Human resource service management system based on big data analysis

Abstract

The invention belongs to the technical field of human resource management, and particularly relates to a human resource service management system based on big data analysis, which comprises a human resource service management platform, a multi-source data capture module, a big data analysis module, an intelligent service decision module and a maintenance supervision evaluation module. According to the invention, human resource data can be efficiently processed and analyzed, intelligent services and management suggestions are provided, powerful support is provided for human resource management of enterprises, and the management efficiency and decision accuracy of the enterprises are remarkably improved; the maintenance supervision evaluation module comprehensively analyzes and comprehensively evaluates the maintenance supervision performance of the human resource service management platform in the monitoring period, and enhances the operation maintenance and information storage supervision of the human resource service management platform when a maintenance supervision abnormal signal is generated. The subsequent safe and stable operation of the human resource service management platform is ensured, and the intelligent degree is high.

Description

Technical Field

[0001] The present invention relates to the technical field of human resource management, and in particular to a human resource service management system based on big data analysis. Background Art

[0002] Human resource management services refer to the rational allocation of human resources in a planned manner according to the requirements of the enterprise development strategy. With the expansion of enterprise scale and the complexity of business, traditional human resource management methods can no longer meet the needs of modern enterprises. At present, it is difficult to efficiently process and analyze human resource data and provide intelligent service management suggestions when using data analysis technology for human resource management services, which is not conducive to improving the management efficiency and decision-making accuracy of enterprises. It is also impossible to conduct a comprehensive analysis and comprehensive evaluation of the maintenance and supervision performance of the human resource service management platform, which is not conducive to ensuring the safe and stable operation of the human resource service management platform, and the degree of intelligence is low; In view of the above technical defects, a solution is now proposed. Summary of the invention

[0003] The purpose of the present invention is to provide a human resources service management system based on big data analysis, which solves the problem that the existing technology is difficult to efficiently process and analyze human resources data and provide intelligent service management suggestions, and is unable to comprehensively analyze and comprehensively evaluate the maintenance and supervision performance of the human resources service management platform, and has a low level of intelligence.

[0004] To achieve the above object, the present invention provides the following technical solutions: A human resource service management system based on big data analysis, comprising a human resource service management platform, a multi-source data capture module, a big data analysis module, an intelligent service decision module and a maintenance supervision evaluation module; The multi-source data capture module collects human resource related data of the corresponding enterprise through multiple channels, and sends the collected raw data to the big data analysis module; The big data analysis module pre-processes the collected raw data, uses big data processing technology and intelligent algorithms to conduct in-depth mining and analysis of the pre-processed data, and sends the analysis results to the intelligent service decision module; The intelligent service decision module automatically generates intelligent human resource service management suggestions based on the analysis results of the big data analysis module, and sends the generated human resource service management suggestions to the user management terminal via the human resource service management platform; The maintenance supervision evaluation module is used to set the monitoring period, evaluate the maintenance supervision performance of the human resources service management platform during the monitoring period, generate a maintenance supervision qualification signal or a maintenance supervision exception signal through analysis, and send the maintenance supervision qualification signal or the maintenance supervision exception signal to the user management terminal via the human resources service management platform.

[0005] Furthermore, the specific analysis process of the maintenance supervision assessment module is as follows: The platform inspection value and information storage value of the monitoring period are obtained, the platform inspection value and the information storage value are numerically calculated to obtain the maintenance monitoring value, and the maintenance monitoring value is numerically compared with the preset maintenance monitoring threshold. If the maintenance monitoring value exceeds the preset maintenance monitoring threshold, a maintenance supervision abnormality signal is generated; if the maintenance monitoring value does not exceed the preset maintenance monitoring threshold, a maintenance supervision qualified signal is generated.

[0006] Furthermore, the maintenance supervision and evaluation module is connected to the operation analysis and judgment module and the information storage control module in communication. The operation analysis and judgment module analyzes the operation status of the human resources service management platform during the monitoring period, obtains the platform inspection value through analysis, and sends the platform inspection value to the maintenance supervision and evaluation module; The information storage management and control module obtains all storage devices used for storing human resource service management information, analyzes the management and control status of all storage devices during the monitoring period, obtains the information storage value through analysis, and sends the information storage value to the maintenance supervision and evaluation module.

[0007] Furthermore, the specific analysis process of running the analysis and judgment module is as follows: The time when the virus vulnerability scan is performed on the human resources service management platform is collected and marked as the virus vulnerability scan time, the interval between two adjacent virus vulnerability scan times in the monitoring period is marked as the vulnerability scan interval time, the virus vulnerability scan interval time is numerically compared with the preset virus vulnerability scan interval time threshold, and the number of virus vulnerability scan interval times that exceed the preset virus vulnerability scan interval time threshold in the monitoring period is marked as the virus vulnerability scan interval abnormal detection value, and all virus vulnerability scan interval times in the monitoring period are averaged to obtain the virus vulnerability scan interval time analysis value; During the corresponding virus vulnerability scanning process, if a virus vulnerability is scanned, the corresponding virus vulnerability scanning end time is marked as the sweeping time, and the timing is started from the sweeping time until the anti-virus repair is completed, thereby obtaining the virus vulnerability elimination time value; and the virus vulnerability elimination time value is numerically compared with the preset virus vulnerability elimination time threshold, and the number of virus vulnerability elimination time values ​​exceeding the preset virus vulnerability elimination time threshold during the monitoring period is marked as the virus vulnerability abnormal elimination value; When the human resources service management platform cannot operate normally, the duration of its inability to operate is collected, and the total abnormal suspension time value is obtained by summing up all the duration of the inability to operate during the monitoring period, and the numerical comparison is made between the duration of the inability to operate and the preset duration threshold of the inability to operate, and the number of durations of the inability to operate that exceed the preset duration threshold of the inability to operate during the monitoring period is marked as the high-inspection value of the suspension; the platform management inspection value is obtained by numerically calculating the virus and leak scanning abnormal inspection value, the virus and leak scanning time analysis value, the virus vulnerability elimination value, the total abnormal suspension time value and the high-inspection value of the suspension.

[0008] Furthermore, the specific analysis process of the information storage control module is as follows: Obtain the remaining storage space value of the corresponding storage device, sum up the remaining storage space values ​​of all storage devices to obtain a storage margin value, obtain the real-time storage consumption rate, and mark the ratio of the storage margin value to the real-time storage consumption rate as a storage insufficiency coefficient; compare the storage insufficiency coefficient with a preset storage insufficiency coefficient threshold, and if the storage insufficiency coefficient does not exceed the preset storage insufficiency coefficient threshold, it is determined that the corresponding moment is in a storage space shortage state; The total duration of storage space shortage during the monitoring period is obtained and marked as the storage space anomaly value, and the storage status value of the corresponding storage device is obtained through analysis. The proportion of storage devices that exceed the preset storage status threshold is marked as the storage device anomaly value, and the storage status values ​​of all storage devices are averaged to obtain the storage device management inspection value. The information storage management value is obtained by numerically calculating the storage space anomaly value, the storage device anomaly value and the storage device management inspection value.

[0009] Furthermore, the specific analysis process of obtaining the storage status value of the corresponding storage device through analysis is as follows: The real-time temperature and real-time humidity of the environment where the corresponding storage device is located are collected, and the deviation value of the real-time temperature compared to the preset ambient temperature standard value is marked as the storage temperature deviation value. Similarly, the storage humidity deviation value is obtained, and the dust concentration of the environment where the corresponding storage device is located is marked as the storage dust detection value; The storage impact value is obtained by numerically calculating the storage temperature deviation value, the storage humidity deviation value and the storage ash detection value, and the storage surface condition value is obtained by averaging all the storage impact values ​​of the corresponding storage device during the monitoring period.

[0010] Furthermore, the human resource service management platform is communicated with the service management effectiveness detection module, which analyzes the human resource service management effects of the enterprise during the monitoring period, generates a service management effectiveness qualified signal or a service management effectiveness abnormal signal through the analysis, and sends the service management effectiveness qualified signal or the service management effectiveness abnormal signal to the user management terminal via the human resource service management platform.

[0011] Furthermore, the specific analysis process of the service management efficiency detection module is as follows: The number of employees who failed to meet attendance standards or performance targets during the monitoring period is collected and the ratio of this to the total number of employees in the enterprise is marked as the attendance performance ratio, the average value of all employees' satisfaction data for the enterprise during the monitoring period is collected and marked as the satisfaction detection value, and the ratio of the number of employees who resigned during the monitoring period to the total number of employees in the enterprise is marked as the resignation detection value; The service management efficiency detection value is obtained by numerically calculating the attendance performance poor value, satisfaction detection value and resignation inspection value, and the service management efficiency detection value is numerically compared with the preset service management efficiency detection threshold. If the service management efficiency detection value exceeds the preset service management efficiency detection threshold, a service management efficiency abnormal signal is generated; if the service management efficiency detection value does not exceed the preset service management efficiency detection threshold, a service management efficiency qualified signal is generated.

[0012] Furthermore, the human resource service management platform is connected to the push exception analysis module, which analyzes the push exception status of the human resource service management suggestions during the monitoring period, generates a push non-optimal signal or a push optimal signal through the analysis, and sends the push non-optimal signal or the push optimal signal to the user management terminal via the human resource service management platform; the specific analysis process is as follows: When sending the human resource service management suggestion to the user management terminal, the delay duration and information omission ratio data of the corresponding sending process are collected and marked as the push delay value and the push omission check value, and the push delay value and the push omission check value are numerically compared with the preset push delay threshold and the preset push omission check threshold respectively. If the push delay value or the push omission check value exceeds the corresponding preset threshold, the corresponding sending process is marked as an abnormal sending process; The proportion of the number of anomalous processes during the monitoring period is obtained and marked as the anomalous detection value, and the push delay values ​​during the monitoring period are averaged to obtain the push anomalous time value, and the push analysis value is averaged to obtain the push analysis value; the push abnormal evaluation value is obtained by numerically calculating the anomalous detection value, the push anomalous time value and the push analysis value, and the push abnormal evaluation value is numerically compared with the preset push abnormal evaluation threshold. If the push abnormal evaluation value exceeds the preset push abnormal evaluation threshold, a push non-optimal signal is generated; if the push abnormal evaluation value does not exceed the preset push abnormal evaluation threshold, a push optimal signal is generated.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. In the present invention, the human resources related data of the corresponding enterprise are collected through the multi-source data capture module, the big data analysis module performs in-depth mining and analysis on the pre-processed data, and the intelligent service decision module automatically generates corresponding human resources service management suggestions according to the analysis results, providing strong support for the human resources management of the enterprise, and the maintenance supervision evaluation module comprehensively analyzes and comprehensively evaluates the maintenance and supervision performance of the human resources service management platform during the monitoring period, and strengthens the operation maintenance and information storage supervision of the human resources service management platform when the maintenance supervision abnormal signal is generated, so as to ensure the subsequent safe and stable operation of the human resources service management platform; 2. In the present invention, the human resource service management effect of the enterprise during the monitoring period is analyzed through the service management efficiency detection module, and when the service management efficiency abnormal signal is generated, the cause investigation and analysis are carried out and corresponding improvement measures are made, so as to ensure the smooth and efficient operation of the enterprise, and the push abnormality analysis module is used to analyze the push abnormality of the human resource service management suggestions during the monitoring period, and when the push non-optimal signal is generated, the cause investigation and tracing are carried out and reasonable optimization measures are made, so as to help avoid affecting the effective management of the management personnel for the enterprise, and the degree of intelligence is high. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiment 2 and Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Embodiment 1: Figure 1 As shown, a human resource service management system based on big data analysis proposed by the present invention includes a human resource service management platform, a multi-source data capture module, a big data analysis module, an intelligent service decision module and a maintenance supervision evaluation module; It should be noted that the multi-source data capture module collects the human resources related data of the corresponding enterprise (including but not limited to basic information of employees, work performance, training records, salary and benefits, recruitment data, etc.) through multiple channels (such as big databases, internal enterprise systems, social media, etc.), and sends the collected raw data to the big data analysis module; The big data analysis module performs preprocessing operations on the collected raw data, such as cleaning and deduplication, to ensure data quality and provide a reliable data basis for subsequent analysis; it uses big data processing technology and intelligent algorithms to conduct in-depth mining and analysis of the preprocessed data, and sends the analysis results to the intelligent service decision module; for example, the following analysis process: Employee behavior analysis: Analyze employee work habits, performance fluctuations, turnover tendencies, etc., to provide a basis for companies to formulate personalized employee care and management strategies; Recruitment effectiveness evaluation: Evaluate the efficiency, cost and talent quality of different recruitment channels, and optimize recruitment strategies; Compensation and benefits optimization: Adjust the compensation structure and welfare policies based on market salary levels and employee satisfaction survey results to improve employee satisfaction and loyalty; Performance forecasting and management: Based on historical performance data, predict employees' future performance and provide a scientific basis for performance management.

[0017] The intelligent service decision module automatically generates intelligent human resource service management suggestions based on the analysis results of the big data analysis module, and sends the generated human resource service management suggestions to the user management terminal via the human resource service management platform; for example, the following operation process: Precise recruitment recommendations: Automatically match and recommend suitable candidates based on the company's recruitment needs and candidate characteristics; Personalized training plans: Develop personalized training plans based on employees' ability shortcomings and development needs; Salary and benefits adjustment suggestions: Propose salary and benefits adjustment suggestions based on market salary levels and employee satisfaction survey results; Performance improvement suggestions: Propose specific improvement suggestions and implementation paths for employee performance issues.

[0018] The maintenance supervision evaluation module is used to set a monitoring period, preferably one month; evaluate the maintenance supervision performance of the human resources service management platform during the monitoring period, and generate a maintenance supervision qualified signal or a maintenance supervision abnormal signal through analysis; The maintenance supervision qualified signal or maintenance supervision abnormal signal is sent to the user management terminal through the human resources service management platform. When the user management terminal receives the maintenance supervision abnormal signal, it issues a corresponding warning to remind the management personnel to promptly strengthen the operation maintenance and information storage supervision of the human resources service management platform in the future, so as to ensure the subsequent safe and stable operation of the human resources service management platform; the specific analysis process of the maintenance supervision evaluation module is as follows: The platform inspection value and information storage value of the monitoring period are obtained, and the platform inspection value LR and the information storage value LX are numerically calculated by the formula LM=uw1*LR+uw2*LX to obtain the maintenance monitoring value LM; wherein, uw1 and uw2 are preset weight coefficients with values ​​greater than zero, and the larger the value of the maintenance monitoring value LM is, the worse the overall maintenance supervision performance of the management personnel during the monitoring period is; The maintenance monitoring and evaluation value is numerically compared with the preset maintenance monitoring and evaluation threshold. If the maintenance monitoring and evaluation value exceeds the preset maintenance monitoring and evaluation threshold, it indicates that the management personnel's maintenance supervision performance during the monitoring period is generally poor, and a maintenance supervision abnormality signal is generated; if the maintenance monitoring and evaluation value does not exceed the preset maintenance monitoring and evaluation threshold, it indicates that the management personnel's maintenance supervision performance during the monitoring period is generally good, and a maintenance supervision qualified signal is generated.

[0019] Specifically, the maintenance supervision and evaluation module is connected to the operation analysis and judgment module and the information storage control module through communication. The operation analysis and judgment module analyzes the operation status of the human resources service management platform during the monitoring period, obtains the platform inspection value LR through analysis, and sends the platform inspection value LR to the maintenance supervision and evaluation module, which can not only accurately feedback the management status of the human resources service management platform during the monitoring period, but also provide data support for the analysis process of the maintenance supervision and evaluation module to ensure the accuracy of its analysis results; the specific analysis process of the operation analysis and judgment module is as follows: The time when the virus vulnerability scan is performed on the human resources service management platform is collected and marked as the virus vulnerability scan time, the interval between two adjacent virus vulnerability scan times in the monitoring period is marked as the vulnerability scan interval time, the virus vulnerability scan interval time is numerically compared with the preset virus vulnerability scan interval time threshold, and the number of virus vulnerability scan interval times that exceed the preset virus vulnerability scan interval time threshold in the monitoring period is marked as the virus vulnerability scan interval abnormal detection value, and all virus vulnerability scan interval times in the monitoring period are averaged to obtain the virus vulnerability scan interval time analysis value; During the corresponding virus vulnerability scanning process, if a virus vulnerability is scanned, the corresponding virus vulnerability scanning end time is marked as the sweeping time, and the time is counted from the sweeping time as the starting point until the antivirus repair is completed, and the virus vulnerability elimination time value is obtained accordingly; wherein, the larger the value of the virus vulnerability elimination time value is, the less timely the corresponding antivirus repair process is; The virus vulnerability elimination time value is numerically compared with a preset virus vulnerability elimination time threshold, and the number of virus vulnerability elimination time values ​​exceeding the preset virus vulnerability elimination time threshold during the monitoring period is marked as a virus vulnerability abnormal elimination value; When the human resources service management platform cannot operate normally (mainly due to factors such as network attacks), the duration of its inability to operate is collected, and all durations of inability to operate during the monitoring period are summed up to obtain the total abnormal stoppage time value, and the duration of inability to operate is numerically compared with the preset duration of inability to operate threshold, and the number of durations of inability to operate that exceed the preset duration of inability to operate threshold during the monitoring period is marked as the high-inspection value of the operation stoppage; By formula The virus and leak scanning and isolation abnormal detection value BY, virus and leak scanning and isolation time analysis value BF, virus and vulnerability elimination value BX, total abnormal suspension time value BS and high operation suspension detection value BK are numerically calculated to obtain the platform management and inspection value LR; among them, k1, k2, k3, k4, k5 are preset proportional coefficients whose values ​​are greater than zero, and the larger the value of the platform management and inspection value LR, the worse the overall management status of the human resources service management platform during the monitoring period.

[0020] Furthermore, the information storage control module obtains all storage devices used for storing human resource service management information, analyzes the control status of all storage devices during the monitoring period, obtains the information storage value LX through analysis, and sends the information storage value LX to the maintenance supervision evaluation module, which can not only accurately feedback the information storage management status during the monitoring period, but also provide data support for the analysis process of the maintenance supervision evaluation module, further ensuring the accuracy of its analysis results; the specific analysis process of the information storage control module is as follows: Obtain the remaining storage space value of the corresponding storage device (i.e., the data volume value of the remaining storage capacity), sum up the remaining storage space values ​​of all storage devices to obtain the storage margin value, and obtain the real-time consumption rate of the storage volume, and mark the ratio of the storage margin value to the real-time consumption rate of the storage volume as the storage insufficiency coefficient; Compare the storage insufficiency coefficient with the preset storage insufficiency coefficient threshold. If the storage insufficiency coefficient does not exceed the preset storage insufficiency coefficient threshold, it is determined that the corresponding moment is in a storage space shortage state, and the total duration of the storage space shortage state during the monitoring period is obtained and marked as the storage space out-of-time value. And obtain the storage status value of the corresponding storage device through analysis, specifically: collect the real-time temperature and real-time humidity of the environment where the corresponding storage device is located, mark the deviation value of the real-time temperature compared to the preset ambient temperature standard value as the storage temperature deviation value, mark the deviation value of the real-time humidity compared to the preset ambient humidity standard value as the storage humidity deviation value, and obtain the dust concentration of the environment where the corresponding storage device is located and mark it as the dust detection value; The storage temperature deviation value QS, the storage humidity deviation value QH and the storage ash detection value QM are numerically calculated by the formula QY=(up1*QS+up2*QH+up3*QM) / 3 to obtain the storage influence value QY, wherein up1, up2 and up3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the storage influence value QY is, the worse the real-time environmental condition of the environment where the corresponding storage device is located is; all the storage influence values ​​of the corresponding storage device in the monitoring period are averaged to obtain the storage table value; Compare the storage status value of the corresponding storage device with the preset storage status threshold, mark the percentage of storage devices that exceed the preset storage status threshold as the storage device abnormal inspection value, and calculate the average of the storage status values ​​of all storage devices to obtain the storage device management inspection value; By formula The storage space abnormal time value YR, the storage device abnormal inspection value YW and the storage device management inspection value YK are numerically calculated to obtain the information storage management value LX; wherein, sq1, sq2, sq3 are preset proportional coefficients, sq2>sq3>sq1>0; and, the larger the value of the information storage management value LX, the worse the information storage management status during the monitoring period is overall.

[0021] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the human resource service management platform is connected to the service management efficiency detection module in communication, and the service management efficiency detection module analyzes the human resource service management effect of the enterprise during the monitoring period, and generates a service management efficiency qualified signal or a service management efficiency abnormal signal through analysis; The qualified service management efficiency signal or abnormal service management efficiency signal is sent to the user management terminal through the human resources service management platform. When the user management terminal receives the abnormal service management efficiency signal, it issues a corresponding warning to remind the management personnel to conduct cause investigation and analysis and make corresponding improvement measures, so as to ensure the smooth and efficient operation of the enterprise. The specific analysis process of the service management efficiency detection module is as follows: The number of employees who failed to meet attendance standards or performance targets during the monitoring period is collected and the ratio of this to the total number of employees in the enterprise is marked as the attendance performance ratio, the average value of all employees' satisfaction data for the enterprise during the monitoring period is collected and marked as the satisfaction detection value, and the ratio of the number of employees who resigned during the monitoring period to the total number of employees in the enterprise is marked as the resignation detection value; The service management efficiency detection value FX is obtained by numerically calculating the attendance performance poor value FY, the satisfaction detection value FM and the resignation detection value FL through the formula FX=(a1*FY+a3*FL) / (1+a2*FM); wherein a1, a2 and a3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the service management efficiency detection value FX is, the worse the overall human resource service management effect for the enterprise during the supervision period is. The service management effectiveness detection value FX is numerically compared with the preset service management effectiveness detection threshold. If the service management effectiveness detection value FX exceeds the preset service management effectiveness detection threshold, indicating that the human resources service management effect for the enterprise during the supervision period is generally poor, then a service management effectiveness abnormal signal is generated; if the service management effectiveness detection value FX does not exceed the preset service management effectiveness detection threshold, indicating that the human resources service management effect for the enterprise during the supervision period is generally good, then a service management effectiveness qualified signal is generated.

[0022] Embodiment 3: Figure 2 As shown, the difference between this embodiment and the first and second embodiments is that the human resource service management platform is connected to the push exception analysis module in communication, and the push exception analysis module analyzes the push exception status of the human resource service management suggestions during the monitoring period, and generates a push non-optimal signal or a push excellent table signal through the analysis; And the push signal of non-optimal or push signal of optimal table is sent to the user management terminal through the human resources service management platform. When the user management terminal receives the push signal of non-optimal, it will issue a corresponding warning to remind the management personnel to conduct timely investigation and tracing of the cause and make reasonable optimization measures, so as to ensure that the management personnel can receive the human resources service management suggestions in a timely and complete manner in the future, so as to avoid affecting the management personnel's effective management of the enterprise; the specific analysis process is as follows: When sending the human resource service management suggestion to the user management terminal, the delay time of the corresponding sending process (i.e., the data volume value of the interval time between the generation time of the corresponding human resource service management suggestion and the reception time of the user management terminal) and the information omission ratio data (i.e., the data volume value of the ratio of the amount of omitted data to the total amount of data required to be sent) are collected and marked as the push delay value and the push omission check value, and the push delay value and the push omission check value are numerically compared with the preset push delay threshold and the preset push omission check threshold respectively. If the push delay value or the push omission check value exceeds the corresponding preset threshold, indicating that the corresponding push process is abnormal, the corresponding sending process is marked as an abnormal sending process; The number ratio of the anomalous processes in the monitoring period is obtained and marked as the anomalous detection value, and all the push delay values ​​in the monitoring period are averaged to obtain the push anomalous value, and all the push legacy detection values ​​in the monitoring period are averaged to obtain the push legacy analysis value; The abnormal detection value TN, the abnormal time value TF and the analysis value TP are numerically calculated by the formula TL=kp1*TN+(kp2*TF+kp3*TP) / kp1 to obtain the abnormal evaluation value TL; wherein, kp1, kp2 and kp3 are preset proportional coefficients, kp1>kp3>kp2>0; and the larger the value of the abnormal evaluation value TL, the worse the performance of pushing suggestions for human resource service management during the supervision period; The push difference evaluation value TL is numerically compared with the preset push difference evaluation threshold. If the push difference evaluation value TL exceeds the preset push difference evaluation threshold, it indicates that the push performance of human resources service management suggestions during the supervision period is poor, and a push non-optimal signal is generated; if the push difference evaluation value TL does not exceed the preset push difference evaluation threshold, it indicates that the push performance of human resources service management suggestions during the supervision period is good, and a push excellent signal is generated.

[0023] The working principle of the present invention is as follows: when in use, the human resources related data of the corresponding enterprise is collected through the multi-source data capture module, the big data analysis module uses big data processing technology and intelligent algorithms to deeply mine and analyze the pre-processed data, and the intelligent service decision module automatically generates corresponding human resources service management suggestions according to the analysis results. It can efficiently process and analyze human resources data and provide intelligent services and management suggestions, provide strong support for the human resources management of the enterprise, and significantly improve the management efficiency and decision-making accuracy of the enterprise. The maintenance and supervision evaluation module will comprehensively analyze and comprehensively evaluate the maintenance and supervision performance of the human resources service management platform during the monitoring period. When the maintenance and supervision abnormality signal is generated, the operation maintenance and information storage supervision of the human resources service management platform will be strengthened to ensure the subsequent safe and stable operation of the human resources service management platform with a high degree of intelligence.

[0024] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A human resource service management system based on big data analysis, characterized in that: It includes a human resources service management platform, a multi-source data capture module, a big data analysis module, an intelligent service decision module, and a maintenance supervision and evaluation module; The multi-source data capture module collects human resource related data of the corresponding enterprise through multiple channels, and sends the collected raw data to the big data analysis module; The big data analysis module pre-processes the collected raw data, uses big data processing technology and intelligent algorithms to conduct in-depth mining and analysis of the pre-processed data, and sends the analysis results to the intelligent service decision module; The intelligent service decision module automatically generates intelligent human resource service management suggestions based on the analysis results of the big data analysis module, and sends the generated human resource service management suggestions to the user management terminal via the human resource service management platform; The maintenance supervision evaluation module is used to set the monitoring period, evaluate the maintenance supervision performance of the human resources service management platform during the monitoring period, generate a maintenance supervision qualification signal or a maintenance supervision exception signal through analysis, and send the maintenance supervision qualification signal or the maintenance supervision exception signal to the user management terminal via the human resources service management platform.

2. According to claim 1, a human resources service management system based on big data analysis is characterized in that: The specific analysis process of the maintenance supervision assessment module is as follows: The platform inspection value and information storage value of the monitoring period are obtained, and the platform inspection value and the information storage value are numerically calculated to obtain the maintenance monitoring value. If the maintenance monitoring value exceeds the preset maintenance monitoring threshold, a maintenance supervision abnormality signal is generated; if the maintenance monitoring value does not exceed the preset maintenance monitoring threshold, a maintenance supervision qualified signal is generated.

3. A human resource service management system based on big data analysis according to claim 2, characterized in that: The maintenance supervision and evaluation module is connected to the operation analysis and judgment module and the information storage control module in communication. The operation analysis and judgment module analyzes the operation status of the human resources service management platform during the monitoring period, obtains the platform inspection value through analysis, and sends the platform inspection value to the maintenance supervision and evaluation module; The information storage management and control module obtains all storage devices used for storing human resource service management information, analyzes the management and control status of all storage devices during the monitoring period, obtains the information storage value through analysis, and sends the information storage value to the maintenance supervision and evaluation module.

4. A human resource service management system based on big data analysis according to claim 3, characterized in that: The specific analysis process of running the analysis and judgment module is as follows: The time when the virus vulnerability scan is performed on the human resources service management platform is collected and marked as the virus vulnerability scan time, the interval between two adjacent virus vulnerability scan times in the monitoring period is marked as the vulnerability scan interval time, and the number of virus vulnerability scan interval times that exceed the preset virus vulnerability scan interval time threshold in the monitoring period is marked as the virus vulnerability scan interval abnormal detection value, and the average of all virus vulnerability scan interval times in the monitoring period is calculated to obtain the virus vulnerability scan interval time analysis value; During the corresponding virus vulnerability scanning process, if a virus vulnerability is scanned, the corresponding virus vulnerability scanning end time is marked as the sweeping time, and the timing is started from the sweeping time until the anti-virus repair is completed, and the virus vulnerability elimination time value is obtained accordingly, and the number of virus vulnerability elimination time values ​​exceeding the preset virus vulnerability elimination time threshold during the monitoring period is marked as the virus vulnerability abnormal elimination value; When the human resources service management platform cannot operate normally, the duration of its inability to operate is collected, and the total abnormal suspension time value is obtained by summing up all the duration of the inability to operate during the monitoring period, and the number of duration of the inability to operate that exceeds the preset threshold of the duration of the inability to operate during the monitoring period is marked as the high-inspection value of the suspension; the platform management inspection value is obtained by numerically calculating the virus and leak scanning abnormal inspection value, the virus and leak scanning time analysis value, the virus vulnerability elimination value, the total abnormal suspension time value and the high-inspection value of the suspension.

5. The human resource service management system based on big data analysis according to claim 3 is characterized in that: The specific analysis process of the information storage control module is as follows: The total duration of storage space shortage during the monitoring period is obtained and marked as the storage space anomaly value, and the storage status value of the corresponding storage device is obtained through analysis. The proportion of storage devices that exceed the preset storage status threshold is marked as the storage device anomaly value, and the storage status values ​​of all storage devices are averaged to obtain the storage device management inspection value. The information storage management value is obtained by numerically calculating the storage space anomaly value, the storage device anomaly value and the storage device management inspection value.

6. A human resource service management system based on big data analysis according to claim 5, characterized in that: The specific analysis process of obtaining the storage status value of the corresponding storage device through analysis is as follows: The real-time temperature and real-time humidity of the environment where the corresponding storage device is located are collected, and the deviation value of the real-time temperature compared to the preset ambient temperature standard value is marked as the storage temperature deviation value. Similarly, the storage humidity deviation value is obtained, and the dust concentration of the environment where the corresponding storage device is located is marked as the storage dust detection value; The storage impact value is obtained by numerically calculating the storage temperature deviation value, the storage humidity deviation value and the storage ash detection value, and the storage surface condition value is obtained by averaging all the storage impact values ​​of the corresponding storage device during the monitoring period.

7. The human resource service management system based on big data analysis according to claim 1 is characterized in that: The human resource service management platform is communicated with the service management effectiveness detection module, which analyzes the human resource service management effect of the enterprise during the monitoring period, generates a service management effectiveness qualified signal or a service management effectiveness abnormal signal through the analysis, and sends the service management effectiveness qualified signal or the service management effectiveness abnormal signal to the user management terminal via the human resource service management platform.

8. According to claim 7, a human resource service management system based on big data analysis is characterized in that: The specific analysis process of the service management efficiency detection module is as follows: The service management efficiency detection value is obtained by numerically calculating the attendance performance poor value, satisfaction detection value and resignation inspection value. If the service management efficiency detection value exceeds the preset service management efficiency detection threshold, a service management efficiency abnormal signal is generated; if the service management efficiency detection value does not exceed the preset service management efficiency detection threshold, a service management efficiency qualified signal is generated.

9. According to claim 7, a human resources service management system based on big data analysis is characterized in that: The human resource service management platform communicates with the push exception analysis module, which analyzes the push exception status of the human resource service management suggestions during the monitoring period, generates a push non-optimal signal or a push optimal signal through the analysis, and sends the push non-optimal signal or the push optimal signal to the user management terminal via the human resource service management platform; the specific analysis process is as follows: The push abnormality value is obtained by numerically calculating the abnormal detection value, the push abnormal time value and the push analysis value. If the push abnormality value exceeds the preset push abnormality threshold, a push non-optimal signal is generated; if the push abnormality value does not exceed the preset push abnormality threshold, a push optimal signal is generated.

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