Human resource management system based on internet of things

Abstract

This invention discloses an Internet of Things-based human resource management system, comprising: a modular cabinet with symmetrically arranged main water inlet and main water outlet pipes inside; multiple heat exchange plates, each with an internal cavity, one end of which is fixedly connected to a secondary water inlet pipe, and the other end of which is fixedly connected to a secondary water outlet pipe. The secondary water inlet pipe is connected to the main water inlet pipe, and the secondary water outlet pipe is connected to the main water outlet pipe. A connecting pipe is also provided between adjacent heat exchange plates, and the multiple connecting pipes are arranged alternately. Multiple first solenoid valves are installed in the main water inlet pipe, second solenoid valves are installed in the secondary water outlet pipes, and third solenoid valves are installed in the connecting pipes; and multiple surrounding plates, which are slidably connected to the corresponding heat exchange plates. The outer wall of the surrounding plates is rotatably connected to the cabinet. This invention can exchange heat with the water temperature in underground tap water pipes, achieving mutual benefit and environmental protection.

Description

Technical Field

[0001] This invention relates to the field of human resources technology, and in particular to a human resources management system based on the Internet of Things. Background Technology

[0002] Traditional human resource management systems typically rely on historical experience or static planning for human resource allocation, lacking precise analysis of real-time production tasks, output-profit conversion rates, and dynamic human resource needs. As businesses grow and business complexity increases, traditional methods struggle to adapt to rapidly changing production demands, easily leading to human resource redundancy or shortages, thus impacting overall operational efficiency.

[0003] In existing technologies, some systems attempt to combine data analysis for manpower demand forecasting, but the following problems still exist: Data collection lag: Reliance on manual entry or periodic summarization makes it impossible to obtain production task and manpower efficiency data in real time, resulting in decision-making delays; The analysis is limited to a single dimension: it only makes simple calculations based on task volume or labor costs, without comprehensively considering key indicators such as output value and profit conversion rate, making it difficult to optimize resource allocation; Poor dynamic adaptability: It cannot respond to sudden tasks or personnel changes in real time, and cross-departmental scheduling relies on manual intervention, resulting in low efficiency.

[0004] The development of Internet of Things (IoT) technology has made real-time data collection and analysis possible, but it has not yet been fully applied to intelligent decision-making in human resource management. Therefore, there is an urgent need for an IoT-based human resource management system to achieve dynamic and accurate forecasting and optimized scheduling of human resource needs. Summary of the Invention

[0005] The purpose of this invention is to provide an Internet of Things-based human resource management system to solve the problems mentioned in the background art.

[0006] The technical solution of this invention is: an Internet of Things-based human resource management system, configured to: continuously acquire departmental work task data, perform conversion rate analysis based on output value and profit to form departmental production efficiency data; collect historical human resource data of the department and perform human resource efficiency analysis to form current human resource efficiency data of the department; perform human resource demand analysis based on the departmental production efficiency data and the departmental current human resource efficiency data to form human resource demand analysis data; acquire dynamic human resource demand information of the department and, in conjunction with the human resource demand analysis data, establish departmental human resource demand data.

[0007] Preferably, continuously acquiring departmental work task data and performing conversion rate analysis based on output value and profit to form departmental production efficiency data includes: setting a production efficiency analysis period, continuously acquiring large departmental work task data within the production efficiency analysis period to form departmental periodic production efficiency data; extracting production task volume information from the departmental periodic production efficiency data and performing task volume change analysis to form departmental periodic task volume change data; extracting production output value information from the departmental periodic production efficiency data and performing output value efficiency analysis to form departmental periodic output value efficiency data; and extracting production profit information from the departmental periodic production efficiency data and performing profit efficiency analysis to form departmental periodic profit efficiency data.

[0008] Preferably, the production task quantity information is extracted from the departmental periodic production efficiency data, and task quantity change analysis is performed to form departmental periodic task quantity change data, including: determining the amount of data processed in different production efficiency analysis periods to form departmental periodic task data quantity; arranging the departmental periodic task data quantity in time dimension order to form task quantity periodic change data.

[0009] Preferably, the production output value information is extracted from the departmental periodic production efficiency data, and output value efficiency analysis is performed to form departmental periodic output value efficiency data, including: determining the output value data in different production efficiency analysis periods to form departmental periodic output value data; calculating the average output value corresponding to the unit task volume to form departmental periodic unit output value efficiency data; and analyzing the trend changes of the departmental periodic output value data in the time dimension to form output value efficiency trend data.

[0010] Preferably, the production profit information is extracted from the departmental periodic production efficiency data, and profit efficiency analysis is performed to form departmental periodic profit efficiency data, including: determining the profit data in different production efficiency analysis periods to form departmental periodic profit data; calculating the average profit corresponding to the unit task volume to form departmental periodic unit profit efficiency data; and analyzing the trend changes of the departmental periodic profit data in the time dimension to form profit efficiency trend data.

[0011] Preferably, historical human resource data of the department is collected and human resource efficiency analysis is performed to form the department's current human resource efficiency data, including: obtaining human resource input data in the historical period to form historical human resource input data of the department; combining the corresponding historical production efficiency data to calculate per capita output value and per capita profit to form historical human resource efficiency indicators; analyzing the changing trend of historical human resource efficiency indicators to predict the human resource efficiency benchmark value for the current period.

[0012] Preferably, based on the department's production efficiency data and the department's existing human resource efficiency data, a human resource demand analysis is performed to form human resource demand analysis data, including: predicting future task demand based on the department's periodic task volume change data; determining the optimal human resource allocation ratio by combining the department's periodic output efficiency data and the department's periodic profit efficiency data; and generating human resource demand suggestions that meet production efficiency targets based on historical human resource efficiency indicators and predicted task volume.

[0013] Preferably, the process involves acquiring dynamic human resource demand information from departments and, in conjunction with the aforementioned human resource demand analysis data, establishing departmental human resource demand data. This includes: receiving real-time information on temporary task adjustments or sudden human resource demand; dynamically revising the human resource demand prediction model based on the human resource demand analysis data; and outputting adjusted short-term, medium-term, and long-term human resource demand plans.

[0014] Preferably, it also includes: collecting employee work status data in real time through IoT devices, including work hour utilization, task completion progress and work intensity indicators; comparing and analyzing the real-time work status data with manpower demand data, and dynamically optimizing manpower scheduling strategies.

[0015] Preferred dynamic optimization strategies for manpower allocation include: identifying departments with surplus or shortage of manpower and generating cross-departmental manpower allocation suggestions; adjusting task allocation priorities based on real-time production efficiency to optimize human resource utilization.

[0016] This invention provides an improved Internet of Things (IoT)-based human resource management system, which has the following improvements and advantages compared to existing technologies: Real-time data-driven decision-making: By continuously collecting data such as production tasks, output value, and profits through IoT devices and combining them with historical human resource efficiency analysis, a dynamic human resource demand model is formed, avoiding the lag of manual data entry and improving the timeliness of decision-making. Multidimensional efficiency analysis optimizes resource allocation: It not only analyzes changes in workload, but also combines indicators such as output efficiency and profit efficiency to calculate the contribution value per unit of manpower, determine the optimal manpower allocation ratio, and avoid resource waste or shortage. Dynamically adapt to business changes: Receive sudden tasks or manpower adjustment needs in real time, dynamically revise the prediction model, and output short-term, medium-term and long-term manpower plans to enhance system flexibility; Intelligent scheduling improves utilization: By monitoring employee work status in real time (such as time utilization and task progress), it identifies whether there is a surplus or shortage of manpower in departments, automatically generates cross-departmental allocation suggestions, and optimizes the overall human resource utilization. Trend forecasting supports strategic planning: Based on historical data and real-time analysis, it predicts future task volume and manpower demand trends, providing data support for the company's long-term human resources strategy; In summary, this system combines IoT technology with intelligent analytics to achieve more precise, dynamic, and automated human resource management, significantly improving enterprise operational efficiency and competitiveness. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0018] The components of the embodiments of the invention typically described and shown herein can be arranged and designed in a variety of different configurations. Therefore, the detailed description of embodiments of the invention provided below is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention.

[0019] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," indicating orientation or positional relationships, are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0021] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0022] This invention provides an Internet of Things (IoT)-based human resource management system, configured to: continuously acquire departmental work task data, perform conversion rate analysis based on output value and profit to form departmental production efficiency data; collect historical human resource data of the department and perform human resource efficiency analysis to form current human resource efficiency data of the department; perform human resource demand analysis based on departmental production efficiency data and current human resource efficiency data of the department to form human resource demand analysis data; acquire dynamic human resource demand information of the department and combine it with human resource demand analysis data to establish departmental human resource demand data.

[0023] Continuously acquire departmental work task data, conduct conversion rate analysis based on output value and profit, and generate departmental production efficiency data. This includes: setting a production efficiency analysis cycle, continuously acquiring departmental work task data within the production efficiency analysis cycle, generating departmental cycle production efficiency data; extracting production task volume information from the departmental cycle production efficiency data and conducting task volume change analysis, generating departmental cycle task volume change data; extracting production output value information from the departmental cycle production efficiency data and conducting output value efficiency analysis, generating departmental cycle output value efficiency data; and extracting production profit information from the departmental cycle production efficiency data and conducting profit efficiency analysis, generating departmental cycle profit efficiency data.

[0024] Extract production task volume information from departmental periodic production efficiency data and perform task volume change analysis to form departmental periodic task volume change data, including: determining the amount of data processed in different production efficiency analysis periods to form departmental periodic task data volume; arranging the departmental periodic task data volume in chronological order to form task volume periodic change data.

[0025] Extract production output information from departmental periodic production efficiency data and perform output efficiency analysis to form departmental periodic output efficiency data. This includes: determining output data within different production efficiency analysis periods to form departmental periodic output data; calculating the average output value corresponding to a unit of task to form departmental periodic unit output efficiency data; and analyzing the trend changes of departmental periodic output data from a time perspective to form output efficiency trend data.

[0026] Extract production profit information from departmental periodic production efficiency data and perform profit efficiency analysis to form departmental periodic profit efficiency data. This includes: determining profit data within different production efficiency analysis periods to form departmental periodic profit data; calculating the average profit per unit of work to form departmental periodic unit profit efficiency data; and analyzing the trend changes of departmental periodic profit data over time to form profit efficiency trend data.

[0027] Collect historical human resource data for the department and conduct human resource efficiency analysis to form current human resource efficiency data for the department. This includes: obtaining human resource input data within the historical period to form historical human resource input data for the department; combining the corresponding historical production efficiency data to calculate per capita output and per capita profit to form historical human resource efficiency indicators; analyzing the changing trends of historical human resource efficiency indicators to predict the baseline value of human resource efficiency for the current period.

[0028] Based on departmental production efficiency data and existing human resource efficiency data, a human resource demand analysis is conducted to generate human resource demand analysis data, including: predicting future task demand based on departmental cyclical task volume change data; determining the optimal human resource allocation ratio by combining departmental cyclical output efficiency data and departmental cyclical profit efficiency data; and generating human resource demand recommendations to meet production efficiency targets based on historical human resource efficiency indicators and predicted task volume.

[0029] Obtain dynamic human resource demand information from departments and, in conjunction with human resource demand analysis data, establish departmental human resource demand data, including: receiving real-time information on temporary task adjustments or sudden human resource demand; dynamically revising the human resource demand forecasting model based on human resource demand analysis data; and outputting adjusted short-term, medium-term, and long-term human resource demand plans.

[0030] Real-time data on employee work status is collected through IoT devices, including work hour utilization, task completion progress, and work intensity indicators. The real-time work status data is compared and analyzed with manpower demand data to dynamically optimize manpower scheduling strategies.

[0031] Dynamic optimization of human resource allocation strategies includes: identifying departments with surplus or shortage of manpower and generating cross-departmental manpower allocation suggestions; adjusting task allocation priorities based on real-time production efficiency to optimize human resource utilization.

[0032] The foregoing description enables those skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A human resource management system based on the Internet of Things, characterized in that, It is configured to: continuously acquire departmental work task data, perform conversion rate analysis based on output value and profit to form departmental production efficiency data; collect historical human resource data of the department and perform human resource efficiency analysis to form current human resource efficiency data of the department; perform human resource demand analysis based on the departmental production efficiency data and the departmental current human resource efficiency data to form human resource demand analysis data; acquire dynamic human resource demand information of the department and combine it with the human resource demand analysis data to establish departmental human resource demand data.

2. The Internet of Things-based human resource management system according to claim 1, characterized in that, Continuously acquire departmental work task data, conduct conversion rate analysis based on output value and profit, and generate departmental production efficiency data. This includes: setting a production efficiency analysis period, continuously acquiring departmental work task data within the production efficiency analysis period, generating departmental periodic production efficiency data; extracting production task volume information from the departmental periodic production efficiency data and conducting task volume change analysis, generating departmental periodic task volume change data; extracting production output value information from the departmental periodic production efficiency data and conducting output value efficiency analysis, generating departmental periodic output value efficiency data; and extracting production profit information from the departmental periodic production efficiency data and conducting profit efficiency analysis, generating departmental periodic profit efficiency data.

3. The Internet of Things-based human resource management system according to claim 2, characterized in that, Extract production task volume information from departmental periodic production efficiency data and perform task volume change analysis to form departmental periodic task volume change data, including: determining the amount of data processed in different production efficiency analysis periods to form departmental periodic task data volume; arranging the departmental periodic task data volume in time dimension order to form task volume periodic change data.

4. The Internet of Things-based human resource management system according to claim 2, characterized in that, Extract production output information from departmental periodic production efficiency data and perform output efficiency analysis to form departmental periodic output efficiency data, including: determining output data within different production efficiency analysis periods to form departmental periodic output data; calculating the average output value corresponding to unit task volume to form departmental periodic unit output efficiency data; and analyzing the trend changes of the departmental periodic output data in the time dimension to form output efficiency trend data.

5. The Internet of Things-based human resource management system according to claim 2, characterized in that, Extract production profit information from departmental periodic production efficiency data and perform profit efficiency analysis to form departmental periodic profit efficiency data. This includes: determining profit data within different production efficiency analysis periods to form departmental periodic profit data; calculating the average profit per unit of work to form departmental periodic unit profit efficiency data; and analyzing the trend changes of the departmental periodic profit data over time to form profit efficiency trend data.

6. The Internet of Things-based human resource management system according to claim 1, characterized in that, Collect historical human resource data for the department and conduct human resource efficiency analysis to form current human resource efficiency data for the department. This includes: obtaining human resource input data within the historical period to form historical human resource input data for the department; combining the corresponding historical production efficiency data to calculate per capita output and per capita profit to form historical human resource efficiency indicators; analyzing the changing trends of historical human resource efficiency indicators to predict the baseline value of human resource efficiency for the current period.

7. The Internet of Things-based human resource management system according to claim 1, characterized in that, Based on the department's production efficiency data and existing human resource efficiency data, a human resource demand analysis is conducted to generate human resource demand analysis data, including: predicting future task demand based on the department's cyclical task volume change data; determining the optimal human resource allocation ratio by combining the department's cyclical output efficiency data and cyclical profit efficiency data; and generating human resource demand recommendations that meet production efficiency targets based on historical human resource efficiency indicators and predicted task volume.

8. The Internet of Things-based human resource management system according to claim 1, characterized in that, Obtain dynamic human resource demand information from departments and, in conjunction with the aforementioned human resource demand analysis data, establish departmental human resource demand data, including: receiving real-time information on temporary task adjustments or sudden human resource demand; dynamically revising the human resource demand forecasting model based on the human resource demand analysis data; and outputting adjusted short-term, medium-term, and long-term human resource demand plans.

9. The Internet of Things-based human resource management system according to claim 1, characterized in that, Also includes: Real-time data on employee work status is collected through IoT devices, including work hour utilization, task completion progress, and work intensity indicators. The real-time work status data is compared and analyzed with manpower demand data to dynamically optimize manpower scheduling strategies.

10. The Internet of Things-based human resource management system according to claim 9, characterized in that, Dynamic optimization of human resource allocation strategies includes: identifying departments with surplus or shortage of manpower and generating cross-departmental manpower allocation suggestions; adjusting task allocation priorities based on real-time production efficiency to optimize human resource utilization.