Human resource software for supply chain

Abstract

The invention discloses human resource software for a supply chain, and relates to the technical field of human resource allocation, and the human resource software comprises the steps of S1, database construction, S2, model establishment, S3, matching degree matrix calculation, S4, weight allocation, S5, real-time optimization, S6, dynamic adjustment, and S7, scheme output. According to the method, the ability evaluation value is dynamically adjusted based on the employee historical task execution time through the skill attenuation factor, the natural decline rule of the employee skill along with time is reflected, and the mismatching problem caused by outdated skill evaluation is avoided; in combination with a five-dimensional scoring system and skill threshold constraints, the system can automatically filter substandard persons, and meanwhile, the comprehensive level of multiple skills is considered during matching; according to the design, the personnel ability evaluation is always kept synchronous with the real-time state, the accuracy of personnel and post matching is improved in a complex and changeable supply chain environment, and the subjective error of artificial evaluation is reduced while the task execution quality is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of human resource allocation, and particularly relates to a human resource software for supply chains. Background Art

[0002] An EHR (Electronic Human Resource) system is a human resource management system in which an enterprise records, analyzes, and processes all aspects of human resources through information technology. Its purpose is to improve the enterprise's human resource management level, give full play to the benefits of human resources, and make human resources more effectively serve the enterprise's goals.

[0003] In the prior art, personnel skill matching usually adopts a binary determination method (qualified / unqualified) or a fixed-weight scoring method, and fails to fully consider the natural attenuation characteristics of skills over time. For example, after an employee obtains a certain skill certification, if they do not participate in relevant practical operations for a long time, their actual ability may degenerate, but the traditional system still regards them as fully qualified resources for scheduling, resulting in quality risks in task execution. To address the above problems, the following solutions are proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a human resource software for supply chains, which dynamically adjusts the ability evaluation value based on the employee's historical task execution time through a skill decay factor, reflects the natural decline law of employees' skills over time, avoids the mismatch problem caused by outdated skill evaluation, and solves the problem in the prior art that the natural attenuation characteristics of skills over time are not fully considered, resulting in quality risks in task execution.

[0005] To solve the above technical problems, the present invention is realized through the following technical solutions: The present invention is a human resource software for supply chains, and the human resource allocation method includes the following steps: Step S1, constructing a database: constructing a multi-dimensional database containing quantitative evaluation and real-time positioning of employees' skills, and establishing a dynamically updated employee ability profile; Step S2, establishing a model: establishing a task model with an emergency coefficient and a dynamic position, and defining a priority calculation rule based on time urgency; Step S3, calculating a matching degree matrix: calculating a double matching degree through a skill decay factor and a real-time position distance to quantify the degree of person-job adaptation; Step S4, weight allocation: automatically balancing the weight ratio of skill matching and position factors according to the global emergency level; Step S5, real-time optimization: establishing an integer programming model with multiple constraints, and using an improved branch-and-price algorithm to calculate the real-time optimal solution; Step S6, Dynamic Adjustment: Set up a loop monitoring and incremental optimization mechanism to achieve dynamic adjustment when the task progress lags or new tasks are added; Step S7, Output Plan: Generate a visual scheduling plan and navigation instructions, display and push the task allocation results to the terminal device through the space-time dimension.

[0006] Furthermore, the specific steps of constructing the database in Step S1 are as follows: Step S11: Collect basic employee information and import employee ID, name, department, and position rank data through the ERP system; Step S12: Establish a skill evaluation matrix, quantitatively evaluate the skills of each employee, and use a five-dimensional scoring method for evaluation from 0 to 5 points: ; In the formula, is the skill vector of the th employee; is the score of the th employee on the rd skill, , , is the "dimension" in the five-dimensional scoring method, referring to the number of types of job skills; is the total number of employees; Step S13: Equip each employee with a positioning terminal and establish a location coordinate update system: , update frequency ≤ 30 seconds; In the formula, is the real-time location coordinate of employee at time , is the longitude coordinate of employee at time , is the latitude coordinate of employee at time ; By constructing a multi-dimensional database containing employee skills and real-time location data, quantitatively evaluating skills and continuously tracking positions, it provides structured data support for dynamic matching.

[0007] Furthermore, the specific steps of establishing the model in Step S2 are as follows: Step S21: Define task characteristic parameters to provide standardized input for subsequent matching algorithms, specifically: ; In the formula, is the parameter set of the th task, is the task duration, is the urgency level of the task, is the minimum requirement threshold of the task for the th skill, is the dynamic position coordinate of the task changing over time ; is the current total number of tasks; Step S22: Construct a weight formula based on urgency and time decay to solve the problem of dynamic priority evaluation of tasks. The formula is: ; In the formula, are all empirical coefficients, is the urgency level of the task, is the current system time, is the task creation timestamp, is the task standard duration; Define the dynamic demand characteristics of the task (skills, urgency, location, etc.), establish a priority calculation model based on time decay, and quantify the real-time impact weight of the task on resource allocation.

[0008] Furthermore, the specific steps of calculating the matching degree matrix in step S3 are as follows: Step S31: Define a weighted threshold filtering algorithm to quantify the matching degree between employee skills and task requirements. The formula is: ; In the formula, is the skill matching degree, is the global importance weight of the skill ; is the employee actual score in the skill ; is the task minimum requirement threshold for the skill ; Step S32: Introduce a skill decay factor. The formula is: ; In the formula, is the skill decay factor, is the employee timestamp of the last execution of the same type of task, is the preset skill refresh period; Step S33: Calculate the location fitness based on real-time position data: ; In the formula, is the location fitness, For employees real-time coordinates, For tasks real-time position coordinates, For employees average moving speed, is the preset time tolerance threshold; Real-time calculate the multi-dimensional matching degree between employees and tasks, combined with a skill decay factor (reflecting the decline of skill proficiency over time) and a position fitness (based on movement cost), to generate a dynamic matching matrix.

[0009] Furthermore, in step S4, the weight assignment specifically includes the following steps: Step S41: Generate dynamic assignment weights by integrating multi-dimensional factors, and construct a composite matching degree function. The formula is: ; In the formula, is the dynamic comprehensive matching degree between employee and task at time , is the skill matching degree, is the skill decay factor, is the position fitness, is the task priority; Step S42: Dynamically adjust the position weight. When the sum of the urgency levels of all tasks , it indicates that the system is in a high-urgency state, and coefficient adjustment is performed: ; ; In the formula, is the threshold, is the total number of tasks; By dynamically adjusting the proportion of skill and position weights, automatically reduce the skill weight and increase the position priority when the global urgency level increases, to achieve adaptive optimization of the resource allocation strategy in emergency scenarios.

[0010] Furthermore, in step S5, the real-time optimization specifically includes the following steps: Step S51: Establish a 0-1 integer programming model. The objective function is: ; The constraint conditions are: (Each person can be assigned at most 1 task); (Meet the minimum number requirement); (Allocation is prohibited if skills do not meet the standard); Wherein, is the composite matching degree, is the decision variable, is the total number of employees, is the total number of tasks, is the minimum number of people required for the task, is the employee skill score, is the task skill threshold; Step S52: Solve using an improved branch-and-price algorithm, initialize the relaxation problem, and set , generate the initial column set, and for each task select the top 3 employees, use column generation method to iteratively improve the lower bound, apply branching strategy to handle fractional solutions, and output the optimal integer solution; Based on the integer programming model and the branch-and-price algorithm, under the constraints of single-task allocation for employees and skill compliance, etc., solve the global optimal matching scheme to maximize the sum of the composite matching degree and task priority.

[0011] Further, step S6, dynamic adjustment specifically includes the following steps: Step S61: Establish a status monitoring loop, and update the allocation plan every . Scan the ongoing tasks. When the actual progress lags behind by more than the threshold, trigger local re-optimization, freeze the completed task set, and re-execute steps S3 - S5 for the affected tasks; Step S62: When dealing with suddenly added tasks, when the new task arrives, retain the remaining available employees in the existing allocation, add the new task to the task set , recalculate , and perform incremental optimization based on the existing solution; Through the periodic scanning and incremental optimization mechanism, trigger local re-allocation for tasks with lagging progress or newly added tasks, and ensure the real-time nature of the scheduling scheme while minimizing the system computing load.

[0012] Further, in step S7, the output scheme specifically includes the following steps: Step S71: Generate the Gantt chart time axis, create dynamic path planning, call the path planning API for each employee assigned a mobile task to calculate the optimal movement path, and estimate the arrival time ETAr = current time + path duration / employee movement speed; Step S72: Generate device terminal instructions, encode the allocation result in JSON format, for example: {"employeeID":"E123","taskID":"T456","start_time":"2024-03-20T14:30:00","nav_points":[ (x1,y1),(x2,y2)... ]}, and push it to the employee's handheld terminal through the MQTT protocol; Convert the optimization result into a visual scheduling instruction, integrate Gantt chart, path navigation and terminal control functions, and realize the closed-loop management from algorithm decision-making to on-site execution.

[0013] The present invention has the following beneficial effects: 1. By means of the skill decay factor, the present invention dynamically adjusts the ability evaluation value based on the employee's historical task execution time, reflects the natural decay law of the employee's skills over time, and avoids the mismatch problem caused by obsolete skill evaluation; combined with the five-dimensional scoring system and skill threshold constraints, the system can automatically filter out unqualified personnel, and at the same time ensure that the comprehensive level of multiple skills is taken into account during matching; this design enables the personnel ability evaluation to always be synchronized with the real-time state, improves the accuracy of personnel-post matching in a complex and changeable supply chain environment, guarantees the quality of task execution while reducing the subjective error of manual evaluation.

[0014] 2. By integrating real-time positioning data with the dynamic position weight algorithm, the system can accurately calculate the spatial matching degree between personnel and task points; based on the exponential decay model of moving speed and path distance, it quantifies and predicts the time cost for employees to reach the target position, and automatically adjusts the proportion of position weight according to the global task urgency; when the system detects a high-urgency task cluster, it dynamically increases the weight of the position factor and preferentially allocates suitable nearby personnel to shorten the response delay of critical tasks; this design not only avoids the skill mismatch caused by mechanical nearest allocation, but also can intelligently optimize the resource allocation path in an emergency state, achieving the balance between spatial efficiency and skill adaptation.

[0015] 3. The system of the present invention continuously monitors the total global task urgency. When the preset threshold is reached, it automatically triggers the iteration of weight parameters, gradually reduces the weight of the skill factor and increases the weight of the position factor; this design enables the system to give priority to ensuring the quality of skill matching under normal conditions, and automatically switches to the response speed priority mode under the pressure of sudden high-load tasks, enhancing the system's adaptability to supply chain environment fluctuations, and realizing dynamic decision-making between quality and efficiency without manual intervention, and can meet the resource scheduling requirements for emergencies such as order surges and equipment failures.

[0016] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages at the same time. Description of the Drawings

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a schematic flow chart of a human resource software for the supply chain of the present invention. Specific embodiments

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0020] Please refer to Figure 1 As shown, the present invention is a human resource software for the supply chain, and the human resource allocation method includes the following steps: Step S1, construct a database: Step S11: Collect basic employee information, and import employee ID, name, department, and position rank data through the ERP system; Step S12: Establish a skill evaluation matrix, quantitatively evaluate the skills of each employee, and use a five-dimensional scoring method for evaluation from 0 to 5 points: ; Wherein, is the skill vector of the th employee; is the score of the th employee on the th skill, , , is the "dimension" in the five-dimensional scoring method, referring to the number of types of position skills; is the total number of employees; Step S13: Equip each employee with a positioning terminal and establish a location coordinate update system: , the update frequency ≤ 30 seconds; Wherein, is the real-time location coordinate of employee at time , is the longitude coordinate of employee at time , is the employee at time latitude coordinate; By constructing a multi-dimensional database containing real-time data on employee skills and locations, quantifying skill scores and continuous location tracking, structured data support is provided for dynamic matching.

[0021] Step S2: Establish a model: Step S21: Define task characteristic parameters to provide standardized input for subsequent matching algorithms, specifically: ; In the formula, is the parameter set of the th task, is the task duration, is the urgency level of the task, is the minimum requirement threshold of the task for the th skill, is the dynamic position coordinate of the task changing with time , is the current total number of tasks; Step S22: Construct a weight formula based on urgency and time decay to solve the problem of dynamic priority evaluation of tasks. The formula is: ; In the formula, are all empirical coefficients, is the urgency level of the task, is the current system time, is the task creation timestamp, is the task standard duration; Define the dynamic demand characteristics of tasks (skills, urgency, location, etc.), establish a priority calculation model based on time decay, and quantify the real-time impact weight of tasks on resource allocation.

[0022] Step S3: Calculate the matching degree matrix: Step S31: Define a weighted threshold filtering algorithm to quantify the matching degree between employee skills and task requirements. The formula is: ; In the formula, is the skill matching degree, is the global importance weight of the skill , is the employee 's actual score in the skill , is the task 's requirement for the skill The minimum requirement threshold; Step S32: Introduce a skill decay factor, with the formula: ; In the formula, is the skill decay factor, is the timestamp of the employee for the last execution of a similar task, is the preset skill refresh period; Step S33: Calculate the position fitness based on real-time position data: ; In the formula, is the position fitness, is the real-time coordinate of the employee , is the real-time position coordinate of the task , is the average moving speed of the employee , is the preset time tolerance threshold; Calculate the multi-dimensional matching degree between the employee and the task in real time, and combine the skill decay factor (reflecting the decline of skill proficiency over time) and the position fitness (based on moving cost) to generate a dynamic matching matrix.

[0023] Step S4, Weight assignment: Step S41: Generate a dynamic assignment weight by synthesizing multi-dimensional factors and construct a composite matching degree function, with the formula: ; In the formula, is the dynamic comprehensive matching degree between the employee and the task at time , is the skill matching degree, is the skill decay factor, is the position fitness, is the task priority; Step S42: Dynamically adjust the position weight. When the sum of the urgency levels of all tasks indicates that the system is in a high-urgency state, perform coefficient adjustment: ; ; In the formula, is the threshold, is the total number of tasks; By dynamically adjusting the proportion of skill and location weights, the skill weight is automatically reduced and the location priority is increased when the global emergency level rises, so as to achieve the adaptive optimization of the resource allocation strategy in emergency scenarios.

[0024] Step S5, Real-time optimization: Step S51: Establish a 0-1 integer programming model, and the objective function is: ; The constraint conditions are: (Each person can be assigned at most 1 task); (Meet the minimum number of people requirements); (Those with unqualified skills are prohibited from being assigned); In the formula, is the composite matching degree, is the decision variable, is the total number of employees, is the total number of tasks, is the minimum number of people required for the task, is the employee skill score, is the task skill threshold; Step S52: Solve it using an improved branch-and-price algorithm, initialize the relaxation problem, and set , generate the initial column set, and for each task select the top 3 employees, use column generation method to iteratively improve the lower bound, apply branch strategy to handle fractional solutions, and output the optimal integer solution; Based on the integer programming model and the branch-and-price algorithm, under the constraints of single-task assignment and skill compliance of employees, solve the global optimal matching scheme to maximize the sum of the composite matching degree and the task priority.

[0025] Step S6, Dynamic adjustment: Step S61: Establish a status monitoring loop, and update the allocation scheme every time, scan the ongoing tasks, and when the actual progress lags behind the threshold, trigger local re-optimization, freeze the completed task set, and re-execute steps S3 - S5 for the affected tasks; Step S62: When dealing with suddenly added tasks, when the new task arrives, retain the remaining available employees in the existing assignments, add the new task to the task set , recalculate , and perform incremental optimization based on the existing solution; Through the periodic scanning and incremental optimization mechanism, local reallocation is triggered for tasks with lagging progress or newly added tasks, ensuring the real-time nature of the scheduling scheme while minimizing the system's computational load.

[0026] Step S7: Output the scheme: Step S71: Generate the Gantt chart time axis, create dynamic path planning, call the path planning API for each employee assigned a moving task to calculate the optimal moving path, and estimate the estimated time of arrival ETAr = current time + path duration / employee moving speed; Step S72: Generate device terminal instructions, encode the allocation result in JSON format, for example: {"employeeID":"E123","taskID":"T456","start_time":"2024-03-20T14:30:00","nav_points":[ (x1,y1),(x2,y2)... ]}, and push it to the employee's handheld terminal through the MQTT protocol; Convert the optimization result into a visual scheduling instruction, integrate the Gantt chart, path navigation, and terminal control functions, and achieve closed-loop management from algorithm decision-making to on-site execution.

[0027] A specific application of this embodiment is: Scenario description: During the "Double 11" promotion period, a cross-border e-commerce warehouse center faced a 300% surge in order volume. The warehouse area is 23,000 square meters, equipped with 120 operating personnel, and needs to handle four types of tasks: sorting, packing, quality inspection, and loading and unloading simultaneously. The task requirements change in real-time with order fluctuations; Step implementation: 1. Construction of the human resources database: (1) Equip 120 employees with smart work badges (integrated with RFID + Bluetooth 5.2), and upload the location coordinates to the central server every 25 seconds (2) Establish a four-dimensional skill assessment system: Sorting speed (pieces / minute); Packaging qualification rate (%); Quality inspection accuracy rate (%); Forklift operation level (levels 1 - 3); (3) Initialize the skill matrix through historical operation data, for example: The skill vector of employee E038 is (22 pieces / min, 98%, 92%, level 2); 2. Dynamic task modeling: (1) Define the characteristics of the four types of tasks: Sorting task: Requires a sorting speed of ≥18 pieces / min, and the emergency coefficient is linked to the order commitment time limit; Packing task: Requires a packaging qualification rate of ≥95%, and the location changes according to the availability of the packing table; Quality inspection task: accuracy rate ≥ 90%, emergency level fixed at level 4; Loading and unloading task: forklift level ≥ 2, location fixed in the platform area; (2)Set the parameters of the dynamic priority formula: ; 3. Real-time matching calculation: (1)When the system detects at 15:00: there are 23 backlogged sorting tasks (average emergency level 4.2); 17 idle employees, among which: Employee E038: skills (22, 98, 92, 2), the last sorting task at 14:20; Employee E045: skills (19, 99, 95, 1), the last sorting task at 13:50; (2)Calculate the attenuation of E038's sorting skills: ; (3)Location fitness calculation: 50 meters away from the nearest sorting area, walking speed 80 meters per minute → ; 4. Dynamic weight adjustment: (1)Total emergency level , threshold ; (2)Trigger weight adjustment: New location weight = , and the skill weight is adjusted to 0.58 accordingly; 5. Optimization allocation implementation: (1)Construct the objective function: ; (2)Compare with employee E045: ; 3)Determine through the branch-and-price algorithm: Assign E038 to the T189 sorting task with the highest priority; 6. Dynamic adjustment mechanism: (1) At 15:02, the system detects a sudden international order, adding 5 quality inspection tasks with an emergency level of 5; (2)Keep the 12 tasks that have been allocated; (3)Recalculate: total emergency level ; The weight is adjusted to location weight 0.45 and skill weight 0.55; (4)Reassign the idle E045 to the newly added quality inspection tasks; 7. Visualization output: (1)Generate a 3D warehouse heat map: Highlight the sorting area B2 with more than 10 backlogged orders in red; The blue arrow guides the optimal path of E038: current position → B2-03 channel → target sorting table; (2)The employee terminal displays: "E038, please arrive at sorting table B2-03 before 15:05 to process VIP order #189 (remaining time limit: 28 minutes)"; (3)The manager's dashboard displays: Real-time labor utilization rate: 89%; Skill matching compliance rate: 92%; Emergency task response delay: <2 minutes; Implementation effect: Compared with the traditional scheduling method, this method increases the task completion rate during peak hours by 37%, reduces the cross-regional movement distance of employees by 42%, and reduces the emergency order overtime rate from 15.7% to 3.2%; The dynamic weight mechanism automatically increases the location weight to 47% during the peak period from 16:00 to 18:00 in the afternoon, effectively coping with the concentrated demand in the loading and unloading area.

[0028] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0029] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the relevant technical fields can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A human resources software for supply chain, characterized in that The human resource allocation method includes the following steps: Step S1, Construct a database: Construct a multi-dimensional database containing the quantitative assessment of employees' skills and real-time positioning, and establish a dynamically updated portrait of employees' capabilities; Step S2, Establish a model: Establish a task model with an emergency coefficient and dynamic location, and define the priority calculation rule based on time urgency; Step S3, Calculate the matching degree matrix: Calculate the double matching degree through the skill attenuation factor and the real-time location distance, and quantify the degree of fit between people and positions; Step S4, Weight allocation: Automatically balance the weight ratio of skill matching and location factors according to the overall emergency level; Step S5, Real-time optimization: Establish an integer programming model with multiple constraints, and use an improved branch-and-price algorithm to calculate the real-time optimal solution; Step S6, Dynamic adjustment: Set up a loop monitoring and incremental optimization mechanism to achieve dynamic adjustment when the task progress lags or new tasks are added; Step S7, Output the plan: Generate a visual scheduling plan and navigation instructions, and display and push the task allocation results to the terminal device through the space-time dimension.

2. The human resource software for a supply chain according to claim 1, wherein In step S1, constructing the database specifically includes the following steps: Step S11: Collect basic employee information, and import employee ID, name, department, and position rank data through the ERP system; Step S12: Establish a skills assessment matrix, quantitatively evaluate the skills of each employee, and use a five-dimensional scoring method for evaluation: ; where is the skill vector of the th employee; is the score of the th employee on the th skill, , , is the "dimension" in the five-dimensional scoring method, referring to the number of types of job skills; is the total number of employees; Step S13: Equip each employee with a positioning terminal and establish a location coordinate update system: , and set the update frequency; In the formula, is the real-time position coordinates of the employee at time , is the longitude coordinate of the employee at time , is the latitude coordinate of the employee at time .

3. The human resource software for a supply chain according to claim 1, characterized in that, In step S2, establishing the model specifically includes the following steps: Step S21: Define task characteristic parameters to provide standardized input for subsequent matching algorithms, specifically: ； In the formula, is the parameter set of the th task, is the task duration, is the urgency level of the task, is the minimum requirement threshold of the task for the th skill, is the dynamic position coordinate of the task changing with time , is the current total number of tasks; Step S22: Construct a weight formula based on emergency level and time decay to solve the problem of dynamic task priority assessment. The formula is: ； In the formula, are all empirical coefficients, is the task urgency level, is the current system time, is the task creation timestamp, is the task standard duration.

4. A human resources software for a supply chain according to claim 1, characterized in that, In step S3, calculating the matching degree matrix specifically includes the following steps: Step S31: Define a weighted threshold filtering algorithm to quantify the matching degree between employees' skills and task requirements. The formula is: ； Wherein, is the skill matching degree, is the global importance weight of the skill , is the actual score of the employee in the skill , is the minimum requirement threshold of the task for the skill ; Step S32: Introduce a skill attenuation factor. The formula is: ； In the formula, is the skill decay factor, is the timestamp of the employee for the last execution of the same type of task, is the preset skill refresh period; Step S33: Calculate the location fitness based on real-time location data; ； In the formula, is the position fitness, is the real-time coordinate of the employee , is the real-time position coordinate of the task , is the average moving speed of the employee , is the preset time tolerance threshold.

5. A human resource software for supply chain according to claim 1, characterized in that, In step S4, weight allocation specifically includes the following steps: Step S41: Generate dynamic allocation weights by integrating multi-dimensional factors, and construct a composite matching degree function. The formula is: ； In the formula, is the dynamic comprehensive matching degree between the employee and the task at time , is the skill matching degree, is the skill attenuation factor, is the position fitness, is the task priority; Step S42: Dynamically adjust the location weight when the sum of the emergency levels of all tasks When it indicates that the system is in a high emergency state, coefficient adjustment is carried out: ； ； In the formula, is the threshold value, is the total number of tasks.

6. The human resource software for a supply chain according to claim 1, characterized in that, In step S5, real-time optimization specifically includes the following steps: Step S51: Establish a 0-1 integer programming model, and the objective function is: ; The constraint conditions are: ； ； ； In the formula, is the composite matching degree, is the decision variable, is the total number of employees, is the total number of tasks, is the minimum number of people required for the task, is the employee skill score, is the task skill threshold; Step S52: Solve using an improved branch-and-price algorithm. Initialize the relaxation problem and , generate an initial column set. For each task select the top 3 employees, iteratively improve the lower bound using column generation, apply the branching strategy to handle fractional solutions, and output the optimal integer solution.

7. A human resource software for a supply chain according to claim 1, characterized in that, In step S6, dynamic adjustment specifically includes the following steps: Step S61: Establish a status monitoring loop. Every time the allocation plan is updated, scan the ongoing tasks. When the actual progress lags behind by more than the threshold, trigger local re-optimization, freeze the completed task set, and re-execute steps S3 to S5 for the affected tasks; Step S62: When processing a suddenly added new task, when the new task arrives, retain the remaining available employees who are already assigned, add the new task to the task set , recalculate , and perform incremental optimization.

8. A human resource software for supply chain according to claim 1, characterized in that, In step S7, outputting the plan specifically includes the following steps: Step S71: Generate a Gantt chart time axis, create a dynamic path planning, and call the path planning API for each employee assigned a mobile task to calculate the optimal mobile path. The estimated arrival time ETAr = current time + path duration / employee movement speed; Step S72: Generate device terminal instructions, encode the allocation results in JSON format, and push them to the employee handheld terminal through the MQTT protocol.

Images