Intelligent talent allocation management system and method for chain enterprises

Abstract

The invention discloses an intelligent talent allocation management system and method for chain enterprises, and the method comprises the steps: collecting the flow data and knowledge transmission records of each store, recognizing organization memory nodes through network analysis, and constructing a knowledge fracture risk conduction diagram; frequency features are extracted based on shock wave simulation and Fourier transform, and a cascade influence path of personnel loss is predicted; obtaining a capability-load mismatch coefficient through phase analysis, and generating a deployment opportunity window; a personnel flow potential field is constructed, capability diffusion simulation is carried out, and a multi-dimensional deployment decision space is generated by using tensor operation; carrying out gradient search in the decision space to identify an optimal balance point, generating a ripple allocation path set, and optimizing to obtain an optimal sequence; detecting tissue knowledge density change after deployment is executed, and triggering knowledge structure recombination based on a phase change critical point. According to the method, conversion from passive response to active prediction is realized, the risk of knowledge fracture can be effectively prevented, and talent resource allocation is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of human resource management, and in particular to an intelligent talent deployment management system and method for chain enterprises. Background Art

[0002] Chain businesses, a key organizational form in modern commerce, typically operate dozens or even hundreds of decentralized stores, posing significant challenges in talent management. Significant disparities in personnel capabilities and uneven workloads exist across stores, leaving some stores oversupplied with talent while others are understaffed, leading to overall operational inefficiency. Furthermore, the sudden departure of key employees often triggers a chain reaction, resulting in a loss of experience and business disruptions, seriously impacting the company's continued development.

[0003] Existing talent deployment methods rely primarily on manual judgment and simple statistical analysis, lacking the ability to deeply analyze and predict personnel turnover trends. Traditional methods are unable to effectively identify risks in key positions within an organization, struggle to predict the cascading effects of personnel changes, and lack a scientific assessment of skill transfer models. These technical limitations result in a lack of foresight and systematic approach to deployment decisions, often resulting in a reactive, "treating the symptoms rather than the root cause" approach, making it difficult to optimize the allocation of talent resources and effectively transfer organizational experience. Summary of the Invention

[0004] The present invention provides an intelligent talent allocation management system and method for chain enterprises. The system aims to achieve accurate prediction of the cascading impact of staff loss and scientific modeling of the law of capability diffusion by constructing a knowledge fracture risk transmission map and a multi-dimensional allocation decision space. Based on ripple-type allocation path optimization and knowledge structure optimization control, an intelligent allocation plan that takes into account both knowledge protection and capability diffusion is formed, providing chain enterprises with a forward-looking and systematic talent resource optimization and allocation solution.

[0005] The first aspect of the present invention provides an intelligent talent deployment management method for chain enterprises, comprising the following steps: Collect personnel flow data and knowledge transfer records from each store of the chain enterprise, perform network analysis on the knowledge transfer records to identify organizational memory nodes, obtain node vulnerabilities based on the organizational memory nodes, and construct a knowledge fracture risk transmission map based on the node vulnerabilities; Based on the knowledge gap risk transmission diagram, shock wave simulation is performed to predict the cascading impact path of staff loss, the shock frequency characteristics are extracted by Fourier transform on the cascading impact path, and the key nodes requiring pre-positioning of buffer personnel are identified based on the shock frequency characteristics; Extracting the personnel capacity density distribution and business load curve of each store based on the personnel flow data, identifying overcapacity areas based on the personnel capacity density distribution, performing phase analysis on the overcapacity areas and the business load curve to obtain a capacity-load mismatch coefficient, and generating a deployment opportunity window based on the capacity-load mismatch coefficient; Constructing a personnel flow potential field based on the deployment opportunity window, performing capability diffusion simulation to deduce the impact radius in the personnel flow potential field, and performing tensor operations on the impact radius, the key nodes, and the capability-load mismatch coefficient to generate a multi-dimensional deployment decision space; Performing a gradient search in the multidimensional deployment decision space to identify an optimal balance point between knowledge protection and capability diffusion, generating a ripple deployment path set based on the optimal balance point, and optimizing the ripple deployment path set to obtain an optimal deployment sequence; After executing the optimal deployment sequence, the organizational knowledge density is detected, the phase transition critical point is identified based on the knowledge density, and the knowledge structure optimization is triggered based on the phase transition critical point to complete the deployment management of talents.

[0006] A second aspect of the present invention provides an intelligent talent deployment and management system for chain enterprises, comprising: The knowledge analysis module is used to collect personnel flow data and knowledge transfer records from each store of the chain enterprise, perform network analysis on the knowledge transfer records to identify organizational memory nodes, obtain node vulnerabilities based on the organizational memory nodes, and construct a knowledge fracture risk transmission map based on the node vulnerabilities; An impact prediction module is configured to perform shock wave simulation based on the knowledge fracture risk transmission diagram to predict the cascading impact path of staff loss, perform Fourier transform on the cascading impact path to extract impact frequency characteristics, and identify key nodes requiring pre-positioning of buffer personnel based on the impact frequency characteristics; A capacity analysis module is configured to extract the personnel capacity density distribution and business load curve of each store, identify overcapacity areas based on the personnel capacity density distribution, perform phase analysis on the overcapacity areas and the business load curve to obtain a capacity-load mismatch coefficient, and generate a deployment opportunity window based on the capacity-load mismatch coefficient; A deployment decision module is configured to construct a personnel flow potential field based on the deployment opportunity window, simulate the diffusion of capabilities in the personnel flow potential field to deduce the impact radius, and perform tensor operations on the impact radius, the key nodes, and the capability-load mismatch coefficient to generate a multidimensional deployment decision space; a path optimization module configured to perform a gradient search in the multidimensional deployment decision space to identify an optimal balance point between knowledge protection and capability diffusion, generate a ripple deployment path set based on the optimal balance point, and optimize the ripple deployment path set to obtain an optimal deployment sequence; The phase change control module is used to detect the organizational knowledge density after executing the optimal deployment sequence, identify the phase change critical point based on the knowledge density, trigger the knowledge structure optimization based on the phase change critical point, and complete the deployment management of talents.

[0007] The beneficial effects of the present invention are reflected in the following points: First, by constructing a knowledge fracture risk transmission map and shock wave simulation technology, the present invention can accurately predict the cascading impact of the loss of key personnel on the organization, identify the key nodes that need to be configured with buffer personnel in advance, and effectively avoid the passive situation of "one person resigns and the business stops" in the traditional method, thereby improving the organization's risk resistance and business continuity. Secondly, the present invention introduces phase analysis and tensor operation technology, which can accurately identify the capacity-load mismatch of each store and generate the best deployment opportunity window, realizing the transformation from extensive personnel transfer to precise capacity matching, improving the efficiency and success rate of talent deployment, and avoiding resource waste and organizational shock. Finally, the present invention adopts a ripple-type deployment path design and knowledge structure optimization control mechanism to reduce the impact of deployment on organizational stability by diffusing layer by layer, and at the same time establishes a dynamic monitoring and feedback optimization system to ensure the continuity and adaptability of the deployment effect, providing a complete technical solution for chain enterprises to achieve intelligent and scientific management of talent resources.

[0008] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The accompanying drawings herein illustrate specific examples of the technical solutions described in the present invention, and together with the specific implementation methods constitute a part of the specification, and are used to explain the technical solutions, principles and effects of the present invention.

[0010] Unless otherwise specified or defined, the same reference numerals in different drawings represent the same or similar technical features, and the same or similar technical features may also be represented by different reference numerals.

[0011] Figure 1 The present invention is a flowchart of an intelligent talent deployment management method for chain enterprises.

[0012] Figure 2 This is a structural diagram of an intelligent talent deployment and management system for chain enterprises according to the present invention. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0014] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0015] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0016] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0017] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0018] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0019] The technical solutions of the embodiments of this application are introduced below.

[0020] like Figure 1As shown, the embodiment of the present invention provides an intelligent talent deployment management method for chain enterprises, including the following steps S110 to S160: Step S110: Collect personnel flow data and knowledge transfer records from each store of the chain enterprise, perform network analysis on the knowledge transfer records to identify organizational memory nodes, obtain node vulnerability based on the organizational memory nodes, and construct a knowledge rupture risk transmission map based on the node vulnerability.

[0021] Specifically, through the chain's human resources and knowledge management platforms, personnel turnover data and knowledge transfer records are collected from each store. This personnel turnover data covers all types of personnel changes, including hires, resignations, transfers, and promotions, and records multi-dimensional attributes such as timestamps, department affiliations, job levels, and professional skills. For example, complete information is recorded for the transfer of top-tier store manager Zhang from store A in the core business district to store B in the newly developed business district as a regional manager, including the transfer date, the original five stores under his supervision, and the new 15 stores under his management. Knowledge transfer records are captured through multiple channels: training records the mentoring relationship in which Manager Zhang imparts sales skills and team management experience to new store managers; project management tracks Manager Zhang's experience sharing and team collaboration during the store opening; internal communication platforms analyze the frequency and depth of discussions between Manager Zhang and other store managers on sales strategies and customer service techniques; and document management records access to the management systems and sales training materials created by Manager Zhang. Data collection utilizes a combination of real-time synchronization and batch import. The real-time interface captures immediate personnel changes and knowledge exchange events, while batch processing integrates historical data. Data preprocessing includes steps such as format standardization, outlier cleaning, and missing value interpolation to ensure data quality. By collecting and standardizing multi-source heterogeneous data, we can obtain personnel flow data and knowledge transfer records.

[0022] Conduct in-depth network analysis on the collected knowledge transfer records to identify the memory nodes that play a key role in the organizational knowledge system. The network is constructed using a directed weighted graph model. Where V represents the set of employee nodes, E represents the set of knowledge transfer edges, and W represents the transfer intensity weight. The definition of edges is based on various knowledge interaction behaviors: direct teaching relationships (mentorship, mentoring) are given the highest weight, knowledge sharing in project collaboration is graded and weighted according to the depth of collaboration, and document co-editing and technical discussions are weighted according to frequency and content complexity. Network centrality analysis identifies key nodes, and degree centrality It measures the number of direct connections between nodes, where aij is an adjacency matrix element and n is the total number of nodes. Betweenness centrality quantifies the role of a node as a bridge in the knowledge transfer path, that is, the degree to which the employee plays a key role in the knowledge transfer path. Eigenvector centrality considers the importance of a node's neighbors and is obtained by solving the characteristic equation. For example, Manager Zhang, as a regional gold medal store manager, imparts sales skills and management experience to five stores in the business district and eight stores in the adjacent area, thus exhibiting high degree centrality and betweenness centrality in the knowledge network. Clustering coefficient analysis identifies knowledge communities and reflects the closeness of node neighborhoods. Community detection algorithms identify subnetworks of knowledge transfer and use modularity optimization methods to divide the network into communities with dense internal connections and sparse external connections. Organizational memory nodes are identified based on a combination of multidimensional centrality indicators and community structure analysis.

[0023] Based on the identified organizational memory nodes, the vulnerability of each node is evaluated and the impact of its loss on the organizational knowledge system is quantified. The calculation of node vulnerability adopts a multi-dimensional evaluation model. , where V(i) is the comprehensive vulnerability of node i, I(i) is the irreplaceability index, R(i) is the knowledge concentration, S(i) is the inheritance risk coefficient, and α, β, and γ are weighting parameters. The irreplaceability index is directly assessed using the node's betweenness centrality. A high betweenness centrality indicates that the node's role as a bridge in the knowledge dissemination path is difficult to replace. This index is also adjusted to account for the proportion of employees with similar skills. For example, Manager Zhang, as the only store manager in the region who possesses VIP customer care skills and high-end product sales strategies, is highly irreplaceable. Knowledge concentration is calculated by combining a node's degree centrality and eigenvector centrality. Degree centrality reflects the number of direct knowledge connections a node has, while eigenvector centrality reflects the node's influence in the overall knowledge network. A weighted combination of the two yields the degree of knowledge concentration. The inheritance risk coefficient is assessed by combining the probability of staff turnover, knowledge transferability, and the node clustering coefficient. A low clustering coefficient indicates that the node's knowledge connections are relatively isolated, resulting in a higher inheritance risk. Although Manager Zhang is knowledgeable, his core skills are primarily passed on through informal communication, and formal knowledge documentation is poor, posing a high inheritance risk. Based on the multi-dimensional evaluation and dynamic analysis mechanism, the node vulnerability is determined.

[0024] Node vulnerability is used to construct a transmission model of knowledge discontinuity risk in organizational networks. The risk transmission graph is represented by a weighted directed graph, where nodes represent employees or departments, edges represent risk transmission paths, and edge weights reflect transmission intensity. For example, when Manager Zhang (a high-vulnerability node) faces the risk of leaving, the five stores under his jurisdiction will be directly affected, and the business fluctuations of these five stores will further affect other stores in adjacent business districts, forming a clear risk transmission chain. The formula for calculating transmission intensity is: , where T(i, j) is the risk transmission intensity from node i to node j, V(i) is the vulnerability of the source node, D(i, j) is the knowledge dependency coefficient, and B(j) is the buffering capacity of the target node. The knowledge dependency coefficient is determined by analyzing the knowledge flow and collaboration between nodes, reflecting the degree of knowledge dependence of one employee on another. Buffering capacity reflects a node's ability to withstand knowledge gaps and is determined by both knowledge redundancy and employee knowledge absorption capacity. For example, if a store managed by Manager Zhang has multiple skilled sales staff and new employees have strong learning abilities, the store's buffering capacity is strong, effectively withstanding the impact of Manager Zhang's departure. The cascading effects of risk transmission are simulated through iterative computation, and the transmission paths are identified using a graph traversal algorithm to locate high-risk transmission links. Through vulnerability-driven transmission model construction and risk path analysis, a knowledge gap risk transmission map is constructed.

[0025] Step S120 , based on the knowledge gap risk transmission diagram, shock wave simulation is performed to predict the cascade impact path of staff loss, the cascade impact path is subjected to Fourier transform to extract the shock frequency characteristics, and the key nodes requiring pre-positioning of buffer personnel are identified based on the shock frequency characteristics.

[0026] In some embodiments, the shock wave simulation based on the knowledge gap risk transmission map to predict the cascading impact path of staff loss includes: setting an initial shock source based on the knowledge gap risk transmission map; analyzing the initial shock source and calculating the shock wave propagation speed; constructing a time series diffusion mechanism based on the propagation speed; and using the time series diffusion mechanism to deduce the cascading impact path.

[0027] According to the node vulnerability ranking and risk concentration area distribution in the knowledge fracture risk conduction diagram, the initial source point of the shock wave simulation is scientifically set. The selection strategy of the initial shock source gives priority to the top 10% nodes with the highest vulnerability. These nodes are usually senior employees who master core technologies or manage key processes. The types of shock sources are divided into three categories: point source, line source and surface source: point source simulates the departure of a single key employee, such as the sudden departure of Manager Zhang, the gold medal store manager; line source simulates the loss of the entire team or department, such as all store managers in Manager Zhang's area collectively jumping ship; surface source simulates large-scale layoffs or organizational reorganization. The setting of shock intensity takes into account the knowledge stock and network position of employees. , where V(i) represents the node vulnerability and C(i) represents the centrality index, derived through a weighted synthesis of the previously calculated degree centrality, betweenness centrality, and eigenvector centrality. The temporal characteristics include instantaneous shocks (sudden departures) and gradual shocks (planned departures), described by step and ramp functions, respectively. The initial shock source was determined based on risk distribution analysis and parameter settings.

[0028] Analyze the initial shock source and calculate the propagation speed of the knowledge fracture shock wave in the organizational network. The propagation speed is affected by the characteristics of the shock source and the network structure. The calculation formula is: ,in is the basic propagation speed of source point i, d(i, j) is the network distance from the source point to the target point, is the initial shock intensity, α is the attenuation coefficient, and β is the intensity amplification factor. Different job types determine the basic speed. The impact of leaving a technical position spreads at a speed of about 2 nodes / week, while the impact of leaving a management position can reach 3-4 nodes / week due to its greater influence. For example, when Manager Zhang resigned as a regional gold medal store manager, his impact spread to the 5 stores he directly managed in the first week, spread to 8 stores in the adjacent area in the second week, and spread to 20 stores in the entire city in the third week. The impact of shock intensity is reflected through nonlinear terms. Strong shocks Stimulates rapid propagation, speed can be increased by 50%; weak impact It propagates slowly and is easily absorbed by the tissue. Directional propagation velocity takes into account the anisotropy of the tissue structure, with the fastest propagation along the hierarchical direction and limited cross-sector propagation. The shock wave propagation velocity is derived based on a differentiated analysis of the impact source intensity and type.

[0029] Using the propagation velocity field, a temporal diffusion mechanism for knowledge discontinuity shocks is constructed. This temporal diffusion model employs a modified SIR (Susceptible-Infectious-Recovered) framework, categorizing employee status into three categories: stable (S), unstable (I), and turnover (R). The probability of state transitions is related to the shock intensity and propagation velocity. The probability of an employee transitioning from a stable state to an unstable state is proportional to the intensity of the shock received, while the probability of transitioning from an unstable state to a turnover state takes into account personal factors and external opportunities. Diffusion directionality is reflected by the anisotropy of organizational structure, with intra-level diffusion being fastest and cross-level diffusion being limited. Time delay effects are addressed by introducing memory effects, where employees' memories of previous shocks influence current state transitions. Multi-scale characteristics are described using a hierarchical diffusion model, with different diffusion rates at different organizational levels. A random perturbation term simulates the impact of uncertainties, using a Gaussian white noise approximation. The temporal diffusion mechanism is constructed through dynamic modeling and parameter configuration driven by propagation velocity.

[0030] The constructed time-series diffusion mechanism was used to deduce the cascading impact paths of knowledge discontinuity shocks. Path deduction employed Monte Carlo simulation to generate multiple sets of possible propagation trajectories, each simulation accounting for random parameter fluctuations and uncertainty in initial conditions. Cascading effects were identified through a threshold mechanism: when the knowledge deficit at a node exceeded a critical value, θ_c, the node became a new shock source, triggering the next level of propagation. Path bifurcation was achieved by calculating propagation probabilities. The primary path was identified using the maximum probability principle, while secondary paths were retained for risk assessment. For example, after Manager Zhang's resignation, the impact path split into two branches: one branch spread to the five stores under his direct management, resulting in a decline in sales performance and team stability; the other branch spread to the regional training system, impacting the development of new employees and the transfer of experience. Time stamps were used to record the moment each node was affected, forming a time-series impact diagram. Critical paths were identified by calculating path importance, visualized using a dynamic network diagram, with node color depth indicating the degree of impact. Cascading impact paths were deduced through iterative simulation and statistical analysis of the diffusion mechanism.

[0031] In some embodiments, performing Fourier transform on the cascade impact path to extract the impact frequency characteristics includes: extracting a time domain impact signal based on the impact path; performing fast Fourier transform on the time domain impact signal to obtain a transformation result; analyzing the transformation result to identify the main frequency component; and generating the impact frequency characteristics based on the main frequency component.

[0032] The signal sequence reflecting the time-varying impact intensity of knowledge rupture is extracted from the cascade impact path. The construction of the time domain signal is based on the time series of the influence degree of each node on the path. , where w(i) is the node weight and I(i, t) is the influence strength of node i at time t. The sampling strategy is determined by the organization's decision-making cycle, typically measured in weeks, with a sampling frequency of f_s = 1 / week. This satisfies the Nyquist sampling theorem to capture monthly and quarterly cyclical changes. Signal preprocessing includes detrending to eliminate long-term growth or decay trends, normalization to map the signal amplitude to the interval [-1, 1], and zero padding to extend the signal length to integer powers of 2 to improve FFT computational efficiency. Principal component analysis is used to process multipath signals to extract the main impact patterns. Time-domain impact signals are obtained through time series feature extraction and signal processing of the impact paths.

[0033] Perform fast Fourier transform on the extracted time domain impulse signal to convert the time domain features to the frequency domain for analysis. The FFT calculation uses the Cooley-Tukey algorithm, and the transformation formula is: , where x(n) is the time domain signal, X(k) is the frequency domain representation, N is the signal sequence length, n is the time domain sampling point index and n=0, 1, 2, ..., N-1, and k is the frequency domain sampling point index and k=0, 1, 2, ..., N-1. exp(-j2πkn / N) is a complex exponential function, representing a complex rotation factor with a specific frequency and phase. During the transformation process, the time domain sampling point x(n) is projected onto the corresponding frequency component, thereby extracting the amplitude and phase characteristics of the signal at different frequencies. Where exp is the natural exponential function and j is the imaginary unit. The fast Fourier transform uses this complex exponential function to convert the time domain discrete signal x(n) of length N into N complex frequency domain coefficients X(k), achieving decomposition and analysis of the signal's frequency components. The choice of window function affects spectral resolution and leakage, and the Hanning window is used to balance the two. The power spectral density calculation provides the energy distribution of each frequency component, and the phase spectrum reveals the phase relationship between different frequency components. Frequency resolution (Δf = f_s / N) determines the minimum resolvable frequency interval. Resolution can be improved by increasing the signal length or interpolating. When converting a double-sided spectrum to a single-sided spectrum, the amplitudes of all components except the DC component are doubled. Cross-spectral analysis calculates the frequency domain correlation of signals from different paths, and the confidence interval of the spectrum estimate is determined using a bootstrap method. The FFT transform result is obtained through the efficient computation of the FFT algorithm and the extraction of frequency domain parameters.

[0034] In-depth analysis of the FFT transform results identifies the dominant frequency components of impulse propagation. A peak detection algorithm searches for local maxima in the power spectrum, using a threshold of three times the average power to screen for significant peaks. The physical interpretation of the dominant frequency provides valuable business guidance: low-frequency components (periods > 3 months) reflect the long-term impact of organizational restructuring, such as annual staff turnover peaks; medium-frequency components (periods 1-3 months) correspond to project cycles and quarterly performance review rhythms, such as staff adjustments following quarterly performance evaluations; and high-frequency components (periods < 1 month) reflect short-term fluctuations in daily operations, such as weekend shift adjustments. For example, analysis of historical data for Manager Zhang's region revealed a distinct seasonal pattern (low frequency) in staff turnover, with peaks occurring after the Spring Festival and at the end of the mid-year promotional season, compounded by medium-frequency fluctuations during the monthly performance review cycle. Harmonic analysis identifies integer multiples of the fundamental frequency, and the energy contribution of the frequency components quantifies the relative importance of each frequency. Time-frequency analysis tracks the temporal evolution of the dominant frequency, with resonant frequencies corresponding to the organization's inherent oscillation modes. The dominant frequency components are identified through analysis and feature extraction of the transform results.

[0035] Based on the identified main frequency components, a frequency eigenvector describing the shock propagation characteristics is constructed. Contains the frequency value f, normalized amplitude A, initial phase φ and quality factor Q of m main frequency components. Quality factor Q reflects the sharpness of the frequency component; a high Q value indicates a stable shock pattern with strong periodicity. Frequency features are categorized by period length: fast shock (T < 1 week), regular shock (1 week ≤ T < 1 month), and slow shock (T ≥ 1 month). The time-varying nature of the features is analyzed using a sliding window with a window length of 3 months and a sliding step of 1 week, forming a feature evolution trajectory. Frequency fingerprints are generated using feature hashing, dominant patterns are extracted through cluster analysis, and anomalous frequencies are labeled based on statistical outlier detection.

[0036] Based on the extracted impact frequency characteristics, the key positions in the organization where buffer personnel need to be deployed are identified through frequency response analysis. The buffer demand is quantified using the formula , where R(i, f) is the response amplitude of node i to frequency f, P(f) is the probability of occurrence of that frequency in the shock signature, and V(i) is the node vulnerability. This formula comprehensively considers the node's dynamic response characteristics, shock frequency distribution, and static vulnerability. For example, analysis revealed that Manager Zhang's position, the top store manager, responds strongly to medium-frequency shocks (quarterly staffing adjustments) and has a high vulnerability. Therefore, it is recommended to pre-position two deputy store managers with potential to become store managers in this area as a buffer. Resonance risk assessment focuses on nodes with natural frequencies close to the main shock frequency. Such nodes will experience an amplification effect under specific frequency shocks. Buffer strategies are differentiated based on frequency characteristics: high-frequency shock nodes are equipped with a rapid-response temporary support mechanism; medium-frequency shock nodes establish a rotation and knowledge backup system; and low-frequency shock nodes implement a long-term successor development plan. Through frequency response analysis and node vulnerability assessment, key nodes requiring pre-positioned buffer personnel are identified.

[0037] Step S130: extract the personnel capacity density distribution and business load curve of each store based on the personnel flow data, identify the excess capacity area based on the personnel capacity density distribution, perform phase analysis on the excess capacity area and the business load curve to obtain the capacity-load mismatch coefficient, and generate a deployment opportunity window based on the capacity-load mismatch coefficient.

[0038] Specifically, we use personnel flow data, combined with the chain enterprise's human resource management and business operation platform, to extract the personnel capacity distribution and business operation load data of each store. The 24-month historical record of personnel flow data provides a statistical basis for the assessment of capacity stability. By analyzing the frequency of resignations in each position, we calculate the turnover rate, identify internal flow patterns based on transfer records, and evaluate the capacity growth trajectory based on promotion data. The definition of capacity density uses the capacity strength of the unit organizational space to represent the capacity. , where S(i) is the skill level score of employee i, ranging from 1 to 10. K(i) is the knowledge value coefficient, determined based on the importance of the position and the scarcity of the skills. F(i) is a stability factor based on turnover data, calculated from the historical turnover rate of the employee's position level. δ is a position function, identifying the employee's position in the organizational space. A is the store's organizational area. For example, Manager Zhang's store in the core business district has two senior sales consultants (with a skill level of 8 or above) and one gold medal store manager per square meter, while the new suburban store under his jurisdiction has only one ordinary salesperson (with a skill level of 5) per square meter, resulting in a significant difference in capability density. Capability assessment dimensions include professional skills, management capabilities, innovation capabilities, and collaboration capabilities. They are determined using a 360-degree assessment combined with performance data. The construction of the business load curve is based on a multi-dimensional analysis of historical store operational data. Key operational indicators such as transaction data (sales, order volume), customer flow data (number of customers, length of stay), and service data (number of inquiries, number of complaints handled) are collected. A comprehensive load model is constructed through time series analysis. . Among them, L_base(t) is the base load calculated based on the historical average business intensity, reflecting the normal operating level of the store; L_season(t) is the periodic load change extracted through seasonal decomposition, including regular fluctuations such as holiday peaks and promotional activities; L_random(t) is the random fluctuation term after deducting trend and seasonal factors, reflecting the impact of emergencies and changes in the market environment. Various types of data are sampled on an hourly or daily basis, and after standardization, they are weighted and combined to form a load time curve that reflects the actual business pressure of the store. Through correlation analysis and standardization of personnel flow data and multi-source business data, the personnel capacity density distribution and business load curve are extracted.

[0039] Based on the extracted capacity density distribution data, spatial analysis methods are used to identify areas in each store where capacity supply exceeds actual demand. The criteria for determining excess capacity are: , where ρ_required is the required capacity density calculated based on business load, and α is a reasonable redundancy coefficient, typically set to 0.2. Hotspot analysis uses the Getis-OrdGi* statistic to identify spatial units with significant capacity clusters. Cluster analysis uses the DBSCAN algorithm to identify spatially adjacent groups of employees with similar capacity levels as capacity clusters. For example, after Manager Zhang was transferred to a regional manager position, Store A in the core business district under his former management experienced a significant overcapacity in management. The store was staffed with one senior deputy store manager, two department heads, and three team leaders, while the actual business load only required one deputy store manager and two heads. Quantitative indicators of overcapacity reflect the relative level of overcapacity. Temporal stability analysis tracks the persistence of overcapacity areas, distinguishing between temporary overcapacity (lasting less than one month) and structural overcapacity (lasting more than three months). Skill dimension decomposition identifies specific types of overcapacity, such as a structural imbalance of excess technical capacity and insufficient management capacity. Cross-store comparative analysis reveals regional differences in the distribution of capacity: stores in first-tier cities generally have an oversupply of high-end talent, while stores in third- and fourth-tier cities exhibit the opposite trend. Identify areas of overcapacity through multi-dimensional spatial statistical analysis and comparative evaluation.

[0040] In some embodiments, the phase analysis of the excess capacity area and the business load curve to obtain the capacity-load mismatch coefficient includes: extracting the capacity distribution timing from the excess area; extracting the load change timing based on the business load curve; performing phase comparison on the capacity distribution timing and the load change timing to calculate the phase difference; and calculating the capacity-load mismatch coefficient based on the phase difference.

[0041] Extract sequence data reflecting the time-varying capacity levels from the identified overcapacity areas. The sampling strategy for time series extraction is determined based on business characteristics. Retail stores sample hourly to capture intraday changes, while the management center samples daily to reflect the impact of the operating cycle. The capacity time series signal is constructed as , where R is the set of employees in the surplus area, Skill(i, t) is the conversion of the static skill score S(i) into a dynamic skill level that takes into account attendance status and work efficiency, and A(i, t) is the attendance status. Taking into account the impact of employee scheduling, the concept of effective capacity is introduced to reflect the actual available capacity level. Seasonal decomposition uses the STL method to decompose the capacity time series into trend, season, and residual terms. Moving average smoothing eliminates short-term random fluctuations, with the window length set to one scheduling cycle. Normalization uses the z-score method to ensure comparability with the load time series. Abnormal event markers include capacity improvements during collective training and sudden capacity drops due to staff turnover. For example, in the first month after Manager Zhang's transfer, management capacity in his original jurisdiction decreased significantly, but sales capacity remained stable due to the deputy store manager's takeover, forming a time series variation characteristic of the capacity structure. After time series extraction and preprocessing, the capacity distribution time series is obtained.

[0042] Load change sequences corresponding to capacity time series are extracted from business load curves. Load time series construction considers a weighted combination of multiple business metrics, including transaction volume, service request volume, and complaint handling volume, with weighting coefficients determined based on business importance. A peak identification algorithm identifies peak load periods, using local extreme value detection combined with threshold screening. Identifying valley periods is equally important, as they represent periods of underutilized capacity. Load pattern classification categorizes time series into stationary, cyclical, trending, and mixed types, with corresponding analysis methods applied to each pattern. Differences in load patterns between weekdays and holidays are addressed through segmented modeling. Abnormal load, including load surges caused by promotional events and emergencies, is addressed using robust statistical methods to mitigate their impact. The inclusion of a predictive component is achieved through an ARIMA model, providing forward-looking load forecasts. Normalization ensures that the load time series and the capacity time series are in the same dimension. Load change time series are derived based on business data processing and feature extraction.

[0043] Phase analysis is performed on the capacity distribution time series and the load variation time series to quantify the degree of temporal alignment between them. Phase extraction uses the Hilbert transform to convert the real signal into an analytical signal. Phase difference calculation reflects the temporal misalignment between the capacity and load signals. The average phase difference quantifies the overall phase relationship, and the phase synchronization index measures the degree of synchronization between the two sequences, taking values ​​from [0, 1], with 1 indicating complete synchronization. Cross-correlation analysis determines the optimal time lag, and the frequency domain phase spectrum is obtained using the cross-power spectral density, revealing the phase relationship between different frequency components. For example, after Manager Zhang's transfer, a store experienced peak customer traffic (load peak) on weekends while management staff were on rotation (capacity trough). This temporal misalignment results in a high phase difference, indicating the need for scheduling adjustments or staffing redeployment. Phase wrapping solves the 2π jump problem and ensures the continuity of the phase difference. The time-varying phase difference is obtained through sliding window analysis, and the probability density of the phase difference is analyzed using statistical distribution to identify the dominant phase relationship pattern. The phase difference value is accurately determined using signal processing techniques.

[0044] Based on the calculated phase difference, a quantitative model of the capacity-load mismatch coefficient is established. The basic form of the capacity-load mismatch coefficient is , where PSI is the phase synchronization index, which is obtained by calculating the average modulus of the phase difference vector and has a value range of [0, 1]. The closer the PSI is to 1, the better the time synchronization between the capability and the load. φ_mean is the average phase difference, that is, the arithmetic mean of the phase difference at each moment. K_amp is the amplitude mismatch factor, which is calculated by comparing the amplitude difference between the capability signal and the load signal. , where A_capability is the capability signal amplitude and A_load is the load signal amplitude, reflecting the degree of matching between the two in terms of intensity. The mismatch coefficient M ranges from [0 to ∞). A larger M value indicates a more severe mismatch between capability and load. When M < 0.3, it is considered a mild mismatch; when 0.3 ≤ M < 0.7, it is considered a moderate mismatch; and when M ≥ 0.7, it is considered a severe mismatch. Through a comprehensive modeling approach combining phase analysis and amplitude comparison, the degree of matching between capability and load in terms of time and intensity is quantified.

[0045] Based on the calculated capacity-load mismatch coefficient, a spatiotemporal analysis is used to generate optimal staffing redeployment windows. By analyzing the temporal variation of the mismatch coefficient, M, the optimal redeployment window is identified, avoiding staffing shifts during peak business periods or periods of severe capacity-load imbalance. The spatial dimension, combining the geographic distribution and mismatch level of overcapacity areas, prioritizes regions with mild mismatch (M < 0.3) and convenient locations for redeployment as sources of staffing, while simultaneously identifying regions with moderate to severe mismatch (M ≥ 0.3) as targets. Redeployment windows are generated using a sliding time window mechanism, with a window length of 2-4 weeks and a sliding step of 1 week. Within each window, the feasibility and expected effectiveness of redeployment are evaluated. For example, when the mismatch coefficient of the overcapacity area in Store A drops to 0.25, while the mismatch coefficient of the new store in Area B reaches 0.65, the optimal window for redeploying staff from Store A to Area B is identified as within the next three weeks. Through dynamic monitoring of the mismatch coefficient and multi-dimensional analysis, a spatiotemporally coupled redeployment window is generated.

[0046] Step S140: construct a personnel flow potential field based on the deployment opportunity window, simulate the diffusion of capabilities in the personnel flow potential field to deduce the impact radius, and perform tensor operations on the impact radius, key nodes, and capability-load mismatch coefficients to generate a multi-dimensional deployment decision space.

[0047] Specifically, the time constraints and deployment feasibility information provided by the deployment opportunity window are used to construct a potential field model that describes the flow trend of personnel between organizations. The deployment opportunity window clarifies the period of excess capacity on the supply side, the period of capacity gap on the demand side, and the optimal deployment time. This information provides boundary conditions for the construction of the potential field. The potential field model uses the method of analogy with the physical field to define the potential function , where the first term represents the attractive potential, q i is the capacity gap of demand node i, r i is the organizational distance to node i; the second term represents the repulsive potential, p j is the excess capacity of supply node j, r jis the distance to node j. For example, after Manager Zhang's transfer, Store A in the core business district under his former supervision generates a repulsive potential for management capacity (oversupply), while the three new stores in Area B under his new jurisdiction generate a strong attractive potential (urgent need). Consequently, talented managers tend to move from Store A (high potential) to the new stores in Area B (low potential). The definition of organizational distance comprehensively considers factors such as geographic location, business similarity, and cultural differences, with weighting coefficients determined based on firm characteristics. The potential field gradient represents the direction and intensity of the driving force behind personnel mobility. Time-varying characteristics are reflected by introducing a time decay factor, reflecting the expiration date of the opportunity window. Boundary conditions are set based on organizational boundaries and policy constraints, such as cross-regional transfer restrictions and job level matching requirements. The potential field is discretized using a grid, with each grid point representing an organizational unit.

[0048] In some embodiments, the capability diffusion simulation and deduction of the impact radius in the personnel flow potential field includes: setting a diffusion source point based on the personnel flow potential field; constructing capability diffusion rules according to the diffusion source point; performing iterative calculations on the diffusion rule to obtain a diffusion trajectory; and determining the impact radius based on the diffusion trajectory.

[0049] Exemplarily, the setting of diffusion source points based on the personnel flow potential field includes: performing gradient analysis on the personnel flow potential field to identify positions where the potential field gradient is zero; screening points with minimum potential energy values ​​at positions where the gradient is zero as candidate positions for diffusion source points; performing capability assessment on personnel at the candidate positions to calculate comprehensive capability indicators; and sorting according to the comprehensive capability indicators to select the position with the highest capability indicator as the diffusion source point.

[0050] First, a comprehensive gradient analysis of the personnel flow potential field is performed by calculating the partial derivatives of the potential field function U(x, y, t) at each spatial position. , identifying locations where the potential field gradient is zero. The gradient is calculated using a central difference scheme, and the scanning strategy employs adaptive mesh refinement, automatically increasing the mesh points in areas of sharp gradient change to improve zero point identification accuracy. These gradient zero points represent critical locations where forces in the potential field are balanced and are key nodes where personnel flow trends shift. Zero point screening utilizes a threshold judgment mechanism, and stability is verified by applying small perturbations near the zero point to observe whether the gradient points toward zero. Next, potential energy minima are further screened from the identified gradient zero locations as candidate diffusion source locations. The Hessian matrices of these locations are calculated to verify their positive definiteness and confirm their local minima. Positive definiteness is verified through eigenvalue analysis, and the potential well depth is calculated using a radial search, searching for the nearest potential barrier in all directions around the candidate point. These minimum points have a natural attraction property, making them ideal starting points for capacity diffusion. A comprehensive capacity assessment is then conducted on the personnel at these selected candidate locations to establish a multi-dimensional capacity indicator system. The skill level uses the S(i) value calculated in S130, the knowledge value coefficient uses the K(i) parameter in S130, and the redeployment willingness A(i) is weighted based on the historical redeployment success rate from the S110 personnel flow data. Finally, the candidate positions are ranked and compared based on their comprehensive capability indicators, and the position with the highest capability indicator is selected as the final diffusion source. The ranking algorithm uses a multi-criteria decision-making approach that considers not only the comprehensive capability indicator but also the spatial distribution balance and capability type diversity of the positions. A greedy algorithm is used to select the source point. The position with the highest comprehensive capability value is selected as the first source point. Subsequent source points select the position with the highest capability indicator, while satisfying spatial and type constraints, to determine the final diffusion source point.

[0051] Based on the determined diffusion source characteristics and organizational network structure, a rule system for the diffusion of capabilities among organizations is established. The basic diffusion equation adopts the modified diffusion equation , where C is the capability concentration (a function of spatial coordinates x, y and time t), D is the diffusion coefficient matrix, v is the drift velocity field (determined by the potential field gradient), and S is the source term. The anisotropy of the diffusion coefficient reflects organizational structural characteristics, with intra-level diffusion being fastest and cross-level diffusion being limited. The propagation rate is inversely proportional to the path impedance, employing a migration model driven by the potential field gradient. A capability decay rule ensures the localization of capability diffusion, a branching rule allows capability flow to diverge at nodes, and a feedback inhibition mechanism prevents over-concentration. For example, Manager Li, as a diffusion source, first spreads his management capability to other managers at the same level (diffusion coefficient D = 0.8), then to subordinate employees (D = 0.6), and finally to his superior manager (D = 0.4), demonstrating a distinct anisotropy. A threshold mechanism sets a minimum effective concentration; below this value, propagation automatically terminates, preventing ineffective diffusion. Capability propagation rules are constructed through the integrated modeling of diffusion characteristics and organizational constraints.

[0052] Using the constructed diffusion rules, an iterative calculation simulates the dynamic diffusion of capabilities within an organizational network. The numerical solution employs the finite difference method, with the spatial discretization step size determined based on the smallest organizational unit and the time step size satisfying stability requirements. The iterative update formula reflects the temporal evolution of capability concentration, and boundary treatment employs a hybrid condition: organizational boundaries are characterized by a no-flux condition, while open boundaries represent exchange with the external environment. The acceleration algorithm employs a multigrid approach, with coarse grids rapidly capturing global distributions and fine grids accurately capturing local features. Convergence criteria are set as a relative error less than a set threshold or reaching a maximum number of iterations. Intermediate results are stored for trajectory tracking, with the complete concentration field distribution regularly recorded. For example, Manager Li's management capabilities diffused to two adjacent departments in the first week, to four teams on the same floor in the second week, and to 12 positions throughout the entire store in the third week, forming a clear diffusion trajectory. A parallel computing strategy decomposes the spatial domain, with each subdomain independently computed and then exchanging boundary information. An exception handling mechanism detects numerical instability and automatically adjusts computational parameters and recalculates. Through precise control of the iterative process and numerical optimization, the diffusion trajectory is obtained.

[0053] The spatial distribution characteristics of capability diffusion trajectories were analyzed to determine the effective influence range of each source. The influence radius was defined as the distance at which capability concentration decayed to 10% of its initial value. A family of concentration distribution curves was constructed using isoconcentration analysis. The area enclosed by the outermost effective isoconcentration line was the influence range. Statistical methods were used to calculate the weighted average radius, reflecting the center of gravity of the capability distribution. Directional differences were considered to describe non-circular influence areas, and the time-varying radius characterized the dynamic expansion of the influence range. For example, after Manager Li was transferred to Area B, his management experience and team-building skills had a diffusion effect on six stores within a 2-kilometer radius. Through regular store visits and team training, the overall management level of these stores improved by 12% within three months. Multi-source overlay employed reasonable overlay rules, and effectiveness was verified by calculating the capability increment within the influence area to ensure the expected capability transfer. Visualization employed heat maps overlaid with isoconcentration lines to intuitively display the intensity distribution of the influence range. The influence radius was determined through quantitative analysis of diffusion characteristics and geometric representation.

[0054] In some embodiments, the tensor operation of the influence radius, the key node and the capacity-load mismatch coefficient is performed to generate a multi-dimensional allocation decision space, including: converting the influence radius into a spatial dimension vector; constructing a node weight matrix based on the key node; generating a time dimension tensor according to the capacity-load mismatch coefficient; performing a tensor product operation on the spatial dimension vector, the node weight matrix and the time dimension tensor to construct a multi-dimensional allocation decision space.

[0055] The influence radius data in scalar or vector form is converted into a spatial vector representation suitable for tensor operations. The conversion process first performs principal component analysis on the two-dimensional influence range to extract the main axis direction and characteristic scale, and construct the basic vector , where R_max and R_min are the primary and secondary axis radii, θ_major is the primary axis direction, and A_eff is the effective impact area. The multi-source case is expanded into a matrix format, with each column corresponding to the spatial characteristics of a source point. Normalization is used to ensure that each component is within a reasonable range. Feature encoding incorporates location information, and the expanded vector contains the coordinates of the center of mass of the impact area. For example, Director Li's impact radius is converted to the spatial vector [2.5, 1.8, 45°, 8.2], representing a primary axis radius of 2.5 kilometers, a secondary axis radius of 1.8 kilometers, a primary axis direction of 45 degrees northeast, and an effective impact area of ​​8.2 square kilometers. Sparse representation and vector dimensionality expansion are considered to handle different capability types. Timestamps are used to ensure temporal consistency with other dimensions, and quality weights are attached to the vector to reflect the relative importance of the impact area. After data conversion and vector construction, a spatial dimension vector is generated.

[0056] Using key nodes, a weight matrix is ​​constructed to reflect the importance of nodes in talent allocation. Matrix construction starts with the adjacency matrix, which indicates whether there is an allocation path between nodes. Node weights are determined directly based on their roles in the allocation network: key nodes are given high weights of 0.8-1.0, important nodes (management, technical backbones) are given medium weights of 0.5-0.8, and ordinary nodes are given standard weights of 0.2-0.5. For example, Manager Zhang, as a regional manager and identified as a key node, has a weight coefficient of 0.95, Supervisor Li, as a core store supervisor, has a weight of 0.82, and ordinary employees of new stores have a weight of 0.45, forming a clear hierarchical weight distribution. A diagonal weight matrix is ​​constructed to encode the importance of the node itself, and an associated weight matrix combines adjacency relationships and an attenuation function based on organizational distance. Graph theory analysis methods are used to analyze the connectivity and bottlenecks of the network, and sparse processing retains important connections to improve computational efficiency. Based on direct weight allocation and matrix operations of key node information, a node weight matrix is ​​constructed. .

[0057] The capacity-load mismatch coefficients at multiple time points are organized into a third-order tensor form to capture the spatiotemporal evolution characteristics of the mismatch state. , where n is the number of stores, m is the number of capability dimensions, and t is the number of time steps. The first dimension encodes the mismatch status of different stores; the second dimension distinguishes between mismatch types, such as skills, time, space, and hierarchy; and the third dimension represents the temporal evolution. Time slices represent snapshots of mismatch at specific moments and are populated using the capability-load mismatch coefficients calculated in S130. Interpolation is used to expand the data from discrete time points into a continuous-time representation, and frequency domain analysis is used to extract cyclical patterns in mismatch. For example, the mismatch tensor for the first week after Manager Zhang's transfer shows an excess of 0.3 in management capability at Store A, a shortage of 0.6 in Management Area B, and a skill mismatch index of 0.45. After the fourth week, the excess in Store A drops to 0.1, the shortage in Area B eases to 0.2, and the mismatch index drops to 0.15. Trend, seasonal, and random fluctuation components are separated, and appropriate tensor normalization methods are used to ensure comparability across dimensions. For sparse mismatches, a compressed storage format is used, and data quality is assessed using tensor analysis methods. Through the multi-dimensional organization and tensor representation of mismatched data, a time dimension tensor is generated.

[0058] The tensor product operation is performed on the three constructed tensor components to generate a high-dimensional space that describes the entire deployment decision. The tensor product is calculated using standard tensor operations. , where W_node is the node weight matrix, Represents the tensor outer product operation, generating a fourth-order decision tensor , where d is the spatial feature dimension, n is the number of source nodes, m is the number of target nodes, and t is the number of time steps. Physical Explanation: The physical meaning of the fourth-order decision tensor D(i, j, k, t) is: the deployment feasibility score from the jth source node to the kth target node at the tth time, given the i-th spatial feature. This comprehensively captures the four key dimensions of deployment decisions. Dimensionality reduction is performed using tensor decomposition methods to extract core patterns and principal components. Constraint masks are used to implement various business constraints and policy restrictions, and appropriate tensor metrics are defined to measure the distance between decision points. Multi-dimensional slicing operations are supported for flexible querying, and dimensionality reduction visualization methods are used to intuitively display high-dimensional decision spaces. Analysis of tensor sparsity guides storage and computational optimization, encoding physical constraints and business rules as tensor boundaries to ensure the enforceability of decisions. The tensor integration of the three types of information and the construction of high-dimensional spaces ultimately complete the construction of a multidimensional deployment decision space.

[0059] Step S150: perform gradient search in the multidimensional deployment decision space to identify the optimal balance point between knowledge protection and capability diffusion, generate a ripple deployment path set based on the optimal balance point, and optimize the ripple deployment path set to obtain the optimal deployment sequence.

[0060] Specifically, in the multi-dimensional deployment decision space, deployment feasibility information is extracted through tensor slicing operations for gradient search. The decision variable is represented by a three-dimensional index as x_ijt, which represents a binary decision variable for deploying from node i to node j at time t. The value 0 indicates no deployment, and the value 1 indicates deployment. The tensor slice D(:, i, j, t) extracts the deployment feasibility score from source node i to target node j at time t to construct the data basis of the objective function. The knowledge protection index is obtained by aggregating the source node dimension of the tensor. , where D(1, i, j, t) represents the feasibility of deployment under the first type of spatial characteristics (influence radius). x_ijt is the decision variable, V(i) is the vulnerability of node i (obtained based on step S110), and K(i) is the knowledge value coefficient of node i (obtained based on step S130). For example, when considering deploying Manager Li from Store A to Area B, the knowledge protection index needs to assess the degree of knowledge loss after Store A loses Manager Li, including the risk of loss of his management experience, customer relationships, and team training capabilities. The capability diffusion index is calculated through the target node dimension of the tensor, , where D(2, i, j, t) corresponds to the second type of spatial feature (capability diffusion range). x_ijt is a binary decision variable, R_eff(j) is the effective influence radius of node j (kilometers), and E_impact(j) is the impact coefficient of node j, with a value range of [0, 1]. The cost indicator is weighted based on the time dimension of the tensor, comprehensively considering the deployment cost and time value. The multi-objective optimization problem is converted into a single objective function through the weighted summation method. , where the weight coefficients are determined based on the company's strategic priorities, and J_cost is the deployment cost indicator, representing the total cost required to implement the personnel deployment plan. Automatic differentiation techniques are used to calculate the gradient, with each component reflecting the marginal utility of the corresponding deployment decision. The constraint matrix is ​​directly extracted from the weight matrix dimension of the tensor to ensure that the deployment plan meets the node weight and time window constraints. Based on data extraction from the decision tensor and the construction of a multi-objective function, a gradient search in the decision space is completed.

[0061] Based on the convergence characteristics and objective function distribution of the gradient search process, the optimal balance point between knowledge protection and capability diffusion is identified. The mathematical definition of the balance point is the zero point of the gradient vector of the comprehensive objective function F(x), where ▽F(x*)=0. This indicates that at this point, the three objectives of knowledge protection, capability diffusion, and cost control achieve an optimal trade-off. The trade-off analysis between knowledge protection and capability diffusion is identified using the Pareto frontier. When increasing knowledge protection inevitably leads to a decrease in capability diffusion, the trade-off point lies within the Pareto optimal set. For example, in the deployment decision within Manager Zhang's jurisdiction, completely protecting store A from knowledge loss (by retaining core personnel such as Manager Li) would result in new stores in region B lacking the necessary management support, while over-deployment would weaken the operational stability of store A. The optimal balance point is determined to be deploying Manager Li and one senior sales representative to region B while simultaneously developing two management reserve personnel in store A. The stability of the balance point is verified by the positive definiteness of the Hessian matrix, ensuring the reliability of the solution. Sensitivity analysis quantifies the response of the balance point to parameter perturbations, as highly sensitive parameters require precise control. Multi-objective decision-making uses a weighted summation method to transform the Pareto front into a single objective. The weight vector is determined by combining the analytic hierarchy process with expert judgment. Based on multi-objective optimization theory and stability analysis, the optimal balance between knowledge protection and capability diffusion is identified.

[0062] Using the identified optimal balance point parameter vector x*, the core node set of ripple deployment is determined through the node mapping mechanism. Ripple deployment has obvious advantages over traditional one-time large-scale deployment: reducing organizational shock, reducing the risk of knowledge breakage, facilitating process monitoring and timely adjustment, and ensuring the stability and controllability of the deployment process. The non-zero element x_ijt in the balance point parameter x identifies the optimal deployment decision, and the node with the highest deployment frequency is extracted as the ripple center. For example, in Manager Zhang’s regional deployment plan, Store A is identified as the main capacity output center, the three new stores in Region B are capacity receiving centers, and Supervisor Li is the core seed for the first batch of deployments. Ripple path generation starts with these central nodes. The first-level path directly connects the central node with its adjacent node with the highest feasibility score in the decision tensor D. , where D(1, i, k, t_optimal) is the deployment feasibility score at the optimal time given the influence radius. The second-level paths continue to propagate from the target node in the first level, with increasing time indices reflecting the sequential nature of deployments. For example, the first-level path is Manager Li's transfer from Store A to the main store in Region B. The second-level path is the transfer of two sales elites from Store A to a branch store in Region B after Manager Li stabilizes. The third-level path is the newly trained management reserve filling the vacancy at Store A. Path feasibility is verified by querying the corresponding elements of the decision tensor. Only paths exceeding the feasibility threshold are included in the path set. Ripple strength weights are directly determined based on the statistical characteristics of the equilibrium point parameters, reflecting a layer-by-layer attenuation characteristic. Path capacity constraints are extracted from the capacity dimension of the tensor. The branch selection strategy sorts the elements in the tensor by relative size, prioritizing paths with higher scores. A merging mechanism handles the situation where multiple paths converge to the same target, preventing over-deployment. Ripple boundaries are determined based on the principle of diminishing returns for deployment effectiveness, with propagation stopping when the marginal benefit falls below a set threshold. Through node mapping of equilibrium point parameters and tensor-guided path diffusion, a ripple-like deployment path set is generated.

[0063] The generated ripple deployment path set is optimized to obtain the optimal execution sequence that takes into account both efficiency and stability. The path set optimization problem is expressed as a combinatorial optimization model. , where x ij is the decision variable for whether to choose a path, c ij is the path cost, p k is a binary decision variable for whether to use the kth time slot, y kThe cost of using the kth time slot is used. Constraints include personnel uniqueness, skill matching, and capacity constraints to ensure the feasibility of the deployment plan. For example, Manager Li can only be deployed to one position at a time, his skills must match the requirements of the target position, and the management capacity of the target store cannot be overloaded. The optimization algorithm uses mixed integer programming combined with heuristic search. It first obtains a lower bound through linear relaxation and then uses a genetic algorithm to find a feasible solution. The fitness function comprehensively considers cost, feasibility, and solution diversity. The selection strategy uses tournament selection, the crossover operator uses partial matching crossover, and the mutation operator uses insertion mutation. Local search uses neighborhood operations to improve solution quality. Path conflict detection identifies resource contention and time conflicts. For example, when multiple stores require Manager Li's management guidance simultaneously, priority scheduling and resource reallocation are used to resolve conflicts. Sequential optimization considers the time dependency of deployments. Subsequent deployments depend on the completion status of previous deployments. For example, the deployment of branch personnel in region B must wait until Manager Li stabilizes at the main store before it can begin. Robust optimization handles uncertainty through scenario analysis to generate robust solutions that perform well under various circumstances. The final optimization plan was determined as follows: in weeks 1-2, Manager Li was transferred to the main store in area B and the business was handed over; in weeks 3-4, the sales elites of store A were transferred in batches to the branches in area B; in weeks 5-6, management reserve personnel filled the vacant positions in store A; in weeks 7-8, the results were evaluated and fine-tuned, and the optimal deployment sequence was finally obtained.

[0064] Step S160 , after executing the optimal deployment sequence, detect the organizational knowledge density, identify the phase transition critical point based on the knowledge density, trigger the knowledge structure optimization based on the phase transition critical point, and complete the deployment management of talents.

[0065] Specifically, after executing the optimal deployment sequence, the dynamic changes in the organizational knowledge density are monitored in real time by comparing the staffing structure of each store before and after the deployment. Changes in knowledge density are primarily quantified by counting personnel changes in key positions. When a store loses or gains key personnel, its knowledge density changes accordingly. For example, when Manager Li is transferred from Store A to the main store in Region B, the management knowledge density of Store A decreases from high to medium, while the management knowledge density of the main store in Region B increases from low to high. Monitoring utilizes an event-driven mechanism. After each batch of deployment actions is completed, the change in knowledge density in the affected areas is immediately calculated, and the magnitude and scope of the change are recorded. Through comparative analysis and change monitoring before and after deployment, the dynamic distribution of organizational knowledge density is obtained. Organizational knowledge density is detected through the execution of the optimal deployment sequence and the synchronous monitoring mechanism.

[0066] In some embodiments, the identifying of the phase transition critical point based on the knowledge density includes: calculating a density gradient based on the tissue knowledge density, the density gradient including a strong gradient region, a medium gradient region, and a weak gradient region; preferentially searching and identifying the core mutation interval in the strong gradient region, and supplementarily searching and identifying the edge mutation interval in the medium gradient region and the weak gradient region; performing a deep second-order derivative analysis on the core mutation interval to obtain a main peak point, and performing a fast second-order derivative analysis on the edge mutation interval to obtain a secondary peak point; and accurately locating the phase transition critical point by comprehensively analyzing the distribution characteristics of the main peak point and the secondary peak point.

[0067] Based on the detected tissue knowledge density field ρ_k(x, y, t), calculate its spatial gradient , quantifies the spatial rate of change of knowledge density. Gradient modulus Reflects the unevenness of knowledge distribution. Gradient partitioning uses an adaptive threshold method, and the strong gradient area is defined as , the middle gradient region is , the weak gradient region is , where μ_grad and σ_grad are the mean and standard deviation of the gradient, respectively. For example, after Manager Li's deployment, a strong gradient zone (gradient value 3.8) was formed between Store A and its adjacent stores, indicating a sharp change in knowledge distribution; a medium gradient zone (gradient value 1.2) was formed between stores within Area B, reflecting a gradual knowledge transition; and a weak gradient zone (gradient value 0.3) was formed within the same store, indicating a relatively uniform knowledge distribution. Strong gradient zones usually appear at department boundaries, skill gaps, and other locations. The gradient direction Indicates the dominant direction of knowledge flow. Gradient in the time dimension Reflecting the dynamic evolution of the gradient field. Through the calculation and adaptive partitioning of the gradient field, a density gradient distribution with three levels of strong, medium and weak is formed.

[0068] In the divided strong gradient area, we focus on searching for mutation locations of knowledge density and use the sliding window detection algorithm to identify discontinuities of density function. The criterion for determining core mutation is: , where ε is the small neighborhood radius and θ_core is the core mutation threshold. The search strategy uses an adaptive step size, which is reduced to improve accuracy in areas with drastic gradient changes. The supplementary search in the medium and weak gradient areas uses a sparse sampling strategy, focusing on the vicinity of local extreme points. The recognition threshold for edge mutations is reduced accordingly. , to capture weaker but still meaningful changes in knowledge states. For example, a core mutation interval was discovered at the junction of Store A and Area B, where knowledge density jumped from 6.2 points / square meter to 2.8 points / square meter, with a mutation intensity of 3.4. Edge mutation intervals were identified between stores in Area B, where density changes were relatively mild but still exhibited a distinct step-like distribution. The determination of mutation intervals not only considers single-point mutations but also identifies mutation bands—regions where multiple consecutive points meet the mutation conditions. Temporal persistence verification ensures the stability of identified mutations. Core and edge mutation intervals were identified through a hierarchical search strategy and a multi-verification mechanism.

[0069] Perform deep second-order derivative analysis on the identified core mutation intervals and calculate and , and mixed partial derivatives , construct the Hessian matrix The main peak points are identified by finding the locations where det(H)=0 and tr(H)<0, which correspond to the local maxima of knowledge density. Deep analysis includes calculating the eigenvalues The sign and magnitude of the eigenvalue reflect the morphological characteristics of the peak. Rapid analysis of edge mutation intervals uses a simplified one-dimensional second-order derivative, calculating d²ρ_k / ds² along the gradient direction, where s is the arc length parameter. Secondary peak points are identified through threshold screening, retaining points where the absolute value of the second-order derivative exceeds the set threshold. For example, within the entire area under Manager Zhang's jurisdiction, three primary peaks were identified: the customer service area of ​​Store A (peak knowledge density of 9.2 points / square meter), the management center of the main store in Area B (peak of 7.8 points / square meter), and the training center (peak of 8.1 points / square meter). Eight secondary peaks were also identified, distributed across key positions within each store. Peak sharpness is quantified by curvature, with high curvature indicating a sharp transition in knowledge status. Primary and secondary peak points are identified through a differentiated analysis strategy of second-order derivatives and morphological identification.

[0070] Comprehensively analyze the spatial distribution, intensity characteristics and connection relationship of the main peak points and secondary peak points to accurately determine the critical position of the knowledge phase transition. The determination of the critical point of phase transition adopts a multi-criteria fusion method. , where F_main and F_sub are the characteristic functions of the main and secondary peak points respectively, W_main and W_sub are the corresponding weights, and θ_critical is the critical determination threshold for phase transition. For example, through analysis, it is found that after the regional deployment by Manager Zhang, two main phase transition critical points are formed: the knowledge fault interface between Store A and adjacent stores and the knowledge gradient transition zone within Region B. The intensities of these two critical points are 0.82 and 0.67 respectively, both exceeding the set critical threshold of 0.6. The type classification of critical points includes first-order phase transition (jump in knowledge density) and second-order phase transition (continuous knowledge density but discontinuous derivative). Corresponding processing strategies are adopted for different types. The quantitative index of critical intensity reflects the severity of the phase transition and guides the subsequent intervention intensity, where Δρ_k is the change in knowledge density and ρ_k_avg is the average knowledge density of the region. Through comprehensive analysis and strict verification of multi-dimensional features, the phase transition critical points are accurately located.

[0071] In some embodiments, the triggering of knowledge structure optimization based on the phase transition critical points includes: setting strong trigger conditions, medium trigger conditions, and weak trigger conditions based on the phase transition critical points; activating the deep knowledge recombination mechanism based on the strong trigger conditions, and activating the shallow knowledge recombination mechanism based on the medium trigger conditions and weak trigger conditions; and synergistically executing knowledge structure reconstruction through the deep knowledge recombination mechanism and the shallow knowledge recombination mechanism to achieve knowledge structure optimization.

[0072] According to the intensity I_critical and the influence range of the phase transition critical points, a differentiated trigger condition system is formulated. The strong trigger condition is set as: I_critical > 0.7 and the influence range covers the core business department, or the single-point knowledge density drops by more than 50%, or multiple key nodes approach the critical state simultaneously. The medium trigger condition is: 0.3 < I_critical ≤ 0.7 and the influence is limited to a specific team, or the knowledge density fluctuates between 20% - 50%. The weak trigger condition is: I_critical ≤ 0.3 or only involves knowledge adjustment of individual non-critical positions. For example, after the regional deployment by Manager Zhang, the critical intensity I_critical of Store A is 0.82, triggering the strong trigger condition and requiring the activation of the deep knowledge recombination; the critical intensity I_critical within Region B is 0.45, triggering the medium trigger condition, and shallow knowledge recombination can be adopted. The dynamic adjustment mechanism of trigger conditions is optimized in real time according to the organization's risk tolerance and strategic goals. The time window constraint ensures the timeliness of triggering, and the trigger level is automatically upgraded if the response time limit is exceeded. Through the scientific setting and dynamic management of hierarchical trigger conditions, a precise knowledge recombination activation mechanism is established.

[0073] When strong trigger conditions are activated, a comprehensive restructuring mechanism involving the organization's deep knowledge structure is initiated. Deep restructuring encompasses the reconstruction of core knowledge systems, the reengineering of key processes, and the updating of organizational memory. For example, in response to strong trigger conditions in Store A, a deep restructuring mechanism was initiated: the customer service knowledge system was rebuilt, transforming Manager Li's implicit experience into standardized service processes; a cross-store knowledge sharing platform was established to ensure effective maintenance of Store A's core customer relationships; and a mentoring system was established to facilitate deep knowledge transfer between senior employees and newly promoted managers. This mentoring system strengthened pairing, establishing one-on-one mentoring relationships between senior experts in knowledge-rich areas and key employees in knowledge-scarce areas. Shallow restructuring under medium-weak trigger conditions focuses on local optimization, including measures such as strengthened knowledge sharing within the team, accelerated job rotation, and just-in-time training. In response to medium trigger conditions in Region B, a shallow restructuring mechanism was implemented: Manager Li strengthened daily communication with store employees, transferring knowledge through regular store visits and business guidance; and best practices sharing sessions were established within the region to promote experience sharing among stores. The intensity of the restructuring was adaptively adjusted based on feedback from implementation results. The design and differentiated implementation of deep and shallow restructuring mechanisms activated knowledge restructuring at different levels.

[0074] Deep and shallow knowledge restructuring mechanisms work together to achieve systematic optimization of the organization's knowledge structure. Optimization strategies are developed based on the strength of the critical phase transition point: strong triggers lead to the reconstruction of the knowledge system for core positions, while medium and weak triggers lead to knowledge adjustments for local teams. Once deep restructuring determines the overall direction and resource allocation for knowledge optimization, shallow restructuring mechanisms are activated to implement specific knowledge transfer through mentoring, job rotation, and skills sharing. To ensure optimization effectiveness, changes in knowledge density are continuously monitored. This value reflects the degree of improvement in knowledge levels in each region before and after the adjustments. Furthermore, periodic checkpoints are established to assess the changes in each store's business capabilities after each round of restructuring. Successful optimization models are documented and standardized. For example, after knowledge structure optimization in Manager Zhang's region, Store A established a new customer service knowledge system through deep restructuring, significantly improving customer satisfaction. Region B achieved effective transfer of management experience through shallow restructuring, significantly improving operational efficiency across all stores. When knowledge level differences between stores are narrowed and knowledge gaps in key positions are effectively filled, knowledge structure optimization has achieved its intended goals. Through the coordinated coordination of deep and shallow mechanisms and continuous evaluation of results, talent deployment and management are ultimately achieved.

[0075] In order to implement the intelligent talent deployment management method for chain enterprises corresponding to the above method embodiment, to achieve the corresponding functions and technical effects. Figure 2 , Figure 2The following is a block diagram of a smart talent allocation and management system 200 for chain enterprises provided by an embodiment of the present application. For ease of explanation, only the parts related to this embodiment are shown. The smart talent allocation and management system 200 for chain enterprises provided by an embodiment of the present application includes: The knowledge analysis module 201 is used to collect personnel flow data and knowledge transfer records from each store of the chain enterprise, perform network analysis on the knowledge transfer records to identify organizational memory nodes, obtain node vulnerabilities based on the organizational memory nodes, and construct a knowledge fracture risk transmission map based on the node vulnerabilities; An impact prediction module 202 is configured to perform a shock wave simulation based on the knowledge fracture risk transmission diagram to predict the cascading impact path of staff loss, perform a Fourier transform on the cascading impact path to extract impact frequency characteristics, and identify key nodes requiring pre-placement of buffer personnel based on the impact frequency characteristics; Capacity analysis module 203 is configured to extract the personnel capacity density distribution and business load curve of each store, identify overcapacity areas based on the personnel capacity density distribution, perform phase analysis on the overcapacity areas and the business load curve to obtain a capacity-load mismatch coefficient, and generate a deployment opportunity window based on the capacity-load mismatch coefficient; A deployment decision module 204 is configured to construct a personnel flow potential field based on the deployment opportunity window, simulate the diffusion of capabilities in the personnel flow potential field to deduce the impact radius, and perform tensor operations on the impact radius, the key nodes, and the capability-load mismatch coefficient to generate a multi-dimensional deployment decision space; a path optimization module 205 configured to perform a gradient search in the multi-dimensional deployment decision space to identify an optimal balance point between knowledge protection and capability diffusion, generate a ripple deployment path set based on the optimal balance point, and optimize the ripple deployment path set to obtain an optimal deployment sequence; The phase change control module 206 is used to detect the organizational knowledge density after executing the optimal deployment sequence, identify the phase change critical point based on the knowledge density, trigger the knowledge structure optimization based on the phase change critical point, and complete the deployment management of talents.

[0076] The aforementioned intelligent talent allocation and management system 200 for chain enterprises can implement the intelligent talent allocation and management method for chain enterprises described in the aforementioned method embodiment. The optional options in the aforementioned method embodiment also apply to this embodiment and will not be described in detail here. The remaining contents of the present application embodiment can be referenced to the contents of the aforementioned method embodiment and will not be further described in this embodiment.

[0077] The purpose of the above embodiments is to exemplify and deduce the technical solution of the present invention, and to fully describe the technical solution, purpose and effect of the present invention. Its purpose is to enable the public to have a more thorough and comprehensive understanding of the disclosed content of the present invention, and it does not limit the scope of protection of the present invention.

[0078] The above embodiments are not exhaustive and may include many other embodiments not listed above. Any replacements and improvements made without violating the concept of the present invention are within the scope of protection of the present invention.

Claims

1. An intelligent talent deployment management method for chain enterprises, characterized by: include: Collect personnel flow data and knowledge transfer records from each store of the chain enterprise, perform network analysis on the knowledge transfer records to identify organizational memory nodes, obtain node vulnerabilities based on the organizational memory nodes, and construct a knowledge fracture risk transmission map based on the node vulnerabilities; Based on the knowledge gap risk transmission diagram, shock wave simulation is performed to predict the cascading impact path of staff loss, the shock frequency characteristics are extracted by Fourier transform on the cascading impact path, and the key nodes requiring pre-positioning of buffer personnel are identified based on the shock frequency characteristics; Extracting the personnel capacity density distribution and business load curve of each store based on the personnel flow data, identifying overcapacity areas based on the personnel capacity density distribution, performing phase analysis on the overcapacity areas and the business load curve to obtain a capacity-load mismatch coefficient, and generating a deployment opportunity window based on the capacity-load mismatch coefficient; Constructing a personnel flow potential field based on the deployment opportunity window, performing capability diffusion simulation to deduce the impact radius in the personnel flow potential field, and performing tensor operations on the impact radius, the key nodes, and the capability-load mismatch coefficient to generate a multi-dimensional deployment decision space; Performing a gradient search in the multidimensional deployment decision space to identify an optimal balance point between knowledge protection and capability diffusion, generating a ripple deployment path set based on the optimal balance point, and optimizing the ripple deployment path set to obtain an optimal deployment sequence; After executing the optimal deployment sequence, the organizational knowledge density is detected, the phase transition critical point is identified based on the knowledge density, and the knowledge structure optimization is triggered based on the phase transition critical point to complete the deployment management of talents.

2. The method according to claim 1, characterized in that The shock wave simulation based on the knowledge gap risk transmission map predicts the cascading impact path of staff loss, including: Setting an initial impact source based on the knowledge fracture risk transmission diagram; Analyzing the initial shock source and calculating the shock wave propagation velocity; Constructing a time diffusion mechanism based on the propagation speed; The temporal diffusion mechanism is used to deduce the cascading impact path.

3. The method according to claim 1, characterized in that The performing Fourier transform on the cascade impact path to extract the impact frequency feature includes: extracting a time domain impact signal based on the impact path; Performing a fast Fourier transform on the time domain impulse signal to obtain a transformation result; Analyzing the transformation result to identify the main frequency component; An impact frequency feature is generated according to the main frequency component.

4. The method according to claim 1, wherein The performing phase analysis on the overcapacity area and the service load curve to obtain a capacity-load mismatch coefficient includes: extracting a capacity distribution time series from the excess area; Extracting a load change time series based on the service load curve; Performing a phase comparison between the capacity distribution time series and the load change time series, and calculating a phase difference; A capacity-load mismatch coefficient is calculated based on the phase difference value.

5. The method according to claim 1, wherein The capability diffusion simulation and deduction of the impact radius in the personnel flow potential field includes: Setting diffusion source points based on the personnel flow potential field; Constructing capability propagation rules based on the diffusion source points; Performing iterative calculation on the propagation rule to obtain a diffusion trajectory; An influence radius is determined based on the diffusion trajectory.

6. The method according to claim 1, characterized in that The performing of tensor operations on the influence radius, the key nodes, and the capacity-load mismatch coefficient to generate a multi-dimensional deployment decision space includes: Converting the influence radius into a spatial dimension vector; Constructing a node weight matrix based on the key nodes; generating a time dimension tensor according to the capability-load mismatch coefficient; A tensor product operation is performed on the spatial dimension vector, the node weight matrix, and the time dimension tensor to construct a multi-dimensional deployment decision space.

7. The method according to claim 1, characterized in that The identifying of the phase transition critical point based on the knowledge density includes: Calculating a density gradient based on the tissue knowledge density, wherein the density gradient includes a strong gradient region, a medium gradient region, and a weak gradient region; Prioritizing the search and identification of core mutation intervals in the strong gradient region, and supplementing the search and identification of edge mutation intervals in the medium gradient region and the weak gradient region; Performing a deep second-order derivative analysis on the core mutation interval to obtain a main peak point, and performing a fast second-order derivative analysis on the edge mutation interval to obtain a secondary peak point; The distribution characteristics of the main peak point and the secondary peak point are comprehensively considered to accurately locate the phase transition critical point.

8. The method according to claim 1, characterized in that The knowledge structure optimization triggered based on the phase transition critical point includes: Setting a strong trigger condition, a medium trigger condition and a weak trigger condition based on the phase change critical point; Activate the deep knowledge reorganization mechanism based on the strong trigger condition, and activate the shallow knowledge reorganization mechanism based on the medium trigger condition and the weak trigger condition; The deep knowledge reorganization mechanism and the shallow knowledge reorganization mechanism are used to collaboratively perform knowledge structure reconstruction to achieve knowledge structure optimization.

9. The method according to claim 5, characterized in that The setting of diffusion source points based on the personnel flow potential field includes: Performing a gradient analysis on the personnel flow potential field to identify locations where the potential field gradient is zero; Screening potential energy minimum points at positions where the gradient is zero as candidate positions of diffusion source points; Conducting capability assessment on the candidates for the positions and calculating comprehensive capability indicators; According to the ranking of the comprehensive capability indicators, the position with the highest capability indicator is selected as the diffusion source point.

10. An intelligent talent deployment and management system for chain enterprises, characterized by: include: The knowledge analysis module is used to collect personnel flow data and knowledge transfer records from each store of the chain enterprise, perform network analysis on the knowledge transfer records to identify organizational memory nodes, obtain node vulnerabilities based on the organizational memory nodes, and construct a knowledge fracture risk transmission map based on the node vulnerabilities; An impact prediction module is configured to perform shock wave simulation based on the knowledge fracture risk transmission diagram to predict the cascading impact path of staff loss, perform Fourier transform on the cascading impact path to extract impact frequency characteristics, and identify key nodes requiring pre-positioning of buffer personnel based on the impact frequency characteristics; A capacity analysis module is configured to extract the personnel capacity density distribution and business load curve of each store, identify overcapacity areas based on the personnel capacity density distribution, perform phase analysis on the overcapacity areas and the business load curve to obtain a capacity-load mismatch coefficient, and generate a deployment opportunity window based on the capacity-load mismatch coefficient; A deployment decision module is configured to construct a personnel flow potential field based on the deployment opportunity window, simulate the diffusion of capabilities in the personnel flow potential field to deduce the impact radius, and perform tensor operations on the impact radius, the key nodes, and the capability-load mismatch coefficient to generate a multidimensional deployment decision space; a path optimization module configured to perform a gradient search in the multidimensional deployment decision space to identify an optimal balance point between knowledge protection and capability diffusion, generate a ripple deployment path set based on the optimal balance point, and optimize the ripple deployment path set to obtain an optimal deployment sequence; The phase change control module is used to detect the organizational knowledge density after executing the optimal deployment sequence, identify the phase change critical point based on the knowledge density, trigger the knowledge structure optimization based on the phase change critical point, and complete the deployment management of talents.

Images