CN113506023A - Working behavior data analysis method, device, equipment and storage medium - Google Patents
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Abstract
The embodiment of the application belongs to the field of artificial intelligence and relates to a working behavior data analysis method which comprises the steps of obtaining working behavior data of staff to be assessed; inputting the working behavior data into a preset staff behavior feature matching model for working behavior feature matching to obtain the working behavior features of the staff; and comparing the working behavior characteristics with a preset performance measuring standard to obtain a performance measuring result of the employee. Compared with the manual assessment of assessment personnel, the work behavior data is objective, the work behavior analysis is carried out based on the objective data, the obtained performance measurement result is more objective compared with the manual assessment, the coverage range of the work behavior data is wide, the factors of the performance measurement are more comprehensive, and the result is more fair and fair. The application also provides a working behavior data analysis device, computer equipment and a storage medium.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to a method, an apparatus, a device, and a storage medium for analyzing work behavior data.
Background
The staff performance measurement is an important task of human resource management, is a basic consideration of staff correcting, raising and paying for the staff, and is a basic means for excellent staff identification and excitation for enterprises. The equitable and fair staff performance measurement can effectively stimulate the staff, and the effective performance management can promote the benign development of enterprises.
Currently, most of employee performance measurement is performed on employees by assessment personnel, the subjectivity is strong, in order to be fair and fair, a plurality of assessment personnel are introduced to perform performance assessment on the employees, the problem of strong subjectivity cannot be avoided, and the workload of coordinating the plurality of assessment personnel is large.
Disclosure of Invention
The embodiment of the application aims to provide a working behavior data analysis method, a working behavior data analysis device, computer equipment and a storage medium, so as to solve the problems of strong subjectivity and large workload of performance assessment manually.
In order to solve the above technical problem, an embodiment of the present application provides a method for analyzing work behavior data, which adopts the following technical solutions:
acquiring working behavior data of an employee to be assessed;
inputting the working behavior data into a preset staff behavior feature matching model for working behavior feature matching to obtain the working behavior features of the staff;
and comparing the working behavior characteristics with a preset performance measuring standard to obtain a performance measuring result of the employee.
Further, in the step of obtaining the work behavior data of the employee to be assessed, the method further includes:
acquiring a working behavior record of the employee in a preset assessment period;
storing the working behavior record in a preset Kafka-based message queue of a message publishing system;
and calling a Spark analysis engine to perform statistical analysis on the message queue to obtain the working behavior data of the employee.
Further, the preset employee behavior feature matching model includes N submodels, each submodel corresponds to a preset employee category, and before the step of obtaining the work behavior data of the employee to be assessed, the method further includes:
acquiring static attribute data of an employee to be checked;
determining the employee category of the employee according to the static attribute data;
and inputting the working behavior data into a sub-model corresponding to the employee category to perform working behavior characteristic matching, so as to obtain the working behavior characteristics of the employee.
Further, the step of calling a Spark analysis engine to perform statistical analysis on the message queue to obtain the work behavior data of the employee includes the following specific steps:
and calling a Spark analysis engine to perform statistical analysis on the on-off time record in the message queue, and/or the mail receiving-sending time record of the employee, and/or the estimated working hours and the actual working hours of the working tasks of the employee, so as to obtain the working time of the employee in each unit time period, and/or the mail response time of the employee, and/or the efficiency of each working task of the employee.
Further, the preset employee behavior feature matching model is based on a deep learning neural network model, and before the step of inputting the work behavior data into the preset employee behavior feature matching model for performing work behavior feature matching to obtain the work behavior features of the employees, the method further includes:
acquiring a training sample, wherein the training sample comprises a working behavior data sample and labeled working behavior characteristics;
inputting the training samples into the employee behavior feature matching model to predict the working behavior features, and obtaining working behavior prediction features corresponding to the working behavior data samples;
comparing whether the marked working behavior characteristics are consistent with the working behavior prediction characteristics through a loss function;
and adjusting parameters of each node of the staff behavior feature matching model until the loss function reaches the minimum value, and obtaining the trained staff behavior feature matching model.
Further, the step of comparing the working behavior characteristics with a preset performance measurement standard to obtain the performance measurement result of the employee specifically includes:
inputting the working behavior data into a preset staff behavior feature matching model for working behavior feature matching, and obtaining the attendance checking specification degree and/or team cooperation degree and/or working efficiency features of the staff;
calculating the working behavior score of the employee according to the attendance specification degree and/or the team cooperation degree and/or the working efficiency of the employee;
and comparing the work behavior score with a preset performance measuring standard to obtain a performance measuring result of the employee.
Further, after the step of obtaining the work behavior data of the employee to be assessed, the method further comprises the following steps:
and storing the working behavior data into a block chain.
In order to solve the above technical problem, an embodiment of the present application further provides a device for analyzing work behavior data, which adopts the following technical solutions:
the acquisition module is used for acquiring the working behavior data of the staff to be assessed;
the matching module is used for inputting the working behavior data into a preset staff behavior feature matching model to perform working behavior feature matching so as to obtain the working behavior features of the staff;
and the measuring module is used for comparing the working behavior characteristics with a preset performance measuring standard to obtain a performance measuring result of the employee.
Further, in the obtaining module, the method further includes:
the first acquisition submodule is used for acquiring the working behavior record of the employee in a preset assessment period;
the first storage submodule is used for storing the working behavior record in a preset message queue of a Kafka-based message publishing system;
and the first calculation submodule is used for calling a Spark analysis engine to perform statistical analysis on the message queue to obtain the working behavior data of the employee.
Further, the working behavior data analysis device further includes:
the second acquisition submodule is used for acquiring static attribute data of the employee to be checked;
the first determining submodule is used for determining the employee category of the employee according to the static attribute data;
and the first matching submodule is used for inputting the working behavior data into the submodel corresponding to the employee category to carry out working behavior characteristic matching so as to obtain the working behavior characteristics of the employee.
Further, the first computation submodule includes:
and the first calculating subunit is used for calling a Spark analysis engine to perform statistical analysis on the attendance time record in the message queue, and/or the mail receiving and sending time record of the employee, and/or the estimated working hours and actual working hours of the work task of the employee, so as to obtain the working time of the employee in each unit time period, and/or the mail response time of the employee, and/or the efficiency of each work task of the employee.
Further, the working behavior data analysis device further includes:
the third obtaining submodule is used for obtaining a training sample, and the training sample comprises a working behavior data sample and labeled working behavior characteristics;
the first prediction submodule is used for inputting the training sample into the employee behavior feature matching model to perform work behavior feature prediction to obtain work behavior prediction features corresponding to the work behavior data samples;
the first comparison submodule is used for comparing whether the marked working behavior characteristics are consistent with the working behavior prediction characteristics through a loss function;
and the first adjusting submodule is used for adjusting the parameters of each node of the staff behavior feature matching model until the loss function reaches the minimum value, and obtaining the trained staff behavior feature matching model.
Further, the measuring module further includes:
the second matching sub-module is used for inputting the working behavior data into a preset staff behavior feature matching model for working behavior feature matching to obtain the features of attendance checking specification degree and/or team cooperation degree and/or working efficiency of the staff;
the second calculation submodule is used for calculating the working behavior score of the employee according to the attendance standardization degree and/or the team cooperation degree and/or the working efficiency of the employee;
and the first measuring submodule is used for comparing the working behavior score with a preset performance measuring standard to obtain a performance measuring result of the employee.
Further, the working behavior data analysis device further includes:
and the second storage submodule is used for storing the working behavior data into a block chain.
In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:
a computer device comprising a memory and a processor, the memory having stored therein computer readable instructions which, when executed by the processor, implement the steps of the method of work behavior data analysis as described above.
In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:
a computer readable storage medium having computer readable instructions stored thereon which, when executed by a processor, implement the steps of the above-described method of work behavior data analysis.
Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:
the method comprises the steps of obtaining working behavior data of employees to be assessed; inputting the working behavior data into a preset staff behavior feature matching model for working behavior feature matching to obtain the working behavior features of the staff; and comparing the working behavior characteristics with a preset performance measuring standard to obtain a performance measuring result of the employee. Compared with the manual assessment of assessment personnel, the work behavior data is objective, the work behavior analysis is carried out based on the objective data, the obtained performance measurement result is more objective compared with the manual assessment, the coverage range of the work behavior data is wide, the factors of the performance measurement are more comprehensive, and the result is more fair and fair.
Drawings
In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a method of work behavior data analysis according to the present application;
FIG. 3 is a flowchart of one embodiment of step S201 in FIG. 2;
FIG. 4 is a flow diagram for one embodiment prior to step S202 in FIG. 2;
FIG. 5 is a schematic block diagram of one embodiment of an operational behavior data analysis apparatus according to the present application;
FIG. 6 is a schematic block diagram of one embodiment of a computer device according to the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that the working behavior data analysis method provided in the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the working behavior data analysis apparatus is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow diagram of one embodiment of a method of work behavior data analysis is shown, in accordance with the present application. The working behavior data analysis method comprises the following steps:
step S201, acquiring the working behavior data of the staff to be checked.
In this embodiment, the electronic device (for example, the server/terminal device shown in fig. 1) on which the work behavior data analysis method operates may obtain the work behavior data of the employee to be checked in a wired connection manner or a wireless connection manner. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.
The working behavior data at least comprises working dynamic data of the staff in a preset assessment period, wherein the working dynamic data is data defined relative to staff attribute static data. The static attribute data comprises the demographic attributes, the business attributes and the life forms of the staff, such as age, gender, employment property, position, entertainment hobbies, social habits and the like, which do not change in a longer time period; the attribute static data can be prestored in a preset human resource management database, and the employee attribute static data is inquired through the employee ID. The dynamic working data is obtained by asynchronously recording daily operation, working hours, attendance and the like of the employee through the message queue Kafka and then performing offline/real-time analysis on the message queue through the big data unified analysis engine Spark.
Step S202, inputting the working behavior data into a preset employee behavior feature matching model for working behavior feature matching, and obtaining the working behavior features of the employees.
In this embodiment, the work behavior data is input to a preset employee behavior feature matching model to perform work behavior feature matching. The preset employee behavior feature matching model can be based on a benchmark comparison algorithm, namely, a benchmark of the work behavior feature is set, the work behavior data is compared with the benchmark, and the work behavior feature of the employee is judged. For example, the work efficiency is set as one dimension of the work behavior characteristics, and the characteristics of the work efficiency are divided into three levels: high, medium and low, and set the benchmark of each grade, the benchmark that work efficiency is high is: the duty ratio of the working tasks with actual working hours smaller than expected working hours is more than 90 percent; the benchmark of work efficiency is: the working task proportion of actual working hours smaller than expected working hours is more than 80 percent; the low working efficiency is based on: the work task with actual working hours smaller than the expected working hours accounts for less than 80 percent.
And comparing the work efficiency data in the work behavior data with a reference, and determining that the work efficiency of the employee is one of high, medium and low as the work behavior characteristic of the employee.
The working behavior data includes multidimensional data, for example, the working duration of the employee in a unit time period may also be included, the working duration in the unit time period is set as another dimension of the working behavior feature, and similarly, the feature of the working duration is divided into three levels: setting a reference of each grade, comparing the working time length data in the working behavior data with the reference, and determining that the working time length of the employee is one of high, medium and low as the working behavior characteristic of the employee.
The preset employee behavior feature matching model can also be based on a pre-trained deep learning neural network model. Please refer to fig. 4.
And step S203, comparing the working behavior characteristics with a preset performance measuring standard to obtain a performance measuring result of the employee.
In this embodiment, the working behavior characteristics are compared with preset performance metrics, for example, the performance metrics are set as: when the working behavior characteristics are high working efficiency, high team cooperation degree and high attendance checking specification degree, the performance measurement result is excellent; when the working behavior characteristics are low working efficiency, low team cooperation degree and low attendance checking specification degree, the performance measurement result is to be improved; and when the working behavior characteristics are other, the performance measurement result is qualified. And comparing the working behavior characteristics with the performance measuring standard to obtain the performance measuring result of the employee.
The method comprises the steps of obtaining working behavior data of the staff to be assessed; inputting the working behavior data into a preset staff behavior feature matching model for working behavior feature matching to obtain the working behavior features of the staff; and comparing the working behavior characteristics with a preset performance measuring standard to obtain a performance measuring result of the employee. Compared with the manual assessment of assessment personnel, the work behavior data is objective, the work behavior analysis is carried out based on the objective data, the obtained performance measurement result is more objective compared with the manual assessment, the coverage range of the work behavior data is wide, the factors of the performance measurement are more comprehensive, and the result is more fair and fair.
Referring to fig. 3, in some optional implementations of the embodiment, in step S201, the electronic device may further perform the following steps:
step S301, acquiring a working behavior record of the employee in a preset assessment period;
step S302, storing the working behavior record in a preset Kafka-based message queue of a message publishing system;
step S303, calling a Spark analysis engine to perform statistical analysis on the message queue to obtain the working behavior data of the employee.
In this implementation, the work behavior record of the employee includes the work attendance time, mail record, work hours, and the like of the employee. The dynamic data is obtained by asynchronously recording daily operations, working hours, attendance checking and the like of the staff through the message queue Kafka and then performing offline or real-time analysis on the message data through the big data unified analysis engine Spark. Kafka is a high-throughput distributed publish-subscribe messaging system that can handle all the action flow data of a consumer in a web site. The purpose of Kafka is to unify message processing both online and offline through the parallel loading mechanism of Hadoop. Each message issued to the Kafka cluster has a category, which is called Topic. The messages of physically different topics are stored separately, and logically the message of one Topic is stored on one or more servers, but the user only needs to specify the Topic of the message to produce or consume the data without having to care about where the data is stored.
Spark is a similar open source clustered computing environment as Hadoop, but there are some differences between the two that make Spark superior in terms of some workloads, in other words Spark enables memory distributed datasets that, in addition to being able to provide interactive queries, can also optimize iterative workloads.
Spark is implemented in the Scala language, which uses Scala as its application framework. Unlike Hadoop, Spark and Scala can be tightly integrated, where Scala can manipulate distributed datasets as easily as manipulating local collection objects.
In some optional implementation manners, if the preset employee behavior feature matching model includes N sub-models, each sub-model corresponding to a preset employee category, before the step S201, the electronic device may further perform the following steps:
acquiring static attribute data of an employee to be checked;
determining the employee category of the employee according to the static attribute data;
and inputting the working behavior data into a sub-model corresponding to the employee category to perform working behavior characteristic matching, so as to obtain the working behavior characteristics of the employee.
In this implementation, the static attribute data includes demographic attributes, business attributes, and life forms of the employee, such as age, gender, employment property, position, entertainment, social habits, and the like, which do not change for a long period of time; the attribute static data can be prestored in a preset human resource management database, and the employee attribute static data is inquired through the employee ID.
The static attribute data is used for distinguishing different employee types, and the work behavior characteristics of different employee types are different, so that a work behavior characteristic matching sub-model is set corresponding to each employee type. Therefore, the problem that all staff conduct examination by adopting the same standard and do not accord with reality is avoided.
In some optional implementations, if the work behavior record includes the work attendance time record of the employee, and/or the mailing time record of the employee, and/or the estimated man-hours and actual man-hours of the work task of the employee, in step S303, the electronic device may further perform the following steps:
and calling a Spark analysis engine to perform statistical analysis on the on-off time record in the message queue, and/or the mail receiving-sending time record of the employee, and/or the estimated working hours and the actual working hours of the working tasks of the employee, so as to obtain the working time of the employee in each unit time period, and/or the mail response time of the employee, and/or the efficiency of each working task of the employee.
In the implementation mode, the employee work behavior record comprises the work attendance time, the employee records the work attendance time through an attendance system, the work attendance time record of the employee is obtained and stored in a preset message queue of a Kafka-based message issuing system, a Spark analysis engine is called to read the work attendance time in the message queue, and the work duration of each unit time period of the employee is calculated. The unit time period herein may be daily, weekly, monthly. The working duration of the unit time period is applied to performance measurement, so that the standardization of attendance checking of the employee can be measured, for example, the working duration of 8 hours per day is required, and whether the employee meets the requirement or not is judged.
The employee work behavior record can also comprise the mail sending and receiving time, the mail sending and receiving time is recorded in a mail sending and receiving system, the mail sending and receiving time is stored in a preset message queue of a Kafka-based message publishing system, a Kafka high-throughput distributed processing mechanism can be utilized, then a Spark analysis engine is called to read the mail sending and receiving time in the message queue for statistical analysis, and the mail response time of the employee is obtained. The mail response time can be applied to performance measurement to measure the team cooperation degree of the staff, and the short mail response time can be considered as high team cooperation degree of the staff.
The employee work activity record also includes projected and actual man-hours for each work task scheduled for the employee. And recording the estimated working hours and the actual working hours of each work task, storing the records into a preset message queue of a Kafka-based message publishing system, calling a Spark analysis engine to read the estimated working hours and the actual working hours of the work tasks in the message queue, and performing statistical analysis to obtain the efficiency of each work task of the staff. The working efficiency can be measured by the ratio of the actual man-hours to the estimated man-hours, and the greater the ratio of the actual man-hours to the estimated man-hours, the lower the efficiency can be considered.
Referring to fig. 4, in some alternative implementations, the preset employee behavior feature matching model is based on a deep learning neural network model, and before the step S202, the electronic device may further perform the following steps:
acquiring a training sample, wherein the training sample comprises a working behavior data sample and labeled working behavior characteristics;
inputting the training samples into the employee behavior feature matching model to predict the working behavior features, and obtaining working behavior prediction features corresponding to the working behavior data samples;
comparing whether the marked working behavior characteristics are consistent with the working behavior prediction characteristics through a loss function;
and adjusting parameters of each node of the staff behavior feature matching model until the loss function reaches the minimum value, and obtaining the trained staff behavior feature matching model.
In the present embodiment, the preset employee behavior feature matching model is based on a deep learning neural network model DNN trained in advance. Training a staff behavior feature matching model in advance, marking the working behavior features of corresponding staff for a plurality of pieces of working behavior data and each piece of working behavior data by a training sample, and adjusting the parameters of each node of the staff behavior feature matching model to make the working behavior prediction features output by the staff behavior feature matching model consistent with the marked working behavior features. Specifically, the loss function may adopt a cross entropy loss function by comparing whether the labeled working behavior feature is consistent with the working behavior prediction feature. And when the loss function reaches the minimum value, the working behavior prediction characteristic is considered to be consistent with the marked working behavior characteristic, and the training of the employee behavior characteristic matching model is finished. The deep learning neural network model is trained in advance, so that not only can the explicit relation between the working behavior data and the working behavior characteristics be learned, but also the implicit relation between the working behavior data and the working behavior characteristics can be learned, and the working behavior characteristic analysis is more accurate and scientific.
In some optional implementations, the work behavior data includes a work duration of the employee in each unit time period, and/or a mail response time of the employee, and/or an efficiency of each work task of the employee, and in step S203, the electronic device may further perform the following steps:
inputting the working behavior data into a preset staff behavior feature matching model for working behavior feature matching, and obtaining the attendance checking specification degree and/or team cooperation degree and/or working efficiency features of the staff;
calculating the working behavior score of the employee according to the attendance specification degree and/or the team cooperation degree and/or the working efficiency of the employee;
and comparing the work behavior score with a preset performance measuring standard to obtain a performance measuring result of the employee.
In this embodiment, in some business scenarios, for the oriented incentive employee, different weights are set for behavior characteristics of different dimensions, the work behavior score of the employee is calculated, and the performance measurement result of the employee is determined according to the work behavior score. For example, the working behavior characteristics comprise working efficiency, team cooperation degree and attendance checking specification degree, and the working behavior score of the employee is calculated through a weighted summation algorithm:
s ═ a × work efficiency + b × team cooperation degree + c × attendance normalization degree.
a. b and c are adjustable weight coefficients.
Comparing the work behavior score with a preset performance measuring standard, wherein the preset performance measuring standard is a result corresponding to the work behavior score, for example, the result is more than 90 points, and the performance measuring result is excellent; if the score is more than 70, the performance measurement result is qualified; other scores, performance measures, are to be improved.
The work behavior score is calculated through a weighted sum algorithm, the work behavior score is compared with a preset performance measuring standard to obtain a performance measuring result, different weights can be set for behavior characteristics of different dimensionalities, and a guiding incentive effect is achieved for employees.
In some optional implementation manners, after the step S201, the electronic device may further perform the following steps:
and storing the working behavior data into a block chain.
It is emphasized that, in order to further ensure the privacy and security of the working behavior data, the working behavior data may also be stored in a node of a blockchain.
The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer readable instructions, which can be stored in a computer readable storage medium, and when executed, the processes of the embodiments of the methods described above can be included. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
With further reference to fig. 5, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an operational behavior data analysis apparatus, which corresponds to the embodiment of the method shown in fig. 2, and which can be applied to various electronic devices.
As shown in fig. 5, the work behavior data analysis device 500 according to the present embodiment includes: the device comprises an acquisition module 501, a matching module 502 and a measuring module 503. Wherein:
the acquisition module 501 is used for acquiring the working behavior data of the staff to be assessed;
the matching module 502 is used for inputting the working behavior data into a preset staff behavior feature matching model for working behavior feature matching to obtain the working behavior features of the staff;
and the measuring module 503 is configured to compare the working behavior characteristics with a preset performance measuring standard to obtain a performance measuring result of the employee.
The method comprises the steps of obtaining working behavior data of employees to be assessed; inputting the working behavior data into a preset staff behavior feature matching model for working behavior feature matching to obtain the working behavior features of the staff; and comparing the working behavior characteristics with a preset performance measuring standard to obtain a performance measuring result of the employee. Compared with the manual assessment of assessment personnel, the work behavior data is objective, the work behavior analysis is carried out based on the objective data, the obtained performance measurement result is more objective compared with the manual assessment, the coverage range of the work behavior data is wide, the factors of the performance measurement are more comprehensive, and the result is more fair and fair.
In some optional implementation manners of this embodiment, the obtaining module 501 further includes:
the first acquisition submodule is used for acquiring the working behavior record of the employee in a preset assessment period;
the first storage submodule is used for storing the working behavior record in a preset message queue of a Kafka-based message publishing system;
and the first calculation submodule is used for calling a Spark analysis engine to perform statistical analysis on the message queue to obtain the working behavior data of the employee.
In some optional implementations of this embodiment, the working behavior data analysis apparatus 500 further includes:
the second acquisition submodule is used for acquiring static attribute data of the employee to be checked;
the first determining submodule is used for determining the employee category of the employee according to the static attribute data;
and the first matching submodule is used for inputting the working behavior data into the submodel corresponding to the employee category to carry out working behavior characteristic matching so as to obtain the working behavior characteristics of the employee.
In some optional implementations of this embodiment, the first computing submodule includes:
and the first calculating subunit is used for calling a Spark analysis engine to perform statistical analysis on the attendance time record in the message queue, and/or the mail receiving and sending time record of the employee, and/or the estimated working hours and actual working hours of the work task of the employee, so as to obtain the working time of the employee in each unit time period, and/or the mail response time of the employee, and/or the efficiency of each work task of the employee.
In some optional implementations of this embodiment, the working behavior data analysis apparatus 500 further includes:
the third obtaining submodule is used for obtaining a training sample, and the training sample comprises a working behavior data sample and labeled working behavior characteristics;
the first prediction submodule is used for inputting the training sample into the employee behavior feature matching model to perform work behavior feature prediction to obtain work behavior prediction features corresponding to the work behavior data samples;
the first comparison submodule is used for comparing whether the marked working behavior characteristics are consistent with the working behavior prediction characteristics through a loss function;
and the first adjusting submodule is used for adjusting the parameters of each node of the staff behavior feature matching model until the loss function reaches the minimum value, and obtaining the trained staff behavior feature matching model.
In some optional implementations of this embodiment, the measurement module 503 further includes:
the second matching sub-module is used for inputting the working behavior data into a preset staff behavior feature matching model for working behavior feature matching to obtain the features of attendance checking specification degree and/or team cooperation degree and/or working efficiency of the staff;
the second calculation submodule is used for calculating the working behavior score of the employee according to the attendance standardization degree and/or the team cooperation degree and/or the working efficiency of the employee;
and the first measuring submodule is used for comparing the working behavior score with a preset performance measuring standard to obtain a performance measuring result of the employee.
In some optional implementations of this embodiment, the working behavior data analysis apparatus 500 further includes:
and the second storage submodule is used for storing the working behavior data into a block chain.
In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 6, fig. 6 is a block diagram of a basic structure of a computer device according to the present embodiment.
The computer device 6 comprises a memory 61, a processor 62, a network interface 63 communicatively connected to each other via a system bus. It is noted that only a computer device 6 having components 61-63 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 61 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 61 may be an internal storage unit of the computer device 6, such as a hard disk or a memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 6. Of course, the memory 61 may also comprise both an internal storage unit of the computer device 6 and an external storage device thereof. In this embodiment, the memory 61 is generally used for storing an operating system installed in the computer device 6 and various types of application software, such as computer readable instructions of a work behavior data analysis method. Further, the memory 61 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 62 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 62 is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is configured to execute computer readable instructions stored in the memory 61 or process data, such as computer readable instructions for executing the working behavior data analysis method.
The network interface 63 may comprise a wireless network interface or a wired network interface, and the network interface 63 is typically used for establishing a communication connection between the computer device 6 and other electronic devices.
The method comprises the steps of obtaining working behavior data of employees to be assessed; inputting the working behavior data into a preset staff behavior feature matching model for working behavior feature matching to obtain the working behavior features of the staff; and comparing the working behavior characteristics with a preset performance measuring standard to obtain a performance measuring result of the employee. Compared with the manual assessment of assessment personnel, the work behavior data is objective, the work behavior analysis is carried out based on the objective data, the obtained performance measurement result is more objective compared with the manual assessment, the coverage range of the work behavior data is wide, the factors of the performance measurement are more comprehensive, and the result is more fair and fair.
The present application further provides another embodiment, which is to provide a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the work behavior data analysis method as described above.
The method comprises the steps of obtaining working behavior data of employees to be assessed; inputting the working behavior data into a preset staff behavior feature matching model for working behavior feature matching to obtain the working behavior features of the staff; and comparing the working behavior characteristics with a preset performance measuring standard to obtain a performance measuring result of the employee. Compared with the manual assessment of assessment personnel, the work behavior data is objective, the work behavior analysis is carried out based on the objective data, the obtained performance measurement result is more objective compared with the manual assessment, the coverage range of the work behavior data is wide, the factors of the performance measurement are more comprehensive, and the result is more fair and fair.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.
Claims (10)
1. A working behavior data analysis method is characterized by comprising the following steps:
acquiring working behavior data of an employee to be assessed;
inputting the working behavior data into a preset staff behavior feature matching model for working behavior feature matching to obtain the working behavior features of the staff;
and comparing the working behavior characteristics with a preset performance measuring standard to obtain a performance measuring result of the employee.
2. The work behavior data analysis method according to claim 1, wherein in the step of obtaining the work behavior data of the employee to be assessed, the method further comprises:
acquiring a working behavior record of the employee in a preset assessment period;
storing the working behavior record in a preset Kafka-based message queue of a message publishing system;
and calling a Spark analysis engine to perform statistical analysis on the message queue to obtain the working behavior data of the employee.
3. The work behavior data analysis method according to claim 1, wherein the preset employee behavior feature matching model comprises N submodels, each submodel corresponds to a preset employee category, and before the step of obtaining the work behavior data of the employee to be assessed, the method further comprises:
acquiring static attribute data of an employee to be checked;
determining the employee category of the employee according to the static attribute data;
and inputting the working behavior data into a sub-model corresponding to the employee category to perform working behavior characteristic matching, so as to obtain the working behavior characteristics of the employee.
4. The method according to claim 2, wherein the work behavior records include the work attendance time records of the employee, and/or the mailing time records of the employee, and/or the estimated man-hours and actual man-hours of the work tasks of the employee, and the step of calling a Spark analysis engine to perform statistical analysis on the message queue to obtain the work behavior data of the employee specifically includes:
and calling a Spark analysis engine to perform statistical analysis on the on-off time record in the message queue, and/or the mail receiving-sending time record of the employee, and/or the estimated working hours and the actual working hours of the working tasks of the employee, so as to obtain the working time of the employee in each unit time period, and/or the mail response time of the employee, and/or the efficiency of each working task of the employee.
5. The work behavior data analysis method according to claim 1, wherein the preset employee behavior feature matching model is based on a deep learning neural network model, and before the step of inputting the work behavior data into the preset employee behavior feature matching model for work behavior feature matching to obtain the work behavior features of the employees, the method further comprises:
acquiring a training sample, wherein the training sample comprises a working behavior data sample and labeled working behavior characteristics;
inputting the training samples into the employee behavior feature matching model to predict the working behavior features, and obtaining working behavior prediction features corresponding to the working behavior data samples;
comparing whether the marked working behavior characteristics are consistent with the working behavior prediction characteristics through a loss function;
and adjusting parameters of each node of the staff behavior feature matching model until the loss function reaches the minimum value, and obtaining the trained staff behavior feature matching model.
6. The work behavior data analysis method according to claim 4, wherein the step of comparing the work behavior characteristics with a preset performance measurement standard to obtain the performance measurement result of the employee specifically comprises:
inputting the working behavior data into a preset staff behavior feature matching model for working behavior feature matching, and obtaining the attendance checking specification degree and/or team cooperation degree and/or working efficiency features of the staff;
calculating the working behavior score of the employee according to the attendance specification degree and/or the team cooperation degree and/or the working efficiency of the employee;
and comparing the work behavior score with a preset performance measuring standard to obtain a performance measuring result of the employee.
7. The method for analyzing work behavior data according to claim 1, further comprising, after the step of obtaining the work behavior data of the employee to be assessed:
and storing the working behavior data into a block chain.
8. An operational behavior data analysis device, comprising:
the acquisition module is used for acquiring the working behavior data of the staff to be assessed;
the matching module is used for inputting the working behavior data into a preset staff behavior feature matching model to perform working behavior feature matching so as to obtain the working behavior features of the staff;
and the measuring module is used for comparing the working behavior characteristics with a preset performance measuring standard to obtain a performance measuring result of the employee.
9. A computer device comprising a memory having computer readable instructions stored therein and a processor which when executed implements the steps of a method of work behavior data analysis according to any of claims 1 to 7.
10. A computer-readable storage medium, having computer-readable instructions stored thereon, which, when executed by a processor, implement the steps of the work behavior data analysis method of any of claims 1 to 7.
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