Employee loss prediction method based on multi-source employee behavior data fusion analysis
By constructing basic feature vectors and periodic structure data of multi-source employee behavior data, and calculating steady-state and mutation risk values, the problem of difficulty in identifying short-cycle behavioral anomalies of employees in existing technologies is solved, and high-precision and real-time early warning of employee turnover risk is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-31
- Publication Date
- 2026-04-14
AI Technical Summary
Existing employee turnover prediction technologies struggle to identify sudden behavioral anomalies in a timely manner and cannot distinguish between long-term stable risks and short-term sudden risks, resulting in delayed prediction results and failing to meet the need for early warning.
By collecting multi-source behavioral data from employees, we construct basic behavioral feature vectors and cyclical behavioral structure data, calculate steady-state risk values, mutation indices, and cyclical structure shift risk values, perform weighted fusion calculations, generate comprehensive turnover risk values, and identify long-term stable risks and short-term sudden risks in employee behavior.
It enables the identification of changes in employee behavior structure, improves the accuracy and lead time of prediction, can promptly identify short-cycle behavioral mutations and structural shifts, distinguishes between long-term and short-term risks, and enhances the real-time nature and accuracy of prediction.
Smart Images
Figure CN121860131A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of data processing technology, and in particular to a method for predicting employee turnover based on the fusion analysis of multi-source employee behavior data. Background Technology
[0002] Currently, existing employee turnover prediction technologies typically collect employee behavior data from multiple business systems within an enterprise, perform data fusion processing and feature extraction, and then output the employee's turnover risk or probability based on statistical or machine learning models.
[0003] Existing solutions mainly rely on long-term trends or statistical distributions for prediction, assuming that changes in employee behavior are continuous, making it difficult to identify sudden behavioral anomalies in a short period of time.
[0004] Existing solutions mostly use a single risk value or probability as the output, which cannot distinguish the different sources of long-term stable risks and short-term sudden risks;
[0005] While existing solutions construct behavioral feature vectors, they do not model the structural patterns of employee behavior over continuous periods and lack the ability to identify behavioral structural shifts.
[0006] Therefore, existing technologies often lag in predicting employee behavior when it changes discontinuously or abruptly, making it difficult to meet the need for early warning of employee turnover risks.
[0007] It is evident that there is an urgent need for an employee turnover prediction method based on the fusion analysis of multi-source employee behavior data, which offers both high accuracy and real-time performance. Summary of the Invention
[0008] In view of this, the present disclosure provides an employee turnover prediction method based on the fusion analysis of multi-source employee behavior data, which at least partially solves the problems of poor prediction accuracy and real-time performance in the prior art.
[0009] This disclosure provides a method for predicting employee turnover based on the fusion analysis of multi-source employee behavior data, including:
[0010] Step 1: Collect multi-source behavioral data of employees within a preset time period, and after standardizing the behavioral data, construct a basic behavioral feature vector for each employee. The basic behavioral feature vector is used to describe the employee's behavioral state in the current period.
[0011] Step 2: Based on the basic behavioral feature vectors of multiple consecutive time periods, construct the periodic behavioral structure data of employees to describe the overall pattern of employee behavior in the time dimension.
[0012] Step 3: Analyze the changes in the basic behavioral feature vectors of employees within adjacent periods, calculate the rate of change of each behavioral feature, and generate steady-state risk values accordingly.
[0013] Step 4: Based on the statistical characteristics of employee behavior over multiple historical periods, calculate the mutation index of employee behavior to identify abnormal changes in employee behavior in a short period of time, and generate a mutation risk signal when the mutation index exceeds the corresponding threshold.
[0014] Step 5: Calculate the cyclical structure similarity based on the cyclical behavior structure data of employees in adjacent cycles, and generate the cyclical structure offset risk value accordingly.
[0015] Step 6: Based on the steady-state risk value, the mutation risk value corresponding to the mutation risk signal, and the periodic structure shift risk value, perform weighted fusion calculation to obtain the comprehensive employee turnover risk value, and output the corresponding risk level according to the preset threshold.
[0016] According to a specific implementation of this disclosure, the expression for the basic behavioral feature vector is:
[0017]
[0018] in, Indicates employees In time period Inner Dimensional behavioral feature value, This indicates the total number of feature dimensions.
[0019] According to a specific implementation of this disclosure, the expression for the periodic behavior structure data is:
[0020]
[0021] in, Indicates the period length.
[0022] According to a specific implementation of this disclosure, the expression for calculating the rate of change of each behavioral feature is as follows:
[0023]
[0024] in, This indicates a very small positive number that prevents the denominator from being zero;
[0025] The expression for the steady-state risk value is:
[0026]
[0027] in, This represents the normalization function, used to map risk values to a preset range. This represents the weight coefficient of the k-th behavioral feature in the steady-state risk calculation.
[0028] According to a specific implementation of this disclosure, the expression for the mutation index is:
[0029]
[0030] in, Let represent the mean of employee i's k-th behavioral characteristic within a historical period. This represents the standard deviation of employee i's k-th behavioral characteristic within a historical period;
[0031] The expression for the mutation risk signal is:
[0032]
[0033] in, This represents the mutation determination threshold corresponding to the k-th behavioral feature. This indicates an indicator function that takes the value 1 when the condition is true and 0 otherwise.
[0034] According to a specific implementation of this disclosure, the expression for the periodic structure similarity is:
[0035]
[0036] in, This represents the inner product operation. Represents norm operations;
[0037] The expression for the risk value of the periodic structure offset is:
[0038] .
[0039] According to a specific implementation of this disclosure, the expression for the comprehensive churn risk value is:
[0040]
[0041] in, The weights are dynamic, and .
[0042] According to one specific implementation of this disclosure, the multi-source behavioral data includes data from the attendance system, project management system, performance system, and internal office system.
[0043] The employee turnover prediction scheme based on multi-source employee behavior data fusion analysis in this embodiment includes: Step 1, collecting multi-source behavior data of employees within a preset time period, and standardizing the behavior data to construct a basic behavior feature vector for each employee, wherein the basic behavior feature vector is used to describe the employee's behavior state in the current period; Step 2, constructing periodic behavior structure data of employees based on the basic behavior feature vectors of multiple consecutive time periods to describe the overall pattern of employee behavior in the time dimension; Step 3, analyzing the changes in the employee's basic behavior feature vector in adjacent periods and calculating the characteristics of each behavior. Step 4: Based on the statistical characteristics of employee behavior over multiple historical periods, calculate the mutation index of employee behavior to identify abnormal changes in employee behavior within a short period of time, and generate a mutation risk signal when the mutation index exceeds the corresponding threshold; Step 5: Calculate the periodic structure similarity based on the periodic behavioral structure data of employees in adjacent periods, and generate a periodic structure shift risk value accordingly; Step 6: Perform weighted fusion calculation based on the steady-state risk value, the mutation risk value corresponding to the mutation risk signal, and the periodic structure shift risk value to obtain the comprehensive employee turnover risk value, and output the corresponding risk level according to the preset threshold.
[0044] The beneficial effects of the embodiments disclosed herein are as follows:
[0045] 1. It can identify short-cycle behavioral mutations that cannot be detected by existing trend analysis techniques;
[0046] 2. By using periodic behavior templates and self-similarity analysis, the changes in employee behavior structure can be identified;
[0047] 3. By using a dual-risk model to distinguish between long-term risks and sudden risks, the lead time for forecasting can be improved. Attached Figure Description
[0048] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0049] Figure 1 This is a flowchart illustrating an employee turnover prediction method based on the fusion analysis of multi-source employee behavior data, provided in an embodiment of this disclosure. Detailed Implementation
[0050] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.
[0051] The following specific examples illustrate the implementation of this disclosure. Those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.
[0052] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.
[0053] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The illustrations only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.
[0054] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.
[0055] This disclosure provides a method for predicting employee turnover based on the fusion analysis of multi-source employee behavior data. This method can be applied to the process of predicting employee turnover in enterprise management scenarios.
[0056] See Figure 1 This is a flowchart illustrating a method for predicting employee turnover based on the fusion analysis of multi-source employee behavior data, provided in an embodiment of this disclosure. Figure 1 As shown, the method mainly includes the following steps:
[0057] Step 1: Collect multi-source behavioral data of employees within a preset time period, and after standardizing the behavioral data, construct a basic behavioral feature vector for each employee. The basic behavioral feature vector is used to describe the employee's behavioral state in the current period.
[0058] In practice, employee behavior data is collected from multiple systems within the enterprise, including but not limited to attendance systems, project management systems, performance systems, and internal office systems.
[0059] Unlike existing technologies that only collect behavioral data for long-term statistical analysis, this application introduces behavioral information for mutation identification and periodic modeling during the data collection stage, including short-cycle behavioral density changes, system usage interruption information, and behavioral interval change information.
[0060] For example, collect multi-source behavioral data of employees within a preset time period, and perform standardized processing on the behavioral data;
[0061] After standardization, a basic behavioral feature vector is constructed for each employee to describe their behavioral state within the current period, in the form of:
[0062]
[0063] in, Indicates employees In time period Inner Dimensional behavioral feature values, This indicates the total number of feature dimensions.
[0064] Step 2: Based on the basic behavioral feature vectors of multiple consecutive time periods, construct the periodic behavioral structure data of employees to describe the overall pattern of employee behavior in the time dimension.
[0065] In practice, based on time alignment, anomaly removal and normalization of the collected data, this application further divides the employee behavior data into periods according to a preset time period and constructs a continuous periodic behavior dataset for subsequent periodic behavior template construction and mutation analysis.
[0066] This processing method allows employee behavior data to no longer exist as isolated feature values, but rather as a continuous periodic structure.
[0067] In constructing employee behavior feature vectors, this disclosure, in addition to including traditional multidimensional behavior features, further introduces periodic behavior structure features to form an extended behavior feature vector:
[0068] Basic behavioral characteristics are used to describe an employee's long-term work status;
[0069] Cyclical structure features are used to describe the overall pattern changes in employee behavior over a continuous period.
[0070] This feature construction method can reflect whether there is a structural shift in employee behavior patterns, which is something that existing technical solutions that only construct behavioral feature vectors do not have.
[0071] For example, based on basic behavioral feature vectors from multiple consecutive time periods, cyclical behavioral structure data of employees can be constructed to describe the overall pattern changes of employee behavior over time. The construction method is as follows:
[0072]
[0073] in, Indicates the period length.
[0074] Step 3: Analyze the changes in the basic behavioral feature vector of employees in adjacent periods, calculate the rate of change of each behavioral feature, and generate steady-state risk values accordingly.
[0075] In practice, a dual-path analysis mechanism is adopted in the behavior analysis phase:
[0076] 1. Conduct time series analysis on employee behavioral characteristics to calculate long-term behavioral trends;
[0077] 2. Based on continuous cycle behavior templates, calculate the self-similarity between adjacent cycles to determine whether employee behavior patterns have shifted.
[0078] When the periodic self-similarity decreases, it is determined that the employee behavior structure has changed, and this judgment is independent of long-term trend analysis.
[0079] Specifically, the changes in the basic behavioral characteristic vectors of employees within adjacent periods are analyzed, the rate of change of each behavioral characteristic is calculated, and a steady-state risk value is generated based on the rate of change.
[0080]
[0081] .
[0082] Step 4: Based on the statistical characteristics of employee behavior over multiple historical periods, calculate the mutation index of employee behavior to identify abnormal changes in employee behavior in a short period of time, and generate a mutation risk signal when the mutation index exceeds the corresponding threshold.
[0083] In practice, a behavioral mutation identification mechanism is further introduced. By comparing short-cycle behavioral data of employees with periodic statistical characteristics, a mutation index is calculated to identify abnormal changes in employee behavior within a short period of time.
[0084] This mutation identification mechanism does not rely on trend fitting results, but makes judgments based on periodic statistical distributions, thus enabling it to output mutation risk signals as soon as abnormal employee behavior occurs.
[0085] Cyclical structure shift risk is used to identify situations where employees adjust their behavioral structure without significant changes in behavioral intensity, such as changes in work time distribution or task participation methods. Such changes are usually not detectable through single trend analysis.
[0086] For example, based on the statistical characteristics of employee behavior over multiple historical periods, the degree of abrupt change in employee behavior can be calculated to identify abnormal changes in employee behavior within a short period of time. The abrupt change index is calculated as follows:
[0087]
[0088] When the mutation index exceeds the corresponding threshold, a mutation risk signal is generated:
[0089] .
[0090] Step 5: Calculate the cyclical structure similarity based on the cyclical behavior structure data of employees in adjacent cycles, and generate the cyclical structure offset risk value accordingly.
[0091] In practice, the similarity of the cyclical behavioral structures of employees in adjacent cycles is calculated to determine whether a structural shift has occurred in the employee's behavioral patterns, and a cyclical structural shift risk value is generated accordingly. The calculation method is as follows:
[0092]
[0093] .
[0094] Step 6: Based on the steady-state risk value, the mutation risk value corresponding to the mutation risk signal, and the periodic structure shift risk value, perform weighted fusion calculation to obtain the comprehensive employee turnover risk value, and output the corresponding risk level according to the preset threshold.
[0095] In practice, after the above calculations, a weighted fusion calculation can be performed based on the steady-state risk value, the mutation risk value corresponding to the mutation risk signal, and the periodic structure shift risk value to obtain the comprehensive employee turnover risk value, and the corresponding risk level can be output according to the preset threshold.
[0096] .
[0097] The employee turnover prediction method based on multi-source employee behavior data fusion analysis provided in this embodiment identifies structural shifts in employee behavior patterns that traditional trend analysis cannot detect by constructing continuous periodic behavioral structure data and analyzing its self-similarity. It introduces a mutation identification mechanism based on periodic statistical distribution to achieve timely detection of abnormal behavioral changes within short periods, overcoming the problem of delayed response to discontinuous changes in traditional methods. It establishes a dual-risk model that separates steady-state risk, mutation risk, and periodic structural shift risk, and dynamically weights and fuses the three to achieve comprehensive assessment and differentiated early warning of long-term stable causes and short-term sudden signals of employee turnover, thereby improving the lead time and accuracy of prediction.
[0098] It should be understood that the various parts of this disclosure can be implemented in hardware, software, firmware, or a combination thereof.
[0099] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.
Claims
1. A method for predicting employee turnover based on the fusion analysis of multi-source employee behavior data, characterized in that, include: Step 1: Collect multi-source behavioral data of employees within a preset time period, and after standardizing the behavioral data, construct a basic behavioral feature vector for each employee. The basic behavioral feature vector is used to describe the employee's behavioral state in the current period. Step 2: Based on the basic behavioral feature vectors of multiple consecutive time periods, construct the periodic behavioral structure data of employees to describe the overall pattern of employee behavior in the time dimension. Step 3: Analyze the changes in the basic behavioral feature vector of employees in adjacent periods, calculate the rate of change of each behavioral feature, and generate steady-state risk values accordingly. Step 4: Based on the statistical characteristics of employee behavior over multiple historical periods, calculate the mutation index of employee behavior to identify abnormal changes in employee behavior in a short period of time, and generate a mutation risk signal when the mutation index exceeds the corresponding threshold. Step 5: Calculate the cyclical structure similarity based on the cyclical behavior structure data of employees in adjacent cycles, and generate the cyclical structure offset risk value accordingly. Step 6: Based on the steady-state risk value, the mutation risk value corresponding to the mutation risk signal, and the periodic structure shift risk value, perform weighted fusion calculation to obtain the comprehensive employee turnover risk value, and output the corresponding risk level according to the preset threshold.
2. The method according to claim 1, characterized in that, The expression for the basic behavioral feature vector is: in, Indicates employees In time period Inner Dimensional behavioral feature value, This indicates the total number of feature dimensions.
3. The method according to claim 2, characterized in that, The expression for the periodic behavior structure data is: in, Indicates the period length.
4. The method according to claim 3, characterized in that, The expression for calculating the rate of change of each behavioral feature is as follows: in, This indicates an extremely small positive number that prevents the denominator from being zero; The expression for the steady-state risk value is: in, This represents the normalization function, used to map risk values to a preset range. This represents the weight coefficient of the k-th behavioral feature in the steady-state risk calculation.
5. The method according to claim 4, characterized in that, The expression for the mutation index is: in, Let represent the mean of employee i's k-th behavioral characteristic within a historical period. This represents the standard deviation of employee i's k-th behavioral characteristic within a historical period; The expression for the mutation risk signal is: in, This represents the mutation determination threshold corresponding to the k-th behavioral feature. This indicates an indicator function that takes the value 1 when the condition is true and 0 otherwise.
6. The method according to claim 5, characterized in that, The expression for the periodic structure similarity is: in, This represents the inner product operation. Represents norm operations; The expression for the risk value of the periodic structure offset is: 。 7. The method according to claim 6, characterized in that, The expression for the comprehensive churn risk value is: in, The weights are dynamic, and .
8. The method according to claim 1, characterized in that, The multi-source behavioral data includes data from attendance systems, project management systems, performance systems, and internal office systems.