Labor service force and energy distribution method and device based on dynamic weight, medium and product

Abstract

The invention discloses a labor capability allocation method and device based on a dynamic weight, a medium and a product, and the method comprises the steps: obtaining the complexity and personnel skill data of each task in response to a project labor capability allocation request; based on the dynamic coupling model, calculating the real-time matching degree of the personnel and the tasks in combination with the dynamic weight factors corresponding to the tasks; then, according to a matching result and multi-dimensional constraints such as project cost and time, a multi-objective optimization algorithm is adopted to generate an optimal labor force and energy distribution scheme and expected progress data; after the scheme is executed, the actual progress of the project is monitored in real time and compared with expected data; if the deviation exceeds a set threshold value, a feedback mechanism is triggered, the personnel skill data and the dynamic weight factor are updated, the matching and optimizing process is restarted, and closed-loop self-adaptive management of the whole period of the project is achieved until the project is completed, and the technical scheme of the embodiment of the invention remarkably improves the accuracy of resource allocation and the self-adaptive capacity of project management.

Description

Technical Field

[0001] This invention relates to the field of human resource management technology, and in particular to a method, equipment, medium and product for allocating labor capacity based on dynamic weights. Background Technology

[0002] As enterprises accelerate their digital transformation and projects become increasingly complex, the precise allocation and dynamic optimization of human resources have become crucial factors determining project success or failure. However, traditional project management models face prominent challenges such as mismatches between personnel skills and task requirements, rigid resource allocation, and difficulties in coordinating multiple objectives. Particularly in complex, cross-departmental projects with long timelines, static human resource allocation schemes struggle to adapt to changing needs, schedule adjustments, and risk management during project execution, leading to frequent problems such as low resource utilization, project delays, and cost overruns.

[0003] Existing labor capacity optimization technologies mostly employ fixed-weight matching algorithms or single-dimensional scheduling rules: either they statically assign tasks based solely on personnel skill tags, ignoring the dynamic changes in capability requirements due to project phases; or they simply schedule resources based on task priority, lacking comprehensive optimization of multi-dimensional objectives such as cost constraints and risk control. These methods cannot achieve real-time and accurate measurement of the matching relationship between employees and projects, nor can they establish a full lifecycle adaptive adjustment mechanism, resulting in low project management efficiency. Summary of the Invention

[0004] This invention provides a method, equipment, medium, and product for allocating labor capacity based on dynamic weights, which can realize dynamic optimization and adaptive management of human resources throughout the entire project lifecycle.

[0005] According to one aspect of the present invention, a method for allocating labor capacity based on dynamic weights is provided, the method comprising:

[0006] In response to a request for the allocation of labor resources for a target project within a target enterprise, the task complexity data for each task in the target project and the skill data for each skilled worker in the target enterprise are obtained; wherein each task belongs to at least two task phases;

[0007] Skill data and task complexity data are input into the dynamic coupling model. Based on the dynamic weight factors built into the dynamic coupling model that correspond to each task, the real-time matching degree between each skilled person and each task is calculated.

[0008] Based on the real-time matching degree and the multi-dimensional constraint parameters of the target project, a target labor force allocation plan and the expected progress data of the target labor force allocation plan are generated through a multi-objective optimization algorithm.

[0009] According to the target labor capacity allocation plan, each task in the target project is assigned to the matching skilled personnel for execution, and the current progress data of the target project is obtained in real time during the project execution process;

[0010] The system matches the current progress data with the expected progress data. When the matching result meets the deviation threshold condition, it calculates new skill data and dynamic weight factors, and returns to execute the operation of inputting skill data and task complexity data into the dynamic coupling model until the target project is completed.

[0011] According to another aspect of the present invention, a labor capacity allocation device based on dynamic weights is provided, the device comprising:

[0012] The allocation request and response module is used to respond to labor allocation requests for target projects in target enterprises, and to obtain task complexity data for each task in the target project and skill data for each skilled worker in the target enterprise; wherein each task belongs to at least two task phases;

[0013] The real-time model matching module is used to input skill data and task complexity data into the dynamic coupling model, and calculate the real-time matching degree between each skilled person and each task based on the dynamic weight factors built into the dynamic coupling model that correspond to each task.

[0014] The optimization decision module is used to generate a target labor force allocation plan and the expected progress data of the target labor force allocation plan through a multi-objective optimization algorithm based on the real-time matching degree and the multi-dimensional constraint parameters of the target project.

[0015] The execution plan module is used to assign each task in the target project to the matching skilled personnel according to the target labor capacity allocation plan, and to obtain the current progress data of the target project in real time during the project execution process;

[0016] The adaptive update module is used to match the current progress data with the expected progress data. When the matching result meets the deviation threshold condition, it calculates new skill data and dynamic weight factors, and returns to execute the operation of inputting skill data and task complexity data into the dynamic coupling model until the target project is completed.

[0017] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0018] At least one processor; and

[0019] A memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform a dynamic weight-based labor capacity allocation method according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement a labor capacity allocation method based on dynamic weights as described in any embodiment of the present invention.

[0022] According to another aspect of the present invention, a computer program product is also provided, including computer instructions that, when executed by a processor, implement the steps of the method as described in any embodiment of the present invention.

[0023] The technical solution of this invention, in response to a request for labor allocation for a target project within a target enterprise, firstly acquires the task complexity data of each task in the target project and the skill data of each skilled worker in the target enterprise. Then, the skill data and task complexity data are input into a dynamic coupling model. Based on the dynamic weight factors built into the model corresponding to each task, the real-time matching degree between each skilled worker and each task is calculated. Next, based on the real-time matching degrees and the multi-dimensional constraint parameters of the target project, a target labor allocation scheme and its expected progress data are generated through a multi-objective optimization algorithm. Each task is assigned to the matched skilled worker according to the allocation scheme. During project execution, the current progress data of the target project is acquired in real time. Finally, the current progress data is matched with the expected progress data. If the matching result meets the deviation threshold condition, new skill data and dynamic weight factors are calculated, and the data input and matching degree calculation process is re-executed until the target project is completed. This novel labor allocation method based on dynamic weights improves the dynamic adaptation accuracy of human resource allocation and project task requirements, effectively solving the problems of resource mismatch and response lag.

[0024] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of a labor capacity allocation method based on dynamic weights according to Embodiment 1 of the present invention;

[0027] Figure 2 This is a flowchart of another method for allocating labor capacity based on dynamic weights according to Embodiment 2 of the present invention;

[0028] Figure 3 This is a schematic diagram of a labor capacity allocation device based on dynamic weights according to Embodiment 3 of the present invention;

[0029] Figure 4 This is a schematic diagram of the structure of an electronic device that implements a dynamic weight-based labor capacity allocation method according to an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] Example 1

[0033] Figure 1 This is a flowchart of a labor capacity allocation method based on dynamic weights provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where project human resources are dynamically optimized and adjusted in real time. The method can be executed by a labor capacity allocation device based on dynamic weights. This device can be implemented in hardware and / or software and is generally configured in electronic devices.

[0034] Correspondingly, such as Figure 1As shown, the method includes:

[0035] S110. In response to a request for the allocation of labor capacity for a target project within a target enterprise, obtain task complexity data for each task in the target project and skill data for each skilled worker in the target enterprise; wherein each task belongs to at least two task phases.

[0036] Labor capacity can be understood as the sum of skills, efficiency, and execution capabilities inherent in labor resources. It is a comprehensive quantitative concept that emphasizes the dynamic adaptation between personnel skill levels and task complexity under specific project tasks. It not only focuses on the static ability indicators of individual personnel but also emphasizes their effectiveness in translating into actual output in specific work scenarios. Task complexity can be understood as a quantitative assessment of the inherent difficulty, scale, and uncertainty of a work task. It is not a subjective judgment but rather a comprehensive consideration of various factors such as the required working hours, types and quantities of resources, technical difficulty, dependencies, and potential risks after breaking down the project layer by layer into specific tasks through a work breakdown structure. Skill data of skilled personnel can be understood as a quantitative description of the professional ability, experience level, and performance of personnel performing tasks.

[0037] In this embodiment, when a resource allocation request for a specific project is received, the project tasks and personnel capabilities must first be quantified. Specifically, project tasks are hierarchically analyzed using a work breakdown structure to obtain a list of specific tasks with clear hierarchical relationships. Simultaneously, professional evaluation methods are used to comprehensively assess and quantify the technical skills, experience, and other capabilities of each member of the team, forming structured skill data. All these tasks are clearly categorized into different implementation phases within the project. The project implementation phase can be divided into four stages: initiation, design, execution, and closure, each with different task requirements.

[0038] Optionally, based on the above embodiments, obtaining task complexity data for each task in the target project and skill data for each skilled worker in the target enterprise may include:

[0039] Using 360-degree assessment technology and historical project performance data, the skills of various skilled personnel in the target company are quantified in multiple dimensions, and the quantified skill values ​​of each skilled personnel in the target company are obtained as skill data through weighted fusion.

[0040] The target project is decomposed layer by layer using the work breakdown structure technique to obtain all the tasks contained in each task stage. Based on the Monte Carlo simulation technique, the uncertainty of the time and resources required for each task is quantitatively evaluated, and the complexity rating of each task in the target project is obtained as task complexity data.

[0041] Generally, quantitative assessment of personnel skills requires a comprehensive and objective approach, typically employing a 360-degree evaluation technique. This involves combining evaluations from superiors, colleagues, and the individual themselves, while also deeply analyzing historical performance data from past projects. This allows for the assessment of diverse qualities such as technical skills and teamwork. Subsequently, these evaluations from different dimensions are weighted and integrated to calculate a dynamically updated, quantifiable skill score, which serves as a precise skill profile for the individual.

[0042] In one optional implementation of this embodiment, we take Zhang San, a senior structural engineer at the company, as an example. The company quantifies his skills using 360-degree evaluation technology: his superiors evaluate his extensive experience in complex structural calculations; colleagues report his outstanding ability to solve on-site technical problems; and Zhang San's self-evaluation emphasizes his expertise in the application of new building materials. Simultaneously, the system retrieves Zhang San's historical project performance data: it was found that in four of the five large projects he previously led, zero major changes were achieved in the structural design phase, and his average work-hour efficiency was 15% higher than the team average. Finally, the system weights and integrates this multi-dimensional information (superiors, colleagues, self-evaluation, and historical performance) according to preset weights (e.g., historical performance accounts for 50%, superior evaluation accounts for 30%), calculating Zhang San's quantitative score of 92 points (out of 100) in the "structural design" skill dimension and 88 points in the "on-site problem-solving" dimension. This integrated score represents Zhang San's current skill data.

[0043] Generally, task complexity analysis begins with the structured decomposition of the project. This involves using a work breakdown structure (WBS) technique to break down the overall project into different task phases from top to bottom, until individual work tasks are identified. Building on this, Monte Carlo simulation techniques are used to conduct extensive simulations and probabilistic analyses to address the uncertainties in the time and resource input required for each task. This quantitatively assesses its potential risks and difficulties, ultimately generating a rating index representing its complexity level. For example, in a software development project, the complexity rating of a coding task will be significantly higher than that of a data processing task because the former has greater uncertainty in terms of technical implementation and time estimation.

[0044] In an optional implementation of this embodiment, the company utilizes work breakdown structure (WBS) technology to decompose the "high-rise commercial building project" into stages such as "schematic design," "preliminary design," "construction drawing design," and "construction supervision." Furthermore, the "construction drawing design" stage is further decomposed into specific tasks such as "foundation construction drawing design," "main structure construction drawing design," and "mechanical and electrical piping construction drawing design." Now, the complexity of the "main structure construction drawing design" task is assessed. Based on Monte Carlo simulation technology, multiple uncertainties of this task are considered: for example, the required man-hours may fluctuate between 400 and 600 hours depending on the complexity of the structural form; the number of professional collaborators involved may be between 5 and 8 people; simultaneously, this task needs to comply with the latest seismic codes, posing a risk of rework due to disagreements in code interpretation. Through tens of thousands of simulations, it is calculated that there is a 90% probability that this task will require more than 500 man-hours, and a 15% probability that schedule delays will occur due to coordination issues. Considering these quantitative results of uncertainty, the "main structure construction drawing design" task is ultimately assigned a high complexity rating, such as Grade A (representing high complexity and high risk). The relatively simple task of "compiling a drawing catalog" has less uncertainty, and the complexity rating obtained after simulation may only be level C.

[0045] S120. Input skill data and task complexity data into the dynamic coupling model, and calculate the real-time matching degree between each skill personnel and each task based on the dynamic weight factors built into the dynamic coupling model that correspond to each task.

[0046] The dynamic coupling model can be understood as an intelligent computing framework for real-time calculation of the fit between personnel and tasks. Its core function lies in flexibly adjusting the relative importance of employees and projects during the matching process by introducing a key variable—the dynamic weighting factor. The dynamic weighting factor can be understood as a key adjustment parameter in the dynamic coupling model, determining whether, at a specific stage, the calculation of the fit focuses more on the personnel's skill level or the complexity requirements of the task.

[0047] In this embodiment, the acquired quantitative data is input into a dynamically coupled model. This model assigns a variable weight coefficient to each stage of every task. This coefficient determines the relative importance of the task's complexity and the individual's skill level when calculating the matching degree. Through the model's internal calculation mechanism, a quantitative matching score is generated for each individual's combination with each task, thereby accurately reflecting the suitability of each individual for each task.

[0048] Optionally, based on the above embodiments, calculating the real-time matching degree between each skilled worker and each task according to the dynamic weight factors built into the dynamic coupling model corresponding to each task may include:

[0049] Based on the formula using the dynamic coupling model: Calculate the real-time matching degree between each skilled worker and each task;

[0050] in, Let j be the dynamic weight factor for the j-th task. Let i be the quantified skill value of the i-th skilled worker. Rate the complexity of the j-th task. The real-time matching degree between the i-th skilled worker and the j-th task; each dynamic weight factor built into the dynamic coupling model has a preset initial value.

[0051] Generally, real-time matching calculations rely on a core quantitative formula. This formula uses a dynamic weighting factor to flexibly adjust the relative importance of personnel skill levels and task complexity requirements in the final matching result. Specifically, the calculation involves substituting the quantified skill score of a specific skilled worker, the assessed complexity level of the task to be assigned, and the corresponding dynamic weighting factor into a linear weighting function. This function dynamically adjusts based on the core requirements of different project stages, prioritizing either leveraging the strengths of highly skilled personnel or controlling the risks and resource consumption of complex tasks. This yields a precise numerical value characterizing the suitability of the individual for the task. The model presets an initial weighting factor for each task, providing a starting point for this dynamic calculation. For example, in the creative design phase of an architectural project, the dynamic weighting factor for tasks at this stage is set to a higher initial value, meaning that the matching calculation places greater emphasis on the architect's creative ability score. Conversely, when the project enters the standardized construction drawing phase, the initial value of the corresponding dynamic weighting factor is lower, focusing more on assessing the complexity of the task itself and the requirements for drawing efficiency.

[0052] S130. Based on the real-time matching degree and the multi-dimensional constraint parameters of the target project, generate the target labor force allocation plan and the expected progress data of the target labor force allocation plan through a multi-objective optimization algorithm.

[0053] Among these, multidimensional constraint parameters can be understood as a set of key boundary conditions that constrain the labor allocation scheme in a real project environment. These parameters ensure that the generated scheme not only has a high degree of matching but is also feasible in reality. Expected schedule data can be understood as a detailed time plan and resource consumption forecast for the project execution process derived from a specific labor allocation scheme.

[0054] In this embodiment, based on the calculated fine-grained matching results, the overall constraints of the project in terms of cost, time, and resource load are comprehensively considered. Then, using advanced optimization algorithms, the allocation scheme that maximizes the overall matching degree while satisfying all preset constraints is found from countless possible combinations. Finally, not only is this optimal personnel task allocation scheme output, but a corresponding detailed expected schedule is also generated.

[0055] S140. According to the target labor capacity allocation plan, assign each task in the target project to the matching skilled personnel for execution, and obtain the current progress data of the target project in real time during the project execution process.

[0056] In this embodiment, the optimal allocation scheme is implemented, and specific tasks are assigned to corresponding personnel. During the actual progress of the project, a continuous monitoring mechanism is established to collect data reflecting the true progress of the project in real time, such as the actual start and completion times of each task and the actual costs incurred, through reliable data channels.

[0057] S150. Match the current progress data with the expected progress data, and when the matching result meets the deviation threshold condition, calculate the new skill data and dynamic weight factor, and return to execute the operation of inputting the skill data and task complexity data into the dynamic coupling model until the target project is completed.

[0058] In this embodiment, the real-time collected progress data is precisely compared with the expected progress data generated at the time the plan was created. When the difference between the two exceeds a pre-set allowable range, an adjustment mechanism is triggered. This mechanism reassesses the capabilities of relevant personnel based on the latest actual project performance and adjusts the weight coefficients of each task in the coupling model. Subsequently, this updated data is re-input into the previous coupling model for a new round of calculations, thus initiating a cyclical process of reassessment and optimization of resource allocation. This ensures that resource allocation dynamically adapts to the actual progress of the project until its completion.

[0059] The technical solution of this invention, in response to a request for labor allocation for a target project within a target enterprise, firstly acquires the task complexity data of each task in the target project and the skill data of each skilled worker in the target enterprise. Then, the skill data and task complexity data are input into a dynamic coupling model. Based on the dynamic weight factors built into the model corresponding to each task, the real-time matching degree between each skilled worker and each task is calculated. Next, based on the real-time matching degrees and the multi-dimensional constraint parameters of the target project, a target labor allocation scheme and its expected progress data are generated through a multi-objective optimization algorithm. Each task is assigned to the matched skilled worker according to the allocation scheme. During project execution, the current progress data of the target project is acquired in real time. Finally, the current progress data is matched with the expected progress data. If the matching result meets the deviation threshold condition, new skill data and dynamic weight factors are calculated, and the data input and matching degree calculation process is re-executed until the target project is completed. This novel labor allocation method based on dynamic weights improves the dynamic adaptation accuracy of human resource allocation and project task requirements, effectively solving the problems of resource mismatch and response lag.

[0060] Example 2

[0061] Figure 2 This is a flowchart of another dynamic weight-based labor allocation method provided in Embodiment 2 of the present invention. This embodiment is based on the above embodiments and optimized. Specifically, the step of "generating a target labor allocation scheme and the expected progress data of the target labor allocation scheme through a multi-objective optimization algorithm according to the real-time matching degree and the multi-dimensional constraint parameters of the target project" has been refined.

[0062] S210. In response to a request for the allocation of labor capacity for a target project within a target enterprise, obtain task complexity data for each task in the target project and skill data for each skilled worker in the target enterprise; wherein each task belongs to at least two task phases.

[0063] S220. Input skill data and task complexity data into the dynamic coupling model, and calculate the real-time matching degree between each skill personnel and each task based on the dynamic weight factors built into the dynamic coupling model that correspond to each task.

[0064] S230. Taking the maximization of the overall matching degree of skill personnel allocation for all tasks as the objective function, and taking the total cost not exceeding the budget, the total project duration meeting the requirements, and the workload of a single person not exceeding the upper limit as multi-dimensional constraints, an integer programming model is established.

[0065] In this embodiment, the problem of matching personnel with tasks is transformed into a mathematical optimization model with clear objectives and constraints. The core objective of the model is to maximize the total matching degree obtained after allocating all tasks to personnel. Simultaneously, the model must strictly adhere to several key constraints inherent in real-world projects, including that the total cost of the entire project cannot exceed the approved budget, the total completion time must meet the established schedule requirements, and that the total workload undertaken by any individual is within their reasonable capacity limit.

[0066] S240. Solve the integer programming model using a genetic algorithm or a particle swarm optimization algorithm, and search for one or more Pareto optimal solutions that satisfy all constraints in the solution space.

[0067] Genetic algorithms can be understood as intelligent search algorithms that simulate the evolutionary process in the biological world. A possible solution is likened to an individual with a certain genetic makeup. Through simulated selection, crossover, and mutation, superior "genes" are preserved and inferior ones are eliminated in generations of evolution, thus gradually approaching the optimal solution to the problem. Particle swarm optimization (PSO) can be understood as intelligent search algorithms that simulate the social behavior of flocks of birds or schools of fish. Each possible solution is considered a "particle" in the search space. Each particle dynamically adjusts its flight speed and direction based on its own flight experience and that of its companions.

[0068] Integer programming models can be understood as a type of mathematical programming model that requires all or some of the decision variables in the model to take integer values. Pareto optimal solutions can be understood as solutions that, without worsening any other objective, cannot improve any particular objective. They do not represent a single solution, but rather a set of "optimal trade-offs," each representing a balance among multiple objectives.

[0069] In this embodiment, after establishing the aforementioned mathematical model, an advanced search algorithm is needed to find the optimal solution. Since the number of possible allocation combinations is extremely large, intelligent optimization methods such as genetic algorithms or particle swarm optimization are employed to perform efficient searches within the vast solution space. These algorithms intelligently explore various possible allocation methods, aiming to find a set of solutions that simultaneously satisfy all preset constraints and achieve a good balance among multiple optimization objectives—the so-called Pareto optimal solution set.

[0070] S250. Transform one or more Pareto optimal solutions obtained from the solution into a matching labor capacity allocation scheme.

[0071] In this embodiment, after the optimization algorithm finds one or more satisfactory non-dominated solutions, these mathematical solutions need to be transformed into practically executable resource allocation plans. Each solution corresponds to a specific personnel task arrangement, that is, it clarifies which tasks each skilled worker is specifically responsible for. Therefore, this step maps the mathematical calculation results into a clear and explicit task assignment list, forming several selectable and complete labor capacity allocation schemes.

[0072] S260. Obtain the target labor allocation scheme from the various labor allocation schemes obtained through the transformation.

[0073] In this embodiment, from several feasible allocation schemes, one scheme is ultimately determined as the target scheme to be implemented. This selection may be based on additional decision-making criteria, such as selecting the scheme with the highest overall matching degree, or selecting the scheme with the lowest total cost while meeting basic requirements. Finally, the decision-maker makes the final decision on the optimal scheme as the basis for execution.

[0074] S270. Based on the personnel assigned to each task in the target labor capacity allocation plan, estimate the theoretical working hours and theoretical resource consumption required for each task phase to form the expected progress data.

[0075] In this embodiment, after the target labor capacity allocation plan is finalized, it is necessary to further derive a detailed schedule and resource plan that matches the plan. Specifically, based on the personnel and their skill levels assigned to each task in the plan, it is necessary to estimate the theoretical working time required to complete each task phase and the consumption of various resources (such as manpower and equipment). These estimated working hours and resource data together form a complete and quantified expected schedule dataset, providing a clear benchmark for the subsequent execution and monitoring of the project.

[0076] S280. According to the target labor capacity allocation plan, assign each task in the target project to the matching skilled personnel for execution, and obtain the current progress data of the target project in real time during the project execution process.

[0077] Optionally, based on the above embodiments, acquiring the current progress data of the target project in real time during project execution may include:

[0078] Through IoT sensors and / or the application programming interface of project management software, the actual completion time of each task in each task phase of the target project, as well as the actual amount of resources consumed, are collected in real time as the current progress data of the target project.

[0079] Generally, to accurately grasp the true progress of a project, a reliable data acquisition mechanism is needed to obtain real-time information during execution. This typically involves using IoT sensors deployed at the project site to automatically capture changes in the physical world, such as equipment hours and material consumption. Simultaneously, this is combined with standard data interfaces provided by project management software to automatically read the actual start and end times of various tasks, whether manually entered or generated by the software, as well as the specific quantities of manpower and materials consumed. These real-time data from different channels, after being aggregated and verified, together constitute progress data reflecting the current true state of the project, providing a basis for subsequent comparison and analysis. For example, in a production line renovation project, sensors installed at key workstations can automatically record the time when equipment debugging is completed, while the project management system simultaneously records the number of parts consumed and manpower hours for that task. These data together form the actual progress record of the task.

[0080] S290. Match the current progress data with the expected progress data, and when the matching result meets the deviation threshold condition, calculate the new skill data and dynamic weight factor, and return to execute the operation of inputting the skill data and task complexity data into the dynamic coupling model until the target project is completed.

[0081] Optionally, based on the above embodiments, when the matching result meets the deviation threshold condition, new skill data and dynamic weighting factors are calculated, including:

[0082] If the difference between the actual completion time of all tasks in the target task phase and the theoretical working hours required for the target task phase is greater than the deviation threshold, or if the difference between the actual resource consumption when completing all tasks in the target task phase and the theoretical resource consumption of the target task phase is greater than the deviation threshold, then the new skill data of each skilled personnel will be updated based on the current progress data of the target project.

[0083] Based on the degree of deviation in the matching results, a preset reinforcement learning algorithm is used to update the dynamic weight factors in the dynamic coupling model.

[0084] Generally, when the actual time or resource consumption of a certain task phase deviates from the planned value beyond the allowable range, the personnel skill data update mechanism is triggered. Specifically, if the total actual completion time of all tasks in that phase far exceeds the original planned working hours, or the total amount of resources actually consumed significantly exceeds the budget, it indicates that the personnel's capabilities initially assessed based on historical data may differ from their actual performance. At this time, the skill level of the relevant personnel will be reassessed based on the latest collected performance data. For example, if a task is severely delayed due to technical difficulties, the corresponding skill scores of the personnel involved in the task will be appropriately lowered, thereby achieving dynamic calibration of the skill profile.

[0085] Generally, adjusting weighting factors is a feedback-based self-learning process. Based on the specific magnitude and direction of progress or cost deviations, reinforcement learning strategies are used to analyze whether the current weighting settings are reasonable. For example, when a project leads to uncontrolled costs due to an excessive pursuit of technical perfection, the algorithm will automatically reduce the weight of skills and increase the weight of cost efficiency, making subsequent resource allocation more inclined to choose pragmatic solutions that can balance quality and economic benefits, thereby achieving adaptive optimization of model parameters.

[0086] In an optional implementation of this embodiment, the model's self-updating mechanism is triggered when the actual execution of a task phase in a project deviates significantly from the expected outcome. For example, in the core engine development phase of a software company's "intelligent customer service platform" project, the theoretical working hours for the critical task were set at 200 hours, with a cost budget of 50,000 yuan. However, in actual execution, although programmer A achieved a technical score of 95, his excessive pursuit of technical perfection led to an actual working time of 300 hours and an actual cost increase to 70,000 yuan. When the schedule deviation of 100 hours and the cost deviation of 20,000 yuan both exceeded the set thresholds of 50 hours and 10,000 yuan respectively, two critical updates were executed: First, based on the overdue data, the programmer's efficiency dimension skill score was lowered from 80 to 70; second, through a reinforcement learning algorithm, the dynamic weight factor for subsequent similar tasks was adjusted from the initial value of 0.7 to 0.5, changing the weight ratio of skill and efficiency from the original 7:3 to a balanced 5:5, thereby optimizing the subsequent resource allocation strategy.

[0087] The technical solution of this invention, in response to a labor allocation request for a target project within a target enterprise, firstly acquires the task complexity data of each task in the target project and the skill data of each skilled worker in the target enterprise, wherein each task belongs to at least two task phases. Then, the skill data and task complexity data are input into a dynamic coupling model. Based on the dynamic weight factors built into the model corresponding to each task, the real-time matching degree between each skilled worker and each task is calculated. Next, with maximizing the total matching degree of skilled worker allocation for all tasks as the objective function, and with multi-dimensional constraints such as total cost not exceeding the budget, total project duration meeting requirements, and individual worker workload not exceeding the upper limit, an integer programming model is established. A genetic algorithm or particle swarm optimization algorithm is used to solve this integer programming model. One or more Pareto optimal solutions satisfying all constraints are searched in the solution space. The obtained optimal solutions are transformed into matching labor allocation schemes. After determining the target labor allocation scheme, the assigned task executors are determined according to the scheme. This novel dynamic weighted labor allocation method estimates the theoretical working hours and resource consumption required for each task phase to form the expected progress data. Then, according to the target labor allocation scheme, each task is assigned to matching skilled personnel. During project execution, the current progress data of the target project is acquired in real time. Finally, the current progress data is matched with the expected progress data. If the matching result meets the deviation threshold condition, new skill data and dynamic weight factors are calculated, and the process is returned to input the skill data and task complexity data into the dynamic coupling model until the target project is completed. By establishing a multi-objective constrained optimization model and solving for Pareto optimal solutions, this method can achieve optimal allocation of human resources under multiple constraints such as cost and schedule, significantly improving the efficiency of comprehensive resource utilization. Simultaneously, with the help of a full-process feedback mechanism, the method can dynamically adjust personnel skill assessment and weight strategies based on actual project execution deviations, effectively enhancing the adaptability and accuracy of project management and ensuring the robust achievement of project goals.

[0088] Example 4

[0089] Figure 3 This is a schematic diagram of a labor capacity allocation device based on dynamic weights provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: a request and response module 310, a real-time model matching module 320, an optimization decision module 330, an execution plan module 340, and an adaptive update module 350, wherein:

[0090] The allocation request response module 310 is used to respond to the labor capacity allocation request for the target project in the target enterprise, and to obtain the task complexity data of each task in the target project and the skill data of each skilled person in the target enterprise; wherein each task belongs to at least two task stages;

[0091] The real-time matching module 320 is used to input skill data and task complexity data into the dynamic coupling model, and calculate the real-time matching degree between each skilled person and each task based on the dynamic weight factors built into the dynamic coupling model that correspond to each task.

[0092] The optimization decision module 330 is used to generate a target labor force allocation plan and the expected progress data of the target labor force allocation plan through a multi-objective optimization algorithm based on the real-time matching degree and the multi-dimensional constraint parameters of the target project.

[0093] The execution plan module 340 is used to assign each task in the target project to the matching skilled personnel according to the target labor capacity allocation plan, and to obtain the current progress data of the target project in real time during the project execution process;

[0094] The adaptive update module 350 is used to match the current progress data with the expected progress data, and when the matching result meets the deviation threshold condition, calculate the new skill data and dynamic weight factor, and return to execute the operation of inputting the skill data and task complexity data into the dynamic coupling model until the target project is completed.

[0095] The technical solution of this invention, in response to a request for labor allocation for a target project within a target enterprise, firstly acquires the task complexity data of each task in the target project and the skill data of each skilled worker in the target enterprise. Then, the skill data and task complexity data are input into a dynamic coupling model. Based on the dynamic weight factors built into the model corresponding to each task, the real-time matching degree between each skilled worker and each task is calculated. Next, based on the real-time matching degrees and the multi-dimensional constraint parameters of the target project, a target labor allocation scheme and its expected progress data are generated through a multi-objective optimization algorithm. Each task is assigned to the matched skilled worker according to the allocation scheme. During project execution, the current progress data of the target project is acquired in real time. Finally, the current progress data is matched with the expected progress data. If the matching result meets the deviation threshold condition, new skill data and dynamic weight factors are calculated, and the data input and matching degree calculation process is re-executed until the target project is completed. This novel labor allocation method based on dynamic weights improves the dynamic adaptation accuracy of human resource allocation and project task requirements, effectively solving the problems of resource mismatch and response lag.

[0096] Based on the above embodiments, the allocation request-response module 310 can be specifically used for:

[0097] Using 360-degree assessment technology and historical project performance data, the skills of various skilled personnel in the target company are quantified in multiple dimensions, and the quantified skill values ​​of each skilled personnel in the target company are obtained as skill data through weighted fusion.

[0098] The target project is decomposed layer by layer using the work breakdown structure technique to obtain all the tasks contained in each task stage. Based on the Monte Carlo simulation technique, the uncertainty of the time and resources required for each task is quantitatively evaluated, and the complexity rating of each task in the target project is obtained as task complexity data.

[0099] Based on the above embodiments, the real-time model matching module 320 can be specifically used for:

[0100] Based on the formula using the dynamic coupling model: Calculate the real-time matching degree between each skilled worker and each task;

[0101] in, Let j be the dynamic weight factor for the j-th task. Let i be the quantified skill value of the i-th skilled worker. Rate the complexity of the j-th task. The real-time matching degree between the i-th skilled worker and the j-th task; each dynamic weight factor built into the dynamic coupling model has a preset initial value.

[0102] Based on the above embodiments, the optimized decision module 330 can be specifically used for:

[0103] An integer programming model is established with the objective function of maximizing the overall matching degree of skill personnel allocation for all tasks, and with the constraints of total cost not exceeding the budget, total project duration meeting requirements, and workload of individual personnel not exceeding the upper limit.

[0104] The integer programming model is solved using a genetic algorithm or a particle swarm optimization algorithm, and one or more Pareto optimal solutions that satisfy all constraints are searched in the solution space.

[0105] The one or more Pareto optimal solutions obtained from the solution are transformed into matching labor capacity allocation schemes;

[0106] Obtain the target labor allocation scheme from the various labor allocation schemes obtained through transformation;

[0107] Based on the personnel assigned to each task in the target labor capacity allocation plan, the theoretical working hours and theoretical resource consumption required for each task phase are estimated to form the expected progress data.

[0108] Based on the above embodiments, the execution scheme module 340 can be specifically used for:

[0109] Through IoT sensors and / or the application programming interface of project management software, the actual completion time of each task in each task phase of the target project, as well as the actual amount of resources consumed, are collected in real time as the current progress data of the target project.

[0110] Based on the above embodiments, the adaptive update module 350 can be specifically used for:

[0111] If the difference between the actual completion time of all tasks in the target task phase and the theoretical working hours required for the target task phase is greater than the deviation threshold, or if the difference between the actual resource consumption when completing all tasks in the target task phase and the theoretical resource consumption of the target task phase is greater than the deviation threshold, then the new skill data of each skilled person will be updated based on the current progress data of the target project.

[0112] Based on the degree of deviation in the matching results, a preset reinforcement learning algorithm is used to update the dynamic weight factors in the dynamic coupling model.

[0113] The labor capacity allocation device based on dynamic weights provided in this embodiment of the invention can execute the labor capacity allocation method based on dynamic weights provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0114] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0115] Example 4

[0116] Figure 4 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0117] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0118] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0119] Processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as performing a dynamic weight-based labor capacity allocation method as described in any embodiment of the present invention, i.e.:

[0120] In response to a request for the allocation of labor resources for a target project within a target enterprise, the task complexity data for each task in the target project and the skill data for each skilled worker in the target enterprise are obtained; wherein each task belongs to at least two task phases;

[0121] Skill data and task complexity data are input into the dynamic coupling model. Based on the dynamic weight factors built into the dynamic coupling model that correspond to each task, the real-time matching degree between each skilled person and each task is calculated.

[0122] Based on the real-time matching degree and the multi-dimensional constraint parameters of the target project, a target labor force allocation plan and the expected progress data of the target labor force allocation plan are generated through a multi-objective optimization algorithm.

[0123] According to the target labor capacity allocation plan, each task in the target project is assigned to the matching skilled personnel for execution, and the current progress data of the target project is obtained in real time during the project execution process;

[0124] The system matches the current progress data with the expected progress data. When the matching result meets the deviation threshold condition, it calculates new skill data and dynamic weight factors, and returns to execute the operation of inputting skill data and task complexity data into the dynamic coupling model until the target project is completed.

[0125] In some embodiments, a labor capacity allocation method based on dynamic weights as described in any of the embodiments of the present invention can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the labor capacity allocation method based on dynamic weights as described above can be performed. Alternatively, in other embodiments, processor 11 can be configured by any other suitable means (e.g., by means of firmware) to perform a labor capacity allocation method based on dynamic weights as described in any of the embodiments of the present invention.

[0126] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0127] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0128] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0129] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0130] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0131] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0132] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0133] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for allocating labor capacity based on dynamic weights, characterized in that, The method includes: In response to a request for the allocation of labor resources for a target project within a target enterprise, the task complexity data for each task in the target project and the skill data for each skilled worker in the target enterprise are obtained; wherein each task belongs to at least two task phases; Skill data and task complexity data are input into the dynamic coupling model. Based on the dynamic weight factors built into the dynamic coupling model that correspond to each task, the real-time matching degree between each skilled person and each task is calculated. Based on the real-time matching degree and the multi-dimensional constraint parameters of the target project, a target labor force allocation plan and the expected progress data of the target labor force allocation plan are generated through a multi-objective optimization algorithm. According to the target labor capacity allocation plan, each task in the target project is assigned to the matching skilled personnel for execution, and the current progress data of the target project is obtained in real time during the project execution process; The system matches the current progress data with the expected progress data. When the matching result meets the deviation threshold condition, it calculates new skill data and dynamic weight factors, and returns to execute the operation of inputting skill data and task complexity data into the dynamic coupling model until the target project is completed.

2. The method according to claim 1, characterized in that, Obtain task complexity data for each task in the target project and skill data for each skill level of personnel in the target company, including: Using 360-degree assessment technology and historical project performance data, the skills of various skilled personnel in the target company are quantified in multiple dimensions, and the quantified skill values ​​of each skilled personnel in the target company are obtained as skill data through weighted fusion. The target project is decomposed layer by layer using the work breakdown structure technique to obtain all the tasks contained in each task stage. Based on the Monte Carlo simulation technique, the uncertainty of the time and resources required for each task is quantitatively evaluated, and the complexity rating of each task in the target project is obtained as task complexity data.

3. The method according to claim 2, characterized in that, Based on the dynamic weight factors built into the dynamic coupling model that correspond to each task, the real-time matching degree between each skilled worker and each task is calculated, including: Based on the formula using the dynamic coupling model: Calculate the real-time match between each skilled worker and each task; in, Let j be the dynamic weight factor for the j-th task. Let i be the quantified skill value of the i-th skilled worker. Rate the complexity of the j-th task. The real-time matching degree between the i-th skilled worker and the j-th task; each dynamic weight factor built into the dynamic coupling model has a preset initial value.

4. The method according to any one of claims 1-3, characterized in that, Based on the real-time matching degree and the multi-dimensional constraint parameters of the target project, a target labor force allocation plan and the expected progress data of the target labor force allocation plan are generated through a multi-objective optimization algorithm, including: An integer programming model is established with the objective function of maximizing the overall matching degree of skill personnel allocation for all tasks, and with the constraints of total cost not exceeding the budget, total project duration meeting requirements, and workload of individual personnel not exceeding the upper limit. The integer programming model is solved using a genetic algorithm or a particle swarm optimization algorithm, and one or more Pareto optimal solutions that satisfy all constraints are searched in the solution space. The one or more Pareto optimal solutions obtained from the solution are transformed into matching labor capacity allocation schemes; Obtain the target labor allocation scheme from the various labor allocation schemes obtained through transformation; Based on the personnel assigned to each task in the target labor capacity allocation plan, the theoretical working hours and theoretical resource consumption required for each task phase are estimated to form the expected progress data.

5. The method according to claim 4, characterized in that, During project execution, real-time progress data of the target project is acquired, including: Through IoT sensors and / or the application programming interface of project management software, the actual completion time of each task in each task phase of the target project, as well as the actual amount of resources consumed, are collected in real time as the current progress data of the target project.

6. The method according to claim 5, characterized in that, When the matching results meet the deviation threshold condition, new skill data and dynamic weighting factors are calculated, including: If the difference between the actual completion time of all tasks in the target task phase and the theoretical working hours required for the target task phase is greater than the deviation threshold, or if the difference between the actual resource consumption when completing all tasks in the target task phase and the theoretical resource consumption of the target task phase is greater than the deviation threshold, then the new skill data of each skilled personnel will be updated based on the current progress data of the target project. Based on the degree of deviation in the matching results, a preset reinforcement learning algorithm is used to update the dynamic weight factors in the dynamic coupling model.

7. A labor capacity allocation device based on dynamic weights, characterized in that, The device includes: The allocation request and response module is used to respond to labor allocation requests for target projects in target enterprises, and to obtain task complexity data for each task in the target project and skill data for each skilled worker in the target enterprise; wherein each task belongs to at least two task phases; The real-time model matching module is used to input skill data and task complexity data into the dynamic coupling model, and calculate the real-time matching degree between each skilled person and each task based on the dynamic weight factors built into the dynamic coupling model that correspond to each task. The optimization decision module is used to generate a target labor force allocation plan and the expected progress data of the target labor force allocation plan through a multi-objective optimization algorithm based on the real-time matching degree and the multi-dimensional constraint parameters of the target project. The execution plan module is used to assign each task in the target project to the matching skilled personnel according to the target labor capacity allocation plan, and to obtain the current progress data of the target project in real time during the project execution process; The adaptive update module is used to match the current progress data with the expected progress data. When the matching result meets the deviation threshold condition, it calculates new skill data and dynamic weight factors, and returns to execute the operation of inputting skill data and task complexity data into the dynamic coupling model until the target project is completed.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the labor capacity allocation method based on dynamic weights as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the labor capacity allocation method based on dynamic weights as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the labor capacity allocation method based on dynamic weights according to any one of claims 1-6.

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