CN119962500B - Performance form creation method and system of human resource system - Google Patents
Performance form creation method and system of human resource systemInfo
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Abstract
本发明公开了一种人力资源系统的绩效表单创建方法及系统，属于人工智能技术领域，能够便于生成适用于不同部门考核需求的绩效表单；包括获取第一部门的历史多版本绩效表单数据以及各版本对应的实际考核场景信息；对历史多版本绩效表单数据进行文本解析，提取出现频率达到设定阈值的文本片段，并转化为结构化数据，记为历史关键数据集合；获取实时业务需求数据；将历史关键数据集合与实时业务需求数据进行对比分析，判断二者的匹配程度；若匹配度低于设定阈值，则对历史关键数据集合进行优化调整；根据优化调整后的数据，结合预设的表单布局规则，动态生成适应第一部门新业务场景的绩效表单。
The present invention discloses a performance form creation method and system for a human resources system, which belongs to the field of artificial intelligence technology and can facilitate the generation of performance forms suitable for the assessment needs of different departments; the method comprises obtaining historical multi-version performance form data of a first department and actual assessment scenario information corresponding to each version; performing text parsing on the historical multi-version performance form data, extracting text fragments whose occurrence frequency reaches a set threshold, and converting them into structured data, which are recorded as a historical key data set; obtaining real-time business demand data; comparing and analyzing the historical key data set with the real-time business demand data to determine the degree of matching between the two; if the matching degree is lower than the set threshold, optimizing and adjusting the historical key data set; and dynamically generating a performance form adapted to the new business scenario of the first department based on the optimized and adjusted data and in combination with preset form layout rules.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a performance form creation method and system for a human resource system.
Background
With the rapid development of modern enterprises and the increasing complexity of competing environments, performance management has become one of the core content of enterprise human resource management. Performance management can help enterprises effectively improve staff work efficiency, optimize resource allocation and enhance the overall competitiveness of organizations. In the performance management system, the performance form is taken as an important tool and plays important roles of recording assessment content, quantifying staff performance and feeding back assessment results.
However, with the continuous development of the concept of performance management, the requirements of enterprises on performance assessment in practical applications are increasingly diversified. Different enterprises often have different performance assessment flows and rules due to the differences of industry background, organization architecture and development strategy. Even the same enterprise may have significant differences in performance assessment methods for different departments, posts, and staff groups. For example, the sales department may require quantitative assessment based on performance data, while the research and development department may be more concerned with qualitative assessment of project quality and innovation ability. Therefore, how to flexibly design and generate performance forms adapting to diversified assessment requirements becomes an important challenge in enterprise human resource management.
The foregoing background is only for the purpose of facilitating an understanding of the principles and concepts of the application and is not necessarily in the prior art to the present application and is not intended to be used as an admission that such background is not entitled to antedate such novelty and creativity by virtue of prior application or that it is already disclosed at the date of filing of this application.
Disclosure of Invention
The application provides a performance form creation method for a human resource system, which can be used for conveniently generating performance forms suitable for assessment requirements of different departments.
In order to achieve the above purpose, the embodiment of the application discloses the following technical scheme:
in a first aspect, an embodiment of the present application provides a performance form creation method for a human resource system, including the following steps:
acquiring historical multi-version performance form data of a first department and actual assessment scene information corresponding to each version;
Text analysis is carried out on the historical multi-version performance form data, and key index text fragments, flow node text fragments and association relation text fragments with occurrence frequency reaching a set threshold are extracted;
Converting the key index text segment, the flow node text segment and the association relation text segment into structural data by using a natural language processing technology, and recording the structural data as a historical key data set;
Acquiring real-time service demand data;
Comparing and analyzing the historical key data set with the real-time business demand data, and judging the matching degree of the historical key data set and the real-time business demand data according to a predefined similarity algorithm;
if the matching degree is lower than the set threshold value, optimizing and adjusting the historical key data set by using a preset machine learning model;
and dynamically generating a performance form adapting to the new business scene of the first department according to the optimized and adjusted data and in combination with a preset form layout rule.
In the embodiment of the application, the historical multi-version performance form data and the actual assessment scene information corresponding to each version are firstly acquired. The historical data contains rich experience accumulated by departments in the past when performance assessment is carried out on different business objects in different business stages, covers various assessment modes, index setting and other contents, and lays a foundation for the subsequent construction of an adaptability form.
And then, carrying out text analysis on the historical multi-version performance form data, and extracting key index text fragments, flow node text fragments and association relation text fragments, wherein the occurrence frequency of the key index text fragments reaches a set threshold value, for example, key indexes such as performance completion rate and the like frequently appearing in past evaluation of a sales department, and related indexes such as on-time delivery rate of projects of research and development departments can be extracted. By extracting these core elements, performance assessment features of each department and post can be captured, and representative assessment patterns can be extracted.
And then the data are further converted into structured data by utilizing a natural language processing technology and recorded as a history key data set, and the original disordered unstructured history form data can be arranged into a standard form which is convenient for subsequent analysis, comparison and utilization, so that a general assessment mode and key elements hidden in the history data are mined. The real-time business demand data is acquired, the change condition of the current business of departments can be captured sharply, and a practical basis is provided for timely adjusting performance forms.
And comparing and analyzing the sorted historical key data set with the real-time business demand data, judging the matching degree of the historical key data set and the real-time business demand data according to a predefined similarity algorithm, and accurately judging whether the current historical data can meet the real-time business demand or not according to the fit conditions between the historical data and the real-time demand in a plurality of dimensions. If the matching degree is lower than the set threshold, the historical experience is insufficient to be directly applied to the current service scene, at the moment, the historical key data set is optimally adjusted by utilizing a preset machine learning model based on the existing historical data and real-time service demand data, then, according to the optimized and adjusted data, elements such as optimized key indexes, flow nodes and the like are reasonably presented on a form according to a given layout rule, a performance form suitable for the new service scene is dynamically generated, the generated performance form can reflect the new demand, and meanwhile, the applicability of the historical experience is reserved.
If the matching degree is higher, the historical data can be well adapted to the current requirement, and no further adjustment is needed. Through the real-time monitoring mechanism, the examination form can timely adapt to new requirements generated by business adjustment or organization change.
Therefore, performance forms of assessment requirements of different departments can be conveniently generated, the generated forms are ensured to conform to habits of departments in terms of display and use, and the actual assessment requirements of current business are closely attached, so that the performance forms which are flexibly designed and generated according to different business scenes are realized, and the problem that performance assessment modes of different departments, posts and staff groups of different enterprises and the same enterprise are large in difference is effectively solved.
In some possible implementations of the first aspect, the historical multi-version performance form data includes performance forms of a plurality of different periods and different business lines, and the actual assessment scenario information includes organization architecture, business objectives and assessment personnel categories.
In some possible implementations of the first aspect, the step of obtaining real-time service requirement data includes:
establishing connection with databases of business systems in enterprises by utilizing a database connection technology and a pre-developed API interface, wherein the business systems comprise a project management system, an ERP system, a CRM system and a human resource management system;
determining key data sources reflecting real-time service demands therein by analyzing data of each service system, and unique identifiers of data in each data source;
Periodically monitoring data changes of the key data sources through triggers or timing tasks of the database;
When the data update is detected, extracting updated data through an API interface according to a preset data acquisition rule;
cleaning the acquired data, removing repeated, erroneous and incomplete data, and integrating the data from different data sources according to a preset data association rule;
updating the cleaned and integrated data to a preset business requirement database;
And calling the service demand data from the service demand database as real-time service demand data. Therefore, the latest business demand data can be timely obtained, the real-time performance and accuracy of the data are guaranteed, and a reliable data base is provided for dynamic generation of performance forms. In addition, the collected data are cleaned and integrated, invalid data can be removed, the quality of the data is improved, and the situation that the form is generated in error or inaccurate due to the data problem is reduced. In addition, the data from different business systems are integrated, the data island is broken, business conditions of enterprises can be more comprehensively known, and therefore performance forms which are more in line with actual business requirements are generated.
In some possible implementations of the first aspect, the specific formula of the predefined similarity algorithm is as follows:
wherein:
h i, representing the ith keyword in the historical keyword set;
r j represents the jth keyword in the real-time business requirement set;
S, representing the comprehensive matching degree between the keywords in the two sets, wherein the larger the value is, the higher the matching degree between the two sets is;
n represents the total number of history keywords in the collection;
m is the total number of the keywords required in real time in the collection;
w ij, the weight, which represents the association strength between the history keyword h i and the real-time business demand keyword r j, the greater the weight value, the closer the relation between the history keyword h i and the real-time business demand keyword r j is described;
f (h i,rj) a matching function for judging whether the keyword h i is matched with the keyword r j, which is defined as follows:
In some possible implementations of the first aspect, the preset machine learning model is a classification model based on a decision tree algorithm, and when the preset machine learning model is constructed, key index text segments, flow node text segments and association relation text segments in a historical key data set are used as feature vectors, and an adaptation effect of a performance form in a past actual business scene is used as tag data for training. The model based on the decision tree algorithm can automatically learn rules and modes in historical data, classify performance form requirements under different service scenes, and provide intelligent decision support for subsequent optimization adjustment. In addition, the adaptation effect under the past actual business scene is used as the label data for training, so that the model can fully utilize the historical experience of enterprises, the accuracy and the reliability of the model are improved, and the suitability of the performance form is further improved.
In some possible implementations of the first aspect, the preset machine learning model specific training process includes:
Preprocessing historical data, converting text features into numerical features by adopting a single-hot coding mode, screening out features with obvious influence on performance form adaptation by utilizing an information gain algorithm, and constructing node branches of a decision tree;
The training data set is divided in a recursion mode, so that the structure of the decision tree is continuously optimized, and the performance form demand modes under different service scenes can be accurately classified;
When the similarity score is lower than a set threshold value, the current real-time business demand data is also converted into a feature vector form required by a decision tree model, the feature vector form is input into a trained model, the model is gradually judged from a root node according to a path of a feature value in the decision tree, the current business scene is finally classified into a corresponding performance form demand category, and an optimization adjustment strategy is extracted from a well-adapted performance form case in the past in a targeted manner based on the category information, so that the historical key data set is optimized, wherein the optimization adjustment strategy comprises weight adjustment of key indexes, increase and decrease of flow nodes and reconfiguration of association relations.
In a second aspect, an embodiment of the present application provides a performance form creation system of a human resource system, including:
The first acquisition module is used for acquiring historical multi-version performance form data of a first department and actual assessment scene information corresponding to each version;
the first analysis module is used for carrying out text analysis on the historical multi-version performance form data and extracting key index text fragments, flow node text fragments and association relation text fragments, the occurrence frequency of which reaches a set threshold value;
The first conversion module is used for converting the key index text segment, the flow node text segment and the association relation text segment into structural data by using a natural language processing technology and recording the structural data as a historical key data set;
The second acquisition module is used for acquiring real-time service demand data;
the first comparison module is used for comparing and analyzing the historical key data set and the real-time business demand data, and judging the matching degree of the historical key data set and the real-time business demand data according to a predefined similarity algorithm;
the first adjusting module is used for optimizing and adjusting the historical key data set by using a preset machine learning model if the matching degree is lower than a set threshold value;
and the table generation module is used for dynamically generating a performance table adapting to the new business scene of the first department according to the optimized and adjusted data and in combination with a preset table layout rule.
In some possible implementations of the second aspect, the historical multi-version performance form data includes performance forms of a plurality of different periods and different business lines, and the actual assessment scenario information includes organization architecture, business objectives and assessment personnel categories.
In some possible embodiments of the second aspect, the second obtaining module is specifically configured to establish a connection with a database of each service system in the enterprise by using a database connection technology and a pre-developed API interface, where the service systems include a project management system, an ERP system, a CRM system, and a human resource management system, determine a key data source reflecting a real-time service requirement and a unique identifier of data in each data source by analyzing data of each service system, periodically monitor a data change of the key data source by a trigger or a timing task of the database, extract updated data through the API interface according to a preset data acquisition rule when data update is detected, clean the acquired data, integrate data from different data sources according to a preset data association rule, update the cleaned integrated data into a preset service requirement database, and call the service requirement data from the service requirement database as real-time service requirement data.
In some possible embodiments of the second aspect, the specific formula of the predefined similarity algorithm is as follows:
wherein:
h i, representing the ith keyword in the historical keyword set;
r j represents the jth keyword in the real-time business requirement set;
S, representing the comprehensive matching degree between the keywords in the two sets, wherein the larger the value is, the higher the matching degree between the two sets is;
n represents the total number of history keywords in the collection;
m is the total number of the keywords required in real time in the collection;
w ij, the weight, which represents the association strength between the history keyword h i and the real-time business demand keyword r j, the greater the weight value, the closer the relation between the history keyword h i and the real-time business demand keyword r j is described;
f (h i,rj) a matching function for judging whether the keyword h i is matched with the keyword r j, which is defined as follows:
In some possible implementations of the second aspect, the preset machine learning model is a classification model based on a decision tree algorithm, and when the preset machine learning model is constructed, key index text segments, flow node text segments and association relation text segments in a historical key data set are used as feature vectors, and the adaptation effect of the performance form in a past actual business scene is used as tag data for training.
In some possible embodiments of the second aspect, the preset machine learning model specific training process includes:
Preprocessing historical data, converting text features into numerical features by adopting a single-hot coding mode, screening out features with obvious influence on performance form adaptation by utilizing an information gain algorithm, and constructing node branches of a decision tree;
The training data set is divided in a recursion mode, so that the structure of the decision tree is continuously optimized, and the performance form demand modes under different service scenes can be accurately classified;
When the similarity score is lower than a set threshold value, the current real-time business demand data is also converted into a feature vector form required by a decision tree model, the feature vector form is input into a trained model, the model is gradually judged from a root node according to a path of a feature value in the decision tree, the current business scene is finally classified into a corresponding performance form demand category, and an optimization adjustment strategy is extracted from a well-adapted performance form case in the past in a targeted manner based on the category information, so that the historical key data set is optimized, wherein the optimization adjustment strategy comprises weight adjustment of key indexes, increase and decrease of flow nodes and reconfiguration of association relations.
In a third aspect, an embodiment of the present application provides an electronic device, including one or more processors, a storage device having one or more programs stored thereon, where the one or more programs, when executed by the one or more processors, cause the one or more processors to implement a method according to any one of the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements a method according to any one of the solutions of the first aspect.
In a fifth aspect, an embodiment of the present application provides a computer program product, comprising a computer program, which when executed by a processor implements the method according to any one of the claims of the first aspect.
The technical effects of any one of the design manners of the second aspect to the fifth aspect may be referred to the technical effects of the different design manners of the first aspect, and will not be repeated here.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those of ordinary skill in the art that the drawings in the following description are exemplary only and that other implementations can be obtained from the extensions of the drawings provided without inventive effort.
Fig. 1 is a flow chart of a performance form creation method of a human resource system according to some embodiments of the present application;
fig. 2 is a schematic structural diagram of a performance form creation system of a human resource system according to some embodiments of the present application;
Fig. 3 is a schematic diagram of an electronic device suitable for use in implementing some embodiments of the application.
Detailed Description
Reference will now be made in detail to specific embodiments of the invention. While the invention will be described in conjunction with these specific embodiments, it will be understood that they are not intended to limit the invention to these specific embodiments. On the contrary, these embodiments are intended to cover alternatives, modifications and equivalents, which may be included within the spirit and scope of the invention as defined by the appended claims. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The present invention may be practiced without some or all of these specific details.
When used in conjunction with the description herein and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. 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 invention belongs.
Summary of the application with the rapid development of modern enterprises and the increasing complexity of competing environments, performance management has become one of the core content of enterprise human resource management. Performance management can help enterprises effectively improve staff work efficiency, optimize resource allocation and enhance the overall competitiveness of organizations. In the performance management system, the performance form is taken as an important tool and plays important roles of recording assessment content, quantifying staff performance and feeding back assessment results.
However, with the continuous development of the concept of performance management, the requirements of enterprises on performance assessment in practical applications are increasingly diversified. Different enterprises often have different performance assessment flows and rules due to the differences of industry background, organization architecture and development strategy. Even the same enterprise may have significant differences in performance assessment methods for different departments, posts, and staff groups. For example, the sales department may require quantitative assessment based on performance data, while the research and development department may be more concerned with qualitative assessment of project quality and innovation ability. Therefore, how to flexibly design and generate performance forms adapting to diversified assessment requirements becomes an important challenge in enterprise human resource management.
Aiming at the technical problems, the technical scheme overall thought is that a performance form creation method of a human resource system is provided, and the performance form creation method comprises the following steps of obtaining historical multi-version performance form data of a first department and actual assessment scene information corresponding to each version, conducting text analysis on the historical multi-version performance form data, extracting key index text fragments, flow node text fragments and association relation text fragments, which have the occurrence frequency reaching a set threshold value, converting the key index text fragments, the flow node text fragments and the association relation text fragments into structural data by using a natural language processing technology, recording the structural data as a historical key data set, obtaining real-time service demand data, comparing and analyzing the historical key data set and the real-time service demand data, judging the matching degree of the historical key data set and the real-time service demand data according to a predefined similarity algorithm, conducting optimization adjustment on the historical key data set by using a preset machine learning model if the matching degree is lower than the set threshold value, and dynamically generating a performance form suitable for a new service scene of the first department according to the optimized and adjusted data and combining preset form layout rules.
The method comprises the steps of firstly obtaining historical multi-version performance form data and actual assessment scene information corresponding to each version. The historical data contains rich experience accumulated by departments in the past when performance assessment is carried out on different business objects in different business stages, covers various assessment modes, index setting and other contents, and lays a foundation for the subsequent construction of an adaptability form.
Then, text analysis is carried out on the historical multi-version performance form data, key index text fragments, flow node text fragments and association relation text fragments with the occurrence frequency reaching a set threshold are extracted,
For example, key indexes such as performance completion rate and the like frequently appearing in past examination of the sales department, and relevant indexes such as on-time delivery rate and the like of projects of the research and development department can be extracted. By extracting these core elements, performance assessment features of each department and post can be captured, and representative assessment patterns can be extracted.
And then the data are further converted into structured data by utilizing a natural language processing technology and recorded as a history key data set, and the original disordered unstructured history form data can be arranged into a standard form which is convenient for subsequent analysis, comparison and utilization, so that a general assessment mode and key elements hidden in the history data are mined. The real-time business demand data is acquired, the change condition of the current business of departments can be captured sharply, and a practical basis is provided for timely adjusting performance forms.
And comparing and analyzing the sorted historical key data set with the real-time business demand data, judging the matching degree of the historical key data set and the real-time business demand data according to a predefined similarity algorithm, and accurately judging whether the current historical data can meet the real-time business demand or not according to the fit conditions between the historical data and the real-time demand in a plurality of dimensions. If the matching degree is lower than the set threshold, the historical experience is insufficient to be directly applied to the current service scene, at the moment, the historical key data set is optimally adjusted by utilizing a preset machine learning model based on the existing historical data and real-time service demand data, then, according to the optimized and adjusted data, elements such as optimized key indexes, flow nodes and the like are reasonably presented on a form according to a given layout rule, a performance form suitable for the new service scene is dynamically generated, the generated performance form can reflect the new demand, and meanwhile, the applicability of the historical experience is reserved.
If the matching degree is higher, the historical data can be well adapted to the current requirement, and no further adjustment is needed. Through the real-time monitoring mechanism, the examination form can timely adapt to new requirements generated by business adjustment or organization change. For example, according to the higher requirements of real-time business on innovation capability, the weights of new related indexes in the performance forms of research and development departments can be adjusted through a machine learning model.
Therefore, performance forms of assessment requirements of different departments can be conveniently generated, the generated forms are ensured to conform to habits of departments in terms of display and use, and the actual assessment requirements of current business are closely attached, so that the performance forms which are flexibly designed and generated according to different business scenes are realized, and the problem that performance assessment modes of different departments, posts and staff groups of different enterprises and the same enterprise are large in difference is effectively solved.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings. Referring to fig. 1, an embodiment of the present application provides a performance form creation method for a human resource system, including the following steps:
s101, acquiring historical multi-version performance form data of a first department and actual assessment scene information corresponding to each version;
Specifically, in some embodiments, the historical multi-version performance form data includes performance forms of a plurality of different periods and different service lines, and the actual assessment scenario information includes an organization structure, a service objective and an assessment personnel category. For example, in some embodiments, the historical multi-version performance form data and the actual assessment scenario information corresponding to each version may be actively acquired from a preset database by an execution body of the performance form creation method of the human resource system, and may be manually uploaded to the execution body by a human resource department.
S102, carrying out text analysis on historical multi-version performance form data, and extracting key index text fragments, flow node text fragments and association relation text fragments with occurrence frequency reaching a set threshold;
s103, converting the key index text segment, the flow node text segment and the association relation text segment into structural data by using a natural language processing technology, and recording the structural data as a historical key data set;
S104, acquiring real-time service demand data;
real-time business requirement data includes, but is not limited to, current business objectives, employee post distribution, and assessment cycle requirements.
Specifically, in some embodiments, the real-time business requirement data may be obtained by:
the method comprises the steps that firstly, a database connection technology and a pre-developed API interface are utilized to establish connection with databases of business systems in enterprises, and the business systems comprise a project management system, an ERP system, a CRM system and a human resource management system;
Secondly, determining key data sources reflecting real-time service demands and unique identifiers of data in each data source through data analysis of each service system;
third, periodically monitoring the data change of the key data source through a trigger or a timing task of the database;
fourth, when data update is detected, extracting updated data through an API interface according to a preset data acquisition rule;
fifthly, cleaning the collected data to remove repeated, wrong and incomplete data, and integrating the data from different data sources according to a preset data association rule;
sixth, updating the data after cleaning and integrating to a preset business requirement database;
Seventh, the service demand data is fetched from the service demand database as real-time service demand data. Therefore, the latest business demand data can be timely obtained, the real-time performance and accuracy of the data are guaranteed, and a reliable data base is provided for dynamic generation of performance forms. In addition, the collected data are cleaned and integrated, invalid data can be removed, the quality of the data is improved, and the situation that the form is generated in error or inaccurate due to the data problem is reduced. In addition, the data from different business systems are integrated, the data island is broken, business conditions of enterprises can be more comprehensively known, and therefore performance forms which are more in line with actual business requirements are generated. Of course, the present application is not limited thereto. In other embodiments, the uploading may also be performed manually by the human resources department at fixed time intervals.
S105, comparing and analyzing the historical key data set and the real-time business demand data, and judging the matching degree of the historical key data set and the real-time business demand data according to a predefined similarity algorithm;
Specifically, in some embodiments, the specific formula of the predefined similarity algorithm is as follows:
wherein:
h i, representing the ith keyword in the historical keyword set;
r j represents the jth keyword in the real-time business requirement set;
S, representing the comprehensive matching degree between the keywords in the two sets, wherein the larger the value is, the higher the matching degree between the two sets is;
n represents the total number of history keywords in the collection;
m is the total number of the keywords required in real time in the collection;
w ij, the weight, which represents the association strength between the history keyword h i and the real-time business demand keyword r j, the greater the weight value, the closer the relation between the history keyword h i and the real-time business demand keyword r j is described;
f (h i,rj) a matching function for judging whether the keyword h i is matched with the keyword r j, which is defined as follows:
S106, if the matching degree is lower than a set threshold value, optimizing and adjusting the historical key data set by using a preset machine learning model;
Specifically, in some embodiments, the preset machine learning model is a classification model based on a decision tree algorithm, and when the preset machine learning model is constructed, key index text segments, flow node text segments and association relation text segments in a historical key data set are used as feature vectors, and the adaptation effect of a performance form in a past actual business scene is used as tag data for training. The model based on the decision tree algorithm can automatically learn rules and modes in historical data, classify performance form requirements under different service scenes, and provide intelligent decision support for subsequent optimization adjustment. In addition, the adaptation effect under the past actual business scene is used as the label data for training, so that the model can fully utilize the historical experience of enterprises, the accuracy and the reliability of the model are improved, and the suitability of the performance form is further improved.
Based on the above embodiment, the specific training process of the preset machine learning model includes:
firstly, preprocessing historical data, converting text features into numerical features by adopting a single-heat coding mode, screening out features with obvious influence on performance form adaptation by utilizing an information gain algorithm, and constructing node branches of a decision tree;
secondly, the structure of the decision tree is continuously optimized by recursively dividing the training data set, so that the decision tree can accurately classify performance form demand modes in different service scenes;
And thirdly, when the similarity score is lower than a set threshold value, the current real-time business demand data is also converted into a feature vector form required by a decision tree model, the feature vector form is input into a trained model, the model is gradually judged from a root node according to the path of the feature value in the decision tree, the current business scene is finally classified into a corresponding performance form demand category, and based on the information, an optimization adjustment strategy is extracted from the past well-adapted performance form cases in a targeted manner, and the historical key data set is optimized, wherein the optimization adjustment strategy comprises weight adjustment of key indexes, increase and decrease of flow nodes and reconfiguration of association relations.
And S107, dynamically generating a performance form adapting to the new business scene of the first department according to the optimized and adjusted data and in combination with a preset form layout rule.
The specific case is as follows:
A human resources department of a company needs to formulate a new performance form for a flagged marketing department (abbreviated as "first department") for evaluating the staff's work performance. The main responsibility of the marketing department is to increase the market share of the company through advertisement putting, client development, brand promotion and other modes. As the company just develops a market popularization plan of a new product, the existing performance forms are not completely suitable for new requirements, so that a performance form which accords with the current new business scene needs to be dynamically generated.
The following is a specific procedure for performing performance form creation according to the steps in the claims:
1. Acquiring historical performance form data
The performance form history data used by the marketing department in the past 12 months is extracted from the database of the department, and the performance form history data comprises the following contents:
Index data of different versions of forms, for example:
index 1 advertisement putting ROI (return on investment)
Index 2 customer conversion
Index 3 brand awareness percentage improvement
Index 4 social media interaction volume
Flow node data, for example:
Node 1 first-line employee self-evaluation
Node 2 department manager evaluation
Node 3 human resource portion assessment
Association relationship data such as:
The advertisement delivery ROI and the conversion rate of the client are in direct positive correlation, and the weights are 40% and 35%, respectively.
Meanwhile, the actual examination scene information corresponding to the forms is also acquired, for example:
the main tasks of departments are conventional marketing, quarterly sales promotion and the like.
Department organization architecture including marketing manager, advertising specialist, customer manager, etc.
2. Text parsing and key information extraction
Parsing and analyzing text contents of the history form through a Natural Language Processing (NLP) algorithm:
key indicator text segments are extracted from the form using word segmentation techniques and frequency statistics, such as:
The indexes with higher frequency of occurrence are 'advertisement putting ROI' (more than 10 occurrences) 'client conversion rate' (more than 8 occurrences).
Extracting a flow node text segment, for example:
"department manager rating" appears in all version forms.
Extracting text segments of key association relations, for example:
the "advertisement placement ROI" is positively correlated with the "customer conversion rate".
The key indexes, flow nodes and text fragments with the occurrence frequency reaching a set threshold (such as 30%) are arranged into a historical key data set, which comprises the following steps:
and the index set is an advertisement putting ROI, a client conversion rate, brand awareness improvement and the like.
And the process node set comprises employee self-evaluation, manager evaluation and personnel assessment.
Correlation, advertisement delivery ROI→customer conversion rate (positive correlation).
3. Acquiring real-time business demand data
In the current business environment, real-time business demand data is collected by:
The method comprises the following steps of obtaining from a task management system of a marketing department:
the current business goal is that new products are marketed and popularized, and 50 thousands of people are expected to be covered by the target client group.
The important task is to improve the brand recognition degree through social media advertisement and short video popularization.
Data input from department responsible person is obtained:
the new assessment index is 'short video click rate', 'new customer acquisition quantity'.
The current assessment period is 1 month (short term performance for promotional activities).
From historical business data analysis:
the advertisement putting quantity is 50 ten thousand times of exposure.
Current customer conversion is 5%.
The real-time business demand data is determined as:
Business objective, new product popularization.
New index, short video click rate (30% improvement expected), new customer acquisition (1000 people target).
The examination period is 1 month.
4. Matching degree analysis
Comparing and analyzing the real-time business demand data with the historical key data set:
similarity calculation:
The indexes such as 'advertisement putting ROI' and 'client conversion rate' in the historical key data set are highly correlated with the current business target, and the similarity score is 85%.
The index of short video click rate does not appear in the history form, and the index is partially not matched with the current demand.
Matching degree result:
The matching degree score is 70%, and is lower than a set threshold (75%), and the historical key data set needs to be optimally adjusted.
5. Data optimization adjustment
Optimizing the historical key data set by using a preset machine learning model (such as a random forest algorithm) and a rule engine:
and (3) adding indexes, namely introducing the indexes of short video click rate and new client acquisition quantity, and respectively giving weights of 25% and 15%.
Weight adjustment-weight reduction of "advertisement placement ROI" (from 40% to 20%) to highlight the emphasis of the current short-term promotional campaign.
And updating a flow node, namely adding a social media operation special person evaluation link in the assessment node and taking charge of evaluating the short video delivery effect.
And verifying the result, namely ensuring that the new index and the weight distribution meet the actual requirements through service rationality verification.
Optimizing the adjusted data:
Index set, advertisement placement ROI (20%), customer conversion rate (30%), short video click rate (25%), new customer acquisition (15%).
And the process nodes comprise employee self-evaluation, social media operation special personnel evaluation, manager evaluation and personnel assessment.
6. Dynamically generating performance forms
According to the optimized data, combining form layout rules, dynamically generating a new performance form:
Form content:
index weight distribution:
advertisement putting ROI (20%)
Customer conversion (30%)
Short video click rate (25%)
New customer acquisition (15%)
And (3) checking:
step 1, staff self-evaluation
Step 2, social media operation expert evaluation
Step 3, department manager evaluation
Step 4, final evaluation of human resources department
Form format:
and dynamically generated HTML pages support online filling and submission.
Can be exported in PDF format for archiving.
Referring to fig. 2, based on the same inventive concept as the performance form creation method of a human resource system in the foregoing embodiment, an embodiment of the present application provides a performance form creation system of a human resource system, including:
The first obtaining module 201 is configured to obtain historical multi-version performance form data of a first department and actual assessment scenario information corresponding to each version;
the first parsing module 202 is configured to perform text parsing on the historical multi-version performance form data, and extract a key indicator text segment, a process node text segment and an association relation text segment, where the occurrence frequency of the key indicator text segment reaches a set threshold;
The first conversion module 203 converts the key index text segment, the process node text segment and the association relation text segment into structural data by using a natural language processing technology, and records the structural data as a historical key data set;
A second obtaining module 204, configured to obtain real-time service requirement data;
The first comparison module 205 is configured to compare and analyze the historical key data set and the real-time service demand data, and determine a matching degree of the historical key data set and the real-time service demand data according to a predefined similarity algorithm;
the first adjustment module 206 is configured to perform optimization adjustment on the historical key data set by using a preset machine learning model if the matching degree is lower than a set threshold;
The table generating module 207 is configured to dynamically generate a performance table adapted to the new business scenario of the first department according to the optimized and adjusted data and in combination with a preset table layout rule.
In some embodiments, the historical multi-version performance form data includes performance forms of a plurality of different periods and different business lines, and the actual assessment scenario information includes organization architecture, business objectives and assessment personnel categories.
In some embodiments, the second obtaining module 204 is specifically configured to establish a connection with a database of each service system inside the enterprise by using a database connection technology and a pre-developed API interface, wherein the service system includes a project management system, an ERP system, a CRM system, and a human resource management system, determine a key data source reflecting real-time service requirements and a unique identifier of data in each data source by analyzing data of each service system, periodically monitor a data change of the key data source by a trigger or a timing task of the database, extract updated data through the API interface according to a preset data acquisition rule when data update is detected, clean the acquired data, integrate data from different data sources according to a preset data association rule, update the cleaned and integrated data into a preset service requirement database, and retrieve service requirement data from the service requirement database as real-time service requirement data.
In some embodiments, the specific formula of the predefined similarity algorithm is as follows:
wherein:
h i, representing the ith keyword in the historical keyword set;
r j represents the jth keyword in the real-time business requirement set;
S, representing the comprehensive matching degree between the keywords in the two sets, wherein the larger the value is, the higher the matching degree between the two sets is;
n represents the total number of history keywords in the collection;
m is the total number of the keywords required in real time in the collection;
w ij, the weight, which represents the association strength between the history keyword h i and the real-time business demand keyword r j, the greater the weight value, the closer the relation between the history keyword h i and the real-time business demand keyword r j is described;
f (h i,rj) a matching function for judging whether the keyword h i is matched with the keyword r j, which is defined as follows:
In some embodiments, the preset machine learning model is a classification model based on a decision tree algorithm, and when the preset machine learning model is constructed, key index text segments, flow node text segments and association relation text segments in a historical key data set are used as feature vectors, and the adaptation effect of a performance form in a past actual business scene is used as label data for training.
In some embodiments, the preset machine learning model specific training process includes:
Preprocessing historical data, converting text features into numerical features by adopting a single-hot coding mode, screening out features with obvious influence on performance form adaptation by utilizing an information gain algorithm, and constructing node branches of a decision tree;
The training data set is divided in a recursion mode, so that the structure of the decision tree is continuously optimized, and the performance form demand modes under different service scenes can be accurately classified;
When the similarity score is lower than a set threshold value, the current real-time business demand data is also converted into a feature vector form required by a decision tree model, the feature vector form is input into a trained model, the model is gradually judged from a root node according to a path of a feature value in the decision tree, the current business scene is finally classified into a corresponding performance form demand category, and an optimization adjustment strategy is extracted from a well-adapted performance form case in the past in a targeted manner based on the category information, so that the historical key data set is optimized, wherein the optimization adjustment strategy comprises weight adjustment of key indexes, increase and decrease of flow nodes and reconfiguration of association relations.
It will be appreciated that the modules described by the performance form creation system of the human resource system correspond to the steps in the performance form creation method of the human resource system described with reference to fig. 1. Thus, the operations, features and beneficial effects described above with respect to the method are equally applicable to the performance form creation system of the human resource system and the modules contained therein, and are not described here again.
Referring to fig. 3, an embodiment of the present application provides an electronic device based on the inventive concept of a performance form creation method of a human resource system in the foregoing embodiment. The electronic device may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), etc., and a fixed terminal such as a digital TV, a desktop computer, etc. The electronic device includes a processing means 301 (e.g., a central processor, a graphics processor, etc.) which can perform various appropriate actions and processes according to a program stored in a ROM302 (read only memory) or a program loaded from a storage means 308 into a RAM303 (random access memory). In the RAM303, various programs and data required for the operation of the electronic device are also stored. The processing device 301, the ROM302, and the RAM303 are connected to each other via a bus 304. An input/output interface (i.e., I/O interface 305) is also connected to bus 304.
In general, devices may be connected to I/O interface 305 including input devices 306 such as a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc., output devices 307 including a Liquid Crystal Display (LCD), speaker, vibrator, etc., storage devices 308 including, for example, magnetic tape, hard disk, etc., and communication devices 309. The communication means 309 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data.
In particular, according to some embodiments of the application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 309, or from storage device 308, or from ROM 302. The above-described functions defined in the methods of some embodiments of the present application are performed when the computer program is executed by the processing means 301.
The computer readable medium described in some embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of a computer-readable storage medium may include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the application, however, the computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to electrical wiring, fiber optic cable, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperTextTransferProtoco l ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be included in the electronic device or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs, when the one or more programs are executed by the electronic equipment, the electronic equipment is enabled to acquire historical multi-version performance form data of a first department and actual assessment scene information corresponding to each version, conduct text analysis on the historical multi-version performance form data, extract key index text fragments, flow node text fragments and association relation text fragments, the key index text fragments, the flow node text fragments and the association relation text fragments, convert the key index text fragments, the flow node text fragments and the association relation text fragments into structural data by using a natural language processing technology, record the structural data as a historical key data set, acquire real-time business demand data, conduct comparison analysis on the historical key data set and the real-time business demand data, judge the matching degree of the historical key data set and the real-time business demand data according to a predefined similarity algorithm, conduct optimization adjustment on the historical key data set by using a preset machine learning model if the matching degree is lower than the set threshold, dynamically generate a performance form suitable for a new business scene of the first department according to the optimized and adjusted data and combining with preset form layout rules.
Computer program code for carrying out operations for some embodiments of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, SMA L LTA L K, C++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in some embodiments of the application may be implemented in software or in hardware. The described modules may also be provided in the processor, and may be described as, for example, a first acquisition module, a first parsing module, a first conversion module, a second acquisition module, a first comparison module, a first adjustment module, and a table generation module. The names of these modules do not constitute a limitation on the module itself in some cases, and for example, the first acquisition module may also be described as "historical performance form receiving module".
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic that may be used include Field Programmable Gate Arrays (FPGAs), application specific integrated circuits (AS ICs), application Specific Standard Products (ASSPs), systems-on-a-chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.
Some embodiments of the present application also provide a computer program product comprising a computer program which, when executed by a processor, implements a performance form creation method of any of the human resource systems described above.
While the invention has been described in detail in the foregoing general description and specific examples, it will be apparent to those skilled in the art that modifications and improvements can be made thereto. Accordingly, such modifications or improvements may be made without departing from the spirit of the invention and are intended to be within the scope of the invention as claimed.
Claims (8)
1. The performance form creation method of the human resource system is characterized by comprising the following steps of:
acquiring historical multi-version performance form data of a first department and actual assessment scene information corresponding to each version;
text analysis is carried out on the historical multi-version performance form data, and key index text fragments, flow node text fragments and association relation text fragments with occurrence frequency reaching a set threshold are extracted;
Converting the key index text segment, the flow node text segment and the association relation text segment into structural data by using a natural language processing technology, and recording the structural data as a historical key data set;
Acquiring real-time service demand data;
Comparing and analyzing the historical key data set and the real-time business demand data, and judging the matching degree of the historical key data set and the real-time business demand data according to a predefined similarity algorithm, wherein the specific formula of the predefined similarity algorithm is as follows:
wherein:
h i, representing the ith keyword in the historical keyword set;
r j represents the jth keyword in the real-time business requirement set;
S, representing the comprehensive matching degree between the keywords in the two sets, wherein the larger the value is, the higher the matching degree between the two sets is;
n represents the total number of history keywords in the collection;
m is the total number of the keywords required in real time in the collection;
w ij, the weight, which represents the association strength between the history keyword h i and the real-time business demand keyword r j, the greater the weight value, the closer the relation between the history keyword h i and the real-time business demand keyword r j is described;
f (h i,rj) a matching function for judging whether the keyword h i is matched with the keyword r j, which is defined as follows:
if the matching degree is lower than a set threshold value, optimizing and adjusting the historical key data set by using a preset machine learning model, wherein the preset machine learning model is a classification model based on a decision tree algorithm, and when the preset machine learning model is constructed, key index text fragments, flow node text fragments and association relation text fragments in the historical key data set are used as feature vectors, and the adaptation effect of a performance form in a past actual business scene is used as label data for training;
and dynamically generating a performance form adapting to the new business scene of the first department according to the optimized and adjusted data and in combination with a preset form layout rule.
2. The performance form creation method of a human resource system according to claim 1, wherein the historical multi-version performance form data includes performance forms of different periods and different business lines, and the actual assessment scenario information includes organization architecture, business objective and assessment personnel category.
3. The performance form creation method of a human resource system according to claim 2, wherein the acquiring step of the real-time business demand data includes:
Establishing connection with databases of business systems in enterprises by utilizing a database connection technology and a pre-developed API interface, wherein the business systems comprise a project management system, an ERP system, a CRM system and a human resource management system;
determining key data sources reflecting real-time service demands therein by analyzing data of each service system, and unique identifiers of data in each data source;
Periodically monitoring data changes of the key data sources through triggers or timing tasks of the database;
When the data update is detected, extracting updated data through the API interface according to a preset data acquisition rule;
cleaning the acquired data, removing repeated, erroneous and incomplete data, and integrating the data from different data sources according to a preset data association rule;
updating the cleaned and integrated data to a preset business requirement database;
and calling the business requirement data from the business requirement database to serve as the real-time business requirement data.
4. The performance form creation method of a human resources system according to claim 1, wherein the preset machine learning model specific training process includes:
Preprocessing historical data, converting text features into numerical features by adopting a single-hot coding mode, screening out features with obvious influence on performance form adaptation by utilizing an information gain algorithm, and constructing node branches of a decision tree;
The training data set is divided in a recursion mode, so that the structure of the decision tree is continuously optimized, and the performance form demand modes under different service scenes can be accurately classified;
When the similarity score is lower than a set threshold value, the current real-time business demand data is also converted into a feature vector form required by a decision tree model, the feature vector form is input into a trained model, the model is gradually judged from a root node according to a path of a feature value in the decision tree, the current business scene is finally classified into a corresponding performance form demand category, and an optimization adjustment strategy is extracted from a well-adapted performance form case in the past in a targeted manner based on the category information, so that the historical key data set is optimized, wherein the optimization adjustment strategy comprises weight adjustment of key indexes, increase and decrease of flow nodes and reconfiguration of association relations.
5. A performance form creation system for a human resources system, comprising:
The first acquisition module is used for acquiring historical multi-version performance form data of a first department and actual assessment scene information corresponding to each version;
the first analysis module is used for carrying out text analysis on the historical multi-version performance form data and extracting key index text fragments, flow node text fragments and association relation text fragments, the occurrence frequency of which reaches a set threshold value;
The first conversion module is used for converting the key index text segment, the flow node text segment and the association relation text segment into structural data by using a natural language processing technology and recording the structural data as a historical key data set;
The second acquisition module is used for acquiring real-time service demand data;
The first comparison module is used for comparing and analyzing the historical key data set and the real-time business demand data, and judging the matching degree of the historical key data set and the real-time business demand data according to a predefined similarity algorithm, wherein the specific formula of the predefined similarity algorithm is as follows:
wherein:
h i, representing the ith keyword in the historical keyword set;
r j represents the jth keyword in the real-time business requirement set;
S, representing the comprehensive matching degree between the keywords in the two sets, wherein the larger the value is, the higher the matching degree between the two sets is;
n represents the total number of history keywords in the collection;
m is the total number of the keywords required in real time in the collection;
w ij, the weight, which represents the association strength between the history keyword h i and the real-time business demand keyword r j, the greater the weight value, the closer the relation between the history keyword h i and the real-time business demand keyword r j is described;
f (h i,rj) a matching function for judging whether the keyword h i is matched with the keyword r j, which is defined as follows:
The system comprises a first adjustment module, a first adjustment module and a second adjustment module, wherein the first adjustment module is used for optimizing and adjusting a historical key data set by using a preset machine learning model if the matching degree is lower than a set threshold value;
and the table generation module is used for dynamically generating a performance table adapting to the new business scene of the first department according to the optimized and adjusted data and in combination with a preset table layout rule.
6. An electronic device, comprising:
One or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1 to 4.
7. A computer readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processing device, implements the method according to any of claims 1 to 4.
8. A computer program product comprising a computer program, characterized in that the computer program, when executed by processing means, implements the method of any one of claims 1 to 4.
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