File management method and system based on multi-source heterogeneity

Abstract

The invention belongs to the technical field of archive management, and discloses an archive management method and system based on multi-source heterogeneity. A sales archive information data set and a personnel management archive data set are collected and preprocessed, and the archive information data set comprises user consumption data, user purchase data and user single item data; analyzing the sales archive information data set to obtain user state values, classifying the user state values to obtain a classification result, and providing decision options based on the classification result; storing the collected archive information data set, the personnel management archive data set, the commodity state reference value and the classification result; data access is limited based on the data storage module and the personnel management archive data set, and generally, the system has the remarkable effects of being high in data analysis capacity, good in auxiliary decision making effect and high in information storage confidentiality degree.

Description

Technical Field

[0001] The present invention relates to the technical field of archive management, and more specifically, to a multi-source heterogeneous archive management method and system. Background Art

[0002] Archives management, also known as archives work, is the general term for various business operations of archives (rooms) to directly manage archive entities and archives information and provide utilization services. It is also the most basic component of the national archives cause. With the development of social modernization, the emergence of office automation and paperless things has greatly changed the way archives are generated. Its archives management is in the system, and the operation processes such as document drafting, issuance, urging and archiving are carried out in computers and communication lines. In this way, the predecessor of the archives must be in the form of machine-readable documents, so the archives naturally exist in machine-readable form. The utilization method of these archives is very different from that of paper-based archives. This change indicates that archivists will face more machine-readable archives with disks as carriers. Therefore, the electronicization of archive information is also an inevitable trend in the development of archive utilization work. Nowadays, how to better manage and utilize archives has become a major problem that many companies need to face when archives exist from multiple sources, in large quantities and with extremely high complexity.

[0003] The patent application with the publication number CN117609153A discloses an electronic archive management method and device based on multi-source heterogeneity. By storing business data in layers based on business types and establishing a physical storage structure for each electronic archive file, a strict distinction between business types is achieved, which facilitates the subsequent differentiated display of business data of different business types. By broadening the acquisition scope, it helps the person who is searching to find the corresponding electronic archive file, solving the problem that it is difficult to obtain matching electronic archive files for query information.

[0004] However, although the above-mentioned electronic archive management method and equipment based on multi-source heterogeneity can achieve the purpose of facilitating query and enhancing query speed to a certain extent by classifying the types of archives, the current archive management work not only requires the archive query to have the ability of convenient query and fast query speed, but also how to reduce data storage risks, reduce the probability of data leakage and enhance data utilization, which is also an extremely urgent need at present.

[0005] In view of this, the present invention proposes a multi-source heterogeneous archive management method and system to solve the above problems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solutions: comprising: The data collection module is used to collect and pre-process the sales archive information data set and the personnel management archive data set. The sales archive information data set includes user consumption data, user consumption frequency data and user consumption time data. Furthermore, the method of collecting the sales archive information data set and the personnel management archive data set includes: By connecting to the enterprise sales management platform, the user's consumption amount is collected to obtain the user's consumption data, the number of user consumption times within the budget time unit is collected to obtain the user's consumption frequency data, the user's most recent consumption time is collected, with the day as the time basis, and the user's most recent consumption time is subtracted from the current time to obtain the user's consumption time data; By connecting to the enterprise personnel management platform, the information of the enterprise's employees is collected to obtain the personnel management file data set. The information of the employees includes job level, name, enterprise platform login account and contact information; The preprocessing methods include data cleaning, data format conversion and data denoising; The data processing module is used to analyze the sales file information data set, obtain the user status value, classify it, obtain the classification result, and provide decision options based on the classification result; Further, the analysis methods include: Get the sales archive information dataset by substituting into the calculation formula: Get the reference value of the product status, where For consumption data, is the consumption frequency data, is consumption time data; Obtain L groups of historical commodity status reference values ​​as sample sets, and divide the sample sets into 70%L training sets, 15%L test sets, and 15%L validation sets; A data classification model is established based on the sample set, K initial cluster centers are preset, and the calculation formula is substituted: Get the distance from the data point to the initial cluster center, assign the data point to the cluster with the nearest preset initial cluster center, recalculate the cluster average of the cluster with the preset initial cluster center after assignment, substitute the cluster average as the new initial cluster center for calculation again, and obtain the intelligent allocation model after the calculation reaches the preset number of iterations, where and is the coordinate value of the data point, is the number of data items; Input the commodity status reference value into the intelligent allocation model, and output the user status value; Further, the classification methods include: Based on the user status value, a user classification threshold interval (C1, C2) is preset. When the user status value is less than C1, it is marked as an important retention customer. When the user status value is greater than C1 and less than C2, it is marked as an important deep cultivation customer. When the user status value is greater than C2, it is marked as an important value customer. Package important retained customers, important deep-cultivation customers and important valuable customers to obtain classification results; Further, the methods of providing decision options include: When the user status value is an important customer to retain, the retention decision information is displayed through the display device; when the user status value is an important customer to deepen, the deepening decision information is displayed through the display device; when the user status value is an important customer to value, the value decision information is displayed through the display device; Retention decision information includes explaining that the customer has a low willingness to buy and requiring staff to communicate with the customer to understand the reasons for the low shopping interest, and adjusting the service content and providing special discounts based on the reasons; Deepening decision information includes explaining that the customer has a general willingness to buy and requiring staff to provide customers with preferred product recommendations based on the sales file information data set; Value decision information includes explaining that the customer has a strong willingness to buy and spending power, and requiring staff to provide customers with customized services based on the sales file information data set; A data storage module, used to store the collected sales archive information data set, personnel management archive data set, commodity status reference values ​​and classification results; Furthermore, the step of storing includes: Q1: Based on the customer number, the sales file information data set, commodity status reference value and classification results are packaged to obtain customer information data; Q2: Based on blockchain storage technology, the customer information data and the personnel management file data set are sharded to obtain customer shard data and personnel file shard data; Q3: Based on the hash algorithm, calculate the customer shard data and the personnel file shard data to obtain the hash values ​​corresponding to the customer shard data and the personnel file shard data; Q4: Copy the customer shard data and personnel profile shard data and hash values ​​to generate P redundant copies, and upload the customer shard data and personnel profile shard data, hash values ​​and redundant copies to the blockchain network for storage; Q5: Package the customer shard data and personnel profile shard data, hash value and storage location as upload records, and record the transaction records on the blockchain ledger; The data management module restricts data access based on the data storage module and the personnel management archive data set; Further, the restriction methods include: Based on the personnel management file data set, access permission ranges are preset, and staff are divided into ordinary staff and senior staff according to their job levels. The access permission range includes data access and personnel data modification; Based on the data storage module, ordinary staff can query and access the sales archive information data set, personnel management archive data set, commodity status reference value and classification results, and senior staff can query and access the sales archive information data set, personnel management archive data set, commodity status reference value and classification results, and send a custom character to the central server. After the central server conducts identity verification through the platform login account and name, the access rights of the account user can be changed or cancelled; Further, S1: collect sales archive information data set and personnel management archive data set, and pre-process, the sales archive information data set includes user consumption data, user purchase data and user single product data; S2: Analyze the sales file information data set to obtain the user status value, classify it, obtain the classification result, and provide decision options based on the classification result; S3: storing the sales archive information data set, the personnel management archive data set, the commodity status reference value and the classification result; S4: Restrict data access based on the data storage module and personnel management archive dataset.

[0007] The technical effects and advantages of the multi-source heterogeneous archive management method and system of the present invention are as follows: The present invention analyzes the sales archive information data set through the data processing module, so that more information beneficial to the development of the enterprise can be extracted from the complex data, which greatly improves the information structure ability of the enterprise and enhances the competitiveness of the enterprise. Through the established data storage module, not only the security of data storage can be effectively enhanced, but also the cost of enterprise data storage can be greatly reduced, so that the enterprise has a better and more optimal storage environment in terms of data, and the ability to provide decision-making options can greatly reduce the workload required by staff when facing different customers, thereby effectively improving the work effect of staff. Through the established data management module, data leakage through the access level can be effectively prevented, thereby greatly enhancing the customer information management ability of the enterprise and reducing the customer loss problem caused by customer information leakage. Generally speaking, the present invention has the remarkable effects of strong data analysis ability, good auxiliary decision-making effect and high information storage confidentiality. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic diagram of a multi-source heterogeneous archive management system according to the present invention; Figure 2 The present invention is a schematic diagram of a multi-source heterogeneous archive management method. DETAILED DESCRIPTION

[0009] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example 1

[0010] See also Figure 1 As shown, the multi-source heterogeneous archive management system described in this embodiment includes: The data collection module is used to collect and pre-process the sales archive information data set and the personnel management archive data set. The sales archive information data set includes user consumption data, user consumption frequency data and user consumption time data. Furthermore, the method of collecting the sales archive information data set and the personnel management archive data set includes: By connecting to the enterprise sales management platform, the user's consumption amount is collected to obtain the user's consumption data, the number of user consumption times within the budget time unit is collected to obtain the user's consumption frequency data, the user's most recent consumption time is collected, with the day as the time basis, and the user's most recent consumption time is subtracted from the current time to obtain the user's consumption time data; By connecting to the enterprise personnel management platform, the information of the enterprise's employees is collected to obtain the personnel management file data set. The information of the employees includes job level, name, enterprise platform login account and contact information; The preprocessing methods include data cleaning, data format conversion and data denoising; The data processing module is used to analyze the sales file information data set, obtain the user status value, classify it, obtain the classification result, and provide decision options based on the classification result; Further, the analysis methods include: Get the sales archive information dataset by substituting into the calculation formula: Get the reference value of the product status, where For consumption data, is the consumption frequency data, is consumption time data; Obtain L groups of historical commodity status reference values ​​as sample sets, and divide the sample sets into 70%L training sets, 15%L test sets, and 15%L validation sets; A data classification model is established based on the sample set, K initial cluster centers are preset, and the calculation formula is substituted: Get the distance from the data point to the initial cluster center, assign the data point to the cluster with the nearest preset initial cluster center, recalculate the cluster average of the cluster with the preset initial cluster center after assignment, substitute the cluster average as the new initial cluster center for calculation again, and obtain the intelligent allocation model after the calculation reaches the preset number of iterations, where and is the coordinate value of the data point, is the number of data items; Input the commodity status reference value into the intelligent allocation model, and output the user status value; Further classification methods include: Based on the user status value, a user classification threshold interval (C1, C2) is preset. When the user status value is less than C1, it is marked as an important retention customer. When the user status value is greater than C1 and less than C2, it is marked as an important deep cultivation customer. When the user status value is greater than C2, it is marked as an important value customer. Package important retained customers, important deep-cultivation customers and important valuable customers to obtain classification results; Further, the methods of providing decision options include: When the user status value is an important customer to retain, the retention decision information is displayed through the display device; when the user status value is an important customer to deepen, the deepening decision information is displayed through the display device; when the user status value is an important customer to value, the value decision information is displayed through the display device; Retention decision information includes explaining that the customer has low purchase intention and requiring staff to communicate with the customer to understand the reason for the low shopping interest, and adjust the service content and provide special discounts based on the reason; Deep decision information includes stating that the customer's willingness to buy is average and requiring staff to provide customers with preferred product recommendations based on the sales file information data set; value decision information includes stating that the customer's willingness to buy and spending power are strong and requiring staff to provide customers with customized services based on the sales file information data set; A data storage module, used to store the collected sales archive information data set, personnel management archive data set, commodity status reference values ​​and classification results; Furthermore, the step of storing includes: Q1: Based on the customer number, the sales file information data set, commodity status reference value and classification results are packaged to obtain customer information data; It should be explained that the customer number is a preset number for distinguishing different customers, and is implemented as a natural number such as 1, 2, 3...M; Q2: Based on blockchain storage technology, the customer information data and the personnel management file data set are sharded to obtain customer shard data and personnel file shard data; Q3: Based on the hash algorithm, calculate the customer shard data and the personnel file shard data to obtain the hash values ​​corresponding to the customer shard data and the personnel file shard data; Q4: Copy the customer shard data and personnel file shard data and hash values ​​to generate P redundant copies, and upload the customer shard data and personnel file shard data, hash values ​​and redundant copies to the blockchain network for storage; Q5: Package the customer shard data and personnel profile shard data, hash value and storage location as upload records, and record the transaction records on the blockchain ledger; The data management module restricts data access based on the data storage module and the personnel management archive data set; Further, the methods of restriction include: Based on the personnel management archive data set, access permission ranges are preset, and staff are divided into ordinary staff and senior staff according to their job levels. The access permission range includes data access and personnel data modification; Based on the data storage module, ordinary staff can query and access the sales archive information data set, personnel management archive data set, commodity status reference value and classification results, and senior staff can query and access the sales archive information data set, personnel management archive data set, commodity status reference value and classification results, and send a custom character to the central server. After the central server conducts identity verification through the platform login account and name, the access rights of the account user can be changed or cancelled; The beneficial effect of this embodiment is that the sales archive information data set is analyzed through the data processing module, so that more information beneficial to the development of the enterprise can be extracted from the complex data, which greatly improves the information structure ability of the enterprise and enhances the competitiveness of the enterprise. Through the established data storage module, not only the security of data storage can be effectively enhanced, but also the cost of enterprise data storage can be greatly reduced, so that the enterprise has a better and more optimal storage environment in terms of data, and the ability to provide decision-making options can greatly reduce the workload required by staff when facing different customers, thereby effectively improving the work effect of staff. Through the established data management module, data leakage through the access level can be effectively prevented, thereby greatly enhancing the customer information management ability of the enterprise and reducing customer churn caused by customer information leakage. In general, the present invention has the remarkable effects of strong data analysis ability, good auxiliary decision-making effect and high degree of information storage confidentiality. Example 2

[0011] See also Figure 2As shown, the part not described in detail in this embodiment is described in Example 1, and a multi-source heterogeneous archive management method is provided, including: S1: collecting a sales archive information data set and a personnel management archive data set, and pre-processing, the sales archive information data set includes user consumption data, user purchase data and user single product data; S2: Analyze the sales file information data set to obtain the user status value, classify it, obtain the classification result, and provide decision options based on the classification result; S3: storing the collected sales archive information data set, personnel management archive data set, commodity status reference value and classification results; S4: Restrict data access based on the data storage module and personnel management archive dataset. Example 3

[0012] This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the operation mode of the archive management method based on multi-source heterogeneity provided above is implemented.

[0013] Since the electronic device introduced in this embodiment is an electronic device used to implement a method for archive management based on multi-source heterogeneity in the embodiment of the present application, based on the method for archive management based on multi-source heterogeneity introduced in the embodiment of the present application, a person skilled in the art can understand the specific implementation of the electronic device of the present embodiment and its various variations, so how the electronic device implements the method in the embodiment of the present application is not described in detail here. As long as a person skilled in the art implements an electronic device used in a method for archive management based on multi-source heterogeneity in the embodiment of the present application, it belongs to the scope of protection of the present application.

[0014] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0015] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical users in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A multi-source heterogeneous archive management system, characterized in that: include: The data collection module is used to collect and pre-process the sales archive information data set and the personnel management archive data set. The sales archive information data set includes user consumption data, user consumption frequency data and user consumption time data. The data processing module is used to analyze the sales file information data set, obtain the user status value, classify it, obtain the classification result, and provide decision options based on the classification result; A data storage module, used to store sales archive information data sets, personnel management archive data sets, commodity status reference values ​​and classification results; The data management module restricts data access based on the data storage module and the personnel management archive data set.

2. The multi-source heterogeneous archive management system according to claim 1, characterized in that: Methods for collecting sales archive information data sets and personnel management archive data sets include: By connecting to the enterprise sales management platform, the user's consumption amount is collected to obtain the user's consumption data, the number of user consumption times within the budget time unit is collected to obtain the user's consumption frequency data, the user's most recent consumption time is collected, with the day as the time basis, and the user's most recent consumption time is subtracted from the current time to obtain the user's consumption time data; By connecting to the enterprise personnel management platform, the information of the enterprise's employees is collected to obtain the personnel management file data set. The information of the employees includes job level, name, enterprise platform login account and contact information; Preprocessing methods include data cleaning, data format conversion and data denoising.

3. The multi-source heterogeneous archive management system according to claim 2 is characterized in that: The analysis can be done by: Get the sales archive information dataset by substituting into the calculation formula: Get the reference value of the product status, where For consumption data, is the consumption frequency data, is consumption time data; Obtain L groups of historical commodity status reference values ​​as sample sets, and divide the sample sets into 70%L training sets, 15%L test sets, and 15%L validation sets; A data classification model is established based on the sample set, K initial cluster centers are preset, and the calculation formula is substituted: Get the distance from the data point to the initial cluster center, assign the data point to the cluster with the nearest preset initial cluster center, recalculate the cluster average of the cluster with the preset initial cluster center after assignment, substitute the cluster average as the new initial cluster center for calculation again, and obtain the intelligent allocation model after the calculation reaches the preset number of iterations, where and is the coordinate value of the data point, is the number of data items; The commodity status reference value is input into the intelligent allocation model, and the user status value is output.

4. The multi-source heterogeneous archive management system according to claim 3 is characterized in that: The classification methods include: Based on the user status value, a user classification threshold interval (C1, C2) is preset. When the user status value is less than C1, it is marked as an important retention customer. When the user status value is greater than C1 and less than C2, it is marked as an important deep cultivation customer. When the user status value is greater than C2, it is marked as an important value customer. Package important retained customers, important deep-cultivation customers and important valuable customers to obtain classification results.

5. The multi-source heterogeneous archive management system according to claim 4 is characterized in that: Ways to provide decision options include: When the user status value is an important customer to retain, the retention decision information is displayed through the display device; when the user status value is an important customer to deepen, the deepening decision information is displayed through the display device; when the user status value is an important customer to value, the value decision information is displayed through the display device; Retention decision information includes explaining that the customer has a low willingness to buy and requiring staff to communicate with the customer to understand the reasons for the low shopping interest, and adjust the service content and provide special discounts based on the reasons; deep-diving decision information includes explaining that the customer has a general willingness to buy and requiring staff to provide customers with preferred product recommendations based on the sales file information data set; value decision information includes explaining that the customer has a strong willingness to buy and consumption capacity, and requiring staff to provide customers with customized services based on the sales file information data set.

6. The multi-source heterogeneous archive management system according to claim 5, characterized in that: The steps to store include: Q1: Based on the customer number, the sales file information data set, commodity status reference value and classification results are packaged to obtain customer information data; Q2: Based on blockchain storage technology, the customer information data and the personnel management file data set are sharded to obtain customer shard data and personnel file shard data; Q3: Based on the hash algorithm, calculate the customer shard data and the personnel file shard data to obtain the hash values ​​corresponding to the customer shard data and the personnel file shard data; Q4: Copy the customer shard data and personnel profile shard data and hash values ​​to generate P redundant copies, and upload the customer shard data and personnel profile shard data, hash values ​​and redundant copies to the blockchain network for storage; Q5: Package the customer shard data and personnel profile shard data, hash value and storage location as upload records, and record the transaction records on the blockchain ledger.

7. The multi-source heterogeneous archive management system according to claim 6 is characterized in that: Restrictions can be imposed by: Based on the personnel management file data set, access permission ranges are preset, and staff are divided into ordinary staff and senior staff according to their job levels. The access permission range includes data access and personnel data modification; Based on the data storage module, ordinary staff can query and access the sales file information data set, personnel management file data set, commodity status reference values ​​and classification results; senior staff can query and access the sales file information data set, personnel management file data set, commodity status reference values ​​and classification results, and by sending a custom character to the central server, the central server can change or cancel the access rights of the account user after conducting an identity verification through the platform login account and name.

8. A method for managing archives based on multi-source heterogeneity, implemented according to the archive management system based on multi-source heterogeneity according to claims 1-7, characterized in that: S1: Collect sales archive information data set and personnel management archive data set, and perform preprocessing. The sales archive information data set includes user consumption data, user purchase data, and user single product data; S2: Analyze the sales file information data set to obtain the user status value, classify it, obtain the classification result, and provide decision options based on the classification result; S3: storing the collected sales archive information data set, personnel management archive data set, commodity status reference value and classification results; S4: Restrict data access based on the data storage module and personnel management archive dataset.

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