Intelligent human resource allocation optimization system

Abstract

The invention relates to the technical field of human resources, in particular to a human resource intelligent allocation optimization system, which comprises an occupational analysis and prediction module, an occupational path planning module, an emergency allocation management module and a human resource optimization module. According to the invention, by integrating the occupational history and skill data of the medical personnel, accurate prediction and dynamic deployment of occupational paths are realized, talent matching is more accurate through accurate occupational conversion point analysis, resource waste is reduced, and occupational training recommendation is customized for hospital business targets, so that the pertinence and effectiveness of training are improved; the talent training process is optimized, the training effect of professional talents is improved, under the emergency condition, the system can respond in time through real-time data monitoring, rapid and effective manpower allocation is carried out, it is ensured that a hospital can keep efficient operation before emergency medical requirements, through continuous data updating and feedback, a manpower resource allocation strategy is continuously optimized, and the training effect of the professional talents is improved. And the adaptive capacity and the management efficiency of the hospital in a changeable environment are improved.

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 technical 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 contingency in human resource management and improve operational efficiency.

[0003] Among them, the human resources intelligent allocation optimization system refers to optimizing and improving the allocation efficiency of human resources in the organization through intelligent means, 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 held by the most suitable talent. At the same time, it also helps management monitor and adjust human resource allocation to cope with 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 the career development of employees, resulting in insufficient connection between talent training and market demand, reducing employee job satisfaction and the efficiency of human resource management in hospitals, 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-mentioned purpose, the present invention adopts the following technical scheme: 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 the medical staff's historical work records and skill growth information, predicts the career development direction, and determines the potential career transition points to obtain a career development prediction summary;

[0008] The career path planning module analyzes the matching degree between the current career path and the 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 current operational needs and human resource status of the hospital based on the recommended list of vocational training, collects the availability and emergency demand data 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 according to 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 step of updating the medical staff information set is 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 completeness 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 prediction results of development direction are obtained, among which, represents the predicted value of 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, compared with personal career aspirations and department needs, potential career transition points are determined and a career development prediction summary is obtained.

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

[0021] Based on the career development forecast summary, the hospital's business needs and goals are collected to obtain an expected career and skills data set;

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

[0023]

[0024] Conduct quantitative analysis of career paths and market demand to obtain matching scores for each path, where Match i is the market matching score of the ith 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, the career path associated with the market demand is identified to obtain a matching analysis result.

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

[0027] According to 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 list of career training.

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

[0030] Based on the recommended list of vocational 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 data set;

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

[0032] Based on the resource usage record, the formula is adopted:

[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 factor, UH j is the urgency weight of the jth resource, EH ij is the efficiency score of the i-th department for the j-th resource, PH i is the number of human resources in the ith 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, and obtain real-time human resource 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 human resource demand analysis results, dynamic human resource deployment is performed for departments with human resource shortages and emergency medical tasks, and emergency resource deployment records are obtained.

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

[0042] Based on the emergency resource allocation records, the hospital's human resources and demand information is continuously collected and updated, 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 campus, 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, and the efficiency of human resource allocation is significantly improved. Accurate analysis of career transition points makes talent matching more accurate and reduces resource waste. Career training recommendations customized for hospital 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 a timely manner through real-time data monitoring and conduct rapid and effective human resource 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 It is a flow chart for analyzing the matching degree between the current career path and the market demand 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 monitoring flow chart of operation demand 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 resource adjustments in the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" 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 the medical staff's historical work records and skill growth information, predicts the career development direction, and determines the potential career transition points to obtain a career development prediction summary;

[0061] 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 recommends suitable training and promotion opportunities to obtain a recommended list of career training;

[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 the availability and emergency demand data of medical staff in real time, and dynamically deploys human resources to obtain emergency resource deployment records;

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

[0064] The career development forecast summary includes career growth rate, main skill requirements, and potential career opportunities. The recommended career training list includes training course names, 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 resource adjustment overview includes adjustment details, optimization effect indicators, and human resource status after implementation.

[0065] See also Figure 2 , the specific steps for updating the medical staff information set are:

[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 the job type, task completion rate, duration record and specific content of skill growth such as number of trainings, qualification certification and assessment scores in the work records. Fields of various data were mapped to ensure a unified format. At the same time, data integrity was screened and records with null values ​​or non-standard characters were removed. 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 to clean up redundant items of repeated records by comparing the unique identifier of the work record and the unique index of the skill growth record. The consistency and logical rationality of the records were further verified by combining the correspondence between the time period and the skill improvement content to generate a purified medical staff data set.

[0068] Based on the purified data set, add it to the personnel information, verify the timeliness and completeness of the personnel information, and 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 new 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 the field matching method to verify the field consistency of the new 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 in the personnel information.

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

[0071] Conduct data analysis on the medical staff information set, 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 sequence data in chronological order. The 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 are then grouped and classified, and pivot tables are generated according to dimensions such as job positions, time periods, and skill categories. The trajectory of career progression and key points of skill improvement are extracted based on this, and finally career trend analysis records are generated through comprehensive processing of career data.

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

[0074]

[0075] The prediction results of development direction are obtained, among which, represents the value of the predicted career status at time point t, Y(t-1) represents the career status of the previous period, which is the actual career status of the previous period recorded in the 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 of 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 occupation 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 predicted career status of the medical staff in the next evaluation cycle will be relatively stable, showing moderate career growth potential.

[0079] Based on the development direction prediction results, compare with personal career aspirations and department needs, 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. The preferences are usually obtained through questionnaires or interview records. At the same time, combined with the key competency requirement documents of departmental needs, it is identified whether there are matches or potential vacancies in the current career path. Then the data of the development direction prediction results are broken down into an estimated career distribution table. Using different career directions and corresponding competency requirements as indexes, the predicted career directions are compared item by item to see whether they meet personal aspirations and departmental 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 forecast summary.

[0081] See also Figure 4 , the specific steps for analyzing the matching degree between the current career path 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, especially through the personnel demand tables and target development reports proposed by each department, refining the details of each demand, and organizing and sorting the needs according to 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, checking each item for relevance and logical rationality, and recording the data with differences, we can obtain an integrated set of expected career and skill data.

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

[0085]

[0086] Conduct quantitative analysis of career paths and market demand to obtain matching scores for each path, where Match i is the market matching score of the ith 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, that is, n = 3, weight w 1 =0.5, w 2 =0.3, w 3 =0.2, the responsiveness of career paths to demand is 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 path associated with market demand and obtain the matching analysis results;

[0091] Analyze the urgency of internal needs of the hospital 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 a matching analysis result.

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

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

[0094] Identify career paths with high market demand criticality, 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 overview, use detailed matching rules to mark the priority of each career path, screen out the skills and career directions that need to be focused on, and the screening results are systematically classified as important career path data, providing 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. Targeted training plans are formulated by prioritizing the skills required in the screening path. 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 needs of the hospital 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 vocational 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 data set;

[0099] First, we collect reports submitted by various departments that include operational needs and human resource allocation. The report content includes the staff utilization efficiency of each department, current vacant positions, and urgently needed professional skill categories. Through organizational coordination, we check the data in combination with the survey results, and then integrate the collected information. The data on operational needs and human resource allocation are uniformly entered into the data management platform. The system will generate a complete operational and resource status data set based on the input data. The integrated data content covers resource distribution and specific departmental demand information;

[0100] Based on the operation and resource status data set, 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] By statistically analyzing and summarizing resources by time period, the historical usage and current distribution ratio of each resource can be clarified. Classification analysis can be used to measure the efficiency of resource usage and determine which resources are inefficient or have potential bottlenecks in use. Then, feedback data from various departments can be monitored in real time to record the gap between demand and current resource allocation. The analysis information can be compared with resource status data to form a resource usage record, which 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 for 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 ith department, and m is the total number of resource types;

[0105] A hospital has 3 resources, that is, m = 3, and the optimization coefficient β H =1.2, the urgency weights are UH 1 =0.5, UH 2 =0.3,UH 3 =0.2, 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 the department PH1 =50, the calculation process is as follows:

[0106]

[0107] The result shows that given the urgency weight and efficiency score of the resources, the resource optimization index of the first department is 0.01728, which reflects 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:

[0109] Collect real-time availability data of medical staff, including current attendance, holidays and manpower dynamics of key events, and obtain real-time human resource 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 his or her department. Matrix operations are then used to screen out the current available manpower distribution in key departments, and ultimately real-time human resources data is obtained, 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 5 different emergency medical missions. The first department has 30 medical staff available, with an urgency weight of vx. 1 =0.5, vx 2 =0.2, vx 3 =0.1, vx 4 = 0.1 and vx 5 = 0.1, the available manpower for the task in this department is ax 1，1 =10,ax 1，2 =5, ax1，3 =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 evaluated as 25% efficiency, 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 human resource 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 task urgency level and the type of resources required, further analyze human resource allocation in combination with 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 the deployment plan is generated, 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 specific 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 campus, 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 area 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 numerical 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 personnel allocation is carried out, dynamic adjustments of human resources across campuses are made, and the flow of human resources between multiple hospitals is optimized to obtain 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, and 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 summarized 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 only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope 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 the medical staff's historical work records and skill growth information, predicts the career development direction, and determines the potential career transition points to obtain a career development prediction summary; The career path planning module analyzes the matching degree between the current career path and the 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 current operational needs and human resource status of the hospital based on the recommended list of vocational training, collects the availability and emergency demand data of medical staff in real time, and dynamically deploys human resources to obtain emergency resource deployment records; 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 according to 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 completeness 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 steps for obtaining the career development forecast summary are specifically 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 prediction results of development direction are obtained, among which, represents the predicted value of 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; Based on the development direction prediction results, compared with personal career aspirations and department needs, potential career transition points are determined and a career development prediction summary is obtained.

4. 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 an expected career and skills data set; Based on the expected occupation and skills data set, the formula is used: Conduct quantitative analysis of career paths and market demand to obtain matching scores for each path, where Match i is the market matching score of the ith 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; Based on the matching score of each path, the career path associated with the market demand is identified to obtain a matching analysis result.

5. 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: According to 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 list of career training.

6. The human resources intelligent allocation optimization system according to claim 1 is characterized in that: The specific steps for monitoring the operational requirements and human resource status are as follows: Based on the recommended list of vocational 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 data set; Based on the operation and resource status data set, analyze the trends and patterns in the data, evaluate the real-time status of resource allocation matching with demand, and obtain resource usage records; Based on the resource usage record, the formula is adopted: 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 H is the optimization factor, UH j is the urgency weight of the jth resource, EH ij is the efficiency score of the i-th department for the j-th resource, PH i is the number of human resources in the ith department, and m is the total number of resource types.

7. 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, and obtain real-time human resource data; Based on the real-time human resources data, the formula is adopted: 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; Based on the human resource demand analysis results, dynamic human resource deployment is performed for departments with human resource shortages and emergency medical tasks, and emergency resource deployment records are obtained.

8. 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, the hospital's human resources and demand information is continuously collected and updated, using the formula: 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 campus, 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; 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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