Intelligent data-driven human resource outsourcing service method and system

Abstract

This invention discloses an intelligent data-driven method and system for new-type human resource outsourcing services, comprising: collecting multi-dimensional parameters through an intelligent human resource efficiency analysis platform, generating a feature label set through hierarchical clustering using a science and technology-driven human resource intelligent clustering model; based on this label set, constructing a matrix using a new-type outsourcing dynamic matching algorithm to screen outsourcing resource candidate sets, and then planning an initial scheduling scheme by combining demand priority and constraints through an intelligent outsourcing resource scheduling model; collecting execution status data in real time and dynamically analyzing it to generate efficiency results, and back-optimizing model weights, matching coefficients, and scheduling parameters to form a closed-loop process. Simultaneously, the implementation steps of matching, scheduling, and efficiency analysis are refined to achieve precise matching of resources and demands, dynamic scheduling, and continuous optimization, improving the efficiency and quality of outsourcing services and meeting the intelligent service needs under digital transformation.

Description

Technical Field

[0001] This invention relates to the field of human resource management technology, and in particular to a method and system for intelligent data-driven new-type human resource outsourcing services. Background Technology

[0002] In the context of the deep integration of the digital economy and technological innovation, human resource outsourcing services are transforming towards intelligence, precision, and dynamism. New productivity demands higher levels of flexibility, efficiency, and adaptability in human resource allocation. Traditional human resource outsourcing services rely on manual experience for resource matching and scheduling, making it difficult to handle multi-dimensional, large-scale outsourcing demands and resource supply data processing, and unable to perceive efficiency fluctuations during service execution in real time. Under this trend, there is an urgent need to build a new quality outsourcing service system centered on intelligent algorithms and data-driven approaches. By integrating clustering models, intelligent scheduling models, dynamic matching algorithms, and intelligent analysis platforms, this system can achieve precise matching and dynamic optimization of outsourcing resources and service needs, meeting enterprises' demands for efficient and intelligent human resource outsourcing services during digital transformation, and driving technological upgrades and model innovation in the human resource outsourcing service industry.

[0003] Existing technologies suffer from two significant drawbacks: First, resource matching and clustering analysis lack systematic intelligent support, often relying on single-dimensional or fixed rules for parameter processing. They fail to combine the dynamic characteristics of outsourced services with multi-dimensional parameter relationships for deep clustering, resulting in insufficient targeting of clustering results. This leads to resource-demand mismatches during the matching process, making it difficult to accurately select outsourced resources that fit the actual service scenario. Second, the scheduling mechanism and performance optimization are disconnected, lacking a closed-loop optimization logic. After the resource scheduling plan is generated, it is difficult to adjust clustering weights, matching coefficients, and scheduling parameters based on real-time execution performance data. Furthermore, the scheduling process does not fully integrate the dynamic changes in service demand priorities and resource constraints, resulting in insufficient flexibility in the scheduling plan. Performance analysis results cannot be effectively transformed into core criteria for service optimization, thus hindering the overall quality and efficiency improvement of human resource outsourcing services. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides a method and system for intelligent data-driven new-type human resource outsourcing services.

[0005] The technical solution adopted in this invention is an intelligent data-driven method for new-type human resource outsourcing services, comprising the following steps: S1, collecting multi-dimensional parameters related to new-type human resource outsourcing services through a human resource efficiency intelligent analysis platform, wherein the multi-dimensional parameters include outsourced personnel skill dimension data, service demand scenario characteristic data, resource supply real-time status data, and efficiency evaluation correlation data; S2, calling the science and technology innovation human resource intelligent clustering model to perform hierarchical clustering processing on the collected multi-dimensional parameters, dividing parameter clusters according to preset clustering dimensions, and generating a clustering feature label set; S3, based on the clustering feature label set, constructing a parameter matching dimension matrix using a new-type outsourcing dynamic matching algorithm, and then using the matrix... S4. Array operation filters out a candidate set of outsourced resources that meet the basic matching conditions; S5. The candidate set of outsourced resources is input into the intelligent computing model for outsourced resource scheduling, and scheduling path planning is performed in combination with service demand priority and resource scheduling constraints to output an initial scheduling plan; S6. The execution status data of outsourced resources corresponding to the initial scheduling plan is collected and dynamically analyzed in real time through the intelligent analysis platform for human resource efficiency to generate efficiency analysis results; S7. Based on the efficiency analysis results, the clustering weight of the clustering model driven by science and technology human resources, the matching coefficient of the dynamic matching algorithm for new-quality outsourcing, and the scheduling parameters of the intelligent computing model for outsourced resource scheduling are optimized in reverse to form a closed-loop optimized new-quality human resource outsourcing service process.

[0006] Furthermore, the expression for the science and technology innovation human resource intelligence-driven clustering model is: ,in, To output the clustering results, It is a multidimensional parameter set. The number of parameter dimensions. For the first Clustering weights with dimensional parameters For the first Feature extraction function with dimensional parameters For the first The cluster is related to the first... Membership degree of the dimensional parameter, The number of constraints. For the first The weighting coefficients of each constraint condition. For the first A mapping function for each constraint condition. It serves as a clustering regulator.

[0007] Furthermore, the expression for the outsourced resource scheduling intelligent computing model is: ,in, Output values ​​for the scheduling scheme. For the collection of outsourced resources, For the collection of service demands, The number of scheduling cycles. For the first Periodic resource state weights For the first Periodic resource status assessment function For the first Cyclical demand priority weighting For the first Periodic demand characteristic function For scheduling balance coefficient, For the first The fit function between cyclical resources and demand.

[0008] Furthermore, the expression for the novel quality outsourcing dynamic matching algorithm is: ,in, This is the matching degree value. For the set of outsourced resource parameters, For the set of requirements parameters, The number of dimensions for resource parameters. The number of dimensions for the required parameters. For the first Dimensional resource parameters and the first The correlation coefficient of the dimension requirement parameters, For the first Standardized values ​​of dimensional resource parameters For the first Standardized values ​​of dimensional demand parameters, This is the matching accuracy adjustment factor.

[0009] Furthermore, the performance evaluation model expression of the aforementioned human resource efficiency intelligent analysis platform is as follows: ,in, For the results of the performance analysis, For the execution state data set, Number of state data types, As the benchmark coefficient for performance evaluation, For the first The weights of class state data, For the first Performance transformation function for class state data, For dynamically changing weights, It is a function of the change in state data between adjacent periods.

[0010] Furthermore, the closed-loop optimization model expression for the new-type human resource outsourcing service is as follows: ,in, To optimize the output value for closed-loop operation, For cluster weight set, For the set of scheduling parameters, For the set of matching coefficients, , , These are the optimization weights for clustering, scheduling, and matching, respectively. This is the performance feedback adjustment coefficient. To optimize the clustering results, For scheduling optimization results, To match and optimize the results, This is the feedback value for performance analysis.

[0011] Further, step S3 includes the following sub-steps: S31, extracting the calibration features from the clustering feature label set, classifying and organizing them into three categories: resource attributes, demand attributes, and adaptation rules, and establishing a feature classification index library; S32, based on the feature classification index library, determining the calibration indicators and auxiliary indicators for the matching dimension, where the calibration indicators include skill matching degree, resource availability, and service response speed, and the auxiliary indicators include historical cooperation records and efficiency fluctuation coefficient; S33, mapping the calibration indicators and auxiliary indicators to matrix elements through matrix construction rules, setting element weights, and generating an initial matching dimension matrix, where the matrix row dimensions correspond to outsourced resource features, and the column dimensions correspond to demand features; S34, performing calculations on the initial matching dimension matrix using a matrix similarity calculation method, setting a similarity threshold, and filtering out outsourced resources whose matrix element values ​​are higher than the threshold to form a candidate set of outsourced resources.

[0012] Further, S4 includes the following sub-steps: S41, performing attribute analysis on each outsourced resource in the external resource candidate set, extracting resource capacity, scheduling cost, execution cycle, and constraint limit calibration attribute parameters, and establishing a resource attribute database; S42, obtaining the priority ranking results corresponding to service requirements, determining the priority weight of each requirement based on the urgency of the requirement, service scale, and importance level, and generating a priority sequence; S43, inputting the resource attribute parameters and priority sequence into the outsourced resource scheduling intelligent computing model, setting time constraints, cost constraints, and resource conflict constraints of the scheduling path, and constructing a scheduling objective function; S44, solving the scheduling objective function through model calculation, generating multiple candidate scheduling paths, and selecting the optimal path based on scheduling efficiency, cost control, and resource utilization indicators to form an initial scheduling scheme.

[0013] Further, S5 includes the following sub-steps: S51, continuously collecting data on the work status, task completion progress, and resource consumption of outsourced personnel during the execution of the initial scheduling plan through the real-time acquisition module deployed on the human resource efficiency intelligent analysis platform; the acquisition frequency is dynamically adjusted according to the demand scenario; S52, standardizing the format of the collected execution status data, removing redundant data and abnormal interference data, retaining valid data and storing it in the efficiency analysis database; S53, calling the platform's built-in analysis algorithm to perform multi-dimensional analysis on the valid data, including efficiency trend analysis in the time dimension, efficiency distribution analysis in the resource dimension, and efficiency adaptation analysis in the demand dimension; S54, integrating the multi-dimensional analysis results to generate an efficiency analysis report including efficiency achievement status, deviation data, and potential problem points, as the basis for subsequent optimization.

[0014] A smart data-driven new-type human resource outsourcing service system is disclosed. This system is applied to a smart data-driven new-type human resource outsourcing service method, comprising: a multi-dimensional parameter intelligent acquisition unit, a science and technology innovation-driven human resource intelligent clustering processing unit, a new-type outsourcing dynamic matching unit, an outsourcing resource scheduling intelligent computing unit, a human resource efficiency real-time analysis unit, and a closed-loop parameter optimization unit. The multi-dimensional parameter intelligent acquisition unit is bidirectionally connected to the human resource efficiency real-time analysis unit, and its output is connected to the input of the science and technology innovation-driven human resource intelligent clustering processing unit for transmitting the acquired multi-dimensional parameters to the clustering processing unit. The output of the science and technology innovation-driven human resource intelligent clustering processing unit is connected to the input of the new-type outsourcing dynamic matching unit to transmit cluster feature labels. The data is transmitted to the matching unit; the output of the new-quality outsourcing dynamic matching unit is connected to the input of the outsourcing resource scheduling intelligent computing unit, providing the scheduling intelligent computing unit with a candidate set of outsourcing resources; the output of the outsourcing resource scheduling intelligent computing unit is connected to the input of the human resource efficiency real-time analysis unit, transmitting the initial scheduling plan and execution status related data; the output of the human resource efficiency real-time analysis unit is connected to the input of the closed-loop parameter optimization unit, feeding back the efficiency analysis results; the output of the closed-loop parameter optimization unit is connected to the inputs of the science and technology innovation human resource intelligent clustering processing unit, the new-quality outsourcing dynamic matching unit, and the outsourcing resource scheduling intelligent computing unit, respectively, outputting the optimized parameters to carry out new-quality human resource outsourcing services through the collaborative operation of each unit.

[0015] Beneficial Effects: This invention proposes an intelligent data-driven method and system for new-type human resource outsourcing services. It utilizes a science and technology-driven human resource intelligent clustering model to perform hierarchical clustering of multi-dimensional parameters, and combines this with a dynamic matching algorithm for new-type outsourcing to construct a multi-dimensional matching matrix. This overcomes the limitations of traditional single-dimensional matching. Through deep correlation analysis of resource attributes and demand characteristics, it accurately selects suitable resources, effectively overcoming the shortcomings of existing technologies such as insufficient clustering targeting and large matching deviations. Simultaneously, it integrates demand priorities and multiple constraints into a smart computing model for outsourcing resource scheduling to plan scheduling paths. Relying on a human resource efficiency intelligent analysis platform to collect execution status data in real time, the resulting efficiency analysis results optimize clustering weights, matching coefficients, and scheduling parameters, constructing a closed-loop system of collection-clustering-matching-scheduling-analysis-optimization. This solves the problems of disconnect between scheduling and efficiency optimization and insufficient flexibility in existing technologies. The entire service process can be dynamically adjusted without manual intervention, improving the efficiency and suitability of outsourcing resource allocation, and ensuring steady improvement in service efficiency through a continuous optimization mechanism. This fully meets the intelligent and precise needs of human resource outsourcing services under digital transformation. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention. Figure 2 This is a flowchart of method step S3 of the present invention; Figure 3 This is a flowchart of method step S4 of the present invention; Figure 4 This is a flowchart of step S5 of the method of the present invention; Figure 5 This is a diagram showing the system unit composition of the present invention. Detailed Implementation

[0017] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] like Figure 1 As shown, a new intelligent data-driven human resource outsourcing service method includes the following steps: S1. Collect multi-dimensional parameters related to new-quality human resource outsourcing services through the human resource efficiency intelligent analysis platform. The multi-dimensional parameters include outsourced personnel skill dimension data, service demand scenario characteristic data, real-time resource supply status data, and efficiency evaluation related data. Specifically, step S1, as the data foundation collection link of the entire service method, comprehensively collects the multi-dimensional calibration parameters required for the new quality human resources outsourcing service through the multi-source data collection interface and real-time monitoring module of the human resource efficiency intelligent analysis platform. During the implementation process, the platform captures various types of data at a collection frequency of 20 times per second to ensure the real-time and completeness of the parameters. Among the multidimensional parameters, the outsourced personnel skills dimension data includes 12 specific indicators such as professional skill level, skill proficiency, skill update cycle, and certification status. Each indicator is recorded with raw data according to a quantitative standard of 0-100. The service demand scenario characteristic data includes 8 scenario parameters such as the industry type of the demand, business complexity level, service duration requirements, work location distribution, and team collaboration scale. Among them, the business complexity level is divided into 1-5 levels, and the service duration requirements are accurate to the hour. The real-time status data of resource supply involves 6 dynamic parameters such as the current idle status of outsourced personnel, available time window, geographical location information, and equipment configuration. The geographical location information is accurately recorded using latitude and longitude coordinates. The efficiency evaluation related data includes 10 related indicators such as historical service completion quality score, customer satisfaction feedback, efficiency fluctuation range, and resource utilization rate. The historical service completion quality score is recorded as a cumulative data of the past 12 months on a percentage basis. This step, through the comprehensive collection of the aforementioned 36 specific parameters, provides complete and accurate data support for subsequent clustering processing, matching analysis, scheduling planning, and other stages, eliminating data gaps and ensuring that all subsequent model calculations and platform analyses are based on comprehensive and practically relevant basic data, thus laying a data foundation for the intelligent operation of the entire service process.

[0019] S2, call the science and technology innovation human resources intelligent clustering model to perform hierarchical clustering processing on the collected multi-dimensional parameters, divide the parameter clusters according to the preset clustering dimensions, and generate a cluster feature label set; Specifically, step S2 is a key step in data preprocessing and feature extraction. During implementation, the 36 multi-dimensional parameters collected in step S1 are first fully imported into the science and technology innovation human resource intelligent clustering model through the data transmission interface. The model first classifies and identifies the parameters, and performs preliminary division according to four preset clustering dimensions: personnel attribute class, demand characteristic class, resource status class, and efficiency correlation class. Then, hierarchical clustering operation is started. The first layer divides the data into 10-15 major clusters according to primary classification indicators such as industry type and skill field. The second layer further subdivides each major cluster into 5-8 minor clusters according to secondary classification indicators such as business complexity and skill proficiency, ultimately forming 50-120 refined parameter clusters. During the clustering process, the model automatically extracts labeled features for each cluster, including the mean, distribution range, and key differences of parameters within the cluster. This generates a corresponding clustering feature label set, with 3-5 labeled features assigned to each cluster. The labels use a four-level coding format: "industry-skill-demand-status," such as "Internet-software development-high complexity-ready-to-use." The total number of labels matches the number of parameter clusters. This step, through hierarchical clustering, systematically categorizes massive amounts of dispersed multidimensional parameters, reducing the data processing complexity of subsequent matching and scheduling stages. Simultaneously, feature labeling enables precise extraction of parameter features, allowing subsequent stages to quickly locate key information and improving the overall efficiency of the service process. The implementation method utilizes automatic hierarchical computation and feature extraction, avoiding the subjectivity and lag of manual classification and ensuring the objectivity and accuracy of the clustering results.

[0020] S3, based on the clustering feature label set, uses a novel outsourcing dynamic matching algorithm to construct a parameter matching dimension matrix, and uses matrix operations to filter out a set of outsourcing resource candidates that meet the basic matching conditions; Specifically, step S3 focuses on the precise matching of outsourced resources and service requirements. During implementation, the clustering feature label set generated in step S2 serves as the core input. First, key features are extracted from each label set and correlated with the core demands of the service requirement side. Then, the new-quality outsourcing dynamic matching algorithm is invoked to initiate the construction of the matching dimension matrix. During matrix construction, the row dimension is set to 50-120 rows based on the number of outsourced resource clusters, with each row corresponding to the comprehensive features of a parameter cluster. The column dimension is set to 15-20 columns based on the service requirement's calibration indicators, including key dimensions such as skill matching requirements, response time requirements, resource capacity requirements, and efficiency standard requirements. The matrix element values ​​are obtained by calculating the degree of fit between each resource cluster and the requirement indicators using the algorithm, with values ​​ranging from 0 to 1; higher values ​​indicate a stronger fit. After the matrix is ​​constructed, the algorithm initiates matrix operations, using row-column matrix multiplication to obtain a comprehensive matching score between each resource cluster and the overall demand. Then, a basic matching threshold of 0.6-0.8 is set (the threshold can be dynamically adjusted according to the service scenario), filtering out resource clusters with comprehensive matching scores higher than the threshold. The outsourced resources corresponding to these clusters are then aggregated and integrated to form a candidate set of outsourced resources. The size of the candidate set is controlled within the range of 20-50 outsourced resources to ensure sufficient selection space in subsequent scheduling stages without increasing scheduling difficulty due to an excessive number of resources. This step, through the construction and operation of the matching dimension matrix, achieves multi-dimensional and precise matching of resources and demands, overcoming the limitations of traditional single-dimensional matching. Its implementation method, through automated matrix construction and threshold filtering, ensures the efficiency and accuracy of the matching process, providing a high-quality resource foundation for the optimization and formulation of subsequent scheduling schemes.

[0021] S4: Input the candidate set of outsourced resources into the intelligent computing model for outsourced resource scheduling, combine the service demand priority and resource scheduling constraints to perform scheduling path planning, and output the initial scheduling scheme. Specifically, step S4 is responsible for optimizing the scheduling of outsourced resources. During implementation, the 20-50 candidate outsourced resources generated in step S3 are first fully input into the intelligent computing model for outsourced resource scheduling. Simultaneously, two key auxiliary data categories are imported: service demand priorities and resource scheduling constraints. Service demand priorities are divided into 1-5 levels based on indicators such as urgency, importance, and impact scope, with level 1 being the highest priority. Each demand corresponds to a unique priority level, with priority weights allocated at ratios of 1.0, 0.8, 0.6, 0.4, and 0.2. Resource scheduling constraints include three types: time constraints (service start deadline, task completion cycle), cost constraints (maximum scheduling cost, unit resource cost standard), and resource conflict constraints (outsourced personnel scheduling conflicts, geographical location accessibility limitations). Time constraints are accurate to the hour, cost constraints are set according to a fixed amount range, and resource conflict constraints are automatically identified through preset rules. After receiving input data, the model initiates scheduling path planning calculations. First, candidate resources are initially ranked based on demand priority. Then, the scheduling feasibility of the ranked resources is verified by considering various constraints, eliminating resources that do not meet the constraints. Subsequently, multiple potential scheduling paths are designed for the remaining resources. Each path includes specific details such as resource allocation schemes, scheduling order, and time node arrangements. The model calculates the scheduling efficiency, cost control level, and resource utilization rate of each path, selecting the path with the best overall performance to form the initial scheduling scheme. The initial scheduling scheme includes five core modules: outsourced resource allocation details, task division arrangement, time schedule planning, cost budget details, and constraint satisfaction status. Each module includes specific parameter values ​​and execution standards. This step, through model-based scheduling path planning, optimizes the allocation of outsourced resources, ensuring that the scheduling scheme meets both demand priority requirements and various constraints. Its implementation method, through multi-indicator comprehensive evaluation and path selection, improves the rationality and feasibility of the scheduling scheme, providing a guarantee for efficient service execution.

[0022] S5 uses a human resource efficiency intelligent analysis platform to collect and dynamically analyze the execution status data of outsourced resources corresponding to the initial scheduling plan in real time, and generate efficiency analysis results. Specifically, the S5 process focuses on the performance monitoring and analysis of the outsourced service execution process. During implementation, it relies on the real-time data collection and analysis modules of the human resource efficiency intelligent analysis platform to monitor the entire process of outsourced resource execution status data during the initial scheduling plan execution. The collection module collects data every 5 minutes, including four main categories and 20 specific data items: outsourced personnel work status (work hours, task completion progress, compliance with operational standards), resource consumption data (human resource costs, equipment usage time, auxiliary resource consumption), service quality data (real-time customer feedback, task completion quality inspection results, service standard compliance), and environmental adaptation data (work environment adaptability, cross-departmental collaboration smoothness). All collected data is transmitted in real-time to the platform database for storage, using a distributed storage architecture to ensure data security and ease of access. After extracting valid data from the database, the data analysis module initiates dynamic analysis and computation. First, it verifies data integrity and removes outliers, retaining data with over 95% reliability. Then, it analyzes performance trends from a time perspective (divided by hour, day, and week), performance distribution from a resource perspective (divided by outsourced personnel, equipment type, and service team), and performance suitability from a demand perspective (divided by demand type and priority). Simultaneously, it calculates core analytical indicators such as performance achievement rate, deviation value, and improvement potential value. The performance achievement rate is calculated as the ratio of actual performance to standard performance, and the deviation value is calculated as the difference between actual and standard performance. Finally, the data is integrated to generate performance analysis results, presented in the form of an analysis report. The report includes an overall performance evaluation, detailed analysis of each dimension, explanations of outliers, and diagnosis of potential problems, providing direct data support for subsequent optimization phases. This step enables real-time monitoring and dynamic analysis of the service execution process, allowing for timely understanding of performance status and existing problems. Its implementation method utilizes high-frequency data collection, multi-dimensional analysis, and precise diagnosis to ensure the comprehensiveness and timeliness of performance analysis results, providing a reliable basis for closed-loop optimization.

[0023] S6. Based on the performance analysis results, the clustering weights of the science and technology innovation human resource intelligent clustering model, the matching coefficients of the new quality outsourcing dynamic matching algorithm, and the scheduling parameters of the outsourcing resource scheduling intelligent computing model are optimized in reverse to form a closed-loop optimized human resource new quality outsourcing service process.

[0024] Specifically, step S6 serves as the core of the closed-loop optimization of the entire service method. During implementation, it uses the performance analysis results generated by S5 as a basis to initiate reverse optimization adjustments of multiple model parameters. First, key optimization indicators are extracted from the performance analysis results, including quantitative data corresponding to issues such as clustering accuracy deviation, insufficient matching adaptability, low scheduling efficiency, and excessive performance fluctuations. Then, for the science and technology human resource intelligent clustering model, the clustering weights of each dimension parameter are adjusted based on the degree of fit between clustering characteristics and actual performance. Parameters with high positive correlation to performance (such as skill proficiency and service response speed) have their weights increased by 10%-20%, while parameters with low correlation have their weights appropriately reduced. Simultaneously, the cluster partitioning threshold is optimized to make the clustering results more closely match actual service needs. For the new quality outsourcing dynamic matching algorithm, the matching coefficients of each matching dimension are adjusted based on the deviation data between the matching results and the performance analysis. The coefficient proportion of calibration indicators (such as skill matching degree and resource availability) is increased, while the influence of secondary indicators is reduced. At the same time, the matching threshold is adjusted to make the candidate set selection more accurate. For the outsourcing resource scheduling intelligent computing model, the priority weight allocation ratio of demand, constraint parameter settings, and scheduling path evaluation indicator weights are adjusted based on the execution performance data of the scheduling scheme to improve the flexibility and adaptability of the scheduling scheme. All parameter adjustments are executed automatically through a closed-loop optimization module. The adjusted data is synchronized to the corresponding models and platforms in real time, and the new parameters take effect immediately in subsequent service processes. Simultaneously, the platform records the specific content and effect data of each parameter adjustment, forming an optimization log. This log retains adjustment records for nearly six months for iterative improvement of subsequent optimization strategies. This step constructs a closed-loop mechanism of "execution-analysis-optimization," continuously improving the operational performance of various models and platforms through parameter optimization. Its implementation method, through data-driven precise adjustments, real-time synchronization, and log recording, ensures the scientific nature and sustainability of the optimization process, enabling continuous iteration and upgrading of the new-type human resources outsourcing service process, and constantly improving service quality and efficiency.

[0025] Preferably, the expression of the science and technology innovation human resource intelligence-driven clustering model is: ,in, To output the clustering results, It is a multidimensional parameter set. The number of parameter dimensions. For the first Clustering weights with dimensional parameters For the first Feature extraction function with dimensional parameters For the first The cluster is related to the first... Membership degree of the dimensional parameter, The number of constraints. For the first The weighting coefficients of each constraint condition. For the first A mapping function for each constraint condition. It serves as a clustering regulator.

[0026] Specifically, the Science and Technology Innovation Human Resources Intelligent Clustering Model achieves accurate classification of multi-dimensional parameters. Its implementation process revolves around designing hierarchical clustering logic based on the parameter characteristics of new-type human resources outsourcing services. The model uses a multi-dimensional parameter set as its core processing object. The number of parameter dimensions can be dynamically adjusted between 30 and 50 depending on the actual service scenario. Each dimension parameter is configured with a dedicated clustering weight, with initial weight values ​​divided into gradient ranges of 0.1-0.9 according to parameter importance, and the calibrated parameter weights are no less than 0.7. Feature extraction functions are used to transform the features of each dimension parameter, preserving the key attributes and relationships of the parameters during the transformation process. A cluster membership index is also introduced, with each parameter's membership degree to each cluster ranging from 0 to 1. Parameters with a membership degree higher than 0.6 are assigned to their corresponding clusters. Multiple constraints are also incorporated into the model's computation, with the number of constraints controlled between 5 and 8. Each constraint is configured with a weight coefficient of 0.5-1.0. A mapping function transforms the constraints into a computable numerical form. The clustering adjustment factor is fixed at 0.05 to balance the influence weight of feature extraction results and constraints. This model achieves refined hierarchical clustering of multi-dimensional parameters by integrating multi-parameter weighted operations and constraints, avoiding the one-sidedness of single-dimensional classification. Its implementation method ensures that the clustering results are highly consistent with the outsourcing service demand scenario by dynamically adjusting the weights and membership thresholds, providing accurate feature label support for the subsequent matching process.

[0027] Preferably, the expression for the outsourced resource scheduling intelligent computing model is: ,in, Output values ​​for the scheduling scheme. For the collection of outsourced resources, For the collection of service demands, The number of scheduling cycles. For the first Periodic resource state weights For the first Periodic resource status assessment function For the first Cyclical demand priority weighting For the first Periodic demand characteristic function For scheduling balance coefficient, For the first The fit function between cyclical resources and demand.

[0028] Specifically, the intelligent computing model for outsourced resource scheduling optimizes the allocation of outsourced resources. During implementation, it uses both the outsourced resource set and the service demand set as dual inputs. The number of scheduling cycles is set to 3-10 based on the service project duration. Each cycle independently configures resource status weights and demand priority weights, with the weights adjusted in a gradient over the cycle. Initially, the resource status weight is 0.6 and the demand priority weight is 0.4, with subsequent cycles dynamically fine-tuned based on execution progress. A resource status evaluation function quantifies the outsourced resource capacity, availability, and wear level for each cycle, with evaluation results ranging from 0 to 100. Simultaneously, a demand feature function extracts the demand characteristics and execution requirements for each cycle. A scheduling balance coefficient of 0.8 is used to coordinate the influence ratio of resource status and demand characteristics. A resource-demand fit function calculates the degree of fit between resource attributes and demand characteristics, with a value ranging from 0 to 1. The model uses a product operation logic to integrate the resource evaluation results, demand feature data, and fit values ​​from each cycle to form a comprehensive scheduling output value. This model enables scheduling path optimization under multiple cycles and constraints. Its implementation method ensures that the scheduling scheme not only meets the changing priority of needs in different cycles, but also makes full use of existing outsourced resources, avoids resource conflicts and waste, and improves the feasibility and efficiency of the scheduling scheme through dynamic weight adjustment and multi-factor comprehensive calculation.

[0029] Preferably, the expression for the novel quality outsourcing dynamic matching algorithm is: ,in, This is the matching degree value. For the set of outsourced resource parameters, For the set of requirements parameters, The number of dimensions for resource parameters. The number of dimensions for the required parameters. For the first Dimensional resource parameters and the first The correlation coefficient of the dimension requirement parameters, For the first Standardized values ​​of dimensional resource parameters For the first Standardized values ​​of dimensional demand parameters, This is the matching accuracy adjustment factor.

[0030] Specifically, the novel outsourcing dynamic matching algorithm is a key technology connecting clustering results and the scheduling process. During implementation, it uses the outsourcing resource parameter set and the demand parameter set as input. The number of dimensions for both resource and demand parameters is set to 15-25 based on the actual scenario. Each resource parameter dimension corresponds one-to-one with a demand parameter dimension, and the correlation coefficient is set to 0.3-1.0 according to the degree of correspondence, with the correlation coefficient for core corresponding dimensions not less than 0.8. Standardization transforms the resource and demand parameters into standardized values ​​of 0-1, preserving the relative differences and distribution characteristics of the parameters during the transformation process. During model computation, the cross-product sum of the resource and demand parameters is first calculated. Then, the maximum value of the sum of squares of the resource and demand parameters is used as the denominator, with a matching precision adjustment factor of 0.03 added to avoid computational anomalies due to a zero denominator and to reduce the impact of extreme values ​​on the matching results. The matching degree value ranges from 0-1, with higher values ​​indicating stronger adaptability between resources and demands. This algorithm achieves multi-dimensional and accurate matching of resources and demands. Its implementation method highlights the matching weight of calibration parameters by setting correlation coefficients and standardizing the process. At the same time, it ensures the stability of operation by adjusting the precision factor, effectively screening out a candidate set of outsourced resources that meet the requirements, and providing a high-quality resource foundation for the scheduling process.

[0031] Preferably, the performance evaluation model expression of the human resource efficiency intelligent analysis platform is as follows: ,in, For the results of the performance analysis, For the execution state data set, Number of state data types, As the benchmark coefficient for performance evaluation, For the first The weights of class state data, For the first Performance transformation function for class state data, For dynamically changing weights, It is a function of the change in state data between adjacent periods.

[0032] Specifically, the efficiency evaluation model of the human resource efficiency intelligent analysis platform is used to achieve dynamic efficiency analysis of the service execution process. During implementation, the set of execution status data is used as the analysis object. The number of status data types is divided into 8-12 categories according to monitoring needs, including core types such as work status, resource consumption, and service quality. The efficiency evaluation benchmark coefficient is set to 1.0 as a reference benchmark for efficiency analysis. Each type of status data is assigned a weight of 0.3-1.0, with core status data having a weight of no less than 0.8. Through an efficiency transformation function, various types of status data are converted into standardized efficiency indicators. During the transformation process, the original data is mapped to efficiency values ​​of 0-100 according to preset standards. A dynamic change weight of 0.2 is introduced to amplify the impact of changes in status data between adjacent periods. By calculating the change function of status data between adjacent periods, the model captures the fluctuation trend and magnitude of efficiency changes. The model uses a weighted summation and change superposition operation logic to generate comprehensive efficiency analysis results. This model enables multi-dimensional and dynamic analysis of performance data. Its implementation method highlights the impact of core state data through weight configuration and combines dynamic change analysis to accurately capture performance change trends and potential problems, providing comprehensive and timely performance feedback data for subsequent closed-loop optimization.

[0033] Preferably, the closed-loop optimization model expression for the new human resource outsourcing service is: ,in, To optimize the output value for closed-loop operation, For cluster weight set, For the set of scheduling parameters, For the set of matching coefficients, , , These are the optimization weights for clustering, scheduling, and matching, respectively. This is the performance feedback adjustment coefficient. To optimize the clustering results, For scheduling optimization results, To match and optimize the results, This is the feedback value for performance analysis.

[0034] Specifically, the closed-loop optimization model for new-type human resource outsourcing services is the core of achieving continuous upgrading throughout the entire process. During implementation, the optimization objects are the clustering weight set, scheduling parameter set, and matching coefficient set. The three optimization weights are set to 0.3, 0.4, and 0.3 respectively, highlighting the importance of scheduling parameter optimization. The performance feedback adjustment coefficient is set to 0.2 to control the impact of performance analysis results on the optimization output, avoiding system fluctuations caused by over-adjustment. During model calculation, the clustering optimization results, scheduling optimization results, and matching optimization results are first integrated through weighted summation. Then, 1 is added to the product of the performance analysis feedback value and the adjustment coefficient as the denominator to form the closed-loop optimization output value. The clustering optimization result is obtained by adjusting the clustering weights and cluster thresholds. The scheduling optimization result originates from the dynamic adjustment of scheduling parameters and constraints. The matching optimization result is generated by correcting the matching coefficients and thresholds. The performance analysis feedback value is taken from the output of the human resource performance intelligent analysis platform, with a value range of 0-100. This model constructs a closed-loop mechanism of "optimization-execution-feedback-re-optimization". Its implementation method ensures that the optimization direction is accurately aligned with the actual service performance through the integration of multiple optimization objects and performance feedback adjustment. It achieves the collaborative optimization of clustering models, matching algorithms, and scheduling models, and promotes the continuous iteration and upgrading of outsourcing service processes.

[0035] Preferred, such as Figure 2 As shown, step S3 includes the following sub-steps: S31, extracting the calibration features from the clustering feature label set, classifying and organizing them into three categories: resource attributes, demand attributes, and adaptation rules, and establishing a feature classification index library; S32, based on the feature classification index library, determining the calibration indicators and auxiliary indicators for the matching dimension. The calibration indicators include skill matching degree, resource availability, and service response speed, while the auxiliary indicators include historical cooperation records and efficiency fluctuation coefficient; S33, mapping the calibration indicators and auxiliary indicators to matrix elements through matrix construction rules, setting element weights, and generating an initial matching dimension matrix. The matrix row dimensions correspond to outsourced resource features, and the column dimensions correspond to demand features; S34, performing calculations on the initial matching dimension matrix using a matrix similarity calculation method, setting a similarity threshold, and filtering out outsourced resources whose matrix element values ​​are higher than the threshold to form a candidate set of outsourced resources.

[0036] Specifically, step S3 ensures accurate screening of the outsourced resource candidate set through four coherent steps, revolving around clustered feature label sets and multi-dimensional matching logic. In stage S31, calibrated feature items are extracted from the clustered feature label set and systematically categorized into three types: resource attributes, demand attributes, and adaptation rules. Each category includes 8-12 specific sub-items. A feature classification index library is established to enable rapid retrieval and retrieval of feature items. The index library uses a tree structure for storage, with clear hierarchical division, facilitating rapid positioning of subsequent matching dimensions. In stage S32, based on this index library, the calibration indicators and auxiliary indicators for the matching dimensions are defined. The calibration indicators focus on three key dimensions: skill matching degree, resource availability, and service response speed. Each calibration indicator has 3-5 quantitative sub-indicators. Auxiliary indicators include two supplementary dimensions: historical cooperation records and efficiency fluctuation coefficients. The dominant role of the calibration indicators is highlighted through weight allocation, with the total weight of the calibration indicators accounting for no less than 70%. In stage S33, following preset matrix construction rules, the calibration and auxiliary indicators are mapped one by one to matrix elements. Each matrix element is assigned a weight value of 0.1-0.9, dynamically adjusted based on indicator importance. An initial matching dimension matrix is ​​then generated, with the number of rows matching the number of outsourced resource clusters and the number of columns corresponding to the number of service requirement feature dimensions, ensuring comprehensive coverage of resource and requirement matching dimensions. In stage S34, a matrix similarity calculation method is used to operate on the initial matching dimension matrix. During the calculation, similarity values ​​are calculated separately for each column and row dimension, with a similarity threshold of 0.6-0.8. This threshold can be flexibly adjusted according to the accuracy requirements of the service scenario. Outsourced resources with matrix element values ​​higher than the threshold are selected and sorted in descending order of matching value, ultimately forming a candidate set of 20-50 outsourced resources. This step-by-step, refined operation ensures the systematic and accurate matching process, providing a high-quality resource foundation for subsequent scheduling. The implementation method effectively improves the efficiency and quality of outsourced resource matching through the coordinated operation of classification indexing, indicator weight configuration, matrix operations, and threshold filtering.

[0037] Preferred, such as Figure 3As shown, S4 includes the following sub-steps: S41, performing attribute analysis on each outsourced resource in the external resource candidate set, extracting resource capacity, scheduling cost, execution cycle, and constraint limit calibration attribute parameters, and establishing a resource attribute database; S42, obtaining the priority ranking results corresponding to service requirements, determining the priority weight of each requirement based on the urgency of the requirement, service scale, and importance level, and generating a priority sequence; S43, inputting the resource attribute parameters and priority sequence into the outsourced resource scheduling intelligent computing model, setting time constraints, cost constraints, and resource conflict constraints of the scheduling path, and constructing a scheduling objective function; S44, solving the scheduling objective function through model calculation, generating multiple candidate scheduling paths, and selecting the optimal path based on scheduling efficiency, cost control, and resource utilization indicators to form an initial scheduling scheme.

[0038] Specifically, step S4 optimizes the scheduling of outsourced resources through four sub-steps, focusing on resource attribute analysis, priority setting, constraint fusion, and path selection. In step S41, a comprehensive attribute analysis is performed on each outsourced resource in the candidate set, extracting key attribute parameters such as resource capacity, scheduling cost, execution cycle, and constraints. Resource capacity is quantified by the amount of tasks it can handle, scheduling cost is calculated per unit time or per task, execution cycle is accurate to the hour, and constraints include specific conditions such as scheduling conflicts and geographical accessibility. All analyzed parameters are entered into the resource attribute database in a unified format. The database supports real-time updates and multi-condition searches, ensuring the convenience and accuracy of parameter retrieval. In step S42, the priority ranking results corresponding to service requirements are obtained. The priority weight of each requirement is determined by combining three core factors: urgency, service scale, and importance level. Urgency is divided into levels 1-5, service scale is quantified by task volume or number of people involved, and importance level is set according to the scope of impact. Weight values ​​are distributed in a gradient of 1.0, 0.8, 0.6, 0.4, and 0.2, generating an ordered priority sequence, with each requirement corresponding to a unique priority identifier. In stage S43, parameters and priority sequences from the resource attribute database are input into the outsourced resource scheduling intelligent computing model. Simultaneously, time constraints, cost constraints, and resource conflict constraints are set for the scheduling path. Time constraints specify the service start deadline and the latest task completion deadline. Cost constraints set the upper limit of total scheduling cost and the threshold for unit resource cost. Resource conflict constraints automatically identify and avoid conflicts such as overlapping schedules and geographical inaccessibility through preset rules. Based on these inputs, a scheduling objective function is constructed with the goal of maximizing scheduling efficiency, minimizing cost, and maximizing resource utilization. In stage S44, the scheduling objective function is solved through model computation, generating 5-10 candidate scheduling paths. Each path includes details such as resource allocation, scheduling order, time node arrangement, and cost budget. Subsequently, the candidate paths are comprehensively evaluated based on four calibration indicators: scheduling efficiency, cost control level, resource utilization, and constraint satisfaction. Each indicator is quantified and scored from 0 to 100 points, and a weighted comprehensive score is calculated. The path with the highest comprehensive score is selected as the initial scheduling scheme. By designing a structured scheduling process, the feasibility and optimization of the scheduling scheme are ensured. Its implementation method effectively improves the scientificity and efficiency of outsourced resource scheduling through multi-dimensional parameter integration, constraint control and multi-path evaluation and screening.

[0039] Preferred, such as Figure 4As shown, S5 includes the following steps: S51, continuously collecting data on the work status, task completion progress, and resource consumption of outsourced personnel during the execution of the initial scheduling plan through the real-time acquisition module deployed on the human resource efficiency intelligent analysis platform; the acquisition frequency is dynamically adjusted according to the demand scenario; S52, standardizing the format of the collected execution status data, removing redundant data and abnormal interference data, retaining valid data and storing it in the efficiency analysis database; S53, calling the platform's built-in analysis algorithm to perform multi-dimensional analysis on the valid data, including efficiency trend analysis in the time dimension, efficiency distribution analysis in the resource dimension, and efficiency adaptation analysis in the demand dimension; S54, integrating the multi-dimensional analysis results to generate an efficiency analysis report including efficiency achievement status, deviation data, and potential problem points, as the basis for subsequent optimization.

[0040] Specifically, step S5 achieves real-time monitoring and dynamic performance analysis of the outsourced service execution status through four sub-steps, focusing on the entire process of data collection, processing, analysis, and result generation. In stage S51, relying on the real-time collection module deployed on the human resource efficiency intelligent analysis platform, data on the execution status of outsourced resources during the initial scheduling plan execution process is continuously collected. The collection targets include four categories and 20 specific indicators: outsourced personnel work status, task completion progress, resource consumption, service quality data, and environment adaptation data. Work status includes sub-items such as work duration and compliance with operational standards; task completion progress is quantified as a percentage; resource consumption includes data such as labor costs and equipment usage time. The collection frequency is dynamically adjusted according to the demand scenario: once every 3 minutes for urgent demand scenarios and once every 5 minutes for regular scenarios. The collected data is uploaded to the platform in real time through an encrypted transmission channel. In phase S52, the collected execution status data undergoes format standardization processing. The raw data is converted according to a unified data format specification. An outlier detection algorithm is used to identify and remove outliers exceeding three standard deviations from the normal range. Duplicate records, null values, and other redundant items are also removed, retaining valid data with a reliability of at least 95%. This valid data is stored in a distributed performance analysis database, which employs a multi-replica backup mechanism to ensure data security and read speed. In phase S53, the platform's built-in analysis algorithms perform multi-dimensional in-depth analysis of the valid data. The time dimension is divided into hourly, daily, and weekly periods to analyze performance trends and fluctuations over time. The resource dimension is grouped by outsourced personnel, equipment type, and service team to analyze the performance distribution differences and utilization efficiency of different resources. The demand dimension is categorized by demand type and priority to analyze the fit between performance and demand characteristics. Core analytical indicators such as performance achievement rate, deviation value, and improvement potential value are calculated to quantitatively evaluate service execution performance. The S54 phase integrates multi-dimensional analysis results to generate a structured performance analysis report. This report includes core modules such as overall performance evaluation, detailed analysis of each dimension, explanations of abnormal data, and diagnosis of potential problems. The overall evaluation is divided into four levels: excellent, good, satisfactory, and unsatisfactory. Explanations of abnormal data clearly define the type of abnormality, its occurrence time, and its scope of impact. Diagnosis of potential problems points out the core causes of performance deviations and directions for improvement. The final performance analysis results serve as direct data support for subsequent closed-loop optimization processes, enabling full-process visual monitoring and precise performance evaluation of service execution. Its implementation involves high-frequency data collection, rigorous data processing, multi-dimensional analysis, and structured report generation to ensure the comprehensiveness, accuracy, and timeliness of the performance analysis results, providing a reliable basis for continuous optimization of the entire service process.

[0041] The formula in this invention enables the fusion calculation of different scalar and vector parameters. Its core lies in constructing a unified computational dimension through standardization, dimension mapping, and weight allocation, eliminating computational conflicts caused by differences in parameter types. For scalar parameters, such as undirected single-valued data like outsourced personnel skill proficiency scores or service demand priority weights, the formula transforms them into dimensionless values ​​in the 0-1 range using feature extraction or standardization functions, ensuring a unified computational benchmark for scalar parameters. For vector parameters, such as multi-dimensional data like outsourced resource feature vectors including skill, experience, and adaptation dimensions, and service demand scenario feature vectors, the formula decomposes each dimension of the vector into independently computable components using mapping functions. Then, it establishes a correspondence between vector components and scalar parameters through correlation coefficients, allowing vector parameters to participate in the overall calculation as superimposed components. For example, in the new quality outsourcing dynamic matching algorithm, the components of each dimension of the resource parameter vector are bound to the corresponding components of the demand parameter vector through correlation coefficients. After standardization, these components are transformed into values ​​of a unified magnitude. Then, cross-product summation is used to achieve the fusion calculation of vectors and scalars, ensuring that different types of parameters are additive and comparable within the same formula.

[0042] Meanwhile, the formula incorporates adaptive parameters such as adjustment factors and constraint weights to balance the computational impact of different types of parameters, further ensuring the rationality of the fusion calculation. Addressing the differences in physical meaning and numerical range between scalar and vector parameters, the formula uses parameters such as constraint weight coefficients, matching accuracy adjustment factors, and dynamically changing weights to proportionally calibrate the computational results of different types of parameters, preventing any parameter from being masked due to excessively large or small numerical magnitudes. Taking the science and technology innovation human resource intelligent clustering model as an example, after feature extraction, the multidimensional parameter set in vector form is multiplied by the scalar form of cluster weights and cluster membership degrees. Then, it is normalized using the denominator formed by the sum of squares of the constraint mapping function and the cluster adjustment factor. This preserves the multidimensional characteristics of the vector parameters while balancing the computational results through scalar weights and adjustment factors. This ensures that the fusion calculation of scalar and vector parameters conforms to mathematical logic and aligns with the parameter correlation characteristics of new-type human resource outsourcing services, ultimately achieving collaborative computation and accurate output of different types of parameters within the same formula.

[0043] like Figure 5As shown, an intelligent data-driven new-type human resource outsourcing service system is applied to an intelligent data-driven new-type human resource outsourcing service method. The system includes: a multi-dimensional parameter intelligent acquisition unit, a science and technology innovation-driven human resource intelligent clustering processing unit, a new-type outsourcing dynamic matching unit, an outsourcing resource scheduling intelligent computing unit, a human resource efficiency real-time analysis unit, and a closed-loop parameter optimization unit. The multi-dimensional parameter intelligent acquisition unit is bidirectionally connected to the human resource efficiency real-time analysis unit, and its output end is connected to the input end of the science and technology innovation-driven human resource intelligent clustering processing unit for transmitting the acquired multi-dimensional parameters to the clustering processing unit. The output end of the science and technology innovation-driven human resource intelligent clustering processing unit is connected to the input end of the new-type outsourcing dynamic matching unit to display clustering feature labels. The signature set is transmitted to the matching unit; the output of the new-quality outsourcing dynamic matching unit is connected to the input of the outsourcing resource scheduling intelligent computing unit, providing the scheduling intelligent computing unit with a candidate set of outsourcing resources; the output of the outsourcing resource scheduling intelligent computing unit is connected to the input of the human resource efficiency real-time analysis unit, transmitting the initial scheduling plan and execution status related data; the output of the human resource efficiency real-time analysis unit is connected to the input of the closed-loop parameter optimization unit, feeding back the efficiency analysis results; the output of the closed-loop parameter optimization unit is connected to the input of the science and technology innovation human resource intelligent clustering processing unit, the new-quality outsourcing dynamic matching unit, and the outsourcing resource scheduling intelligent computing unit, respectively, outputting the optimized parameters to carry out new-quality human resource outsourcing services through the collaborative operation of each unit.

[0044] A novel intelligent data-driven human resource outsourcing service method and system achieves precision, dynamism, and efficiency in outsourcing services through the deep integration of multiple models and platforms. It leverages a science and technology-driven human resource intelligent clustering model to perform hierarchical clustering of multi-dimensional parameters, breaking the limitations of traditional single-dimensional processing and making parameter classification more aligned with service scenario characteristics. A new-quality outsourcing dynamic matching algorithm constructs a multi-dimensional matching matrix, enabling deep correlation and screening of resources and needs, significantly improving matching accuracy. An intelligent outsourcing resource scheduling model fully integrates demand priorities and constraints to plan optimal scheduling paths, ensuring the rationality of resource allocation. A human resource efficiency intelligent analysis platform collects execution data in real time and performs multi-dimensional analysis, providing a reliable basis for optimization and adjustment, while a closed-loop optimization mechanism ensures continuous iteration and upgrading of the service process.

[0045] This method addresses the issues of insufficient clustering targeting and large matching bias by combining hierarchical clustering with a multi-dimensional matching matrix. It integrates multiple indicators such as resource attributes, demand characteristics, and adaptation rules, and achieves accurate resource selection through classification indexes and similarity filtering. To address the problems of disconnect between scheduling and performance optimization, and insufficient flexibility, it constructs a closed-loop logic of data collection, clustering, matching, scheduling, analysis, and optimization. It uses real-time performance analysis results to adjust clustering weights, matching coefficients, and scheduling parameters, allowing the scheduling scheme to dynamically adapt to the execution status. Simultaneously, it integrates dynamically changing demand priorities and resource constraints, significantly improving scheduling flexibility and optimization effectiveness, and comprehensively improving the overall quality and efficiency of new-type human resource outsourcing services.

[0046] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0047] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A new type of human resource outsourcing service method driven by intelligent data, characterized in that, Includes the following steps: S1. Collect multi-dimensional parameters related to new-quality human resource outsourcing services through the human resource efficiency intelligent analysis platform. The multi-dimensional parameters include outsourced personnel skill dimension data, service demand scenario characteristic data, resource supply real-time status data, and efficiency evaluation related data. S2. Call the science and technology innovation human resource intelligent clustering model to perform hierarchical clustering processing on the collected multi-dimensional parameters, divide the parameter clusters according to the preset clustering dimensions, and generate a clustering feature label set. S3. Based on the clustering feature label set, a parameter matching dimension matrix is ​​constructed using a novel outsourcing dynamic matching algorithm. Matrix operations are then used to filter out a candidate set of outsourcing resources that meet the basic matching conditions. S4. The candidate set of outsourcing resources is input into the intelligent outsourcing resource scheduling model. Scheduling path planning is performed based on service demand priority and resource scheduling constraints, outputting an initial scheduling scheme. S5. The human resource efficiency intelligent analysis platform collects and dynamically analyzes the execution status data of the outsourcing resources corresponding to the initial scheduling scheme in real time, generating efficiency analysis results. S6. Based on the efficiency analysis results, the clustering weights of the science and technology human resource intelligent clustering model, the matching coefficients of the novel outsourcing dynamic matching algorithm, and the scheduling parameters of the intelligent outsourcing resource scheduling model are optimized in reverse, forming a closed-loop optimized human resource novel outsourcing service process.

2. The intelligent data-driven new-type human resource outsourcing service method according to claim 1, characterized in that, The expression for the science and technology innovation human resource-driven clustering model is: ,in, To output the clustering results, It is a multidimensional parameter set. The number of parameter dimensions. For the first Clustering weights with dimensional parameters For the first Feature extraction function with dimensional parameters For the first The cluster is related to the first... Membership degree of the dimensional parameter, The number of constraints. For the first The weighting coefficients of each constraint condition. For the first A mapping function for each constraint condition. It serves as a clustering regulator.

3. The intelligent data-driven new-type human resource outsourcing service method according to claim 1, characterized in that, The expression for the intelligent computing model for outsourced resource scheduling is: ,in, Output values ​​for the scheduling scheme. For the collection of outsourced resources, For the collection of service demands, The number of scheduling cycles. For the first Periodic resource state weights For the first Periodic resource status assessment function For the first Cyclical demand priority weighting For the first Periodic demand characteristic function For scheduling balance coefficient, For the first The fit function between cyclical resources and demand.

4. The intelligent data-driven new-type human resource outsourcing service method according to claim 1, characterized in that, The expression for the novel quality outsourcing dynamic matching algorithm is: ,in, This is the matching degree value. For the set of outsourced resource parameters, For the set of requirements parameters, The number of dimensions for resource parameters. The number of dimensions for the required parameters. For the first Dimensional resource parameters and the first The correlation coefficient of the dimension requirement parameters, For the first Standardized values ​​of dimensional resource parameters For the first Standardized values ​​of dimensional demand parameters, This is the matching accuracy adjustment factor.

5. The intelligent data-driven new-type human resource outsourcing service method according to claim 1, characterized in that, The performance evaluation model expression of the aforementioned human resource efficiency intelligent analysis platform is as follows: ,in, For the results of the performance analysis, For the execution state data set, Number of state data types, As the benchmark coefficient for performance evaluation, For the first The weights of class state data, For the first Performance transformation function for class state data, For dynamically changing weights, It is a function of the change in state data between adjacent periods.

6. The intelligent data-driven new-type human resource outsourcing service method according to claim 1, characterized in that, The closed-loop optimization model expression for the new-type human resources outsourcing service is as follows: ,in, To optimize the output value for closed-loop operation, For cluster weight set, For the set of scheduling parameters, For the set of matching coefficients, , , These are the optimization weights for clustering, scheduling, and matching, respectively. This is the performance feedback adjustment coefficient. To optimize the clustering results, For scheduling optimization results, To match and optimize the results, This is the feedback value for performance analysis.

7. The intelligent data-driven new-type human resource outsourcing service method according to claim 1, characterized in that, S3 includes the following steps: S31, extracting the calibration features from the clustering feature label set, classifying them into three categories: resource attributes, demand attributes, and adaptation rules, and establishing a feature classification index library; S32, based on the feature classification index library, determining the calibration indicators and auxiliary indicators for the matching dimension. The calibration indicators include skill matching degree, resource availability, and service response speed, while the auxiliary indicators include historical cooperation records and efficiency fluctuation coefficient; S33, mapping the calibration indicators and auxiliary indicators to matrix elements through matrix construction rules, setting element weights, and generating an initial matching dimension matrix. The matrix row dimensions correspond to outsourced resource features, and the column dimensions correspond to demand features; S34, performing calculations on the initial matching dimension matrix using a matrix similarity calculation method, setting a similarity threshold, and filtering out outsourced resources with matrix element values ​​higher than the threshold to form a candidate set of outsourced resources.

8. The intelligent data-driven new-type human resource outsourcing service method according to claim 1, characterized in that, S4 includes the following sub-steps: S41, perform attribute parsing on each outsourced resource in the external resource candidate set, extract resource capacity, scheduling cost, execution cycle, constraint and limit calibration attribute parameters, and establish a resource attribute database; S42, obtain the priority ranking results corresponding to the service requirements, determine the priority weight of each requirement by combining the urgency of the requirement, the service scale, and the importance level, and generate a priority sequence; S43, Input the resource attribute parameters and priority sequence into the outsourced resource scheduling intelligent computing model, set the time constraints, cost constraints and resource conflict constraints of the scheduling path, and construct the scheduling objective function; S44, Solve the scheduling objective function through model calculation, generate multiple candidate scheduling paths, and select the optimal path based on scheduling efficiency, cost control and resource utilization indicators to form an initial scheduling scheme.

9. A method for intelligent data-driven new-type human resource outsourcing services according to claim 1, characterized in that, S5 includes the following steps: S51, continuously collecting data on the work status, task completion progress, and resource consumption of outsourced personnel during the execution of the initial scheduling plan through the real-time acquisition module deployed on the human resource efficiency intelligent analysis platform; the acquisition frequency is dynamically adjusted according to the demand scenario; S52, standardizing the format of the collected execution status data, removing redundant data and abnormal interference data, retaining valid data and storing it in the efficiency analysis database; S53, calling the platform's built-in analysis algorithm to perform multi-dimensional analysis on the valid data, including efficiency trend analysis in the time dimension, efficiency distribution analysis in the resource dimension, and efficiency adaptation analysis in the demand dimension; S54, integrating the multi-dimensional analysis results to generate an efficiency analysis report including efficiency achievement status, deviation data, and potential problem points, as the basis for subsequent optimization.

10. A smart data-driven new-type human resource outsourcing service system, characterized in that, This system is applied to the intelligent data-driven new-type human resource outsourcing service method described in claim 1, comprising: a multi-dimensional parameter intelligent acquisition unit, a science and technology innovation human resource intelligent clustering processing unit, a new-type outsourcing dynamic matching unit, an outsourcing resource scheduling intelligent computing unit, a human resource efficiency real-time analysis unit, and a closed-loop parameter optimization unit; the multi-dimensional parameter intelligent acquisition unit is bidirectionally connected to the human resource efficiency real-time analysis unit, and its output end is connected to the input end of the science and technology innovation human resource intelligent clustering processing unit for transmitting the acquired multi-dimensional parameters to the clustering processing unit; the output end of the science and technology innovation human resource intelligent clustering processing unit is connected to the input end of the new-type outsourcing dynamic matching unit for transmitting the clustering feature label set to the matching unit. The unit comprises three components: a dynamic matching unit for new-type outsourcing, an intelligent computing unit for outsourcing resource scheduling, and a human resource outsourcing service. The output of the dynamic matching unit is connected to the input of the intelligent computing unit for outsourcing resource scheduling, providing a candidate set of outsourcing resources. The output of the intelligent computing unit for outsourcing resource scheduling is connected to the input of the real-time human resource efficiency analysis unit, transmitting initial scheduling plans and execution status related data. The output of the real-time human resource efficiency analysis unit is connected to the input of the closed-loop parameter optimization unit, providing feedback on efficiency analysis results. The output of the closed-loop parameter optimization unit is connected to the inputs of the science and technology innovation human resource intelligent clustering processing unit, the dynamic matching unit for new-type outsourcing, and the intelligent computing unit for outsourcing resource scheduling, respectively, outputting optimized parameters to enable collaborative operation of all units in new-type human resource outsourcing services.

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