Working hour management system

Abstract

The invention discloses a man-hour management system, and the system comprises a man-hour input module which is used for inputting the data related to the man-hour of each person; the man-hour calculation module is used for calculating the effective man-hour of each person according to the man-hour related data of each person, the effective man-hour = [delta t * omega (t)] * (1 + xi), delta t is original man-hour data, omega (t) is a dynamic weight coefficient, and xi is a random disturbance factor; and the analysis module is used for constructing multi-dimensional data visualization analysis.

Description

Technical Field

[0001] The present invention relates to the field of work time management, and in particular to a work time management system. Background Art

[0002] With the increasing complexity of modern enterprise management and the deepening of digital transformation, traditional work time management methods can no longer meet the urgent needs of enterprises for human resource management and project efficiency analysis. The existing work time management system has limitations:

[0003] The problem of single work time calculation Traditional work time management systems usually use a simple linear calculation method, which only performs mechanical calculations based on the start and end times, and cannot reflect the actual intensity and complexity of the work. This calculation method ignores the diversity of work scenarios and makes it difficult to accurately quantify the actual work contribution of employees. Summary of the invention

[0004] To achieve the above objectives and other related objectives, the present invention discloses a work time management system, comprising: Working time entry module, used to enter each person's working time related data; The working time calculation module is used to calculate the effective working time of each person based on the data related to working time of each person, where effective working time = [Δt×Ω(t)]×(1+ξ), where Δt is the original working time data, Ω(t) is the dynamic weight coefficient, and ξ is the random disturbance factor; Analysis module, used to build multi-dimensional data visualization analysis.

[0005] Furthermore, the input of each person's working time-related data includes manual individual input and batch input.

[0006] Further, the Ω(t) includes: Ω(t)=Σ[α(τ)×β(ρ)×γ(ε)×δ(ν)]; Among them, α(τ) is the time type adaptability factor; β(ρ) is the work intensity response function; γ(ε) is the project complexity entropy increase coefficient; δ(ν) is the individual performance correlation function.

[0007] Further, the α(τ) includes: α(τ)=exp(sin(π×w(τ)))×(1+tanh(w(τ)-1)); Among them, τ represents the discrete variable of time type, and w(τ) represents the weight of time type; Working period: w(τ)=1.0; Non-working hours: w(τ)=1.5; Legal holidays: w(τ)=2.0.

[0008] Furthermore, the β(ρ) includes: β(ρ)=e^(-|ln(1+ρ)-μ|^γ)×(1+tanh(κ×(ρ-θ))); Among them, ρ is the work intensity index, μ is the response center parameter, which represents the logarithmic transformation point of the ideal work intensity, γ is the attenuation curvature parameter, κ is the nonlinear gain parameter, and θ is the work intensity threshold, which defines the critical point of the work intensity.

[0009] Further, the γ(ε) includes: γ(ε)=H(P)+Ψ(C)×ln(1+σ(P)); Among them, H(P) is the Shannon entropy basis term; H(P)=-Σ(pi×log(pi)); pi is the probability distribution of complexity of different dimensions of the project; Ψ(C) is the complexity weighting factor, where: Ψ(C)=tanh(∑ωi×ci); ωi is the weight coefficient of each complexity dimension; ci is the complexity index of each dimension; σ(P) is the variance term of the probability distribution; σ(P)=√[Σ(pi-μ)² / n]; μ is the mean of the probability distribution; n is the number of dimensions of the probability distribution.

[0010] Further, the δ(ν) includes: δ(ν)=(v1·v2) / (||v1||×||v2||); v1 and v2 are the individual historical performance vectors at different times.

[0011] Furthermore, the construction of multi-dimensional data visualization analysis includes: Project dimension analysis, including: working hours summary, proportion statistics, and time series data analysis; Personnel dimension analysis, including: personal working hours input and cross-project work distribution.

[0012] By adopting the above technical solutions, we can break the static and linear calculation mode of traditional work time management, introduce a multi-dimensional and nonlinear dynamic modeling method, improve the accuracy and adaptability of work time calculation, and support more intelligent and personalized work time management. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0014] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

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

[0016] Reference Figure 1 The embodiment of the present invention provides a work time management system, which is characterized by comprising: Working time entry module, used to enter each person's working time related data; The working time calculation module is used to calculate the effective working time of each person based on the data related to working time of each person, where effective working time = [Δt×Ω(t)]×(1+ξ), where Δt is the original working time data, Ω(t) is the dynamic weight coefficient, and ξ is the random disturbance factor; Analysis module, used to build multi-dimensional data visualization analysis.

[0017] The input of each person's working time-related data includes manual individual input and batch input.

[0018] The Ω(t) includes: Ω(t)=Σ[α(τ)×β(ρ)×γ(ε)×δ(ν)]; Among them, α(τ) is the time type adaptability factor; β(ρ) is the work intensity response function; γ(ε) is the project complexity entropy increase coefficient; δ(ν) is the individual performance correlation function.

[0019] The above-mentioned methods realize multi-dimensional and dynamic adaptation of working time calculation, break the limitations of traditional linear working time calculation, accurately reflect the actual value of different work scenarios, introduce nonlinear modeling, improve the accuracy and flexibility of working time calculation, and dynamically adjust the working time weight according to the actual working environment.

[0020] The α(τ) includes: α(τ)=exp(sin(π×w(τ)))×(1+tanh(w(τ)-1)); Among them, τ represents the discrete variable of time type, and w(τ) represents the weight of time type; Working period: w(τ)=1.0; Non-working hours: w(τ)=1.5; Legal holidays: w(τ)=2.0.

[0021] The β(ρ) includes: β(ρ)=e^(-|ln(1+ρ)-μ|^γ)×(1+tanh(κ×(ρ-θ))); Among them, ρ is the work intensity index, μ is the response center parameter, which represents the logarithmic transformation point of the ideal work intensity, γ is the attenuation curvature parameter, κ is the nonlinear gain parameter, and θ is the work intensity threshold, which defines the critical point of the work intensity.

[0022] As mentioned above, through the design of composite functions, accurate quantification of work intensity is achieved, and the introduction of multiple parameters enables fine adjustment of work intensity, which can accurately reflect the impact of changes in work intensity on working hours and provide an objective basis for work intensity evaluation.

[0023] The γ(ε) includes: γ(ε)=H(P)+Ψ(C)×ln(1+σ(P)); Among them, H(P) is the Shannon entropy basis term; H(P)=-Σ(pi×log(pi)); pi is the probability distribution of complexity of different dimensions of the project; Ψ(C) is the complexity weighting factor, where: Ψ(C)=tanh(∑ωi×ci); ωi is the weight coefficient of each complexity dimension; ci is the complexity index of each dimension; σ(P) is the variance term of the probability distribution; σ(P)=√[Σ(pi-μ)² / n]; μ is the mean of the probability distribution; n is the number of dimensions of the probability distribution.

[0024] As mentioned above, by quantifying project complexity through information entropy theory and introducing complexity weighting factors, multi-dimensional complexity assessment is achieved. By considering the variance of probability distribution, the accuracy of complexity assessment is improved, and a scientific complexity assessment tool is provided for project management.

[0025] The δ(ν) includes: δ(ν)=(v1·v2) / (||v1||×||v2||); v1 and v2 are the individual historical performance vectors at different times.

[0026] As mentioned above, through the calculation of cosine similarity, objective quantification of performance evaluation is achieved, historical performance data is considered, a continuous performance evaluation mechanism is provided, personalized performance appraisal is supported, and the fairness and accuracy of performance evaluation are promoted.

[0027] The above-mentioned method of calculating working hours breaks the static and linear calculation mode of traditional working hour management, introduces a multi-dimensional and nonlinear dynamic modeling method, improves the accuracy and adaptability of working hour calculation, and supports more intelligent and personalized working hour management.

[0028] Building multi-dimensional data visualization analysis includes: Project dimension analysis, including: working hours summary, proportion statistics, and time series data analysis; Personnel dimension analysis, including: personal working hours input and cross-project work distribution.

[0029] Specifically include: Project category, including: project working hours summary and percentage of total working hours in the statistical interval (table); project working hours summary (pie chart, bar chart, rectangular drill-down chart); project working hours time series data summary (daily data. Pie chart, line chart); project, personnel, working hours data summary (bubble chart); single project main task, subtask working hours data summary (pie chart); single project main task working hours time series chart (daily data, pie chart, line chart); single project personnel working hours time series chart (daily data, pie chart, line chart).

[0030] Personnel category, including: summary of personnel working hours and percentage of working hours in the statistical period (table); statistics of personnel working hours by project dimension (pie chart); time series chart of personnel working hours in different projects (daily data, pie chart, line chart).

[0031] Other categories include: personnel working hours statistics chart (bar chart, which will have standard working hours scale, which can be used to determine the degree of work saturation, whether there are any missing working hours, etc.).

[0032] The above provides a variety of data display forms, including pie charts, bar charts, line charts, etc., supports multi-angle analysis of project dimensions and personnel dimensions, realizes in-depth mining and value discovery of working hour data, provides intuitive data support for management decisions, and facilitates the discovery of abnormal working hours and work saturation problems.

[0033] In actual use, this system has significantly improved the scientificity and accuracy of work time management, greatly improved the analysis efficiency of work time data, provided strong support for enterprise management decisions, promoted the refinement and intelligence of human resource management, and realized the transformation of work time management from passive recording to active analysis.

[0034] Those skilled in the art will appreciate that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined.

[0035] For the method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0036] It can be known from the description of the above implementation modes that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute the methods described in the various implementation modes of the present application or certain parts of the implementation modes.

[0037] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A work time management system, characterized in that: include: Working time entry module, used to enter each person's working time related data; The working time calculation module is used to calculate the effective working time of each person based on the data related to working time of each person, where effective working time = [Δt×Ω(t)]×(1+ξ), where Δt is the original working time data, Ω(t) is the dynamic weight coefficient, and ξ is the random disturbance factor; Analysis module, used to build multi-dimensional data visualization analysis.

2. A work time management system according to claim 1, characterized in that: The input of each person's working time-related data includes manual individual input and batch input.

3. A work time management system according to claim 1, characterized in that: The Ω(t) includes: Ω(t)=Σ[α(τ)×β(ρ)×γ(ε)×δ(ν)]; Among them, α(τ) is the time type adaptability factor; β(ρ) is the work intensity response function; γ(ε) is the project complexity entropy increase coefficient; δ(ν) is the individual performance correlation function.

4. A work time management system according to claim 3, characterized in that: The α(τ) includes: α(τ)=exp(sin(π×w(τ)))×(1+tanh(w(τ)-1)); Among them, τ represents the discrete variable of time type, and w(τ) represents the weight of time type; Working period: w(τ)=1.0; Non-working hours: w(τ)=1.5; Legal holidays: w(τ)=2.

0.

5. A work time management system according to claim 3, characterized in that: The β(ρ) includes: β(ρ)=e^(-|ln(1+ρ)-μ|^γ)×(1+tanh(κ×(ρ-θ))); Among them, ρ is the work intensity index, μ is the response center parameter, which represents the logarithmic transformation point of the ideal work intensity, γ is the attenuation curvature parameter, κ is the nonlinear gain parameter, and θ is the work intensity threshold, which defines the critical point of the work intensity.

6. A work time management system according to claim 3, characterized in that: The γ(ε) includes: γ(ε)=H(P)+Ψ(C)×ln(1+σ(P)); Among them, H(P) is the Shannon entropy basis term; H(P)=-Σ(pi×log(pi)); pi is the probability distribution of complexity of different dimensions of the project; Ψ(C) is the complexity weighting factor, where: Ψ(C)=tanh(∑ωi×ci); ωi is the weight coefficient of each complexity dimension; ci is the complexity index of each dimension; σ(P) is the variance term of the probability distribution; σ(P)=√[Σ(pi-μ)² / n]; μ is the mean of the probability distribution; n is the number of dimensions of the probability distribution.

7. A work time management system according to claim 3, characterized in that: The δ(ν) includes: δ(ν)=(v1·v2) / (||v1||×||v2||); v1 and v2 are the individual historical performance vectors at different times.

8. A work time management system according to claim 1, characterized in that: The construction of multi-dimensional data visualization analysis includes: Project dimension analysis, including: working hours summary, proportion statistics, and time series data analysis; Personnel dimension analysis, including: personal working hours input and cross-project work distribution.

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