WO2022151592A1 - Employee performance assessment method and system, and storage medium - Google Patents

Abstract

An employee performance assessment method and system. The method comprises: obtaining employee performance complexity level factor data and activity level factor data from within a preset system (S110); taking activity level factor data and complexity level factor data conforming to a preset determination condition to respectively serve as activity level information data and complexity level information data, and respectively performing percentile cleanup and long mantissa processing on the complexity level information data and the activity level information data (S120); calculating a personal activity level and a personal complexity level of an employee; and obtaining and presenting a personal productivity result according to the personal complexity level and the personal activity level. The present method further relates to blockchain technology, where data is stored in a blockchain, and the present method is able achieve a technical effect of scientifically assessing aspects of an employee such as an activity level and a work complexity level.

Description

Employee effectiveness evaluation method, system and storage medium

This application claims the priority of the Chinese patent application filed on January 18, 2021 with the application number 202110062598.7 and the invention titled "Employee Effectiveness Evaluation Method, System and Storage Medium", the entire contents of which are incorporated herein by reference Applying.

technical field

The present application relates to big data processing technology, and in particular, to an employee effectiveness evaluation method, system and storage medium.

Background technique

Employee effectiveness evaluation refers to the analysis of the influence of various abilities of employees on their performance. The applicant realizes that the current performance evaluation system is constructed according to the amount of work completed by employees and the attendance status of employees, but the disadvantages are as follows:

1. The manually collected data is single and the calculation method is only simple quantification, resulting in rough and unscientific analysis data obtained;

2. The data of the employee effectiveness evaluation system and the employee management system cannot be shared, resulting in low data acquisition efficiency of the employee effectiveness evaluation system;

Therefore, there is an urgent need for an efficient and scientific evaluation method of employee effectiveness.

SUMMARY OF THE INVENTION

The present application provides an employee performance evaluation method, system and computer-readable storage medium, which mainly solve the problem of poor scientificity in employee performance evaluation.

In order to achieve the above object, the present application provides an employee effectiveness evaluation method, which is applied to an electronic device, and the method includes:

Obtain the complexity factor data and activity factor data of employee effectiveness from the preset system;

The complexity factor data and the activity factor data that meet the preset judgment conditions are respectively used as the complexity information data and the activity information data, and the percentile cleaning and the activity information data are respectively performed on the complexity information data and the activity information data. long-endian processing;

Calculate the personal complexity of the employee according to the complexity information data, and calculate the personal activity of the employee according to the activity information data;

The employee's personal complexity profile is obtained according to the employee's personal complexity, the employee's personal activity profile is obtained according to the employee's personal activity level, and the employee's personal inspection coordinates are obtained according to the employee's personal complexity and the employee's personal activity level.

In order to achieve the above purpose, the present application provides an employee effectiveness evaluation system, including a factor data acquisition unit, a personal activity and personal complexity acquisition unit, and a production capacity result display unit; wherein,

The factor data acquisition unit is used for acquiring complexity factor data and activity factor data of employee effectiveness from a preset system;

The personal activity and personal complexity obtaining unit is used for taking the complexity factor data and the activity factor data that meet the preset judgment conditions as the complexity information data and the activity information data, respectively, and for the complexity information. The data and the activity information data are respectively subjected to percentile cleaning and long endian processing; the employee's personal complexity is calculated according to the complexity information data, and the employee's personal activity is calculated according to the activity information data;

The production capacity result display unit is used to obtain the employee's personal complexity portrait according to the employee's personal complexity, obtain the employee's personal activity portrait according to the employee's personal activity, and obtain the employee's personal activity portrait according to the employee's personal complexity and the employee's personal activity. Degree to obtain the coordinates of the employee's personal inspection.

To achieve the above object, the present application also provides a computer device, the computer device includes: at least one processor; and a memory communicatively connected to the at least one processor; A program executed by a processor, the program being executed by the at least one processor to enable the at least one processor to perform the employee performance assessment method as described above.

In addition, in order to achieve the above object, the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, implements the steps of the above-mentioned employee performance evaluation method.

The employee effectiveness evaluation method, system, computer equipment and computer-readable storage medium proposed in this application obtain the complexity factor data and activity factor data of employee effectiveness from a preset system; Factor data and activity factor data are used as complexity information data and activity information data respectively; perform percentile cleaning and long-end number processing on the complexity information data and activity information data respectively; calculate the personal complexity of employees and personal activity; according to the personal complexity and personal activity, obtain and display personal productivity results; the beneficial effects are as follows: 1) The collected data comprehensively includes the complexity factor data and activity factor data, so as to obtain comprehensive and scientific 2) The employee effectiveness evaluation system directly obtains the database data of the employee management system by establishing an interface, which can not only automatically obtain the complexity factor data and activity factor data of the employee effectiveness, but also realize the incremental synchronization and update of the data. ; 3) Scientific evaluation of employees' activity and work complexity.

Description of drawings

Fig. 1 is the flow chart of the embodiment of the employee effectiveness evaluation method of the present application;

2 is a schematic diagram of the logical structure of the employee effectiveness evaluation system of the present application;

3 is a schematic structural diagram of an embodiment of a computer device of the present application;

Detailed ways

It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application.

In order to improve the user's employee effectiveness evaluation efficiency, the present application provides an employee effectiveness evaluation method. FIG. 1 shows the flow of an embodiment of the employee effectiveness evaluation method of the present application. Referring to FIG. 1 , the method may be performed by an apparatus, and the apparatus may be implemented by software and/or hardware.

The employee effectiveness evaluation method, system, computer equipment and computer-readable storage medium proposed in this application, by acquiring complexity factor data and activity factor data; The data is used as complexity information data and activity information data; perform percentile cleaning and long-end number processing on the complexity information data and activity information data, and obtain to determine the employee's personal complexity activity and personal activity complexity Obtain and present individual productivity results based on the individual activity and individual sophistication obtained.

The employee effectiveness evaluation method includes: step S110-step S140.

S110. Acquire complexity factor data and activity factor data of employee effectiveness from a preset system.

The hardware basis of the employee effectiveness evaluation method of the present application is an application cluster composed of multiple hosts, and the cluster stores synchronization data and operation and maintenance model data through an operation and maintenance database. The application cluster synchronizes the complexity, activity and employee attendance data to the correlation factor database through HTTP requests and Kettle. The correlation factor database is the software of the unit's daily employee management system, such as CMDB. Specifically, the complexity factor data is obtained from the CMDB system; the activity factor data is obtained from the associated problems, events, changes, monitoring and other systems.

The method for obtaining the complexity factor data and the activity factor data includes: connecting with the employee management system database through the ETL (Extract-Transform-Load) interface and the HTTP (HyperText Transfer Protocol, hypertext transfer protocol) interface, and requesting through HTTP And the Kettle tool obtains the complexity factor data and the activity factor data from the employee management system database, not only does not need to manually obtain the data, but also can realize incremental synchronization and update of the data. Among them, Kettle is a foreign open source ETL tool, written in pure Java, with efficient and stable data extraction (data migration tool). There are two script files in Kettle, transformation and job. Transformation completes the basic transformation of data, and job completes the control of the entire workflow.

The activity factor is monitors is the monitoring configuration volume, alerts is the alarm processing volume, versions is the version management volume, changes is the change management volume, profiles is the configuration management volume, problems is the problem management volume, preventions is the active prevention volume, and exceptions is the exception management volume volume, drs is the disaster recovery management volume, emerges is the emergency management volume, risks is the risk management volume, incidents is the incident management volume, and requests is the service request volume.

The complexity factor is sg is the system level, les is the number of logical entities, hosts is the number of hosts, ins is the number of instances, dbs is the number of DB instances, and sc is the number of systems in charge of individuals. Further, the working hours of the employees and the work intensity of the employees are also obtained. The daily workload of the work intensity = the total amount of work / the total number of months / the number of attendance punches.

In the specific implementation process, it is necessary to inspect the human capacity of each operation and maintenance employee. The inspection of capacity will ultimately be reflected in the two dimensions of activity and complexity. The activity is reflected in each node in the ITIL process and the company's customization. The output of the nodes, such as event processing volume, problem processing volume, monitoring configuration and processing volume; in addition, in addition to obtaining the output volume, it is also necessary to obtain the complexity, which is reflected in the various factors of the system that each person is responsible for, and finally combines the active The complexity and complexity of the operation and maintenance work performed by the person can reflect the compliance, intensity and production capacity of the person, so that the intensity and production capacity of the entire group can be calculated. In order to obtain each factor, it is necessary to open up the management system and CMDB system of each node through ETL and HTTP interfaces, and obtain the processing volume and system complexity of each person in the node. A scientific and effective cloud employee performance evaluation system.

S120. Take the complexity factor data and the activity factor data that meet the preset judgment conditions as the complexity information data and the activity information data, respectively, and perform percentiles on the complexity information data and the activity information data respectively. Cleaning and long-endian processing.

In a specific embodiment, the preset determination condition is a normal distribution value of the complexity factor data and the activity factor data determined according to a preset standard, wherein the preset standard is that the normal distribution is 90 % value.

Specifically, normal distributions are constructed respectively for the complexity factor data and the activity factor data; the expected value μ ₁ of the complexity factor data, the standard deviation σ ₁ of the complexity factor data, and the activity factor are calculated respectively. The expected value μ ₂ of the degree factor data and the standard deviation σ ₂ of the activity factor data; according to the expected value μ ₁ and the standard deviation σ ₁ of the complexity factor data, the normal distribution value of the selected complexity factor data that meets the preset conditions is taken as Complexity information data; wherein, the preset condition is x=1.65σ ₁ +μ ₁ ; according to the expected value μ ₂ and standard deviation σ ₂ of the activity factor data, the positive value of the selected activity factor data that meets the preset condition is The state distribution value is used as the activity information data; wherein, the preset condition is x=1.65σ ₂ +μ ₂ .

It should be noted that the normal distribution of the standard activity factor for the complexity factor data or activity factor data acquisition is a value of 90% (or 70%, which can be adjusted specifically), and the maximum value of each factor is μ+λσ, The minimum value is μ-λσ, and the default value is μ-λσ. If μ-λσ is less than 0, the value is 0. μ is the expected value, and σ is the standard deviation; according to the activity factor, the basic portrait of the performance of the operation and maintenance staff can be obtained later. When the value is 90%, (x-μ)/σ=1.65; when the value is 70%, (x-μ)/σ=1.03.

Percentile (Pn) is a statistical term. If a set of data is sorted from large to small and the corresponding cumulative percentile is calculated, the value of the data corresponding to a percentile is called the percentile percentile of the digit. It can be expressed as: a group of n observations are arranged according to the size of the value. For example, the value at the position of P% is called the Pth percentile percentile; Pn means that there are n% of people below this number, such as: P95 That is, 95% of the people who answered are lower than this number.

Calculate the pth percentile, arrange the original data in increasing order, and calculate the index i=np%. If i is not an integer, round up i, and the adjacent integer greater than i is the position of the pth percentile. If i is an integer, then the p-th percentile is the average of the i-th and i+l-th data.

The reason for long-tail processing is that, taking into account the comparability and computability of factors, the influence of each factor can be reflected, and some data that are not in the same order of magnitude need to be taken to the maximum value, or the maximum value is rounded up. The logarithmic value of the logarithm of the whole ten (whole hundred, whole thousand) integer as the base, so that the influence of long-tail data can be dealt with; taking the scenario of 1 host and 100 instances as an example, there is 1 host, but the instance has 100, or even larger, so in order to reflect the influence of 1 and the influence of 100, the logarithm of the maximum value is taken as the base, so that the factors are all between 0-1, in the same order of magnitude.

By performing percentile cleaning and long-endian processing on the complexity information data and the activity information data, more effective and accurate data can be obtained.

S130, calculating the personal complexity of the employee according to the complexity information data, and calculating the personal activity degree of the employee according to the activity information data;

In the specific implementation process, step S120 can also be omitted, and only the complexity factor data and the activity factor data are cleaned by percentiles; or only the complexity factor data and the activity factor data can be cleaned. Normal distribution cleaning, no longer cleaning by percentiles. That is, either percentile cleaning or normal distribution is used.

The method for determining an employee's personal activity level includes:

Personal activity=monitors×0.4+alerts×0.22+versions×0.75+changes×1+profiles×0.1+problems×1.5+preventions×2+exceptions×8+drs×16+emergences×2+risks×16+incidents× 0.5+requests×1;

Among them, monitors is the amount of monitoring configuration, alerts is the amount of alarm processing, versions is the amount of version management, changes is the amount of change management, profiles is the amount of configuration management, problems is the amount of problem management, preventions is the amount of active prevention, and exceptions is the amount of exception management, drs is the amount of disaster recovery management, emerges is the amount of emergency management, risks is the amount of risk management, incidents is the amount of incident management, and requests is the amount of service requests.

Further, preferably, the personal complexity is the personal total system complexity TSC, and the personal total system complexity TSC is obtained by the personal single system complexity PSC;

The method for obtaining the personal total system complexity TSC (Total System Complexity) and the personal single system complexity PSC (Per System Complexity) according to the complexity information data includes:

Among them, sg is the system level, les is the number of logical entities, hosts is the number of hosts, ins is the number of instances, dbs is the number of DB instances, and sc is the number of personally responsible systems.

S140. Obtain the employee's personal complexity portrait according to the employee's personal complexity, obtain the employee's personal activity portrait according to the employee's personal activity, and obtain the employee's personal inspection coordinates according to the employee's personal complexity and the employee's personal activity. .

In the actual implementation process, the personal complexity profile and the personal productivity activity profile can be obtained based on the user profile model in the prior art. The personal complexity and personal activity obtained in step S130 are respectively input into the user portrait model, and the personal complexity portrait and the personal productivity activity portrait are obtained.

In a specific embodiment, for the profile of individual productivity activity, the data is extracted from the data that the factor amount of all employees covers 70% in the normal distribution, and the logarithm of each factor with the maximum value of each factor sequence as the base is obtained, The purpose is to solve the long-tailed data. After each factor of the same order of magnitude is obtained to 70% of the range, the P25-P75 data range within 70% and the average of all data within the range are obtained as the reference standard for user factors. ; For the convenience of observation, on the basis of all data, after *10, to avoid the observation effect caused by 0 and then +10, the original size order has not changed; in the specific implementation process, you can also obtain the normal distribution 90% range or Other ranges of Pn serve as reference standards.

In a specific embodiment, for the complexity of personal portraits, the data is obtained by taking the factor amount of all employees covering 70% of the normal distribution, and obtaining the logarithm of each factor with the maximum value of each factor sequence as the base. It is to solve long-tailed data, so that each factor is in the same order of magnitude; after obtaining 70% of the range, then obtain the P25-P75 data range within 70%, and the average of all data within the range, as the reference standard for user factors ; For the convenience of observation, the logarithmic values are all *10; in the specific implementation process, the 90% range of the normal distribution or other ranges of Pn can also be obtained as the reference standard.

The personal inspection coordinates are obtained from the complexity information data and the activity information data; the personal intensity vector is obtained from the personal working time and work intensity data.

In a specific embodiment, intensity coordinates can also be obtained. The intensity coordinates are used to examine individuals by forming a two-dimensional index of individual working hours and intensity; the origin of the coordinates is (average working intensity, daily working hours).

The inspection coordinates are to inspect individual productivity and compliance through the two-dimensional indicators formed by system activity and complexity. The following coordinate system, the origin of the coordinates is (complexity average, activity average) and the abscissa is the system activity, indicating that the individual Daily operation and maintenance capacity; the ordinate is the complexity of the system, indicating that individuals are responsible for the complexity of the system; high activity and high complexity indicate high productivity and compliance; low activity and high complexity indicate low production capacity. Operation and maintenance specifications are used to manage daily operation and maintenance work; high activity and low complexity indicate high productivity and compliance; low activity and low complexity indicate low productivity and daily operation and maintenance work is not carried out in accordance with the operation and maintenance specifications management. When a person inspects the first quadrant of the coordinates, it can indicate that the employee is highly active, complex, and has high productivity and compliance. You can check whether the employee's activity and complexity are within a reasonable range.

Further, in a specific embodiment, the group capacity can also be obtained.

In order to avoid the change of the base after the group system resources are superimposed, the group capacity calculation needs to logarithmically superimpose the individual complexity * the number of systems in charge of the individual; when comparing the production capacity activity of each group, it cannot be directly compared, and needs to be divided by the number of people in each group. Per capita production capacity comparison.

Among them, in a specific application scenario, the working time data: the effective clock-in time of the department in May, June, and July, the cloud desktop time, the VPN time; the activity data: the event volume, the change volume, etc. in May, June, and July; 90% (70%) refers to sample data covering 90% (70%) of the area of the normal distribution. If the data in the first and fourth quadrants of the intensity coordinates show that the working hours and intensity of most people are within a reasonable range, and the number of people who examine the activity and complexity of the coordinates in the third quadrant is relatively large, it can be shown that a considerable number of people do not follow the standards. The operation and maintenance specification requires the management of the operation and maintenance work.

Through the joint analysis of the two coordinates, the production capacity of a person and group and whether the compliance is met can be obtained. The result is not a clear result, but a comprehensive evaluation; for example, a person can inspect the first quadrant of the coordinates It means that the employee is highly active, complex, and has high productivity and compliance. You can check whether the employee's activity and complexity are within a reasonable range, and you can further see the employee's personal productivity portrait. Get a basic performance evaluation.

The employee effectiveness evaluation method of the present application comprehensively includes the complexity factor data and the activity factor data through the collected data, thereby obtaining comprehensive and scientific employee effectiveness analysis results; the employee effectiveness evaluation system directly obtains the employee management system database data by establishing an interface, Not only can the complexity factor data and activity factor data be automatically obtained, but also the incremental synchronization and update of the data can be achieved; the technical effect of scientifically evaluating the activity and work complexity of employees can be achieved.

FIG. 2 is a schematic diagram of the logical structure of the employee effectiveness evaluation system of the application; refer to FIG. 2 .

In order to achieve the above purpose, the present application provides an employee effectiveness evaluation system 200, including a factor data acquisition unit 210, a personal activity and personal complexity acquisition unit 220, and a productivity result display unit 230; wherein,

The factor data obtaining unit 210 is used to obtain the complexity factor data and activity factor data of employee performance from the preset system;

The personal activity and personal complexity obtaining unit 220 is configured to use the complexity factor data and the activity factor data that meet the preset judgment conditions as the complexity information data and the activity information data, respectively, and determine the complexity Information data and activity information data are respectively subjected to percentile cleaning and long endian processing; the employee's personal complexity is calculated according to the complexity information data, and the employee's personal activity is calculated according to the activity information data;

The production capacity result display unit 230 is configured to obtain the employee's personal complexity portrait according to the employee's personal complexity, obtain the employee's personal activity portrait according to the employee's personal activity, and obtain the employee's personal activity portrait according to the employee's personal complexity and the employee's personal complexity. Activity gets the coordinates of an employee's personal inspection.

Further, preferably, the personal activity and personal complexity obtaining unit 220 includes a determination module 221, a processing module 222 and an obtaining module 223; wherein,

The judging module 221 is configured to use the complexity factor data and activity factor data that meet the preset judgment conditions as the complexity information data and the activity information data respectively; the processing module 222 is used for the complexity information data. Perform percentile cleaning and long-mantissa processing with the activity information data respectively; the acquisition module 223 is used to calculate the personal complexity of the employee according to the complexity information data, and calculate the personal complexity of the employee according to the activity information data. Activity.

Further, preferably, the factor data acquisition unit 210 includes an interface connection module 211 and a data acquisition module 212;

The interface connection module 211 is used to connect with the employee management system database through the ETL interface and the HTTP interface; the data acquisition module 212 is used to obtain the complexity from the employee management system database through HTTP request and Kettle tool Factor data and activity factor data.

The employee effectiveness evaluation system of the present application comprehensively includes the complexity factor data and activity factor data through the collected data, thereby obtaining comprehensive and scientific employee effectiveness analysis results; the employee effectiveness evaluation system directly obtains the employee management system database data by establishing an interface, Not only can the complexity factor data and activity factor data be automatically obtained, but also the incremental synchronization and update of the data can be achieved; the technical effect of scientifically evaluating the activity and work complexity of employees can be achieved.

The present application provides an employee effectiveness evaluation method, which is applied to a computer device 3 .

FIG. 3 shows an application environment according to a preferred embodiment of the employee effectiveness evaluation method of the present application.

Referring to FIG. 3 , in this embodiment, the computer device 3 may be a terminal device with computing functions, such as a server, a smart phone, a tablet computer, a portable computer, and a desktop computer.

The computer device 3 includes: a processor 32 , a memory 31 , a communication bus 33 and a network interface 35 .

The memory 31 includes at least one type of readable storage medium. The readable storage medium may be non-volatile or volatile. The at least one type of readable storage medium may be a non-volatile storage medium such as a flash memory, a hard disk, a multimedia card, a card-type memory 31, or the like. In some embodiments, the readable storage medium may be an internal storage unit of the computer device 3 , such as a hard disk of the computer device 3 . In other embodiments, the readable storage medium may also be the external memory 31 of the computer device 3, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC) equipped on the computer device 3 , Secure Digital (Secure Digital, SD) card, flash memory card (Flash Card) and so on.

In this embodiment, the readable storage medium of the memory 31 is generally used to store the employee performance evaluation program 30 installed in the computer device 3 and the like. The memory 31 can also be used to temporarily store data that has been output or will be output.

The processor 32 may be a central processing unit (Central Processing Unit, CPU), a microprocessor or other data processing chip in some embodiments, for running program codes or processing data stored in the memory 31, such as performing employee performance assessments Procedure 30 et al.

The communication bus 33 is used to realize the connection communication between these components.

The network interface 34 may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), and is generally used to establish a communication connection between the computer device 3 and other electronic devices.

Figure 3 shows only the computer device 3 having components 31-34, but it should be understood that implementation of all of the illustrated components is not required and that more or fewer components may be implemented instead.

Optionally, the computer equipment 3 may also include a user interface, and the user interface may include an input unit such as a keyboard (Keyboard), a voice input device such as a microphone (microphone), etc., a device with a voice recognition function, a voice output device such as a sound box, a headset, etc. , optionally the user interface may also include a standard wired interface and a wireless interface.

Optionally, the computer device 3 may further include a display, which may also be referred to as a display screen or a display unit. In some embodiments, it can be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an organic light-emitting diode (Organic Light-Emitting Diode, OLED) touch device, and the like. The display is used for displaying information processed in the computer device 3 and for displaying a visual user interface.

Optionally, the computer device 3 may further include a radio frequency (Radio Frequency, RF) circuit, a sensor, an audio circuit, and the like, which will not be repeated here.

In the apparatus embodiment shown in FIG. 3 , the memory 31 as a computer storage medium may include an operating system and an employee performance evaluation program 30 ; the processor 32 implements the following when executing the employee performance evaluation program 30 stored in the memory 31 Steps: Obtain the complexity factor data and activity factor data of employee performance from the preset system; take the complexity factor data and activity factor data that meet the preset judgment conditions as the complexity information data and activity information data, respectively, Perform percentile cleaning and long-endian processing on the complexity information data and the activity information data respectively; calculate the employee's personal complexity according to the complexity information data, and calculate the employee's personal complexity according to the activity information data Activity degree; obtain the employee's personal complexity portrait according to the employee's personal complexity, obtain the employee's personal activity portrait according to the employee's personal activity degree, and obtain the employee's personal inspection according to the employee's personal complexity and the employee's personal activity degree coordinate.

In other embodiments, the employee effectiveness assessment program 30 can also be divided into one or more modules, and one or more modules are stored in the memory 31 and executed by the processor 32 to complete the present application. The modules referred to in this application refer to a series of computer program segments that can perform specific functions. The employee effectiveness evaluation program 30 can be divided into a factor data acquisition unit 210 , a personal activity and personal complexity acquisition unit 220 , and a productivity result display unit 230 .

In addition, the present application also proposes a computer-readable storage medium, which is a volatile storage medium or a non-volatile storage medium, and mainly includes a storage data area and a storage program area, wherein the storage data area can store data according to The data created by the use of the blockchain node, etc., the storage program area can store the operating system, the application program required for at least one function, and the computer-readable storage medium includes an employee performance evaluation program, and the employee performance evaluation program is The processor implements operations such as employee performance evaluation methods when executed.

The specific implementations of the computer-readable storage medium of the present application are substantially the same as the specific implementations of the above-mentioned employee performance evaluation method, system, and computer equipment, and are not repeated here.

In general, the employee effectiveness evaluation method, system, computer equipment and computer-readable storage medium of the present application can comprehensively include complexity factor data and activity factor data through the collected data, thereby obtaining comprehensive and scientific employee effectiveness analysis results. ;The employee effectiveness evaluation system directly obtains the database data of the employee management system by establishing an interface, which can not only automatically obtain the complexity factor data and activity factor data, but also realize the incremental synchronization and update of the data; The technical effect of scientific evaluation in terms of degree and other aspects.

The blockchain referred to in this application is a new application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain, essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information to verify its Validity of information (anti-counterfeiting) and generation of the next block. The blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

It should be noted that, herein, the terms "comprising", "comprising" or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, device, article or method comprising a series of elements includes not only those elements, It also includes other elements not expressly listed or inherent to such a process, apparatus, article or method. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, apparatus, article, or method that includes the element.

The above-mentioned serial numbers of the embodiments of the present application are only for description, and do not represent the advantages or disadvantages of the embodiments. From the description of the above embodiments, those skilled in the art can clearly understand that the method of the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is better implementation. Based on this understanding, the technical solutions of the present application can be embodied in the form of software products in essence or the parts that make contributions to the prior art. The computer software products are stored in a storage medium (such as ROM/RAM) as described above. , magnetic disk, optical disc), including several programs to make a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) execute the methods described in the various embodiments of the present application.

The above are only the preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the contents of the description and drawings of the present application, or directly or indirectly applied in other related technical fields , are similarly included within the scope of patent protection of this application.

Claims (20)

1. An employee effectiveness evaluation method, applied to an electronic device, wherein the method comprises:

   Obtain the complexity factor data and activity factor data of employee effectiveness from the preset system;

   The complexity factor data and the activity factor data that meet the preset judgment conditions are respectively used as the complexity information data and the activity information data, and the percentile cleaning and the activity information data are respectively performed on the complexity information data and the activity information data. long-endian processing;

   Calculate the personal complexity of the employee according to the complexity information data, and calculate the personal activity of the employee according to the activity information data;

   The employee's personal complexity profile is obtained according to the employee's personal complexity, the employee's personal activity profile is obtained according to the employee's personal activity level, and the employee's personal inspection coordinates are obtained according to the employee's personal complexity and the employee's personal activity level.
2. The employee effectiveness evaluation method according to claim 1, wherein,

   constructing normal distributions from the complexity factor data and the activity factor data respectively;

   Calculate the expected value μ ₁ of the complexity factor data, the standard deviation σ ₁ of the complexity factor data, the expected value μ ₂ of the activity factor data, and the standard deviation σ ₂ of the activity factor data;

   According to the expected value μ ₁ and the standard deviation σ ₁ of the complexity factor data, the normal distribution value of the complexity factor data that meets the preset condition is selected as the complexity information data; wherein, the preset condition is x=1.65σ ₁ + μ ₁ ;

   According to the expected value μ ₂ and the standard deviation σ ₂ of the activity factor data, the normal distribution value of the activity factor data that meets the preset condition is selected as activity information data; wherein, the preset condition is x=1.65σ ₂ + μ ₂ .
3. The employee effectiveness evaluation method according to claim 1, wherein the method for calculating the personal activity of the employee according to the activity information data comprises:

   Personal activity=monitors×0.4+alerts×0.22+versions×0.75+changes×1+profiles×0.1+problems×1.5+preventions×2+exceptions×8+drs×16+emergences×2+risks×16+incidents× 0.5+requests×1;

   The activity information data includes: monitors is the monitoring configuration amount, alerts is the alarm processing amount, versions is the version management amount, changes is the change management amount, profiles is the configuration management amount, problems is the problem management amount, and preventions is the active prevention amount volume, exceptions is the volume of exception management, drs is the volume of disaster recovery management, emerges is the volume of emergency management, risks is the volume of risk management, incidents is the volume of incident management, and requests is the volume of service requests.
4. The employee effectiveness evaluation method according to claim 1, wherein, in the step of calculating the personal complexity of the employee according to the complexity information data: the personal complexity is a personal total system complexity TSC, and the personal total The system complexity TSC is obtained by the individual single system complexity PSC; where,

   The method for obtaining the personal total system complexity TSC and the personal single system complexity PSC according to the complexity information data includes:

   

   

   The complexity information data includes: sg is the system level, les is the number of logical entities, hosts is the number of hosts, ins is the number of instances, dbs is the number of DB instances, and sc is the number of personally responsible systems.
5. The method for evaluating employee effectiveness according to claim 1, wherein the method for acquiring complexity factor data and activity factor data of employee effectiveness from a preset system comprises:

   Connect with the employee management system database through ETL interface and HTTP interface;

   The complexity factor data and activity factor data are obtained from the employee management system database through an HTTP request and a Kettle tool.
6. The employee effectiveness evaluation method according to claim 1, further comprising obtaining the employee's personal intensity coordinate according to the employee's personal complexity and the employee's personal activity, wherein the personal intensity coordinate includes personal working time data and personal working intensity 2D data for data.
7. The employee effectiveness evaluation method according to claim 6, wherein the coordinate origin of the personal intensity coordinate of the employee includes the average work intensity and the daily work time.
8. An employee effectiveness evaluation system, which includes a factor data acquisition unit, a personal activity and personal complexity acquisition unit, and a production capacity result display unit; wherein,

   The factor data acquisition unit is used for acquiring complexity factor data and activity factor data of employee effectiveness from a preset system;

   The personal activity and personal complexity obtaining unit is used for taking the complexity factor data and the activity factor data that meet the preset judgment conditions as the complexity information data and the activity information data, respectively, and for the complexity information. The data and the activity information data are respectively subjected to percentile cleaning and long endian processing; the employee's personal complexity is calculated according to the complexity information data, and the employee's personal activity is calculated according to the activity information data;

   The production capacity result display unit is used to obtain the employee's personal complexity portrait according to the employee's personal complexity, obtain the employee's personal activity portrait according to the employee's personal activity, and obtain the employee's personal activity portrait according to the employee's personal complexity and the employee's personal activity. Degree to obtain the coordinates of the employee's personal inspection.
9. A computer device comprising: at least one processor; and,

   a memory communicatively coupled to the at least one processor; wherein,

   The memory stores a program executable by the at least one processor, the program being executed by the at least one processor to enable the at least one processor to execute an employee performance assessment method: wherein the employee performance Evaluation methods include:

   Obtain the complexity factor data and activity factor data of employee effectiveness from the preset system;

   The complexity factor data and the activity factor data that meet the preset judgment conditions are respectively used as the complexity information data and the activity information data, and the percentile cleaning and the activity information data are respectively performed on the complexity information data and the activity information data. long-endian processing;

   Calculate the personal complexity of the employee according to the complexity information data, and calculate the personal activity of the employee according to the activity information data;

   The employee's personal complexity profile is obtained according to the employee's personal complexity, the employee's personal activity profile is obtained according to the employee's personal activity level, and the employee's personal inspection coordinates are obtained according to the employee's personal complexity and the employee's personal activity level.
10. The computer device according to claim 9, wherein a normal distribution is constructed from the complexity factor data and the activity factor data respectively;

    Calculate the expected value μ ₁ of the complexity factor data, the standard deviation σ ₁ of the complexity factor data, the expected value μ ₂ of the activity factor data, and the standard deviation σ ₂ of the activity factor data;

    According to the expected value μ ₁ and the standard deviation σ ₁ of the complexity factor data, the normal distribution value of the complexity factor data that meets the preset condition is selected as the complexity information data; wherein, the preset condition is x=1.65σ ₁ + μ ₁ ;

    According to the expected value μ ₂ and the standard deviation σ ₂ of the activity factor data, the normal distribution value of the activity factor data that meets the preset condition is selected as activity information data; wherein, the preset condition is x=1.65σ ₂ + μ ₂ .
11. The computer device according to claim 9, wherein the method for calculating the personal activity of the employee according to the activity information data comprises:

    Personal activity=monitors×0.4+alerts×0.22+versions×0.75+changes×1+profiles×0.1+problems×1.5+preventions×2+exceptions×8+drs×16+emergences×2+risks×16+incidents× 0.5+requests×1;

    The activity information data includes: monitors is the monitoring configuration amount, alerts is the alarm processing amount, versions is the version management amount, changes is the change management amount, profiles is the configuration management amount, problems is the problem management amount, and preventions is the active prevention amount volume, exceptions is the volume of exception management, drs is the volume of disaster recovery management, emerges is the volume of emergency management, risks is the volume of risk management, incidents is the volume of incident management, and requests is the volume of service requests.
12. The computer device according to claim 9, wherein, in the step of calculating the personal complexity of the employee according to the complexity information data: the personal complexity is a personal total system complexity TSC, and the personal total The system complexity TSC is obtained by the individual single system complexity PSC; where,

    The method for obtaining the personal total system complexity TSC and the personal single system complexity PSC according to the complexity information data includes:

    

    

    The complexity information data includes: sg is the system level, les is the number of logical entities, hosts is the number of hosts, ins is the number of instances, dbs is the number of DB instances, and sc is the number of personally responsible systems.
13. A computer device according to claim 9, wherein the method for acquiring complexity factor data and activity factor data of employee effectiveness from a preset system comprises:

    Connect with the employee management system database through ETL interface and HTTP interface;

    The complexity factor data and activity factor data are obtained from the employee management system database through an HTTP request and a Kettle tool.
14. The computer device according to claim 9, further comprising obtaining the personal intensity coordinate of the employee according to the employee's personal complexity and the employee's personal activity, the personal intensity coordinate including personal work duration data and personal work intensity 2D data for data.
15. 15. A computer device according to claim 14, wherein the coordinate origin of the personal intensity coordinate of the employee includes an average work intensity and daily work hours.
16. A computer-readable storage medium storing a computer program, wherein, when the computer program is executed by a processor, a method for evaluating employee performance is implemented, wherein the method for evaluating employee performance includes the following steps:

    Obtain the complexity factor data and activity factor data of employee effectiveness from the preset system;

    The complexity factor data and the activity factor data that meet the preset judgment conditions are respectively used as the complexity information data and the activity information data, and the percentile cleaning and the activity information data are respectively performed on the complexity information data and the activity information data. long-endian processing;

    Calculate the personal complexity of the employee according to the complexity information data, and calculate the personal activity of the employee according to the activity information data;

    The employee's personal complexity portrait is obtained according to the employee's personal complexity, the employee's personal activity portrait is obtained according to the employee's personal activity, and the employee's personal inspection coordinates are obtained according to the employee's personal complexity and the employee's personal activity.
17. The computer-readable storage medium of claim 16, wherein the complexity factor data and the activity factor data are respectively constructed as normal distributions;

    Calculate the expected value μ ₁ of the complexity factor data, the standard deviation σ ₁ of the complexity factor data, the expected value μ ₂ of the activity factor data, and the standard deviation σ ₂ of the activity factor data;

    According to the expected value μ ₁ and the standard deviation σ ₁ of the complexity factor data, the normal distribution value of the complexity factor data that meets the preset condition is selected as the complexity information data; wherein, the preset condition is x=1.65σ ₁ + μ ₁ ;

    According to the expected value μ ₂ and the standard deviation σ ₂ of the activity factor data, the normal distribution value of the activity factor data that meets the preset condition is selected as activity information data; wherein, the preset condition is x=1.65σ ₂ + μ ₂ .
18. The computer-readable storage medium of claim 16, wherein,

    The method for calculating the personal activity of an employee according to the activity information data includes:

    Personal activity=monitors×0.4+alerts×0.22+versions×0.75+changes×1+profiles×0.1+problems×1.5+preventions×2+exceptions×8+drs×16+emergences×2+risks×16+incidents× 0.5+requests×1;

    The activity information data includes: monitors is the monitoring configuration amount, alerts is the alarm processing amount, versions is the version management amount, changes is the change management amount, profiles is the configuration management amount, problems is the problem management amount, and preventions is the active prevention amount volume, exceptions is the volume of exception management, drs is the volume of disaster recovery management, emerges is the volume of emergency management, risks is the volume of risk management, incidents is the volume of incident management, and requests is the volume of service requests.
19. The computer-readable storage medium according to claim 16, wherein, in the step of calculating the personal complexity of the employee according to the complexity information data: the personal complexity is a personal total system complexity TSC, the personal complexity The total system complexity TSC is obtained by the individual single system complexity PSC; where,

    The method for obtaining the personal total system complexity TSC and the personal single system complexity PSC according to the complexity information data includes:

    

    

    The complexity information data includes: sg is the system level, les is the number of logical entities, hosts is the number of hosts, ins is the number of instances, dbs is the number of DB instances, and sc is the number of personally responsible systems.
20. The computer-readable storage medium of claim 16, wherein,

    The method for obtaining complexity factor data and activity factor data of employee effectiveness from a preset system includes:

    Connect with the employee management system database through ETL interface and HTTP interface;

    The complexity factor data and activity factor data are obtained from the employee management system database through an HTTP request and a Kettle tool.

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