CN116629816A - Human resource management and decision-making aid system and method based on big data, electronic equipment and storage medium - Google Patents

Human resource management and decision-making aid system and method based on big data, electronic equipment and storage medium Download PDF

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CN116629816A
CN116629816A CN202310714654.XA CN202310714654A CN116629816A CN 116629816 A CN116629816 A CN 116629816A CN 202310714654 A CN202310714654 A CN 202310714654A CN 116629816 A CN116629816 A CN 116629816A
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data
export
increment
resource management
human resource
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付冠华
赵海荣
厉律阳
叶颖
汪明华
张青
吴逸楠
李南
戴黎旦
吴逸芳
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Zhejiang Zhongxin Electric Power Engineering Construction Co Ltd
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    • G06F11/1446—Point-in-time backing up or restoration of persistent data
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Abstract

The application discloses a human resource management and auxiliary decision-making system and method based on big data, electronic equipment and a storage medium, and belongs to the technical field of computers. Aiming at the problem of insufficient data security of the traditional human resource management and auxiliary decision-making system based on big data, the application provides the human resource management and auxiliary decision-making system based on big data, which comprises a data integration module, a data conversion module and a data processing module, wherein the data integration module is used for accessing, converting and transmitting the data, the data conversion comprises dividing the data into personal data and non-personal data according to preset rules, and dividing the personal data and the non-personal data into a plurality of different types of data according to the preset rules; the data storage module comprises a plurality of mutually independent databases which are in one-to-one correspondence with the data types, and the databases are used for receiving and respectively storing the data correspondingly transmitted by the data integration module; and a data processing analysis module. The application improves the efficiency and effect of human resource management and the data security.

Description

Human resource management and decision-making aid system and method based on big data, electronic equipment and storage medium
Technical Field
The present application relates to the field of computer technologies, and in particular, to a human resource management and decision-making assistance system, method, electronic device, and storage medium based on big data.
Background
Along with continuous progress and development of economy in China, the development scale of enterprises is continuously enlarged, and human resource management is more and more complex. Meanwhile, in recent years, the big data technology obtains good application results in various industry fields, and the defect of small management range of the traditional system can be effectively overcome by providing data support for the traditional management mode, so that the management mode is fairer, more fair and more public.
Therefore, a system for applying the big data technology to the enterprise human resource management work is presented at present, and by means of support of informatization and mass data, cross-department coordination can be realized, and the actual growth and performance of staff can be accurately analyzed. For example, chinese patent application publication No. CN113269535a discloses a human resource management method and system based on big data analysis, which can sort a group of online collaborative data based on collaborative conversion parameter values between online collaborative data and human resource online collaborative services and collaborative operation flow characteristics of the online collaborative data, and further automatically generate a group of ordered collaborative optimization labels for the online collaborative services based on an ordered online collaborative data cluster. Therefore, a more reasonable and accurate collaborative optimization label sequence can be automatically generated, the updating and optimizing cost of collaborative services on a human resource line can be reduced, and the precision and the generating efficiency of the collaborative optimization labels are effectively improved; in addition, the generated collaborative optimization labels have priority, so that the reference degree of the final collaborative optimization labels can be further improved. The scheme has the following defects: data security is not considered.
Disclosure of Invention
In order to solve the problem of insufficient data security of the related human resource management and auxiliary decision-making system based on big data at present, the application provides a human resource management and auxiliary decision-making system based on big data. The technical scheme is as follows:
a big data based human resource management and decision-making aid system comprising:
the data integration module is used for accessing, converting and transmitting the data, and the data conversion comprises dividing the data into personal data and non-personal data according to a preset rule, and dividing the personal data and the non-personal data into a plurality of different types of data according to the preset rule;
the data storage module comprises a plurality of mutually independent databases which are in one-to-one correspondence with the data types, and the databases are used for receiving and respectively storing the data correspondingly transmitted by the data integration module; and, a step of, in the first embodiment,
and the data processing and analyzing module is used for retrieving the data from the databases and processing and analyzing the data according to preset rules and preset models.
By adopting the technical scheme, the collection, integration and processing analysis of the related data of the human resources are realized through the data integration module, the data storage module and the data processing analysis module, the efficiency and the effect of human resource management are improved, and accurate data assistance is provided for enterprise management decisions; meanwhile, the data are divided into personal data and non-personal data, so that the data security level division is realized from the source, and a plurality of databases which are independent from each other and correspond to the data types one by one are matched, so that the system is ensured to provide excellent access performance under the condition of increasing the data quantity, the risk of damaging the databases is reduced, and the data security is further improved.
In some embodiments of the present application, the method further comprises a backup database corresponding to the plurality of databases one to one;
the database storage module further comprises a backup unit, wherein the backup unit is used for regularly exporting and backing up the data in the databases to the corresponding backup database by adopting at least one of complete incremental export, incremental export and accumulated incremental export; wherein,,
the complete increment export refers to exporting and backing up the data in the whole database;
the incremental type incremental export means only exporting and backing up the changed result after the last backup;
the accumulated increment export refers to exporting and backing up the data changed from the database after the last complete increment export.
By adopting the technical scheme, the data integrity can be ensured under the condition of not reducing the performance of the service system through data backup, so that the data safety is improved, and meanwhile, the service continuity, the operation continuity and the quick recovery are ensured.
In some embodiments of the application, the backup unit performs export and backup at a frequency of once per day and cycles through full incremental export-cumulative incremental export-incremental export.
By adopting the technical scheme, the integrity of the data per week and the rapid and maximum data loss during recovery can be ensured.
In some embodiments of the present application, the database storage module further includes an access control unit, where the access control unit is configured to determine whether to allow access of the access request according to a user identifier, a security level, and a permission in the access request, and a type, a security level, an access permission, and an access requirement of the database corresponding to the request; wherein,,
the access accesses include reads, writes, modifications, deletions, and additions to the record.
By adopting the technical scheme, the access can be controlled, thereby avoiding unauthorized access to the information and further improving the data security.
In some embodiments of the present application, the access control unit is configured to directly allow the access request when the access is read and the data stored in the database corresponding to the request is non-personal data.
By adopting the technical scheme, on the basis of ensuring the data security, the access convenience is improved, and the system is ensured to provide excellent access performance.
In some embodiments of the present application, the data integration module converts data, including at least one of automatic word segmentation, keyword extraction and entity recognition, and separates the converted data into personal data and non-personal data based on content or rules, and separates the personal data and non-personal data into a plurality of different types of data based on content or rules;
the data integration module transmits data to the corresponding database based on the data type.
In some embodiments of the present application, the data integration module further includes extracting plain text data from the data before converting the data, and converting the plain text data.
The other technical scheme is as follows:
a human resource management and decision-making aid method based on big data, characterized in that it is applied to any of the above systems, and that the method comprises:
accessing, converting and transmitting the data, wherein the data conversion comprises dividing the data into personal data and non-personal data according to a preset rule, and dividing the personal data and the non-personal data into a plurality of different types of data according to the preset rule;
receiving and respectively and independently storing data of corresponding types;
and retrieving and processing the analysis data according to preset rules and preset models.
In some embodiments of the application, further comprising:
periodically exporting and backing up data by adopting at least one of complete increment export, increment export and accumulation type increment export; and, in addition, the method comprises the steps of,
according to complete increment export-increment export the out-accumulation type increment export-increment type increment export loops.
The other technical scheme is as follows:
an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing any of the methods described above when executing the program.
The other technical scheme is as follows:
a computer readable storage medium storing a computer program for performing any one of the methods described above.
Drawings
FIG. 1 is a system block diagram of the present application;
fig. 2 is a flow chart of the present application.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced in other ways than those described herein, and therefore the scope of the present application is not limited to the specific embodiments disclosed below.
As shown in fig. 1, a human resource management and decision-making aid system based on big data comprises a data integration module, a data storage module and a data processing analysis module. The system realizes collection, integration, processing and analysis of related data of human resources, improves efficiency and effect of human resource management, and provides accurate data assistance for enterprise management decisions. Specifically:
the data integration module is used for accessing, converting and transmitting the data, namely, interfacing with an external system to access the data, preprocessing the data to realize conversion, and transmitting the data to other modules. For example, sqoop, flume, kettle is adopted to realize operations such as data access, conversion, warehouse entry transmission and the like. Further, the data conversion comprises the steps of dividing the data into personal data and non-personal data according to preset rules and dividing the personal data and the non-personal data into a plurality of different types of data according to the preset rules, so that the data security level is divided from the source, and the targeted processing is convenient to follow.
The data storage module comprises a plurality of databases which are mutually independent and correspond to the data types one by one, and the databases are used for receiving and respectively storing the data correspondingly transmitted by the data integration module, so that the system is ensured to provide excellent access performance under the condition of increasing the data quantity, namely, the data processing analysis module can call more accurately and cannot bear great processing analysis load; and the data storage is dispersed, the risk of damaging the database is reduced, and the data security is further improved. The data storage can adopt a Hadoop Distributed File System (HDFS), a distributed database HBase stores data, and a rdbms relational database management system manages a relational database to support services such as mass data storage, efficient indexing and retrieval.
The data processing and analyzing module is used for retrieving from the databases and processing and analyzing data according to preset rules and preset models. For example, based on yarn, zookeeper Hadoop cluster installation, data processing computing services are provided through ooize, spark, hive, etc.; the system is composed of a real-time computing framework (Spark Streaming) and an offline computing framework (MapReduce) in a distributed message queue (Kafka) and a data mining engine (Mahout), and realizes the mining analysis function of large-scale data.
In some embodiments, the system further comprises a backup database in one-to-one correspondence with the plurality of databases. The database storage module further comprises a backup unit, wherein the backup unit is used for periodically backing up data, so that the database can cope with physical threats such as hardware faults caused by flood fires and the like, damage and loss of the data are avoided, namely, the data integrity can be ensured under the condition that the performance of a service system is not reduced, the data safety is further improved, and meanwhile, the service continuity, the operation continuity and the quick recovery are ensured. It will be appreciated that the media of the backup database is stored off-site, remote from the media storage of the database.
In some embodiments, the backup unit may export and backup data in the plurality of databases to the corresponding backup database using at least one of full delta export, delta export, and accumulated delta export. It should be noted that Complete Export refers to exporting and backing up data in the entire database. Incremental delta export (Incremental Export) refers to exporting and backing up only the results of changes after the last backup. An accumulated delta Export (cumulateExport) refers to exporting and backing up data that has changed from the database since the last full delta Export.
In some embodiments, the backup unit performs export and backup at once daily frequency and loops through full incremental export-cumulative incremental export-incremental export, thereby ensuring weekly data integrity and fast and maximum data loss upon recovery.
In some embodiments, the database storage module further comprises an access control unit. The access control unit is used for determining whether to allow the access of the access request according to the user identification, the security level and the authority in the access request and the type, the security level, the access authority and the access requirement of the database corresponding to the access request. It should be noted that access includes reading, writing, modifying, deleting, and adding records. Access control may enable the database to address logical threats, such as unauthorized access to information, thereby improving data security.
In some embodiments, the access control unit is configured to directly allow the access request when the access is read and the data stored in the database corresponding to the request is non-personal data, so that on the basis of ensuring data security, access convenience is improved, and it is ensured that the system can provide excellent access performance.
In some embodiments, the data integration module converts the data, including at least one of automatic word segmentation, keyword extraction, and entity recognition of the data, and separates the converted data into personal data and non-personal data based on content or rules, and separates the personal data and the non-personal data into a plurality of different types of data based on content or rules. The data integration module transmits data to the corresponding database based on the data type.
The automatic word segmentation refers to the segmentation of a text into words, and various text mining operations can be conveniently performed on the basis of word segmentation. For example, a word segmentation technology based on the combination of rules and statistics is adopted to segment a Chinese character sequence into meaningful words, so that various dictionaries can be supported, and the rapidness, accuracy and practicability of word segmentation are ensured; the method can be applied to various fields such as document retrieval, search engines and the like, and improves the accuracy of relevance sorting of retrieval and the like. The main functions also include:
(1) Cutting the text to form word segmentation effect;
(2) The user can define the separator of the word segmentation by himself;
(3) The word segmentation result may be displayed as a result along with the part of speech.
Keyword extraction refers to operations such as extraction of keywords of a text, the number of the keywords can be customized, and the rapidness and accuracy of keyword extraction are ensured.
Entity recognition refers to the operation of recognizing entity words of a text, and extracting entity information such as person names, place names, organization names, identity card numbers, telephones, time, email, license plate numbers, proper nouns and the like contained in the text. Entity identification is based on a technology combining rules and statistics, meaningful entity information is extracted from unstructured text information, the rapidity and accuracy of named entity identification are guaranteed, and the extracted entity information is described in a structured form and can be stored in a structured database for analysis and utilization.
In some embodiments, automatic classification supports confidence limits, and results of classification may be filtered according to confidence, supporting common output of classification results and confidence. Meanwhile, text classification based on rules can input related classification rules, such as industry, region and the like, and the system can realize classification based on the rules.
In some embodiments, the data integration module further includes extracting plain text data from the data and converting the plain text data prior to converting the data. For example, text contents in document files such as doc and pdf are extracted. Meanwhile, the system can also support multiple extraction services, can be expanded, is suitable for using different extraction services under different conditions, and can also poll multiple services until success.
Example 2:
on the basis of embodiment 1, the embodiment provides a human resource management and auxiliary decision-making method based on big data, which is applied to the system of embodiment 1, and improves the efficiency and effect of human resource management, provides accurate data assistance for enterprise management decision-making, and improves data security.
As shown in fig. 2, the method includes:
s1, accessing, converting and transmitting data.
And, the data conversion includes dividing the data into personal data and non-personal data according to a preset rule, and dividing the personal data and the non-personal data into a plurality of different types of data according to the preset rule.
The data security level is classified from the source, and the subsequent targeted processing is facilitated.
S2, receiving and respectively and independently storing the data of the corresponding type.
The step is combined with the last step, so that the system can provide excellent access performance under the condition of increasing the data volume, namely, the data processing analysis module can be more accurately called, and the system is not subjected to great processing analysis load; and the data storage is dispersed, so that the risk of damaging the database is reduced.
S3, retrieving and processing analysis data according to preset rules and preset models.
In some embodiments, the method further comprises:
s4, periodically exporting and backing up data by adopting at least one of complete increment export, increment type increment export and accumulation type increment export. And, in addition, the processing unit, according to complete increment export-increment export the out-accumulation type increment export-increment type increment export loops.
The step can ensure the data integrity under the condition of not reducing the performance of the service system, thereby improving the data security, and simultaneously ensuring the service continuity, the uninterrupted operation and the quick recovery.
Example 3:
on the basis of embodiment 1 and embodiment 2, this embodiment provides an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method of embodiment 2 when executing the program.
Example 4:
on the basis of embodiment 1 and embodiment 2, the present embodiment provides a computer-readable storage medium storing a computer program for executing embodiment 2.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present application may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present application may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (10)

1. A human resource management and decision-making aid system based on big data, comprising:
the data integration module is used for accessing, converting and transmitting the data, and the data conversion comprises dividing the data into personal data and non-personal data according to a preset rule, and dividing the personal data and the non-personal data into a plurality of different types of data according to the preset rule;
the data storage module comprises a plurality of mutually independent databases which are in one-to-one correspondence with the data types, and the databases are used for receiving and respectively storing the data correspondingly transmitted by the data integration module; and, a step of, in the first embodiment,
and the data processing and analyzing module is used for retrieving the data from the databases and processing and analyzing the data according to preset rules and preset models.
2. The big data based human resources management and decision-making aid system according to claim 1, further comprising a backup database in one-to-one correspondence with the plurality of databases;
the database storage module further comprises a backup unit, wherein the backup unit is used for regularly exporting and backing up the data in the databases to the corresponding backup database by adopting at least one of complete incremental export, incremental export and accumulated incremental export; wherein,,
the complete increment export refers to exporting and backing up the data in the whole database;
the incremental type incremental export means only exporting and backing up the changed result after the last backup;
the accumulated increment export refers to exporting and backing up the data changed from the database after the last complete increment export.
3. The big data based human resource management and decision-making aid system according to claim 2, wherein the backup unit performs export and backup at a frequency of once per day and loops in full delta export-accumulation delta export-delta export.
4. The big data based human resources management and assisted decision making system of claim 1, wherein the database storage module further comprises an access control unit for determining whether to allow access of the access request based on the user identification, security level and rights in the access request and the type, security level, access rights and access requirements of the database corresponding to the request; wherein,,
the access accesses include reads, writes, modifications, deletions, and additions to the record.
5. The big data based human resource management and decision-assist system of claim 4 wherein the access control unit is configured to directly allow the access request when the access is read and the data stored in the database corresponding to the request is non-personal data.
6. The big data based human resource management and decision-making aid system according to claim 1, wherein the data integration module converts data including at least one of automatic word segmentation, keyword extraction and entity recognition of the data, and separates the converted data into personal data and non-personal data based on content or rules, and separates the personal data and non-personal data into a plurality of different types of data based on content or rules;
the data integration module transmits data to the corresponding database based on the data type.
7. A human resource management and decision-aid method based on big data, characterized in that it is applied to a system according to any of claims 1-6, and that it comprises:
accessing, converting and transmitting the data, wherein the data conversion comprises dividing the data into personal data and non-personal data according to a preset rule, and dividing the personal data and the non-personal data into a plurality of different types of data according to the preset rule;
receiving and respectively and independently storing data of corresponding types;
and retrieving and processing the analysis data according to preset rules and preset models.
8. The big data based human resource management and decision-assist method of claim 7, further comprising:
periodically exporting and backing up data by adopting at least one of complete increment export, increment export and accumulation type increment export; and, in addition, the method comprises the steps of,
according to complete increment export-increment export the out-accumulation type increment export-increment type increment export loops.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 7-8 when executing the program.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program for executing the method of any one of claims 7-8.
CN202310714654.XA 2023-06-15 2023-06-15 Human resource management and decision-making aid system and method based on big data, electronic equipment and storage medium Pending CN116629816A (en)

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