Human resource management system and method based on block chain

Abstract

The invention relates to the technical field of human resource management, in particular to a human resource management system and method based on a block chain. The method comprises the following steps: firstly, dividing personnel into a plurality of talent unit groups based on functions, professions and levels, performing quantitative evaluation from two dimensions of talent loss risk and post influence degree, and setting evaluation factors such as work satisfaction, occupational development opportunities, work pressure indexes and team relationship scores, so as to obtain evaluation results of the talent loss risk and the post influence degree; calculating a demission tendency index in combination with an analytic hierarchy process; generating an early warning signal based on the evaluation result; and finally, performing credible storage on the whole-process data by utilizing a block chain technology. According to the method, the conversion from passive statistics to active early warning is realized, the evaluation granularity is optimized by introducing the talent unit group, the accuracy of risk identification is improved, and the credibility of data is ensured by using the block chain technology.

Description

Technical Field

[0001] The present application relates to the technical field of human resource management, and in particular to a human resource management system and method based on blockchain. Background Art

[0002] Hospital human resource management is a key link in ensuring the quality of medical services and the normal operation of hospitals. In recent years, with the continuous growth of demand for medical services and the rapid development of medical technology, the problem of talent loss in hospitals has become increasingly prominent, especially in key positions such as specialists and senior nursing staff. Talent loss may lead to a decline in medical quality and damage to service levels.

[0003] At present, hospitals generally use human resource management systems to manage personnel information and carry out talent management by setting assessment indicators and regular evaluations. These systems can realize basic personnel management functions and conduct statistical analysis of personnel turnover.

[0004] However, existing technologies are unable to predict the risk of talent loss and can only conduct passive statistics after talent loss. There is a lack of quantitative assessment of the importance of different positions, making it difficult to identify the risk of talent loss in key positions. This situation needs to be further improved. Summary of the invention

[0005] In order to solve the problem that the existing technology cannot predict the risk of talent loss, can only perform passive statistics after talent loss, lacks quantitative assessment of the importance of different positions, and is difficult to identify the risk of talent loss in key positions, this application provides a human resource management system and method based on blockchain, using the following technical solutions: In a first aspect, the present application provides a human resources management system based on blockchain, comprising: The data collection module is used to obtain the human resource data of each department of the hospital and divide the personnel into several talent unit groups according to their functions, specialties and ranks; A risk assessment module, which is used to evaluate each talent unit group as a basic unit according to preset talent loss evaluation factors, obtain a corresponding turnover tendency index, and calculate a talent loss risk level, wherein the talent loss evaluation factors include job satisfaction, career development opportunities, work stress index, and team relationship score; A position impact module is used to calculate the impact of position vacancies based on preset position criticality evaluation factors, taking each talent unit group as a basic unit; A risk warning module, for generating a human resources warning signal based on the degree of impact of job vacancies and the risk level of talent loss of each talent unit group; The blockchain storage module is used to write the original data, scoring records and warning results in the evaluation process into the blockchain and establish an index structure based on time series.

[0006] By adopting the above technical solution, in order to solve the problem of decreased medical service quality caused by the loss of hospital talents, the existing technology mainly adopts regular assessment and statistical analysis to manage talents, which cannot predict the risk of talent loss and can only perform passive statistics after talent loss; this application first divides personnel into several talent unit groups based on function, profession and rank, and conducts quantitative evaluation from two dimensions: talent loss risk and job impact degree, and calculates the turnover tendency index by setting evaluation factors such as job satisfaction, career development opportunities, work stress index and team relationship score, combined with the hierarchical analysis method; then generates early warning signals based on the evaluation results; finally, uses blockchain technology to reliably store the data of the whole process; not only realizes the transformation from passive statistics to active early warning, but also optimizes the evaluation granularity by introducing talent unit groups, improves the accuracy of risk identification, and uses blockchain technology to ensure the credibility of data.

[0007] Optionally, the data acquisition module specifically includes: The data preprocessing unit is used to standardize and clean the human resource data of each department of the hospital to obtain standardized basic human resource data; An organizational analysis unit, used to perform organizational structure analysis based on the standardized human resources basic data and generate a department function relationship diagram; A collaborative calculation unit, used to calculate a business collaborative index according to the department functional relationship diagram, and generate a talent capability map and a functional connection network based on the business collaborative index; The unit division unit is used to generate an initial talent unit group based on the talent capability map and the functional contact network, and optimize and adjust the initial talent unit group according to the standardized human resource basic data to form a final talent unit group.

[0008] By adopting the above technical solution, this application first standardizes and cleans the original data to establish a unified data specification; generates a departmental functional relationship diagram through organizational structure analysis to reveal the business connections between departments; then calculates the business synergy index based on the functional relationship, and constructs a talent capability map and functional connection network based on this; finally, these multi-dimensional information are used in combination to generate an initial talent unit group and optimize and adjust it; the division of talent units is realized, fully considering the complex functional relationships and business collaboration needs in medical institutions.

[0009] Optionally, the risk assessment module specifically includes: An individual scoring unit, used to score each member in a single talent unit group according to the talent loss evaluation factor to obtain a first member score; A score calculation unit, used for performing weighted average calculation on the first member scores of all members in a single talent unit group to obtain a talent loss evaluation factor discrimination score; The index generating unit is used to calculate the turnover tendency index by using the talent loss evaluation factor discrimination score.

[0010] By adopting the above technical scheme, in order to solve the problems existing in the process of talent loss risk assessment in hospitals, such as inconsistent assessment standards, group characteristics being masked by individual differences, and difficulty in risk quantification; this application first performs individual scoring on each member of the talent unit group, and obtains the overall evaluation factor discrimination score of the team through weighted average calculation, effectively balancing individual differences; finally, the turnover tendency index is calculated based on the discrimination score to achieve quantitative expression of risk; by establishing a progressive calculation model, it not only retains individual difference information, but also reflects the overall trend of the team.

[0011] Optionally, the index generating unit performs the following steps: According to the pre-constructed judgment matrix A, solve the characteristic equation |λI-A|=0 to obtain the maximum eigenvalue λmax; Calculate the consistency index CI = (λmax-n) / (n-1), and obtain the average random consistency index RI by looking up the table according to the matrix order n, calculate the consistency ratio CR = CI / RI, and when CR<0.1, obtain the standardized weight vector W = (w1,w2,w3,w4); Using the discrimination scores of the talent loss evaluation factors, an evaluation matrix R = [rij]4×5 is established, where rij represents the degree of membership of the discrimination score of the i-th talent loss evaluation factor to the j-th evaluation level; Based on the weight vector W and the evaluation matrix R, the comprehensive evaluation vector B = W⊗R is obtained, and the final turnover tendency index I is calculated through the inner product operation with the quantitative score vector V=(20,40,60,80,100).

[0012] By adopting the above technical scheme, the present application first obtains the maximum eigenvalue by solving the characteristic equation, and performs a consistency check to ensure the rationality of the weight distribution; calculates the standardized weight vector to ensure the scientific nature of the weights of each evaluation factor; then constructs a fuzzy evaluation matrix based on the discrimination score of the talent loss evaluation factor, and establishes the affiliation of the evaluation level; finally, obtains the final turnover tendency index through fuzzy comprehensive operation; ensures the objectivity of the weight distribution, and at the same time uses fuzzy affiliation to deal with the uncertainty of multi-dimensional indicators, thereby realizing the quantification of the turnover tendency.

[0013] Optionally, the risk assessment module further includes: A historical analysis unit, used to obtain historical resignation data, and calculate resignation probability data of a talent unit group based on the historical resignation data; The risk calculation unit is used to calculate the final talent loss risk level by using the turnover tendency index and the turnover probability data.

[0014] By adopting the above technical solution, this application first analyzes the historical resignation data and extracts the resignation probability characteristics of each talent unit group; then these historical data are comprehensively calculated with the currently calculated resignation tendency index to obtain the final risk level determination result; historical data analysis is combined with real-time evaluation to improve the accuracy of level evaluation.

[0015] Optionally, the position impact module specifically includes: A scoring standard unit, used to set scoring standards for job criticality evaluation factors, wherein the job criticality evaluation factors include medical quality impact, patient service impact, department operation impact and training cost; A position scoring unit is used to score each position in a single talent unit group according to a position criticality evaluation factor to obtain a position impact score; A weight calculation unit, used to perform weighted average calculation on the position impact scores of all positions in a single talent unit group to obtain a position criticality discrimination score; The impact generation unit is used to calculate the final impact degree of job vacancy by using the job criticality discrimination score and the hierarchical analysis method.

[0016] By adopting the above technical solution, this application first establishes a standardized job criticality scoring system to ensure the uniformity of the evaluation standards; conducts multi-dimensional scoring of each position within the talent unit group to comprehensively measure the value of the position; then obtains the job criticality judgment score through weighted calculation to reflect the overall importance of the position; finally, uses the hierarchical analysis method to calculate the final degree of impact of vacancies; and achieves an objective evaluation of the impact of positions.

[0017] Optionally, the risk warning module specifically includes: A matrix construction unit is used to take each of the talent unit groups as a basic unit, input the corresponding talent loss risk level as row vector data and the corresponding job vacancy impact degree as column vector data into a preset human resource risk discrimination matrix to obtain the corresponding human resource risk level; The warning generation unit is used to generate a human resource warning signal of a corresponding level according to the human resource risk level corresponding to each talent unit group in combination with preset warning rules.

[0018] By adopting the above technical scheme, in order to solve the problems existing in the hospital human resource risk warning process, such as the risk dimension fragmentation, insensitive warning mechanism and insufficient accuracy of warning signals; this application first inputs the loss risk level of the talent unit group as a row vector and the degree of impact of job vacancies as a column vector into the risk discrimination matrix to establish a mapping relationship between risk levels; then based on the discrimination results, combined with the preset warning rules, a warning signal of the corresponding level is generated; the loss risk and job impact are integrated in the form of a matrix, and by establishing a multi-level warning mechanism, the risk warning is made precise and dynamic.

[0019] In a second aspect, the present application provides a blockchain-based human resources management method, which is applied to the above-mentioned blockchain-based human resources management system, and includes the following steps: Obtain human resource data of each department of the hospital and divide the personnel into several talent unit groups according to their functions, specialties and ranks; Taking each talent unit group as a basic unit, an assessment is performed according to a preset talent loss evaluation factor to obtain a corresponding turnover tendency index, and a talent loss risk level is calculated, wherein the talent loss evaluation factor includes job satisfaction, career development opportunities, work stress index and team relationship score; Taking each talent unit group as a basic unit, the impact degree of job vacancies is calculated according to preset job criticality evaluation factors; Generate a human resources early warning signal based on the degree of impact of job vacancies and the risk level of talent loss for each talent unit group; The original data, scoring records and warning results in the evaluation process are written into the blockchain, and an index structure based on time series is established.

[0020] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps performed by the above-mentioned blockchain-based human resources management system when executing the computer program.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps performed by the above-mentioned blockchain-based human resources management system.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: 1. This application first divides personnel into several talent unit groups based on functions, professions and ranks, and conducts quantitative assessments from two dimensions: talent loss risk and job impact. By setting evaluation factors such as job satisfaction, career development opportunities, work stress index and team relationship score, the turnover tendency index is calculated in combination with the analytic hierarchy process. Then, an early warning signal is generated based on the evaluation results. Finally, blockchain technology is used to reliably store the data of the entire process. This not only realizes the transition from passive statistics to active early warning, but also optimizes the evaluation granularity and improves the accuracy of risk identification by introducing talent unit groups. The use of blockchain technology ensures the credibility of the data. 2. In order to solve the problems of inconsistent assessment standards, group characteristics being masked by individual differences, and difficulty in quantifying risks in the process of assessing the risk of talent loss in hospitals; this application firstly scores each member of the talent unit group individually, and obtains the discrimination score of the overall evaluation factor of the team through weighted average calculation, effectively balancing individual differences; finally, the turnover tendency index is calculated based on the discrimination score to achieve quantitative expression of risk; by establishing a progressive calculation model, it not only retains individual difference information, but also reflects the overall trend of the team; 3. This application first obtains the maximum eigenvalue by solving the characteristic equation, and performs a consistency check to ensure the rationality of the weight distribution; calculates the standardized weight vector to ensure the scientific nature of the weights of each evaluation factor; then constructs a fuzzy evaluation matrix based on the discrimination scores of the talent loss evaluation factors, and establishes the affiliation of the evaluation levels; finally, the final turnover tendency index is obtained through fuzzy comprehensive operations; the objectivity of the weight distribution is ensured, and at the same time, the fuzzy affiliation degree is used to deal with the uncertainty of multi-dimensional indicators, thereby realizing the quantification of the turnover tendency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a structural diagram of a human resources management system based on blockchain in an embodiment of the present application; Figure 2 It is a structural diagram of a data acquisition module in an embodiment of the present application; Figure 3 This is a schematic diagram of the structure of the risk assessment module in the embodiment of the present application. Figure 1 ; Figure 4 is a schematic diagram of a process for calculating a turnover tendency index in an embodiment of the present application; Figure 5 This is a schematic diagram of the structure of the risk assessment module in the embodiment of the present application. Figure 2 ; Figure 6 It is a structural diagram of a position impact module in an embodiment of the present application; Figure 7 This is a schematic diagram of the structure of the risk warning module in the embodiment of the present application; Figure 8It is a flowchart of a human resources management method based on blockchain in an embodiment of the present application; Fig. 9 It is a diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0025] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0026] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0027] In the first aspect, the present application provides a human resources management system based on blockchain, referring to Figure 1 ,include: The data collection module is used to obtain the human resource data of each department of the hospital and divide the personnel into several talent unit groups according to their functions, professions and ranks.

[0028] In this embodiment, human resources data includes multi-dimensional information such as basic employee information (such as age, education, professional title, etc.), work performance data (such as assessment and evaluation, performance records, etc.), personnel change records (such as job adjustment, resignation, etc.). Based on these data, the system will divide the personnel into several talent unit groups according to their functions (such as clinical, medical technology, nursing, etc.), specialties (such as internal medicine, surgery, imaging, etc.) and ranks (such as resident physicians, attending physicians, deputy chief physicians, etc.).

[0029] Specifically, taking a tertiary hospital as an example, the data collection module first extracts raw data from the hospital's human resources management system, and after data cleaning and standardization, divides the hospital's 2,000 medical staff into 120 talent unit groups. For example, the senior cardiology physician group, the emergency department nursing group, and the imaging technician group. Each talent unit group has similar professional backgrounds and job functions, which facilitates subsequent accurate risk assessment and management.

[0030] The risk assessment module is used to take each talent unit group as the basic unit, conduct an assessment based on the preset talent loss evaluation factor, obtain the corresponding resignation tendency index, and calculate the talent loss risk level.

[0031] Among them, talent loss evaluation factors include job satisfaction, career development opportunities, work stress index and team relationship scores.

[0032] Specifically, taking the senior cardiologist group as an example, this module uses questionnaire surveys and daily assessment data analysis to quantify and score four evaluation factors. For example, job satisfaction is assessed through a resignation intention questionnaire, career development opportunities are analyzed through promotion opportunity statistics, work stress index is assessed through working hours and number of patients, and team relationship scores are determined through team collaboration evaluation. These data are processed by algorithms to ultimately derive the resignation tendency index and risk level of the group.

[0033] The position impact module is used to calculate the impact of position vacancies based on each talent unit group as the basic unit and according to the preset position criticality evaluation factors.

[0034] In this embodiment, through comprehensive analysis of various indicators, the possible impact of job vacancies is finally obtained.

[0035] Specifically, taking the oncology department as an example, this module will evaluate the impact of vacancies in specific positions on the department's medical service capabilities. For example, if an oncologist has unique expertise in radiotherapy technology and leaves, it will directly affect the implementation of some special treatment projects in the department. Therefore, the impact of the vacancy in this position is assessed as a high level.

[0036] The risk warning module is used to generate human resource warning signals based on the impact of job vacancies and the risk level of talent loss for each talent unit group.

[0037] In this embodiment, multiple warning levels are set, and the system will trigger warnings of different levels according to specific circumstances.

[0038] Specifically, when the system detects high-risk signals in multiple core positions of a department at the same time, it will immediately trigger a red warning, prompting the hospital management to take timely intervention measures. For example, when it is detected that there are three senior doctors in the anesthesiology department who are at risk of leaving, the system will issue an emergency warning signal.

[0039] The blockchain storage module is used to write the original data, scoring records and warning results in the evaluation process into the blockchain and establish an index structure based on time series.

[0040] The blockchain storage module records the data information generated by each of the above links into the blockchain. In this embodiment, the alliance chain technology is used to ensure the security and traceability of the data.

[0041] Specifically, the system records the original data, scoring records, warning results and other information generated during the evaluation process on the blockchain through timestamps, and establishes an index structure for easy query. For example, it can quickly retrieve the recent trend of talent loss risk changes in a department, or trace the specific reasons for the triggering of a warning signal, providing a reliable basis for management decisions.

[0042] In one embodiment, referring to Figure 2 , the data acquisition module specifically includes: The data preprocessing unit is used to standardize and clean the human resources data of each department of the hospital to obtain standardized basic human resources data.

[0043] In this embodiment, the non-standard original data is converted into basic data in a standard format through preset processing rules.

[0044] Specifically, taking the data processing of a certain hospital as an example, the unit first standardizes the professional title information, unifying different expressions such as "attending doctor", "attending physician", "resident doctor", and "resident physician" into standard titles; at the same time, it cleans the abnormal data, such as correcting and converting obviously erroneous values ​​in age data and numbers in string format, and finally forms a standardized basic data set.

[0045] The organizational analysis unit is used to analyze the organizational structure based on standardized basic human resources data and generate a departmental functional relationship diagram.

[0046] In this embodiment, an organizational structure model is constructed, the business connections between departments are analyzed, and ultimately an intuitive department function relationship diagram is generated.

[0047] Specifically, taking a tertiary hospital as an example, the unit found through analysis that there is a close medical collaboration between the Department of Cardiology and the Department of Cardiology Surgery, and that the medical staff of the two departments often need to conduct joint consultations and surgical cooperation. Based on this, the system establishes a strong correlation connection between the two departments in the functional relationship diagram, and marks the specific frequency and type of collaboration.

[0048] The collaborative calculation unit is used to calculate the business collaborative index according to the department functional relationship diagram, and generate a talent capability map and functional connection network based on the business collaborative index.

[0049] In this embodiment, by calculating the business synergy index, a talent capability map is established to form a functional connection network.

[0050] Specifically, the unit calculates the business synergy index by analyzing the consultation frequency and patient referral volume between the emergency department and various clinical departments. For example, it is found that the synergy index between the emergency department and the neurology department is relatively high. Based on this, key talents with dual-department work experience are marked in the talent capability map, and corresponding connections are established in the functional contact network.

[0051] The unit division unit is used to generate the initial talent unit group based on the talent capability map and functional connection network, and optimize and adjust the initial talent unit group according to the standardized basic human resource data to form the final talent unit group.

[0052] In this embodiment, an initial plan is first generated based on the talent capability map and functional contact network, and then optimized and adjusted based on the actual situation. For example, physicians who have both intensive care and ECMO technology are divided into special talent unit groups, rather than simply grouping them according to their professional title levels, so as to ensure that the grouping results are more in line with actual work needs. Through such optimization and adjustment, a talent unit group that can reflect the professional characteristics of talents and is easy to manage is finally formed.

[0053] In one embodiment, referring to Figure 3 , the risk assessment module specifically includes: The individual scoring unit is used to score each member in a single talent unit group according to the talent loss evaluation factor to obtain the first member score.

[0054] In this embodiment, evaluation information is collected through various methods such as questionnaire surveys, interview records, and behavioral data analysis.

[0055] The score calculation unit is used to perform weighted average calculation on the first member scores of all members in a single talent unit group to obtain the talent loss evaluation factor discrimination score.

[0056] Among them, after obtaining the individual scoring results, the score calculation unit calculates the overall evaluation value through a scientific weighting method. In this embodiment, the unit takes into account the differences in the importance of different evaluation factors, sets corresponding weight coefficients, and calculates the overall discrimination score of the talent unit group through weighted average calculation.

[0057] Specifically, a certain orthopedic physician group has 12 members, and the system sets weights of 0.3, 0.3, 0.2, and 0.2 for each member's four scores. By calculating the weighted average score of the 12 members, the final discriminant scores of the talent unit group on the four evaluation factors are 78 points, 65 points, 82 points, and 88 points, respectively.

[0058] The index generating unit is used to calculate the turnover tendency index by using the talent loss evaluation factor discrimination score.

[0059] The index generation unit calculates the turnover tendency index based on the talent loss evaluation factor discrimination score through a preset algorithm model. In this embodiment, the unit adopts a fuzzy comprehensive evaluation method to convert the multi-dimensional discrimination scores into a single turnover tendency index, providing an important basis for risk warning.

[0060] In one embodiment, referring to Figure 4 , the index generation unit performs the following steps: S410 , according to the pre-constructed judgment matrix A, solving the characteristic equation |λI-A|=0 to obtain the maximum eigenvalue λmax.

[0061] S420, calculate the consistency index, and obtain the average random consistency index RI by looking up the table according to the matrix order n, calculate the consistency ratio, and when CR<0.1, obtain the standardized weight vector W.

[0062] Among them, the consistency index CI = (λmax-n) / (n-1), the consistency ratio CR = CI / RI, and W = (w1,w2,w3,w4).

[0063] S430. Use the talent loss evaluation factor discrimination scores to establish an evaluation matrix R.

[0064] Where R = [r ij ]4×5, r ij It represents the degree of membership of the discrimination score of the i-th talent loss evaluation factor to the j-th evaluation level.

[0065] S440. Based on the weight vector W and the evaluation matrix R, a comprehensive evaluation vector B = W⊗R is obtained, and the final turnover tendency index I is calculated by performing an inner product operation with the quantitative score vector V=(20, 40, 60, 80, 100).

[0066] In one embodiment, referring to Figure 5 , the risk assessment module also includes: The historical analysis unit is used to obtain historical resignation data and calculate the resignation probability data of the talent unit group based on the historical resignation data.

[0067] In this embodiment, the unit establishes a database containing resignation records in the past five years, recording key information such as basic information of resigned personnel, reasons for resignation, and time of resignation, identifies resignation patterns through data mining technology, and establishes a prediction model.

[0068] Specifically, taking the radiology department of a hospital as an example, the unit found through analysis that the department has a high rate of resignation in March-April and September-October each year, and that medical technicians under the age of 35 with 3-5 years of work experience are the main resignation groups. Based on these characteristics, combined with the age structure and years of work distribution of the current radiology talent unit group, the system calculated that the resignation probability of this group in the next 6 months is 15%. This analysis method based on historical data can promptly discover potential resignation risk patterns.

[0069] The risk calculation unit is used to calculate the final talent loss risk level using the turnover tendency index and turnover probability data.

[0070] In this embodiment, the unit designs a two-dimensional risk assessment matrix, where the horizontal axis represents the turnover tendency index and the vertical axis represents the historical turnover probability, and the final risk level is determined through matrix positioning.

[0071] In one embodiment, referring to Figure 6 , the position impact module specifically includes: The scoring standard unit is used to set the scoring standard for job criticality evaluation factors.

[0072] Among them, job criticality evaluation factors include impact on medical quality, impact on patient services, impact on department operations and training costs.

[0073] The position scoring unit is used to score each position within a single talent unit group according to the position criticality evaluation factor to obtain the position impact score.

[0074] In this embodiment, the position scoring unit scores each position in four dimensions by collecting actual operation data and combining expert review opinions.

[0075] Specifically, in the evaluation of a chief physician position in cardiothoracic surgery, the medical quality impact score is 95 points (the physician is good at a unique minimally invasive surgical technique), the patient service impact score is 88 points (large daily average number of patients, high patient evaluation), the department operation impact score is 92 points (undertaking department teaching and management work), and the training cost score is 96 points (more than 15 years of clinical experience is required). These scores together constitute the impact score of the position.

[0076] The weight calculation unit is used to perform weighted average calculation on the job impact scores of all jobs in a single talent unit group to obtain the job criticality judgment score.

[0077] In this embodiment, the unit sets corresponding weight coefficients for positions of different levels and types according to the actual situation of the hospital and the opinions of experts.

[0078] The impact generation unit is used to calculate the final impact degree of job vacancy using the job criticality discrimination score and the hierarchical analysis method.

[0079] In this embodiment, the unit constructs a hierarchical structure of evaluation indicators, determines a discriminant matrix through expert scoring, and finally calculates the impact of job vacancies.

[0080] In one embodiment, referring to Figure 7 , the risk warning module specifically includes: The matrix construction unit is used to take each talent unit group as the basic unit, and input the corresponding talent loss risk level as row vector data and the corresponding job vacancy impact degree as column vector data into the preset human resource risk judgment matrix to obtain the corresponding human resource risk level.

[0081] In this embodiment, the unit uses the talent loss risk level (divided into levels I-IV) as the horizontal dimension and the degree of impact of job vacancies (divided into levels AD) as the vertical dimension, and determines the final human resource risk level through the cross-positioning of the two dimensions.

[0082] The early warning generation unit is used to generate a human resource early warning signal of a corresponding level according to the human resource risk level corresponding to each talent unit group and in combination with preset early warning rules.

[0083] Among them, the warning generation unit is responsible for triggering the corresponding warning mechanism according to the risk level.

[0084] In this embodiment, the unit has designed four levels of warning signals: red, orange, yellow and blue, and has formulated detailed warning rules and response measures.

[0085] Specifically, when the system detects that a group of senior neurosurgery physicians is in a "high-risk" area, an orange warning signal is automatically triggered. The warning information will be pushed to relevant managers through the hospital's OA system, and a detailed risk analysis report will be generated. The report includes the specific reasons for the risk (such as: the group's recent work stress index has increased significantly, the team relationship score has decreased, etc.), and proposes intervention suggestions (such as: adjusting work schedules, conducting team building activities, etc.). The system will also track the improvement effect after the warning, and automatically upgrade to a red warning if the risk continues to rise.

[0086] The early warning mechanism is set up with a gradient response principle. For example, the blue warning is mainly used to remind the management to pay attention to potential risks; the yellow warning requires relevant departments to formulate preventive measures; the orange warning requires the hospital leaders to intervene and take intervention actions; the red warning requires the activation of emergency plans and the adoption of emergency measures to prevent talent loss. Through this graded early warning mechanism, the hospital can take corresponding management measures according to the degree of risk and achieve precise control of human resource risks.

[0087] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0088] On the second aspect, the present application provides a blockchain-based human resources management method. The blockchain-based human resources management method of the present application is described below in combination with the above-mentioned blockchain-based human resources management system.

[0089] Reference Figure 8 , a human resource management method based on blockchain, comprising the following steps: S810. Obtain the human resource data of each department of the hospital and divide the personnel into several talent unit groups according to their functions, professions and ranks.

[0090] S820. Taking each talent unit group as a basic unit, an assessment is performed according to a preset talent loss evaluation factor to obtain a corresponding turnover tendency index, and a talent loss risk level is calculated.

[0091] Among them, talent loss evaluation factors include job satisfaction, career development opportunities, work stress index and team relationship scores.

[0092] S830. Take each talent unit group as the basic unit and calculate the impact of job vacancies based on the preset job criticality evaluation factors.

[0093] S840. Generate human resource early warning signals based on the degree of impact of job vacancies and the risk level of talent loss for each talent unit group.

[0094] S850. Write the original data, scoring records and warning results in the evaluation process into the blockchain, and establish an index structure based on time series.

[0095] In one embodiment, human resource data of each department of a hospital is obtained, and personnel are divided into a number of talent unit groups according to their functions, specialties, and ranks, specifically including the following steps: Standardize and clean the human resource data of each department of the hospital to obtain standardized basic human resource data; Conduct organizational structure analysis based on standardized human resources basic data and generate departmental functional relationship diagrams; Calculate the business synergy index based on the departmental functional relationship diagram, and generate a talent capability map and functional connection network based on the business synergy index; The initial talent unit group is generated based on the talent capability map and functional connection network, and then optimized and adjusted according to the standardized basic human resource data to form the final talent unit group.

[0096] In one embodiment, each talent unit group is taken as a basic unit and an assessment is performed according to a preset talent loss assessment factor, specifically including the following steps: Each member of a single talent unit group is scored according to the talent loss evaluation factor to obtain the first member score; The weighted average calculation of the first member scores of all members in a single talent unit group is performed to obtain the discrimination score of the talent loss evaluation factor; The turnover tendency index is calculated by using the discrimination scores of the talent loss evaluation factors.

[0097] In one embodiment, the turnover tendency index is calculated by using the talent loss evaluation factor discrimination score, which specifically includes the following steps: According to the pre-constructed judgment matrix A, solve the characteristic equation |λI-A|=0 to obtain the maximum eigenvalue λmax; Calculate the consistency index CI = (λmax-n) / (n-1), and obtain the average random consistency index RI by looking up the table according to the matrix order n, calculate the consistency ratio CR = CI / RI, and when CR<0.1, obtain the standardized weight vector W = (w1,w2,w3,w4); Using the discrimination scores of the talent loss evaluation factors, an evaluation matrix R = [rij]4×5 is established, where rij represents the degree of membership of the discrimination score of the i-th talent loss evaluation factor to the j-th evaluation level; Based on the weight vector W and the evaluation matrix R, the comprehensive evaluation vector B = W⊗R is obtained, and the final turnover tendency index I is calculated through the inner product operation with the quantitative score vector V=(20,40,60,80,100).

[0098] In one embodiment, calculating the risk level of talent loss specifically includes the following steps: Obtain historical resignation data, and calculate the resignation probability data of the talent unit group based on the historical resignation data; The final risk level of talent loss is calculated using the turnover intention index and turnover probability data.

[0099] In one embodiment, each talent unit group is used as a basic unit, and the impact degree of job vacancy is calculated according to a preset job criticality evaluation factor, specifically including the following steps: Set scoring criteria for job criticality evaluation factors, where job criticality evaluation factors include impact on medical quality, impact on patient service, impact on department operation, and training cost; Each position in a single talent unit group is scored according to the position criticality evaluation factor to obtain the position impact score; The weighted average calculation of the position impact scores of all positions in a single talent unit group is performed to obtain the position criticality judgment score; The final impact of job vacancies is calculated using job criticality discrimination scores and the analytic hierarchy process.

[0100] In one embodiment, based on the degree of impact of job vacancies and the risk level of talent loss of each talent unit group, a human resources early warning signal is generated, which specifically includes the following steps: Taking each talent unit group as the basic unit, the corresponding talent loss risk level is used as the row vector data, and the corresponding job vacancy impact degree is used as the column vector data, which are input into the preset human resource risk discrimination matrix to obtain the corresponding human resource risk level; According to the human resource risk level corresponding to each talent unit group and combined with the preset warning rules, a human resource warning signal of the corresponding level is generated.

[0101] In one embodiment, the present application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Fig. 9 As shown. The electronic device includes a processor, a memory and a network interface connected by a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements the steps performed by a human resources management system based on blockchain.

[0102] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0103] In one embodiment, an electronic device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0104] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0105] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A human resource management system based on blockchain, characterized in that: include: The data collection module is used to obtain the human resource data of each department of the hospital and divide the personnel into several talent unit groups according to their functions, specialties and ranks; A risk assessment module, which is used to evaluate each talent unit group as a basic unit according to preset talent loss evaluation factors, obtain a corresponding turnover tendency index, and calculate a talent loss risk level, wherein the talent loss evaluation factors include job satisfaction, career development opportunities, work stress index, and team relationship score; A position impact module is used to calculate the impact of position vacancies based on preset position criticality evaluation factors, taking each talent unit group as a basic unit; A risk warning module, for generating a human resources warning signal based on the degree of impact of job vacancies and the risk level of talent loss of each talent unit group; The blockchain storage module is used to write the original data, scoring records and warning results in the evaluation process into the blockchain and establish an index structure based on time series.

2. The human resource management system based on blockchain according to claim 1 is characterized in that: The data acquisition module specifically includes: The data preprocessing unit is used to standardize and clean the human resource data of each department of the hospital to obtain standardized basic human resource data; An organizational analysis unit, used to perform organizational structure analysis based on the standardized human resources basic data and generate a department function relationship diagram; A collaborative calculation unit, used to calculate a business collaborative index according to the department functional relationship diagram, and generate a talent capability map and a functional connection network based on the business collaborative index; The unit division unit is used to generate an initial talent unit group based on the talent capability map and the functional contact network, and optimize and adjust the initial talent unit group according to the standardized human resource basic data to form a final talent unit group.

3. The human resource management system based on blockchain according to claim 1 is characterized in that: The risk assessment module specifically includes: An individual scoring unit, used to score each member in a single talent unit group according to the talent loss evaluation factor to obtain a first member score; A score calculation unit, used for performing weighted average calculation on the first member scores of all members in a single talent unit group to obtain a talent loss evaluation factor discrimination score; The index generating unit is used to calculate the turnover tendency index by using the talent loss evaluation factor discrimination score.

4. The human resource management system based on blockchain according to claim 3 is characterized in that: The index generation unit performs the following steps: According to the pre-constructed judgment matrix A, solve the characteristic equation |λI-A|=0 to obtain the maximum eigenvalue λmax; Calculate the consistency index CI = (λmax-n) / (n-1), and obtain the average random consistency index RI by looking up the table according to the matrix order n, calculate the consistency ratio CR = CI / RI, and when CR<0.1, obtain the standardized weight vector W = (w1,w2,w3,w4); Using the discrimination scores of the talent loss evaluation factors, an evaluation matrix R = [rij]4×5 is established, where rij represents the degree of membership of the discrimination score of the i-th talent loss evaluation factor to the j-th evaluation level; Based on the weight vector W and the evaluation matrix R, the comprehensive evaluation vector B = W⊗R is obtained, and the final turnover tendency index I is calculated through the inner product operation with the quantitative score vector V=(20,40,60,80,100).

5. The human resource management system based on blockchain according to claim 3 is characterized in that: The risk assessment module also includes: A historical analysis unit, used to obtain historical resignation data, and calculate resignation probability data of a talent unit group based on the historical resignation data; The risk calculation unit is used to calculate the final talent loss risk level by using the turnover tendency index and the turnover probability data.

6. The human resource management system based on blockchain according to claim 1 is characterized in that: The position impact module specifically includes: A scoring standard unit, used to set scoring standards for job criticality evaluation factors, wherein the job criticality evaluation factors include medical quality impact, patient service impact, department operation impact and training cost; A position scoring unit is used to score each position in a single talent unit group according to a position criticality evaluation factor to obtain a position impact score; A weight calculation unit, used to perform weighted average calculation on the position impact scores of all positions in a single talent unit group to obtain a position criticality discrimination score; The impact generation unit is used to calculate the final impact degree of job vacancy by using the job criticality discrimination score and the hierarchical analysis method.

7. The blockchain-based human resource management system according to claim 1 is characterized in that: The risk warning module specifically includes: A matrix construction unit is used to take each of the talent unit groups as a basic unit, input the corresponding talent loss risk level as row vector data and the corresponding job vacancy impact degree as column vector data into a preset human resource risk discrimination matrix to obtain the corresponding human resource risk level; The warning generation unit is used to generate a human resource warning signal of a corresponding level according to the human resource risk level corresponding to each talent unit group in combination with preset warning rules.

8. A human resource management method based on blockchain, characterized in that: The human resource management system based on blockchain applied to any one of claims 1 to 7 comprises the following steps: Obtain human resource data of each department of the hospital and divide the personnel into several talent unit groups according to their functions, specialties and ranks; Taking each talent unit group as a basic unit, an assessment is performed according to a preset talent loss evaluation factor to obtain a corresponding turnover tendency index, and a talent loss risk level is calculated, wherein the talent loss evaluation factor includes job satisfaction, career development opportunities, work stress index and team relationship score; Taking each talent unit group as a basic unit, the impact degree of job vacancies is calculated according to preset job criticality evaluation factors; Generate a human resources early warning signal based on the degree of impact of job vacancies and the risk level of talent loss for each talent unit group; The original data, scoring records and warning results in the evaluation process are written into the blockchain, and an index structure based on time series is established.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps performed by the blockchain-based human resources management system described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps performed by the blockchain-based human resources management system according to any one of claims 1 to 7 are implemented.

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