the sum of the products of corresponding elements of two vectors:

[0039]

[0040] The vector modulus ∥A∥ and ∥B∥:

[0041]

[0042] The calculated matching value is between 0-100. When p>95, it means that the information is exactly the same as the information in the database. When p<95, it means that the information is different from the information in the database.

[0043] As a preferred embodiment, the encryption of different information data according to the importance level of the information category includes the following steps:

[0044] S431. Use strong encryption algorithms and complex key management strategies for high-level information, use medium-strength encryption algorithms and medium key management strategies for medium-level information, and use simpler encryption algorithms for low-level information;

[0045] S432. Map each type of information to its corresponding encryption level and encrypt them.

[0046] Stricter encryption of information with high importance levels can effectively protect the security of key data, prevent sensitive information from being illegally obtained and abused, and reduce the risk of data leakage. In addition, encryption based on importance levels can more reasonably allocate encryption resources and avoid the waste of resources caused by encrypting all information with the same strength.

[0047] The beneficial effects of the present invention are:

[0048] 1. The present invention generates information category labels based on existing database information and establishes a data model so that subsequent information can be quickly classified. When a large amount of information data needs to be processed, there is no need to manually classify them one by one, thereby saving a lot of time and labor costs. Secondly, automatic classification can also ensure the accuracy of information classification and avoid classification errors caused by human factors.

[0049] 2. The present invention can greatly improve the efficiency of information matching by automatically matching information with category labels with information of the category in the database. When faced with a huge database, indiscriminate matching and verification of all data will consume a lot of time and computing resources. Through targeted matching, specific categories of information can be quickly locked for verification, which greatly shortens the processing time. In addition, it can also improve the accuracy of matching and reduce the probability of false matching, thereby optimizing the information management process.

[0050] 3. The present invention can effectively save storage space and storage costs by eliminating data with the same matching degree, and avoid confusion and interference caused by these duplicate data in subsequent data processing and analysis;

[0051] When the information data matching values ​​are different, different encryption is performed according to the level of the information, which can more reasonably allocate encryption resources and avoid encrypting all information with the same strength, thereby saving computing resources and time costs.

[0052] 4. The present invention has a high degree of automation. First, it can automatically classify information according to its category. Secondly, it can automatically match the data in the existing database to avoid storing duplicate data. Finally, it has the function of encrypting different levels of information data in different ways. This series of automated operations greatly improves the efficiency and security of information management, thereby facilitating the staff to manage information. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 The present invention is a flowchart of an information management method of an information management system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0056] According to an embodiment of the present invention, an information management method of an information management system is provided.

[0057] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to an information management method of an information management system according to an embodiment of the present invention, the method comprises the following steps:

[0058] S1. Based on the existing database information, clarify the information type, determine the information category label according to the information type, and finally divide the information importance level according to the information type;

[0059] Furthermore, the method of clarifying the information type according to the existing database information, determining the information category label according to the information type, and finally classifying the information importance level according to the information type includes the following steps:

[0060] S11. Extract key features of existing database information, including information type and information category labels. Information category labels include customer contract information, financial information, employee file information, marketing and sales, and personal performance information;

[0061] S12. Use supervised learning method to train with existing labeled data, and use independent test set to verify model performance after training.

[0062] It should be noted that the existing labeled data provides a clear learning goal and direction for the model, enabling the model to quickly and effectively learn specific patterns and rules from a large amount of data. By training the model in this way, the model has higher accuracy and reliability so as to adapt to the subsequent actual information classification.

[0063] S13. Define the importance level of information according to business needs and information characteristics, and divide the information into three levels: high, medium and low

[0064] Furthermore, the step of defining the importance level of information according to business requirements and information characteristics, and classifying the information into three levels of high, medium and low includes the following steps:

[0065] S131. Information categories include customer contract information, financial information, employee file information, marketing and sales, and personal performance information. Customer contract information and financial information are classified as high-level information, marketing information and personal performance information are classified as medium-level information, and employee file information is classified as low-level information.

[0066] S2. Input the collected information, and the system automatically pre-processes the information data and verifies its quality;

[0067] Furthermore, the input of the collected information and the automatic preprocessing and quality verification of the information data by the system include the following steps:

[0068] S21. Clean the information data, including detection of duplicate data and removal of missing values;

[0069] S22, verify the data format and convert the information data into a unified format;

[0070] S23, performing range check, consistency check and integrity verification on the information data. The range check is to verify the reasonable range of the numerical data to ensure that the information value is within a reasonable range; the consistency check is to check the consistency between data records to ensure that there is no logical conflict; the integrity check is to check the values ​​of the required fields to ensure that the records are complete;

[0071] S24. Based on the data quality inspection, a data quality report is automatically generated, including the problems and solutions in the cleaning process, and the specific abnormal information is transmitted to the management personnel for processing.

[0072] It should be noted that quality verification can promptly discover problems in the data, thereby providing a high-quality data foundation for subsequent data analysis and decision-making. In addition, automatically generating quality reports can improve work efficiency and reduce the time and cost of manual inspection of data quality.

[0073] S3. After the quality verification is completed, the system will automatically identify the information type and classify it according to the known category labels;

[0074] Furthermore, after the quality verification is completed, the system will automatically identify the information type and classify it according to the known category labels. The specific steps are as follows:

[0075] S31. Based on known model requirements, extract features from the input information, input the extracted features into the model, and run a recognition algorithm to identify the information type;

[0076] S32. Obtain the recognition result of each piece of information, and match it with a known category label according to the recognized information type.

[0077] It should be noted that automatic identification of information types and matching of category labels can greatly improve the efficiency of information management. Since there is no need for manual classification operations one by one, it can also save a lot of time and labor costs. Automatic information classification can also avoid classification errors caused by human factors. S4. Automatically match the information with category labels with the category information in the database. If the matching degree is exactly the same, the information is automatically eliminated. If the matching degree is different, the information is encrypted according to the importance level of the category;

[0078] Furthermore, the automatic matching of the information with the category label with the category information in the database, when the matching degree is exactly the same, automatically eliminating the information, and when the matching degree is different, encrypting the information according to the importance level of the category includes the following steps:

[0079] S41. Use a matching algorithm to match the newly collected information with the information in the database one by one, and calculate the matching degree between each new information and the corresponding category information in the database. When the matching degree exceeds 95%, it means that the information is exactly the same as the information in the database. When the matching degree is less than 95%, it means that the information data is different.

[0080] Furthermore, the use of a matching algorithm to match the newly collected information with the information in the database one by one, and calculating the matching degree of each new information with the corresponding category information in the database, when the matching degree value exceeds 95, it means that the information is exactly the same as the information in the database, and when the matching degree value is less than 95, it means that the information data is different from the database, including the following steps:

[0081] S411, split each string into words, construct a word frequency vector for each string, representing the number of times each word appears in the string, count the word frequency of each string, obtain the word frequency vector of the new information: A = [f1, f2, ..., fn] and the word frequency vector of the database information: B = [g1, g2, ..., gn], calculate the cosine similarity, the specific formula is:

[0082]

[0083] The dot product A·B represents the sum of the products of corresponding elements of two vectors:

[0084]

[0085] The vector modulus ∥A∥ and ∥B∥:

[0086]

[0087] The calculated matching value is between 0 and 100. When p>95, it means that the information is exactly the same as the information in the database. When p<95, it means that the information is different from the information in the database.

[0088] S42. For completely identical data information, the system automatically removes it from the list of information to be processed and records the removal operation;

[0089] S43. Encrypt different information data according to the importance level of the information category.

[0090] It should be noted that automatic matching and elimination of identical information can avoid data redundancy, save storage space, and improve database operating efficiency and query speed. At the same time, it also helps to maintain the simplicity and accuracy of the database and reduce erroneous analysis and decision-making that may be caused by duplicate data.

[0091] Furthermore, the step of encrypting different information data according to the importance level of the information category includes the following steps:

[0092] S431. Use strong encryption algorithms and complex key management strategies for high-level information, use medium-strength encryption algorithms and medium key management strategies for medium-level information, and use simpler encryption algorithms for low-level information;

[0093] It should be noted that the strong encryption algorithm used for high-level information is AES-256, the encryption algorithm used for medium-level information is AES-128, and the encryption algorithm used for low-level information is SA-1024;

[0094] For high-level information, an independent hardware security module (HSM) is used to manage keys, keys are changed regularly, and key access rights are strictly restricted. For medium-level information, a software key management system is used, passwords are changed regularly,

[0095] Low-level information uses basic key management, and passwords can be set manually but need to be changed regularly.

[0096] S432, mapping each type of information to its corresponding encryption level and encrypting it

[0097] S5. Input the encrypted information into a database for storage.

[0098] In summary, the present invention generates information category labels based on existing database information and establishes a data model so that subsequent information can be quickly classified. When a large amount of information data needs to be processed, there is no need to manually perform classification operations one by one, thereby saving a lot of time and labor costs. Secondly, automatic classification can also ensure the accuracy of information classification and avoid classification errors caused by human factors; the present invention can greatly improve the efficiency of information matching by automatically matching information with category labels with the category information in the database. When faced with a huge database, indiscriminate matching and verification of all data will consume a lot of time and computing resources. Through targeted matching, specific categories of information can be quickly locked for verification, which greatly shortens the processing time. In addition, the matching accuracy can also be improved and errors can be reduced. The probability of matching can be increased, thereby optimizing the information management process; the present invention can effectively save storage space and storage costs by eliminating data with the same matching degree, and avoid confusion and interference caused by these duplicate data in subsequent data processing and analysis; when the information data matching values ​​are different, different encryptions are performed according to the level of the information, which can more reasonably allocate encryption resources and avoid encrypting all information with the same strength, thereby saving computing resources and time costs; the present invention has a high degree of automation. First, it can automatically classify according to the information category, and secondly, it can automatically match the data in the existing database to avoid storing duplicate data. Finally, it also has the function of encrypting different levels of information data in different ways. This series of automated operations greatly improves the efficiency and security of information management, thereby facilitating the staff to manage information.

[0099] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An information management method for an information management system, characterized in that: The method comprises the following steps: S1. Based on the existing database information, clarify the information type, determine the information category label according to the information type, and finally divide the information importance level according to the information type; S2. Input the collected information, and the system automatically pre-processes the information data and verifies its quality; S3. After the quality verification is completed, the system will automatically identify the information type and classify it according to the known category labels; S4, automatically matching the information with the category label with the category information in the database, and automatically eliminating the information when the matching degree is exactly the same, and encrypting the information according to the importance level of the category when the matching degree is different; S5. Input the encrypted information into a database for storage.

2. The information management method of an information management system according to claim 1, characterized in that: The method of clarifying the information type based on the existing database information, determining the information category label based on the information type, and finally classifying the information importance level based on the information type includes the following steps: S11. Extract key features of existing database information, including information type and information category labels. Information category labels include customer contract information, financial information, employee file information, marketing and sales, and personal performance information; S12. Use supervised learning method to train with existing labeled data, and use independent test set to verify model performance after training. S13. Define the importance level of information based on business needs and information characteristics, and divide the information into three levels: high, medium, and low.

3. The information management method of an information management system according to claim 2, characterized in that: Defining the importance level of information according to business requirements and information characteristics, and classifying the information into three levels: high, medium, and low, includes the following steps: S131. Information categories include customer contract information, financial information, employee file information, marketing and sales, and personal performance information. Customer contract information and financial information are classified as high-level information, marketing information and personal performance information are classified as medium-level information, and employee file information is classified as low-level information.

4. The information management method of an information management system according to claim 1, characterized in that: The collected information is input, and the system automatically pre-processes the information data and verifies the quality, including the following steps: S21. Clean the information data, including detection of duplicate data and removal of missing values; S22, verify the data format and convert the information data into a unified format; S23, performing range check, consistency check and integrity verification on the information data. The range check is to verify the reasonable range of the numerical data to ensure that the information value is within a reasonable range; the consistency check is to check the consistency between data records to ensure that there is no logical conflict; the integrity check is to check the values ​​of the required fields to ensure that the records are complete; S24. Based on the data quality inspection, a data quality report is automatically generated, including the problems and solutions in the cleaning process, and the specific abnormal information is transmitted to the management personnel for processing.

5. The information management method of an information management system according to claim 1, characterized in that: After the quality verification is completed, the system will automatically identify the information type and classify it according to the known category labels. The specific steps are as follows: S31. Based on known model requirements, extract features from the input information, input the extracted features into the model, and run a recognition algorithm to identify the information type; S32. Obtain the recognition result of each piece of information, and match it with a known category label according to the recognized information type.

6. The information management method of an information management system according to claim 1, characterized in that: The automatic matching of the information with the category label with the category information in the database, when the matching degree is exactly the same, automatically eliminating the information, and when the matching degree is different, encrypting the information according to the importance level of the category includes the following steps: S41. Use a matching algorithm to match the newly collected information with the information in the database one by one, and calculate the matching degree between each new information and the corresponding category information in the database. When the matching degree exceeds 95%, it means that the information is exactly the same as the information in the database. When the matching degree is less than 95%, it means that the information data is different. S42. For completely identical data information, the system automatically removes it from the list of information to be processed and records the removal operation; S43. Encrypt different information data according to the importance level of the information category.

7. The information management method of an information management system according to claim 6, characterized in that: The method uses a matching algorithm to match the newly collected information with the information in the database one by one, and calculates the matching degree of each new information with the corresponding category information in the database. When the matching degree value exceeds 95, it means that the information is exactly the same as the information in the database. When the matching degree value is less than 95, it means that the information data is different. The method includes the following steps: S411, split each string into words, construct a word frequency vector for each string, representing the number of times each word appears in the string, count the word frequency of each string, obtain the word frequency vector of the new information: A = [f1, f2, ..., fn] and the word frequency vector of the database information: B = [g1, g2, ..., gn], calculate the cosine similarity, the specific formula is: The dot product A·B represents the sum of the products of corresponding elements of two vectors: The vector modulus ∥A∥ and ∥B∥: The calculated matching value is between 0-100. When p>95, it means that the information is exactly the same as the information in the database. When p<95, it means that the information is different from the information in the database.

8. The information management method of an information management system according to claim 6, characterized in that: The encryption of different information data according to the importance level of the information category comprises the following steps: S431. Use strong encryption algorithms and complex key management strategies for high-level information, use medium-strength encryption algorithms and medium key management strategies for medium-level information, and use simpler encryption algorithms for low-level information; S432. Map each type of information to its corresponding encryption level and encrypt them.

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