CN110570097A - business personnel risk identification method and device based on big data and storage medium - Google Patents
business personnel risk identification method and device based on big data and storage medium Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention relates to the technical field of safety protection, business safety and risk assessment rules, and provides a business personnel risk identification method based on big data, which comprises the following steps: acquiring business risk information associated with a business risk identifier in real time based on the business risk identifier preset in the kafka system, and converting the business risk information into business risk data, wherein the business risk information comprises attendance information; classifying and analyzing basic information and service risk data in service personnel information through an HBase structure to determine the risk level of service personnel; and when the risk level exceeds the preset risk level of the business personnel, sending warning information corresponding to the risk level to the business personnel. The data collected by the kafka are simplified, so that various information of a salesman can be obtained, the data volume needing to be processed in the later risk service data analysis process is reduced, and the data calculation precision of the server is improved.
Description
Technical Field
the invention relates to the technical field of safety protection, business safety and risk assessment rules, in particular to a business personnel risk identification method and device based on big data, a storage medium and a server.
Background
in recent years, with the increasing degree of informatization and the increasing popularity of paperless offices, project management systems with internet properties are becoming auxiliary tools for human assessment. With the wide application of the system, convenience is brought to users, and the administrative efficiency is greatly improved. At present, enterprises use the system for assessment to improve the performance of companies and employees, and some service personnel adopt temporary personnel for performing human assessment through assessment, the existing temporary personnel only perform judgment in an interview mode, but the manual mode is time-consuming, labor-consuming, tedious and artificial, so that the judgment result is not objective; the assessment system only collects the relevant information of each salesman, the collected information is little, and temporary personnel cannot be accurately judged or risk identification can be carried out on the relevant salesman; and aiming at enterprises with more salesmen, the assessment information data volume related to the enterprises is large, the system data processing process is slow, and the identification process of the temporary salesmen needs to be carried out quickly, so that the system can only judge the risk level of the salesmen through a small amount of information, and further the temporary salesmen cannot be identified accurately.
Disclosure of Invention
In order to overcome the technical problems, particularly the problems that the risk level of a system for business personnel is slow to identify and inaccurate due to less examination information and large examination data amount at present, the following technical scheme is provided:
The business personnel risk identification method based on big data provided by the embodiment of the invention comprises the following steps:
Acquiring business risk information of business personnel associated with a business risk identifier in real time based on the business risk identifier preset in a kafka system, and converting the business risk information into business risk data;
classifying and analyzing basic information in business personnel information and the business risk data through an HBase structure to determine the risk level of business personnel;
and when the risk level exceeds the preset risk level of the business personnel, sending warning information corresponding to the risk level to the business personnel.
optionally, the classifying and analyzing the basic information in the service staff information and the service risk data through the HBase structure to determine the risk level of the service staff includes:
Importing the business risk data into a database of an HBase structure through a Spark engine;
and acquiring basic information in the business personnel information corresponding to the business risk data, and performing classification analysis on the basic information in the business personnel information and the business risk data through an HBase structure to determine the risk level of business personnel.
Optionally, the importing, by a Spark engine, the business risk data into a database of an HBase structure includes:
classifying and partitioning the business risk data according to preset classification categories through a Spark engine to obtain a distributed data set of the business risk data;
And leading the data sets in the same region into a database of the HBase structure in a centralized manner.
Optionally, the classifying and analyzing the basic information in the service staff information and the service risk data through the HBase structure to determine the risk level of the service staff includes:
And extracting key data in the business risk data according to the job level of business personnel, and performing classification analysis on basic information in business personnel information and the key data through an HBase structure to determine the risk level of the business personnel.
optionally, after determining the risk level of the business person, the method includes:
Acquiring the association relation between the risk level and the job level;
and adjusting the job level of the business personnel according to the risk level and the job level association relation.
the embodiment of the present application further provides a service personnel risk identification device based on big data, including:
The conversion module is used for acquiring the business risk information of business personnel associated with the business risk identification in real time based on the business risk identification preset in the kafka system and converting the business risk information into business risk data;
The risk level determining module is used for classifying and analyzing basic information in the business personnel information and the business risk data through an HBase structure to determine the risk level of the business personnel;
And the warning module is used for sending warning information corresponding to the risk level to the business personnel when the risk level exceeds the preset risk level of the business personnel.
Optionally, the risk level determination module includes:
The first import unit is used for importing the business risk data into a database of an HBase structure through a Spark engine;
And the risk level determining unit is used for acquiring the basic information in the business personnel information corresponding to the business risk data, and performing classification analysis on the basic information in the business personnel information and the business risk data through an HBase structure to determine the risk level of the business personnel.
Optionally, the importing unit includes:
The obtaining unit is used for classifying and partitioning the business risk data according to preset classification categories through a Spark engine to obtain a distributed data set of the business risk data;
and the second import unit is used for intensively importing the data sets in the same region into the database of the HBase structure.
The embodiment of the invention also provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when the program is executed by a processor, the business personnel risk identification method based on the big data in any technical scheme is realized.
an embodiment of the present invention further provides a server, including:
One or more processors;
a memory;
One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to perform the steps of the big data based business personnel risk identification method according to any of the aspects.
compared with the prior art, the invention has the following beneficial effects:
the business personnel risk identification method based on big data provided by the embodiment of the application comprises the following steps: acquiring business risk information of business personnel associated with a business risk identifier in real time based on the business risk identifier preset in a kafka system, and converting the business risk information into business risk data; classifying and analyzing basic information in business personnel information and the business risk data through an HBase structure to determine the risk level of business personnel; and when the risk level exceeds the preset risk level of the business personnel, sending warning information corresponding to the risk level to the business personnel. Firstly, simplifying data collected by kafka, not only obtaining information from multiple aspects of a salesman, but also reducing data quantity to be processed in the later-stage risk business data analysis process when the data quantity is large, classifying and sorting business risk data by spark in the business risk data analysis process, also reducing the data processing quantity of HBase, leading the HBase into corresponding data directly based on preset classification and carrying out risk identification on the salesman, effectively classifying and processing data on the basis of combining spark and HBase frame structures, reducing the data processing quantity while simplifying data when the data quantity is large, obtaining different quantities of risk business data according to different types and grades of salesmen, pertinently improving a certain type of data and obtaining more aspects of data, when the data quantity is large, the server can be reasonably called to calculate, the throughput of server data is improved, the calculation precision of the server data is improved simultaneously under the condition that the calculation process is prevented from being blocked, the accuracy of the risk result of the salesman is further improved, the obtained salesman risk type is more accurate, the examination efficiency is higher, and the risk brought to the enterprise by the salesman is reduced.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
drawings
The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow chart diagram illustrating an implementation manner of an exemplary embodiment of a big data-based business person risk identification method according to the present invention;
FIG. 2 is a schematic structural diagram of an exemplary embodiment of a big data-based business personnel risk identification apparatus according to the present invention;
fig. 3 is a schematic structural diagram of a server according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative only and should not be construed as limiting the invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, or operations, but do not preclude the presence or addition of one or more other features, integers, steps, operations, or groups thereof.
it will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
It will be appreciated by those skilled in the art that the terms "application," "application program," "application software," and the like, as used herein, are intended to refer to a computer software product electronically-adapted to be electronically-constructed, from a collection of computer instructions and associated data resources, in accordance with the principles of the present invention. Unless otherwise specified, such nomenclature is not itself limited by the programming language class, level, or operating system or platform upon which it depends. Of course, such concepts are not limited to any type of terminal.
In an implementation manner of the business personnel risk identification method based on big data provided in the embodiment of the present application, as shown in fig. 1, the method includes: s100, S200 and S300.
S100: acquiring business risk information of business personnel associated with a business risk identifier in real time based on the business risk identifier preset in a kafka system, and converting the business risk information into business risk data;
s200: classifying and analyzing basic information in business personnel information and the business risk data through an HBase structure to determine the risk level of business personnel;
s300: and when the risk level exceeds the preset risk level of the business personnel, sending warning information corresponding to the risk level to the business personnel.
in one embodiment provided herein, the attendance information is collected in real-time by a kafka, a kafka high throughput distributed publish-subscribe messaging system that can handle all action flow data in a consumer-scale website, where actions include web browsing, searching, and other user actions; the business risk information of the user comprises related actions, such as attendance information and the like, performed by business personnel in the business operation process. The attendance information with large data volume can be collected based on the kafka high throughput, the persistence and the stability of the attendance information are guaranteed, the attendance information is prevented from being maliciously tampered by other personnel, meanwhile, the data throughput is high, and then the attendance information with large data volume can be efficiently collected through a distributed data processing system so as to be convenient for extracting the attendance information for analysis.
specifically, a business risk identifier is preset in kafka as a topic (topic) of data acquisition, such as attendance information, wherein the attendance information may further include an attendance name, attendance time, and the like. In order to simplify the later data calculation process, conveniently and quickly calculate out related risk service identification, convert part of Attendance information, if the Attendance status is normal 1, late arrival is 2, and spacious work is 3, further, other risk data of business personnel can also be obtained by simplifying the method, different types of data adopt different simplifying methods, in order to avoid the repetition of the business risk data after the conversion of different business risk information, the simplified business risk data carries the topic identification of the corresponding business risk information, if the above mentioned time of Attendance is simplified and possibly repeated with other types of business risk data, the identification carrying the Attendance status (Attendance status) is: the normal data is 1As, the late data is 2As and the spacious work is 3As, so that the data volume of the business risk data acquired from kafka is reduced, the high throughput of the kafka data is more efficiently utilized, the calculation process of the system is simplified, the system can analyze according to more staff multidimensional data, and the risk identification of business personnel can be performed more quickly and accurately. In one embodiment provided by the present application, staff change information is obtained from historical staff data, and the staff change information includes: information of newly-entered employees in a preset examination period and information of departures employees in an adjacent non-examination period after the preset examination period; by the method, the attendance condition of the new staff in the examination period and the condition outside the examination period can be determined in time, namely the risk level of the new staff can be more conveniently and rapidly acquired, whether the corresponding staff is a salesman or not is judged on the basis, and the staff can be conveniently found from the outside to carry out the manual examination, so that the examination and the management of the salesman are facilitated. Further, the assessment and promotion of business personnel can be affected by the risk level. Therefore, more accurate information of the business personnel needs to be acquired, basic information of the business personnel is acquired, and the risk level of the business personnel is determined according to the business risk data and the basic information; the basic information comprises position and department age information of business personnel. And on the basis, remark information of the business personnel can be acquired, and the risk level of the business personnel is determined according to the business risk data, the positions and the remark information. In order to more accurately determine the risk level of business personnel and avoid misjudgment of the risk level of the business personnel caused by incomplete data acquisition. Therefore, the risk level of the business personnel is comprehensively judged by comprehensively acquiring position, department age information, remarks and the like of the business personnel, for example, when attendance data is lost due to business personnel business trip, in order to avoid misjudging the risk level of the business personnel, the condition that the attendance data of the business personnel is lost on the same day is determined through the remark information, if the business personnel is on the trip, the risk level of the business personnel is the lowest, and further subsequent warning can not be carried out on the business personnel.
The system server can rapidly calculate data by dividing the same level and the same department age, and then can rapidly count and calculate the risk data of the same type of service personnel, thereby avoiding the higher loss of the whole service caused by the service personnel at the high level. The method and the system have the advantages that the server can analyze and calculate data in different categories and analyze risks more accurately, partial data processing amount and reading speed can be reduced due to the fact that only business risk data of the same category are analyzed in the analyzing process, and accordingly the result of the business risk data of the same category can be obtained quickly to determine the risk level of business personnel, system data processing amount is reduced through classification analysis by HBase, the risk level of the business personnel of the same category can be determined more clearly, personnel screening can be performed more reasonably, and meanwhile the obtained result is easier to manage. After the risk level of the business personnel is determined, when the risk level of the business personnel is determined to exceed the preset risk level of the business personnel, for example, the risk level determined by the business personnel is 3, the level of the business personnel sending the risk information warning is 2, and the level 3 exceeds 2, the system can give the risk information warning of business response, early handle and control the risk in business operation, and reduce the loss brought by the risk of the business personnel. Meanwhile, the staff is warned to improve the business quality of the staff, and the staff can conveniently control the business quality of the staff.
Optionally, the classifying and analyzing the basic information in the service staff information and the service risk data through the HBase structure to determine the risk level of the service staff includes:
importing the business risk data into a database of an HBase structure through a Spark engine;
And acquiring basic information in the business personnel information corresponding to the business risk data, and performing classification analysis on the basic information in the business personnel information and the business risk data through an HBase structure to determine the risk level of business personnel.
The data is imported into the HBase through the Spark engine for data storage, the HBase structure can acquire basic information of business personnel corresponding to the business risk data, including the information of department age, level, position and the like of the business personnel, and the HBase structure combines the business risk data and the basic information to perform classification analysis (such as the same level, the same department age and the like) on the risk level of the business personnel, so that the risk level of the business personnel in the same category can be determined more accurately, and the business personnel management based on the risk level can be performed in the later period. In the process, the spark engine performs preliminary data calculation and classification on the risk data of the service staff, the processing amount of the later-stage HBase structural data is reduced, processes such as classification calculation and the like do not need to be performed on the service risk data again after the HBase obtains the data, the data processing amount of the HBase is reduced, the data processing efficiency of the HBase is improved, and due to the fact that classification analysis of aggregation comparison is performed on the service risk data through the HBase again on the basis of spark, the result of service risk data analysis is more accurate, and the determined risk level of the service staff is more accurately avoided being mistakenly judged.
Optionally, the importing, by a Spark engine, the business risk data into a database of an HBase structure includes:
classifying and partitioning the business risk data according to preset classification categories through a Spark engine to obtain a distributed data set of the business risk data;
And leading the data sets in the same region into a database of the HBase structure in a centralized manner.
in the embodiment provided by the application, in order to improve the processing speed and the processing amount of the HBase structure data, classifying and partitioning the business risk data according to preset classification categories through a Spark engine, if the business risk data are classified and partitioned rapidly by spark based on preset business personnel basic information and preset classification categories, for example, the basic information of the business personnel is classified according to one or more of the same department age, the same time of employment, the same level, the same department, the same position and the like, the categories such as business risk categories and the like are preset, and then spark can lead the business risk data of the same business risk category into HBase for concentration, the risk level of the business personnel in the same business risk category is quickly determined, the basic information of the business personnel is combined, so that the comparison benchmark of the risk level of the business personnel is the same, and the obtained result is more accurate. After a distributed data set of business risk data is obtained, the data set in the same region is imported into a database of an HBase structure, then the HBase can rapidly process the imported data, and the data set in the same region is adopted, so that the HBase does not need to classify the imported data, direct analysis of the HBase on the imported data set is realized, meanwhile, due to the fact that the data volume of the data set in the same region is small, after a business risk data processing result is directly obtained, a risk level can be rapidly obtained, and data throughput of the HBase is improved. Correspondingly, a certain type of risk service data table can be preset in the HBase, corresponding data can be directly obtained from spark for analysis in the data importing and calculating process, the time for the HBase to search the data is reduced, the HBase data processing capacity can be reduced based on quick preliminary classification of spark, and the HBase can be directly analyzed according to the data of the same type.
Optionally, the classifying and analyzing the basic information in the service staff information and the service risk data through the HBase structure to determine the risk level of the service staff includes:
And extracting key data in the business risk data according to the job level of business personnel, and performing classification analysis on basic information in business personnel information and the key data through an HBase structure to determine the risk level of the business personnel.
In combination with the foregoing process, in order to further reduce the data processing amount of the HBase and improve the data processing speed, and further to be able to quickly obtain the risk level of the business personnel, in the embodiment provided by the application, the key data in the business risk data is extracted through the HBase structure. Because the higher the level and the higher the department age of the business personnel, the higher the risk level generated by the business personnel, the more the harm to the enterprise is, and the more the related benefits are, further more accurate risk analysis is needed, in order to reduce the harm, aiming at the business personnel HBase of the type, the personnel can be analyzed through more detailed or more accurate analysis rules, and corresponding to the business personnel with low level and small department age, because the risk level can also be used as the basis for promoting the job, and the quantity of the partial personnel is large, a small quantity of personnel need to be screened from the partial personnel, further partial data can be reduced, the HBase only obtains the key partial data to analyze the risk level, thus not only can quickly obtain the risk level of the business personnel, further reduce the system data processing amount, but also can more reasonably screen the personnel, and simultaneously the obtained result is easier to manage, i.e. the risk level of the same type of business personnel can be determined more clearly. For example, for business personnel with low grade and small department age, the key part of data may include attendance information, business completion amount and the like, and in the examination period, the business personnel with high risk is considered if the attendance qualified rate is lower than 60%; the attendance qualification rate is 60-80%, and if the service completion quantity in the assessment period is less than 50%, the attendance is determined as a high-risk service worker; the attendance qualification rate is 80-100%, and the business completion amount in the assessment period is 50-80%, and the personnel are determined as medium-risk business personnel; the attendance qualification rate is 80-100%, the service completion amount in the assessment period is 80-100%, and the performance increase amount is negative, the personnel are determined as medium-risk service personnel; the attendance qualification rate is 80-100%, the service completion amount in the assessment period is 80-100%, and the performance increase amount is positive, the personnel are determined to be low-risk service personnel. In addition, common staff are used for completing business volume by individuals, supervisors are used for completing business volume by teams, different key data are extracted according to different job levels, and the problems that different levels of analysis contents are different and risk levels are accurately positioned are solved.
optionally, after determining the risk level of the business person, the method includes:
acquiring the association relation between the risk level and the job level;
And adjusting the job level of the business personnel according to the risk level and the job level association relation.
The method can dynamically adjust the job-holding condition of the business personnel, for example, if the risk level is high, the job-holding level of the business personnel can be reduced, if the risk level is medium, the job-holding level of the business personnel can be maintained, if the risk level is low, the level of the business personnel can be increased, and therefore, when the staff advance, the business personnel with high quality can be intelligently and automatically selected. Correspondingly, in an implementation manner provided by the application, the business personnel information associated with the business personnel can be acquired, and the reminding information is sent to the associated business personnel based on the risk level of the business personnel. By the method, the associated business personnel can be reminded to warn the checked personnel, meanwhile, the associated business personnel are prevented from finding people outside and checking the inside of the company, and the leaked secrets of the company can be avoided and unnecessary troubles brought to the company are reduced due to the fact that the number of the foreign personnel is reduced.
An embodiment of the present invention further provides a service personnel risk identification device based on big data, and in one implementation, as shown in fig. 2, the service personnel risk identification device includes: the conversion module 100, the risk level determination module 200, and the alert module 300:
the conversion module 100 is used for acquiring business risk information of business personnel associated with the business risk identifier in real time based on a business risk identifier preset in the kafka system, and converting the business risk information into business risk data;
A risk level determination module 200, configured to perform classification analysis on basic information in business personnel information and the business risk data through an HBase structure, and determine a risk level of a business personnel;
And the warning module 300 is configured to send warning information corresponding to the risk level to a business person when the risk level exceeds a preset risk level of the business person.
further, as shown in fig. 2, the service personnel risk identification apparatus based on big data provided in the embodiment of the present invention further includes: a first importing unit 210, configured to import the business risk data into a database of an HBase structure through a Spark engine; and a risk level determining unit 220, configured to obtain basic information in the service staff information corresponding to the service risk data, and perform classification analysis on the basic information in the service staff information and the service risk data through an HBase structure to determine a risk level of a service staff. An obtaining unit 211, configured to classify and partition the business risk data according to preset classification categories through a Spark engine, so as to obtain a distributed data set of the business risk data; a second import unit 212, configured to import the data sets in the same region into the database of the HBase structure collectively. And the extracting unit 230 is configured to extract key data in the business risk data according to the job level of the business personnel, and perform classification analysis on the basic information in the business personnel information and the key data through the HBase structure to determine the risk level of the business personnel. An obtaining module 410, configured to obtain an association relationship between the risk level and the job level; and the adjusting module 420 is configured to adjust the job level of the business personnel according to the risk level and the job level association relationship.
the service personnel risk identification device based on big data provided by the embodiment of the invention can realize the embodiment of the service personnel risk identification method based on big data, and for the specific function realization, reference is made to the description in the embodiment of the method, and the description is omitted here.
in the computer-readable storage medium provided in the embodiment of the present invention, a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the business person risk identification method based on big data according to any technical scheme is implemented. The computer-readable storage medium includes, but is not limited to, any type of disk including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks, ROMs (Read-Only memories), RAMs (Random access memories), EPROMs (EraSable Programmable Read-Only memories), EEPROMs (Electrically EraSable Programmable Read-Only memories), flash memories, magnetic cards, or optical cards. That is, a storage device includes any medium that stores or transmits information in a form readable by a device (e.g., a computer, a cellular phone), and may be a read-only memory, a magnetic or optical disk, or the like.
According to the computer-readable storage medium provided by the embodiment of the invention, the embodiment of the service personnel risk identification method based on big data can be realized, the data collected by kafka is simplified firstly in the application, when the data volume is large, not only can the information in various aspects of a service personnel be obtained, but also the data volume needing to be processed in the later risk service data analysis process is reduced, the calculation precision of server data is improved under the condition that the calculation process is blocked, the precision of the risk result of the service personnel is improved, the obtained risk type of the service personnel is more accurate, the assessment efficiency is higher, and the risk brought to an enterprise by the service personnel is reduced; the business personnel risk identification method based on big data provided by the embodiment of the application comprises the following steps: acquiring business risk information of business personnel associated with a business risk identifier in real time based on the business risk identifier preset in a kafka system, and converting the business risk information into business risk data; classifying and analyzing basic information in business personnel information and the business risk data through an HBase structure to determine the risk level of business personnel; and when the risk level exceeds the preset risk level of the business personnel, sending warning information corresponding to the risk level to the business personnel. The attendance information with large data volume can be collected based on the kafka high throughput, the persistence and the stability of the attendance information are guaranteed, the attendance information is prevented from being maliciously tampered by other personnel, meanwhile, the data throughput is high, and then the attendance information with large data volume can be efficiently collected through a distributed data processing system so as to be convenient for extracting the attendance information for analysis. Specifically, a business risk identifier is preset in kafka as a topic (topic) of data acquisition, such as attendance information, wherein the attendance information may further include an attendance name, attendance time, and the like. In order to simplify the later data calculation process, conveniently and quickly calculate out related risk service identification, convert part of Attendance information, if the Attendance status is normal 1, late arrival is 2, and spacious work is 3, further, other risk data of business personnel can also be obtained by simplifying the method, different types of data adopt different simplifying methods, in order to avoid the repetition of the business risk data after the conversion of different business risk information, the simplified business risk data carries the topic identification of the corresponding business risk information, if the above mentioned time of Attendance is simplified and possibly repeated with other types of business risk data, the identification carrying the Attendance status (Attendance status) is: the normal data is 1As, the late data is 2As and the spacious work is 3As, so that the data volume of the business risk data acquired from kafka is reduced, the high throughput of the kafka data is more efficiently utilized, the calculation process of the system is simplified, the system can analyze according to more staff multidimensional data, and the risk identification of business personnel can be performed more quickly and accurately. In one embodiment provided by the present application, staff change information is obtained from historical staff data, and the staff change information includes: information of newly-entered employees in a preset examination period and information of departures employees in an adjacent non-examination period after the preset examination period; by the method, the attendance condition of the new staff in the examination period and the condition outside the examination period can be determined in time, namely the risk level of the new staff can be more conveniently and rapidly acquired, whether the corresponding staff is a salesman or not is judged on the basis, and the staff can be conveniently found from the outside to carry out the manual examination, so that the examination and the management of the salesman are facilitated. Further, the assessment and promotion of business personnel can be affected by the risk level. Therefore, more accurate information of the business personnel needs to be acquired, basic information of the business personnel is acquired, and the risk level of the business personnel is determined according to the business risk data and the basic information; the basic information comprises position and department age information of business personnel. And on the basis, remark information of the business personnel can be obtained, and the risk level of the business personnel is determined according to the business risk data, the positions and the remark information. In order to more accurately determine the risk level of business personnel and avoid misjudgment of the risk level of the business personnel caused by incomplete data acquisition. Therefore, the risk level of the business personnel is comprehensively judged by comprehensively acquiring position, department age information, remarks and the like of the business personnel, for example, when attendance data is lost due to business personnel business trip, in order to avoid misjudging the risk level of the business personnel, the condition that the attendance data of the business personnel is lost on the same day is determined through the remark information, if the business personnel is on the trip, the risk level of the business personnel is the lowest, and further subsequent warning can not be carried out on the business personnel. The system server can rapidly calculate data by dividing the same level and the same department age, and then can rapidly count and calculate the risk data of the same type of service personnel, thereby avoiding the higher loss of the whole service caused by the service personnel at the high level. The method and the system have the advantages that the server can analyze and calculate data in different categories and analyze risks more accurately, partial data processing amount and reading speed can be reduced due to the fact that only business risk data of the same category are analyzed in the analyzing process, and accordingly the result of the business risk data of the same category can be obtained quickly to determine the risk level of business personnel, system data processing amount is reduced through classification analysis by HBase, the risk level of the business personnel of the same category can be determined more clearly, personnel screening can be performed more reasonably, and meanwhile the obtained result is easier to manage. After the risk level of the business personnel is determined, the system can give out the risk information warning of business response, early controls the risk in business operation, and reduces the loss brought by the risk of the business personnel. Meanwhile, the staff is warned to improve the business quality of the staff, and the staff can conveniently control the business quality of the staff.
In addition, in another embodiment, the present invention further provides a server, as shown in fig. 3, the server includes a processor 503, a memory 505, an input unit 507, and a display unit 509. Those skilled in the art will appreciate that the structural elements shown in fig. 3 do not constitute a limitation of all servers and may include more or fewer components than those shown, or some combination of components. The memory 505 may be used to store the application 501 and various functional modules, and the processor 503 executes the application 501 stored in the memory 505, thereby performing various functional applications of the device and data processing. Memory 505 may be internal memory or external memory, or include both internal and external memory. The internal memory may include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), flash memory, or random access memory. The external memory may include a hard disk, a floppy disk, a ZIP disk, a usb-disk, a magnetic tape, etc. The disclosed memory includes, but is not limited to, these types of memory. The memory 505 disclosed herein is provided by way of example only and not by way of limitation.
The input unit 507 is used for receiving input of signals, and basic information, department age information, job level and the like of service personnel input by a user. The input unit 507 may include a touch panel and other input devices. The touch panel can collect touch operations of a client on or near the touch panel (for example, operations of the client on or near the touch panel by using any suitable object or accessory such as a finger, a stylus and the like) and drive the corresponding connecting device according to a preset program; other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., play control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like. The display unit 509 may be used to display information input by the customer or information provided to the customer and various menus of the computer device. The display unit 509 may take the form of a liquid crystal display, an organic light emitting diode, or the like. The processor 503 is a control center of the computer device, connects various parts of the entire computer using various interfaces and lines, and performs various functions and processes data by operating or executing software programs and/or modules stored in the memory 503 and calling data stored in the memory. The one or more processors 503 shown in fig. 3 are capable of executing, implementing, the functions of the conversion module 100, the risk level determination module 200, the alert module 300, the first import unit 210, the risk level determination unit 220, the obtaining unit 211, the second import unit 212, the extracting unit 230, the obtaining module 410, and the adjusting module 420 shown in fig. 2.
In one embodiment, the server comprises one or more processors 503, one or more memories 505, one or more applications 501, wherein the one or more applications 501 are stored in the memory 505 and configured to be executed by the one or more processors 503, and the one or more applications 301 are configured to perform the big data based business person risk identification method described in the above embodiments.
according to the server provided by the embodiment of the invention, the embodiment of the big data-based business personnel risk identification method can be realized, the data collected by kafka is simplified firstly in the application, when the data volume is large, not only can the information in various aspects of the business personnel be obtained, but also the data volume needing to be processed in the later risk business data analysis process is reduced, the calculation precision of the server data is improved under the condition that the calculation process is blocked, the precision of the risk result of the business personnel is improved, the obtained risk type of the business personnel is more accurate, the assessment efficiency is higher, and the risk brought to an enterprise by the business personnel is reduced; the business personnel risk identification method based on big data provided by the embodiment of the application comprises the following steps: acquiring business risk information of business personnel associated with a business risk identifier in real time based on the business risk identifier preset in a kafka system, and converting the business risk information into business risk data; classifying and analyzing basic information in business personnel information and the business risk data through an HBase structure to determine the risk level of business personnel; and when the risk level exceeds the preset risk level of the business personnel, sending warning information corresponding to the risk level to the business personnel. The attendance information with large data volume can be collected based on the kafka high throughput, the persistence and the stability of the attendance information are guaranteed, the attendance information is prevented from being maliciously tampered by other personnel, meanwhile, the data throughput is high, and then the attendance information with large data volume can be efficiently collected through a distributed data processing system so as to be convenient for extracting the attendance information for analysis. Specifically, a business risk identifier is preset in kafka as a topic (topic) of data acquisition, such as attendance information, wherein the attendance information may further include an attendance name, attendance time, and the like. In order to simplify the later data calculation process, conveniently and quickly calculate out related risk service identification, convert part of Attendance information, if the Attendance status is normal 1, late arrival is 2, and spacious work is 3, further, other risk data of business personnel can also be obtained by simplifying the method, different types of data adopt different simplifying methods, in order to avoid the repetition of the business risk data after the conversion of different business risk information, the simplified business risk data carries the topic identification of the corresponding business risk information, if the above mentioned time of Attendance is simplified and possibly repeated with other types of business risk data, the identification carrying the Attendance status (Attendance status) is: the normal data is 1As, the late data is 2As and the spacious work is 3As, so that the data volume of the business risk data acquired from kafka is reduced, the high throughput of the kafka data is more efficiently utilized, the calculation process of the system is simplified, the system can analyze according to more staff multidimensional data, and the risk identification of business personnel can be performed more quickly and accurately. In one embodiment provided by the present application, staff change information is obtained from historical staff data, and the staff change information includes: information of newly-entered employees in a preset examination period and information of departures employees in an adjacent non-examination period after the preset examination period; by the method, the attendance condition of the new staff in the examination period and the condition outside the examination period can be determined in time, namely the risk level of the new staff can be more conveniently and rapidly acquired, whether the corresponding staff is a salesman or not is judged on the basis, and the staff can be conveniently found from the outside to carry out the manual examination, so that the examination and the management of the salesman are facilitated. Further, the assessment and promotion of business personnel can be affected by the risk level. Therefore, more accurate information of the business personnel needs to be acquired, basic information of the business personnel is acquired, and the risk level of the business personnel is determined according to the business risk data and the basic information; the basic information comprises position and department age information of business personnel. And on the basis, remark information of the business personnel can be obtained, and the risk level of the business personnel is determined according to the business risk data, the positions and the remark information. In order to more accurately determine the risk level of business personnel and avoid misjudgment of the risk level of the business personnel caused by incomplete data acquisition. Therefore, the risk level of the business personnel is comprehensively judged by comprehensively acquiring position, department age information, remarks and the like of the business personnel, for example, when attendance data is lost due to business personnel business trip, in order to avoid misjudging the risk level of the business personnel, the condition that the attendance data of the business personnel is lost on the same day is determined through the remark information, if the business personnel is on the trip, the risk level of the business personnel is the lowest, and further subsequent warning can not be carried out on the business personnel. The system server can rapidly calculate data by dividing the same level and the same department age, and then can rapidly count and calculate the risk data of the same type of service personnel, thereby avoiding the higher loss of the whole service caused by the service personnel at the high level. The method and the system have the advantages that the server can analyze and calculate data in different categories and analyze risks more accurately, partial data processing amount and reading speed can be reduced due to the fact that only business risk data of the same category are analyzed in the analyzing process, and accordingly the result of the business risk data of the same category can be obtained quickly to determine the risk level of business personnel, system data processing amount is reduced through classification analysis by HBase, the risk level of the business personnel of the same category can be determined more clearly, personnel screening can be performed more reasonably, and meanwhile the obtained result is easier to manage. After the risk level of the business personnel is determined, the system can give out the risk information warning of business response, early controls the risk in business operation, and reduces the loss brought by the risk of the business personnel. Meanwhile, the staff is warned to improve the business quality of the staff, and the staff can conveniently control the business quality of the staff.
The server provided by the embodiment of the present invention can implement the embodiment of the service personnel risk identification method based on big data, and for specific function implementation, reference is made to the description in the embodiment of the method, which is not described herein again.
the foregoing is only a partial embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. a business personnel risk identification method based on big data is characterized by comprising the following steps:
acquiring business risk information of business personnel associated with a business risk identifier in real time based on the business risk identifier preset in a kafka system, and converting the business risk information into business risk data;
classifying and analyzing basic information in business personnel information and the business risk data through an HBase structure to determine the risk level of business personnel;
and when the risk level exceeds the preset risk level of the business personnel, sending warning information corresponding to the risk level to the business personnel.
2. The big data-based business personnel risk identification method according to claim 1, wherein the classifying and analyzing the basic information in the business personnel information and the business risk data through the HBase structure to determine the risk level of the business personnel comprises:
Importing the business risk data into a database of an HBase structure through a Spark engine;
and acquiring basic information in the business personnel information corresponding to the business risk data, and performing classification analysis on the basic information in the business personnel information and the business risk data through an HBase structure to determine the risk level of business personnel.
3. The big data-based business personnel risk identification method according to claim 2, wherein the importing the business risk data into the database of the HBase structure through a Spark engine comprises:
classifying and partitioning the business risk data according to preset classification categories through a Spark engine to obtain a distributed data set of the business risk data;
And leading the data sets in the same region into a database of the HBase structure in a centralized manner.
4. The business personnel risk identification method based on big data according to any of claims 1 to 3, wherein the classifying and analyzing the basic information in the business personnel information and the business risk data through the HBase structure to determine the risk level of the business personnel comprises:
and extracting key data in the business risk data according to the job level of business personnel, and performing classification analysis on basic information in business personnel information and the key data through an HBase structure to determine the risk level of the business personnel.
5. the big data-based business person risk identification method according to any one of claims 1 to 3, wherein after determining the risk level of the business person, the method comprises:
acquiring the association relation between the risk level and the job level;
And adjusting the job level of the business personnel according to the risk level and the job level association relation.
6. A business personnel risk identification device based on big data is characterized by comprising:
the conversion module is used for acquiring the business risk information of business personnel associated with the business risk identification in real time based on the business risk identification preset in the kafka system and converting the business risk information into business risk data;
The risk level determining module is used for classifying and analyzing basic information in the business personnel information and the business risk data through an HBase structure to determine the risk level of the business personnel;
and the warning module is used for sending warning information corresponding to the risk level to the business personnel when the risk level exceeds the preset risk level of the business personnel.
7. the big data-based business personnel risk identification device according to claim 6, wherein the risk level determination module comprises:
the first import unit is used for importing the business risk data into a database of an HBase structure through a Spark engine;
and the risk level determining unit is used for acquiring the basic information in the business personnel information corresponding to the business risk data, and performing classification analysis on the basic information in the business personnel information and the business risk data through an HBase structure to determine the risk level of the business personnel.
8. the big data-based business personnel risk identification device according to claim 7, wherein the importing unit comprises:
the obtaining unit is used for classifying and partitioning the business risk data according to preset classification categories through a Spark engine to obtain a distributed data set of the business risk data;
and the second import unit is used for intensively importing the data sets in the same region into the database of the HBase structure.
9. a computer-readable storage medium, wherein the computer-readable storage medium stores thereon a computer program, which when executed by a processor, implements the big data-based business person risk identification method according to any one of claims 1 to 5.
10. a server, comprising:
one or more processors;
A memory;
One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to perform the steps of the big data based business person risk identification method of any of claims 1 to 5.
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