A human resources intelligent allocation optimization system

Abstract

The present invention relates to the field of human resources technology, and specifically to a human resources intelligent allocation and optimization system, which includes a career analysis and prediction module, a career path planning module, an emergency deployment management module, and a human resources optimization module. The present invention realizes the accurate prediction and dynamic deployment of career paths by integrating the career history and skill data of medical staff. The precise career transition point analysis makes talent matching more accurate and reduces resource waste. The career training recommendations customized for the hospital's business goals improve the pertinence and effectiveness of training, optimize the talent training process, and improve the training effect of professional talents. In an emergency, the system can respond in time through real-time data monitoring and conduct rapid and effective manpower deployment to ensure that the hospital can maintain efficient operation in the face of emergency medical needs. Through continuous data updates and feedback, the human resources allocation strategy is continuously optimized, which improves the hospital's adaptability and management efficiency in a changing environment.

Description

Technical Field

[0001] The present invention relates to the technical field of human resources, and in particular to a human resources intelligent allocation optimization system. Background Art

[0002] Human resource technology mainly involves the use of technological means to improve and optimize various aspects of human resource management, including recruitment, placement, training, performance evaluation and employee turnover management. With the development of information technology and artificial intelligence, this field has gradually introduced machine learning, data analysis and automation tools to improve the efficiency and accuracy of human resource decision-making, analyze large amounts of data, predict employee performance, optimize recruitment processes, and develop more effective employee development plans, aiming to reduce subjectivity and randomness in human resource management and improve operational efficiency.

[0003] Among them, the human resources intelligent allocation optimization system refers to the use of intelligent means to optimize and improve the efficiency of human resources allocation in the organization, using data analysis, machine learning and optimization algorithms to analyze employees' skills, experience and performance data, so as to automatically recommend the most suitable candidates to the appropriate positions. Its main uses include improving the efficiency of human resources allocation and ensuring that each position is filled by the most suitable talents. At the same time, it also helps management monitor and adjust human resource allocation to respond to rapidly changing market demands and organizational goals, and achieve higher operational efficiency and employee satisfaction.

[0004] Existing technologies rely on traditional management models and are often inefficient when processing large-scale and complex data. Especially in emergency situations, it is difficult to achieve rapid and effective human resource deployment. In addition, traditional technologies lack targeted and forward-looking career path planning and are unable to accurately predict and plan employees' career development, resulting in insufficient connection between talent training and market demand, reducing employee job satisfaction and the hospital's human resource management efficiency, often leading to talent loss, increased recruitment costs, and insufficient response capabilities to public health emergencies, causing hospitals to fail to fully utilize existing talent resources. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a human resources intelligent allocation optimization system.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solution: a human resources intelligent allocation optimization system, the system comprising:

[0007] The career analysis and prediction module updates the medical staff information set based on their historical work records and skill growth information, predicts their career development direction, identifies potential career transition points, and obtains a career development prediction summary;

[0008] The career path planning module analyzes the matching degree between the current career path and market demand based on the career development forecast summary and the hospital's business needs and goals, plans a career development path that meets the hospital's needs, and obtains a recommended list of career training;

[0009] The emergency deployment management module monitors the hospital's current operational needs and human resource status based on the recommended list of professional training, collects data on the availability and emergency needs of medical staff in real time, and dynamically deploys human resources to obtain emergency resource deployment records;

[0010] The human resource optimization module continuously collects and updates the hospital's human resource and demand information based on the emergency resource allocation records, monitors career planning and personnel allocation in real time based on multiple hospital campuses, and makes continuous adjustments based on feedback information to obtain an overview of human resource adjustments.

[0011] The present invention is improved in that the steps of updating the medical staff information set are specifically as follows:

[0012] Based on the database of multiple hospital campuses, we extracted the historical work records and skill growth information of medical staff, performed data cleaning and verification, removed errors or duplicate records, and obtained a purified data set;

[0013] Based on the purified data set, it is added to the personnel information, and the timeliness and integrity of the personnel information are verified to obtain an updated medical staff information set.

[0014] The present invention is improved in that the steps of obtaining the career development forecast summary are specifically as follows:

[0015] Performing data analysis on the medical staff information set, extracting career progression and skill improvement data, and obtaining career trend analysis records;

[0016] Based on the career trend analysis records, career development direction is predicted using the formula:

[0017]

[0018] The development direction prediction results are obtained, among which, represents the predicted occupational status at time point t, Y(t-1) represents the occupational status in the previous period, X(t) represents the current skill score at time point t, α is the influence weight of the occupational status in the previous period, β is the influence weight of the current skill score, and γ is a constant term;

[0019] Based on the development direction prediction results, compare them with personal career aspirations and department needs, identify potential career transition points, and obtain a career development prediction summary.

[0020] The present invention is improved in that the steps of analyzing the matching degree between the current career path and market demand are specifically as follows:

[0021] Based on the career development forecast summary, the hospital's business needs and goals are collected to obtain a dataset of expected careers and skills;

[0022] Based on the expected occupation and skills dataset, the formula is used:

[0023]

[0024] Conduct quantitative analysis of career paths and market demand to obtain the matching score of each path, where Match i is the market matching score of the i-th career path, w k represents the criticality of the kth business requirement, R ik represents the responsiveness of the i-th career path to the k-th business requirement, and n is the total number of business requirements;

[0025] Based on the matching score of each path, career paths associated with market demand are identified to obtain matching analysis results.

[0026] The present invention is improved in that the steps for obtaining the recommended list of vocational training are specifically as follows:

[0027] Based on the matching degree, the skills and career directions that need to be developed are screened to obtain screened career path data;

[0028] Based on the screened career path data, combined with the personnel development intentions, according to the hospital's development plan and immediate training resources, a career development path that meets the hospital's needs is planned to obtain a recommended career training list.

[0029] The present invention is improved in that the steps of monitoring the operational demand and human resource status are specifically as follows:

[0030] Based on the recommended list of job training, the current operational needs and human resource allocation status of multiple departments in the hospital are collected to obtain an operational and resource status dataset;

[0031] Based on the operational and resource status data sets, analyze trends and patterns in the data, evaluate the real-time status of resource allocation matching demand, and obtain resource usage records;

[0032] Based on the resource usage records, the formula is used:

[0033]

[0034] Evaluate the optimization direction of resource allocation and obtain optimization adjustment information, where OH(i) represents the resource optimization index of the i-th department, β His the optimization coefficient, UH j is the urgency weight of the jth resource, EH ij is the efficiency score of the i-th department on the j-th resource, PH i is the number of human resources in the i-th department, and m is the total number of resource types.

[0035] The present invention is improved in that the steps of obtaining the emergency resource allocation record are specifically as follows:

[0036] Collect real-time availability data of medical staff, including current attendance, holidays, and manpower dynamics of key events, to obtain real-time human resources data;

[0037] Based on the real-time human resources data, the formula is adopted:

[0038]

[0039] Analyze and determine the manpower requirements of key departments and emergency medical tasks, where DA(i) represents the resource allocation index of the i-th department, vx z is the urgency weight of the zth task, ax iz is the available manpower of the i-th department for the z-th task, TX i is the total manpower of the i-th department, n X is the total number of tasks;

[0040] Based on the manpower demand analysis results, human resources are dynamically deployed for manpower-shortage departments and emergency medical tasks, and emergency resource deployment records are obtained.

[0041] The present invention is improved in that the steps for obtaining the human resources adjustment overview are specifically as follows:

[0042] Based on the emergency resource allocation records, continuously collect and update the hospital's human resources and demand information, using the formula:

[0043]

[0044] Calculate the efficiency and satisfaction of human resource allocation in multiple campuses and obtain the human resource adjustment result RI overall , where pI i is the human resource allocation ratio of the i-th hospital area, fI i is the feedback score, indicating the satisfaction with human resource allocation, nI is the total number of campuses, and k is the total number of campuses participating in the evaluation;

[0045] Based on the human resource adjustment results, career planning and personnel allocation are monitored in real time, dynamic human resource adjustments are made across campuses, and the flow of human resources between multiple hospitals is optimized to obtain an overview of human resource adjustments.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are:

[0047] In the present invention, by integrating the career history and skill data of medical staff, accurate prediction and dynamic deployment of career paths are achieved, which significantly improves the efficiency of human resource allocation. Accurate analysis of career transition points makes talent matching more accurate and reduces resource waste. Career training recommendations customized for the hospital's business goals improve the pertinence and effectiveness of training, optimize the talent training process, and improve the training effect of professional talents. In emergency situations, the system can respond in time through real-time data monitoring and conduct rapid and effective manpower deployment to ensure that the hospital can maintain efficient operation in the face of emergency medical needs. Through continuous data updates and feedback, the human resource allocation strategy is continuously optimized, which improves the hospital's adaptability and management efficiency in a changing environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a system flow chart of the present invention;

[0049] Figure 2 A flowchart for updating the medical staff information set in the present invention;

[0050] Figure 3 A flowchart for obtaining a career development prediction summary in the present invention;

[0051] Figure 4 A flowchart for analyzing the matching degree between current career paths and market demands in the present invention;

[0052] Figure 5 A flowchart for obtaining a recommended list of vocational training in the present invention;

[0053] Figure 6 A flow chart for monitoring operational requirements and human resource status in the present invention;

[0054] Figure 7 A flowchart for obtaining emergency resource allocation records in the present invention;

[0055] Figure 8 This is a flowchart for obtaining an overview of human resources adjustments in the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0058] Example

[0059] See also Figure 1 The present invention provides a technical solution: a human resources intelligent allocation optimization system comprising:

[0060] The career analysis and prediction module updates the medical staff information set based on their historical work records and skill growth information, predicts their career development direction, identifies potential career transition points, and obtains a career development prediction summary;

[0061] The career path planning module analyzes the match between current career paths and market demand based on the career development forecast summary and the hospital's business needs and goals. It then plans a career development path that meets the hospital's needs, recommends appropriate training and promotion opportunities, and generates a recommended career training list.

[0062] The emergency deployment management module monitors the hospital's current operational needs and human resource status based on the recommended list of professional training, collects data on the availability and emergency needs of medical staff in real time, and dynamically deploys human resources to obtain emergency resource deployment records;

[0063] The human resources optimization module continuously collects and updates the hospital's human resources and demand information based on emergency resource allocation records. It monitors career planning and staffing allocation in real time across multiple hospital campuses, and makes continuous adjustments based on feedback information to provide an overview of human resources adjustments.

[0064] The career development forecast summary includes career growth rate, main skill requirements, and potential career opportunities. The recommended career training list includes the training course name, skill level improvement results, and promotion path selection table. The emergency resource allocation record includes resource utilization rate, emergency response time, and key resource allocation effect. The human resources adjustment overview includes adjustment details, optimization effect indicators, and human resources status after implementation.

[0065] See also Figure 2 , the steps to update the medical staff information set are as follows:

[0066] Based on the database of multiple hospital campuses, we extracted the historical work records and skill growth information of medical staff, performed data cleaning and verification, removed errors or duplicate records, and obtained a purified data set;

[0067] The historical work records and skill growth information of medical staff were extracted, and the database interfaces of different branches were called. Data items were located according to the query conditions, including job type, task completion rate, duration record and specific content of skill growth such as number of training sessions, qualification certification and assessment scores in the work records. Field mapping of each data was performed to ensure a unified format. At the same time, data integrity was screened and records with null values ​​or non-standard characters were eliminated. Time series were sorted by establishing timestamps to ensure that data could be dynamically tracked by time dimension. Then, duplicate value detection logic was applied. By comparing the unique identifier of the work record and the unique index of the skill growth record, redundant items of multiple duplicate records were cleaned up. The consistency and logical rationality of the records were further verified by combining the correspondence between time periods and skill improvement content to generate a purified medical staff data set.

[0068] Based on the purified data set, it is added to the personnel information and the timeliness and completeness of the personnel information are verified to obtain the updated medical staff information set;

[0069] Append the records to the existing personnel information system in sequence, and verify the timeliness of the newly added data through automated comparison rules, including the time span between the new records and the existing records and the offset value from the current date. Use field matching methods to verify the field consistency of the newly added records in the information system and fix format errors or missing items. Perform logical checks to ensure data integrity, and finally save the processed records as new data to the personnel information.

[0070] See also Figure 3 , the steps for obtaining the career development forecast summary are as follows:

[0071] Conduct data analysis on medical staff information sets, extract career progression and skill improvement data, and obtain career trend analysis records;

[0072] The historical work records of medical staff are processed in time series, and data such as working hours, job changes, and work performance are organized into serial data in chronological order. Skill improvement data are extracted from skill scores and skill categories through evaluation forms or assessment records, classified according to time periods and matched with historical work records. Then, invalid data is eliminated through data cleaning and deduplication rules, including duplicate records, missing fields, and data entries that do not meet the standards. The processed data is then grouped and classified, and a pivot table is generated according to dimensions such as job position, time period, and skill category. The trajectory of career progress and the key points of skill improvement are extracted from this. Finally, career trend analysis records are generated through comprehensive processing of career data.

[0073] Based on career trend analysis records, predict career development direction using the formula:

[0074]

[0075] The development direction prediction results are obtained, among which, represents the predicted career status at time point t, Y(t-1) represents the career status in the previous period, which is the actual career status of the previous period recorded in historical data, X(t) represents the current skill score at time point t, which is the skill data obtained based on the most recent evaluation or performance review, α is the influence weight of the career status in the previous period, β is the influence weight of the current skill score, and γ is a constant term used to adjust the baseline of the prediction result;

[0076] The following data is collected: the historical career status Y(t-1) is 80 (representing the evaluation score), the current skill score X(t) is 85, the weights α = 0.5, β = 0.3, and the constant term γ = 5. The calculation process is as follows:

[0077]

[0078] The results show that based on the comprehensive analysis of historical career status and current skill scores, the predicted future career status score is 70.5, which means that if the current trend continues, the medical staff's predicted career status in the next evaluation cycle will be relatively stable, showing moderate career growth potential.

[0079] Based on the development direction prediction results, compare them with personal career aspirations and department needs to identify potential career transition points and obtain a career development prediction summary;

[0080] The career goal preferences of medical staff are extracted from the records of career aspirations. Preferences are usually obtained through questionnaires or interview records. At the same time, combined with the key competency requirement documents of department needs, whether there are matches or potential vacancies in the current career path is identified. Then the data of the development direction prediction results are broken down into a career estimation distribution table. Using different career directions and corresponding competency requirements as indexes, the career prediction directions are compared item by item to see whether they meet personal aspirations and department needs. Each career path is scored by establishing a matching matrix. Finally, potential career transition points are determined based on the matching scores and the impact of career transitions to obtain a career development prediction summary.

[0081] See also Figure 4 , the specific steps for analyzing the match between current career paths and market demand are:

[0082] Based on the career development forecast summary, the hospital's business needs and goals are collected to obtain the expected career and skills data set;

[0083] By analyzing the hospital's business needs and goals, extracting data from the hospital's internal management and goals, and especially refining the details of each need through the personnel demand table and target development report proposed by each department, the needs are sorted and sorted according to the timeline and priority. Each type of demand requires clear specific skill categories, career levels and job responsibilities. By matching the skills and job data involved in the career development forecast summary with the needs proposed by the hospital, each item is verified for relevance and logical rationality, and the data with differences is recorded to obtain an integrated set of expected career and skill data.

[0084] Based on the expected occupation and skills dataset, the formula is used:

[0085]

[0086] Conduct quantitative analysis of career paths and market demand to obtain the matching score of each path, where Match i is the market matching score of the i-th career path, w k Represents the criticality of the kth business requirement, which is allocated according to the urgency of the requirement and the target value. ik represents the responsiveness of the i-th career path to the k-th business requirement, evaluated by the skills and experience data in the career development forecast profile, and n is the total number of business requirements;

[0087] There are three business requirements, namely n = 3, weights w1 = 0.5, w2 = 0.3, w3 = 0.2, and the responsiveness of career paths to the requirements are R i1 =0.7, R i2 =0.4, R i3 =0.9, the calculation process is as follows:

[0088]

[0089] The results show that given the weight and responsiveness, the market fit of this career path is 0.65, indicating that the path has a high degree of consistency with current market demand and is suitable as a priority development direction.

[0090] Based on the matching score of each path, identify the career paths associated with market demand and obtain matching analysis results;

[0091] Analyze the urgency of internal hospital needs and the quantitative scoring results of the matching degree of each path, classify and organize the high-matching paths, eliminate the paths with matching degrees below the threshold, and screen the career paths with the strongest correlation with market demand from the remaining paths. At the same time, mark the key skill requirements in the high-matching paths. Through further analysis, summarize the paths into a set of paths that can meet the current and future goals of the hospital, forming the matching analysis results.

[0092] See also Figure 5 The specific steps for obtaining the recommended list of vocational training are as follows:

[0093] Based on the matching degree, the skills and career directions that need to be developed are screened to obtain the selected career path data;

[0094] Identify career paths with high market demand criticality, and screen out career directions that can fill current market gaps by comparing the skill requirements of career paths with the technical characteristics of market demand. Further compare the detailed skill descriptions and demand coverage of each path in the career development forecast summary, use detailed matching rules to mark the priority of each career path, and screen out skills and career directions that need to be focused on. The screening results are systematically classified as important career path data, and provide clear direction for subsequent career training recommendations.

[0095] Based on the screened career path data, combined with the personnel development intentions, according to the hospital's development plan and immediate training resources, a career development path that meets the hospital's needs is planned, and a recommended list of career training is obtained;

[0096] In combination with the development plans proposed by various departments of the hospital, immediate training resources are subdivided into two categories: general skills training and specialized skills training. By prioritizing the skills required in the screening path, targeted training plans are formulated. At the same time, the personal career development intentions of medical staff are included in the evaluation basis of the screening path, and the training list is further optimized according to the training direction they voluntarily participate in. Combined with the hospital's resource conditions and long-term development strategy, a recommended list of vocational training that meets the hospital's needs and reasonably allocates resources is planned.

[0097] See also Figure 6 ,The specific steps for monitoring operational needs and human resource status are:

[0098] Based on the recommended list of professional training, the current operational needs and human resource allocation status of multiple departments in the hospital were collected to obtain the operational and resource status dataset;

[0099] First, we collect reports submitted by various departments on operational needs and human resource allocation. These reports include information on staff utilization efficiency, current vacancies, and urgently needed professional skills. Through organizational coordination and combined with survey results, we verify the data. We then integrate the collected information and enter the operational needs and human resource allocation data into a unified data management platform. Based on the input data, the system generates a complete operational and resource status dataset. The integrated data covers resource distribution and specific departmental needs.

[0100] Based on the operational and resource status data sets, analyze the trends and patterns in the data, evaluate the real-time status of resource allocation matching with demand, and obtain resource usage records;

[0101] Through time-based statistics and categorized summaries, the historical usage and current distribution ratio of each resource are clarified. Classification analysis is then used to measure resource utilization efficiency and identify which resources are inefficient or have potential bottlenecks. Real-time monitoring of feedback data from various departments is then used to record the gap between demand and current resource allocation. This analysis information is then compared with resource status data to form a resource usage record. This resource usage record ultimately covers information on time, department, resource type, and corresponding demand status.

[0102] Based on resource usage records, the formula is used:

[0103]

[0104] Evaluate the optimization direction of resource allocation and obtain optimization adjustment information, where OH(i) represents the resource optimization index of the i-th department, which measures the effect of resource allocation optimization, and β H is the optimization coefficient, which is determined based on historical data and strategy adjustments. j is the urgency weight of the jth resource, which is determined based on department feedback and the frequency of historical emergency events. ij is the efficiency score of the i-th department on the j-th resource, which is obtained by evaluating the resource usage and results in historical data. i is the number of human resources in the i-th department, and m is the total number of resource types;

[0105] A hospital has three resources, that is, m=3, and the optimization coefficient β H =1.2, the urgency weights are UH1=0.5, UH2=0.3, UH3=0.2, and the efficiency scores of the first department for resources are EH 1，1 =0.8, EH 1，2 =0.6, EH 1，3 =0.7, the human resources of this department PH1 = 50, the calculation process is as follows:

[0106]

[0107] The results show that given the urgency weight and efficiency score of resources, the resource optimization index of the first department is 0.01728, reflecting the resource utilization efficiency and optimization space of the department under the existing human resource configuration.

[0108] See also Figure 7 , the specific steps for obtaining emergency resource allocation records are as follows:

[0109] Collect real-time availability data of medical staff, including current attendance, holidays, and manpower dynamics of key events, to obtain real-time human resources data;

[0110] The data collection process is completed through the collaboration of multiple departments. The original records submitted by each department contain the personnel's name, title, available time period and reason for absence. They are checked against the historical scheduling data in the hospital management system and the instantly updated attendance system. By classifying the reasons for absence (such as sick leave, annual leave, etc.), a multidimensional matrix is ​​established to associate the status of each medical staff member with the needs of their department. Matrix operations are then used to screen out the current available manpower distribution in key departments, ultimately obtaining real-time human resources data, which includes the availability rate of medical staff, the number of vacant positions and the urgency classification of each position.

[0111] Based on real-time human resources data, the formula is used:

[0112]

[0113] Analyze and determine the manpower requirements of key departments and emergency medical tasks, where DA(i) represents the resource allocation index of the i-th department, vx z is the urgency weight of the zth task, ax iz is the available manpower of the i-th department for the z-th task, TX i is the total manpower of the i-th department, n X is the total number of tasks;

[0114] The hospital has five different emergency medical tasks. The first department has 30 medical staff available, with urgency weights vx1 = 0.5, vx2 = 0.2, vx3 = 0.1, vx4 = 0.1, and vx5 = 0.1. The available manpower for the tasks is ax 1，1 =10,ax 1，2 =5,ax 1，3 =3, ax 1，4 =7 and ax 1，5 =5, the calculation process is as follows:

[0115]

[0116] The result shows that the resource allocation index of the first department is 0.25, which means that under the current configuration of human resources and task urgency, the overall response capability of the department is assessed to be 25% efficient, guiding the management to adjust the resource allocation strategy according to the actual situation.

[0117] Based on the results of human resource demand analysis, dynamic human resource deployment is carried out for departments with manpower shortages and emergency medical tasks, and emergency resource deployment records are obtained;

[0118] Focus on dynamic allocation of resources for departments with manpower shortages. By analyzing the emergency task lists submitted by each department, clarify the urgency level of the task and the type of resources required, further analyze the human resource allocation in combination with the resource status, calculate the current list of medical staff that can be deployed, and screen suitable personnel for cross-departmental reinforcement deployment by cross-comparing job requirements, personal skill matching and available time. After generating the deployment plan, notify the relevant departments in turn for confirmation and implementation, record the actual situation after deployment, including deployment time, participants and task completion status, and generate emergency resource deployment records for subsequent human resource optimization management.

[0119] See also Figure 8 The steps to obtain the human resources adjustment overview are as follows:

[0120] Based on the emergency resource allocation records, continuously collect and update the hospital's human resources and demand information, using the formula:

[0121]

[0122] Calculate the efficiency and satisfaction of human resource allocation in multiple campuses and obtain the human resource adjustment result RI overall , where pI i is the human resource allocation ratio of the i-th hospital area, fI i is the feedback score, indicating the satisfaction with human resource allocation, nI is the total number of campuses, and k is the total number of campuses participating in the evaluation;

[0123] The hospital system consists of three campuses, and the human resources allocation ratio of each campus is pI i The satisfaction feedback scores of each hospital district were 0.5, 0.3 and 0.2 respectively. i They are 80, 90 and 85 respectively, and the total number of campuses nI is 3. The calculation process is as follows:

[0124]

[0125] The results show that the comprehensive index of human resource allocation efficiency and satisfaction of the entire hospital system is 28. The value helps management understand the current human resource allocation efficiency and satisfaction level, thereby guiding future human resource adjustment and optimization strategies.

[0126] Based on the results of human resource adjustments, real-time monitoring of career planning and staffing allocation is carried out, dynamic adjustments of human resources across campuses are made, and human resource flows between multiple hospitals are optimized to provide an overview of human resource adjustments.

[0127] Real-time human resource status data is collected from multiple hospital campuses, including current personnel distribution, skill levels and professional needs, inter-departmental human resource scheduling records and workload assessments. Based on the data obtained, the resource supply-demand ratio of each hospital campus is first calculated. Potential scheduling needs are identified by matching the hospital campus's human resource allocation with demand. At the same time, cross-hospital resource flow data is aggregated and weighted to generate cross-hospital resource optimization indicators. Finally, the career planning and personnel allocation of each hospital campus are dynamically adjusted based on historical records and real-time feedback, and an overview of human resource adjustments is output.

[0128] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A human resources intelligent allocation optimization system, characterized in that: The system comprises: The career analysis and prediction module updates the medical staff information set based on their historical work records and skill growth information, predicts their career development direction, identifies potential career transition points, and obtains a career development prediction summary; The steps for obtaining the career development forecast summary are as follows: Performing data analysis on the medical staff information set, extracting career progression and skill improvement data, and obtaining career trend analysis records; Based on the career trend analysis records, career development direction is predicted using the formula: ； The development direction prediction results are obtained, among which, Indicates the predicted career status at time point The value of Indicates the occupational status of the previous period, Indicates at a point in time Current skill rating of is the influence weight of occupational status in the previous period, is the influence weight of the current skill score, is a constant term; Based on the predicted development direction results, compare them with individual career aspirations and department needs to identify potential career transition points and obtain a career development forecast summary; The career path planning module analyzes the matching degree between the current career path and market demand based on the career development forecast summary and the hospital's business needs and goals, plans a career development path that meets the hospital's needs, and obtains a recommended list of career training; The emergency deployment management module monitors the hospital's current operational needs and human resource status based on the recommended list of professional training, collects data on the availability and emergency needs of medical staff in real time, and dynamically deploys human resources to obtain emergency resource deployment records; The specific steps for monitoring the operational needs and human resource status are as follows: Based on the recommended list of job training, the current operational needs and human resource allocation status of multiple departments in the hospital are collected to obtain an operational and resource status dataset; Based on the operational and resource status data sets, analyze trends and patterns in the data, evaluate the real-time status of resource allocation matching demand, and obtain resource usage records; Based on the resource usage records, the formula is used: ； Evaluate the optimization direction of resource allocation and obtain optimization adjustment information, including: Indicates the Resource optimization index of each department, is the optimization coefficient, It is The urgency weight of each resource, It is Department to The efficiency score of each resource, It is The number of human resources in each department, is the total number of resource types; The human resource optimization module continuously collects and updates the hospital's human resource and demand information based on the emergency resource allocation records, monitors career planning and personnel allocation in real time based on multiple hospital campuses, and makes continuous adjustments based on feedback information to obtain an overview of human resource adjustments.

2. The human resources intelligent allocation optimization system according to claim 1 is characterized in that: The steps of updating the medical staff information set are specifically as follows: Based on the database of multiple hospital campuses, we extracted the historical work records and skill growth information of medical staff, performed data cleaning and verification, removed errors or duplicate records, and obtained a purified data set; Based on the purified data set, it is added to the personnel information, and the timeliness and integrity of the personnel information are verified to obtain an updated medical staff information set.

3. The human resources intelligent allocation optimization system according to claim 1 is characterized in that: The specific steps for analyzing the matching degree between the current career path and market demand are as follows: Based on the career development forecast summary, the hospital's business needs and goals are collected to obtain a dataset of expected careers and skills; Based on the expected occupation and skills dataset, the formula is used: ； Conduct a quantitative analysis of career paths and market demand to obtain a matching score for each path, where: It is The market matching score of each career path, Representative The criticality of a business requirement, Indicates the Career Path Responsiveness to business needs, is the total number of business requirements; Based on the matching score of each path, career paths associated with market demand are identified to obtain matching analysis results.

4. The human resources intelligent allocation optimization system according to claim 1 is characterized in that: The specific steps for obtaining the recommended list of vocational training are as follows: Based on the matching degree, the skills and career directions that need to be developed are screened to obtain screened career path data; Based on the screened career path data, combined with the personnel development intentions, according to the hospital's development plan and immediate training resources, a career development path that meets the hospital's needs is planned to obtain a recommended career training list.

5. The human resources intelligent allocation optimization system according to claim 1 is characterized in that: The steps for obtaining the emergency resource allocation record are specifically as follows: Collect real-time availability data of medical staff, including current attendance, holidays, and manpower dynamics of key events, to obtain real-time human resources data; Based on the real-time human resources data, the formula is adopted: ； Analyze and determine the manpower requirements for critical departments and emergency medical missions, including: Indicates the Resource allocation index of each department, It is The urgency weight of each task, It is Department to Available manpower for each task, It is The total manpower of each department, is the total number of tasks; Based on the manpower demand analysis results, human resources are dynamically deployed for manpower-shortage departments and emergency medical tasks, and emergency resource deployment records are obtained.

6. The human resources intelligent allocation optimization system according to claim 1 is characterized in that: The steps for obtaining the human resources adjustment overview are as follows: Based on the emergency resource allocation records, continuously collect and update the hospital's human resources and demand information, using the formula: ； Calculate the efficiency and satisfaction of human resource allocation in multiple campuses and obtain the results of human resource adjustment ,in, For the The ratio of human resources allocation in each hospital area, is the feedback score, which indicates the satisfaction with human resource allocation. is the total number of campuses, Indicates the total number of hospital districts participating in the assessment; Based on the human resource adjustment results, career planning and personnel allocation are monitored in real time, dynamic human resource adjustments are made across campuses, and the flow of human resources between multiple hospitals is optimized to obtain an overview of human resource adjustments.

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