A method and apparatus for assigning labeling tasks based on artificial intelligence

Abstract

This invention discloses an AI-based method and apparatus for assigning annotation tasks, comprising: acquiring historical behavioral data to construct a multi-dimensional user profile; receiving a task description document, data samples, and quality requirement document to obtain a multi-dimensional task feature vector; calculating a matching score based on the multi-dimensional user profile and the multi-dimensional task feature vector using a multi-objective optimization algorithm to generate an optimal task allocation scheme; optimizing the task structure using a fireworks algorithm based on the Student's t-distribution to generate an optimized task unit structure; performing real-time monitoring using an anomaly detection model and a quality prediction model to trigger quality control measures; and updating model parameters using a reinforcement learning algorithm to generate personalized feedback and capability enhancement strategies. This invention achieves precise matching between annotators and tasks, improves the processing efficiency of complex tasks, enhances annotation quality, strengthens the platform's scalability and response speed, and provides an effective solution for large-scale, high-quality data annotation.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence, and in particular to an AI-based method and apparatus for assigning data annotation tasks, which enables efficient and accurate allocation and quality control of data annotation tasks. Background Technology

[0002] Data annotation is a crucial step in training artificial intelligence models. It provides high-quality training samples for machine learning algorithms, directly impacting model performance and application effectiveness. With the rapid development of artificial intelligence technology, the demand for large-scale, high-quality labeled data is increasing, giving rise to and rapidly expanding the data annotation industry.

[0003] Currently, the data annotation industry mainly adopts two models: one is the traditional crowdsourcing platform model, such as Amazon's Mechanical Turk, which distributes a large number of small tasks to scattered workers; the other is the professional annotation team model, in which well-trained professionals complete more complex annotation tasks. Both models have achieved certain results in practical applications, but with the increasing complexity of AI application scenarios and the refinement of annotation requirements, the traditional models have revealed significant shortcomings.

[0004] In existing technologies, task allocation for annotation mainly relies on manual judgment or simple rule engines, lacking a deep understanding and intelligent matching of annotators' capabilities and task requirements. Under this mechanism, tasks are often assigned on a "first-come, first-served" basis or randomly, without considering key factors such as the annotator's professional background, past performance, and areas of expertise. Meanwhile, annotation quality assessment mainly relies on manual sampling or simple consistency checks, making it difficult to promptly identify and correct annotation deviations.

[0005] This traditional task assignment method has several obvious problems: First, the lack of precise matching leads to low task allocation efficiency, and tasks of different difficulties and types cannot be assigned to the most suitable annotators; second, the inconsistent annotation quality directly affects the training effect of downstream AI models; third, a large amount of manual intervention and complex processes limit the platform's scalability and response speed, making it difficult to meet the explosive growth in data annotation demand; and finally, the lack of personalized guidance and growth support for annotators makes it impossible to continuously improve the overall capability level of the annotation team. Summary of the Invention

[0006] The purpose of this invention is to provide an AI-based annotation task assignment method and apparatus, which aims to solve the technical problems in the prior art, such as the lack of accurate matching in annotation task allocation, inconsistent annotation quality, limited platform scalability and response speed, and lack of personalized guidance.

[0007] To achieve the above objectives, this invention provides an AI-based annotation task assignment method, comprising the following steps: acquiring historical behavior data; performing feature extraction and deep learning processing on the historical behavior data to construct a multi-dimensional user profile including professional skills, areas of expertise, annotation quality, and efficiency performance; receiving a task description document, data samples, and a quality requirement document; parsing the task description document using natural language processing; analyzing the data samples using computer vision or natural language processing; extracting indicators from the quality requirement document to obtain a multi-dimensional task feature vector including task difficulty, domain attributes, time urgency, and professional knowledge requirements; and calculating the matching score between the annotator and the task using a multi-objective optimization algorithm based on the multi-dimensional user profile and the multi-dimensional task feature vector to generate an optimal task assignment scheme. The process involves: identifying and evaluating the complexity of complex tasks within the optimal task allocation scheme using multimodal feature recognition; optimizing the task structure using a fireworks algorithm based on the Student t-distribution to generate an optimized task unit structure; labeling the optimized task unit structure to obtain labeling results and task execution status data; performing real-time anomaly detection on the labeling results using a preset anomaly detection model to automatically identify anomalous samples; predicting the labeling quality of the labeling results using a preset quality prediction model to obtain detection and prediction results, triggering quality control measures, and outputting quality control results; and updating the model parameters of the multidimensional user profile and the multidimensional task feature vector using a reinforcement learning algorithm to generate personalized feedback and capability enhancement strategies.

[0008] Furthermore, the step of performing feature extraction and deep learning processing on the historical behavior data to construct a multi-dimensional user profile including professional skills, areas of expertise, annotation quality, and efficiency performance includes: generating a standardized historical behavior dataset based on the historical behavior data through data cleaning, normalization, and outlier processing; performing statistical analysis and feature engineering on the standardized historical behavior dataset to extract and quantify multi-dimensional feature indicators of the annotator's professional skill level, distribution of areas of expertise, stability of annotation quality, efficiency performance, and time availability; using deep neural networks or graph neural networks for deep learning modeling based on the multi-dimensional feature indicators to construct a comprehensive user profile model that can capture the annotator's abilities, behavioral patterns, and development potential; designing a weight adjustment algorithm based on time decay and latest performance, and combining incremental behavior data including task completion data and quality feedback to update the parameters of the comprehensive user profile model to obtain a real-time dynamically updated user profile model; and generating a multi-dimensional user profile including professional skills, areas of expertise, annotation quality, and efficiency performance based on the real-time dynamically updated user profile model.

[0009] Further, the step of parsing the task description document using natural language processing, analyzing the data samples using computer vision or natural language processing, and extracting indicators from the quality requirement document to obtain a multi-dimensional task feature vector containing task difficulty, domain attributes, time urgency, and professional knowledge requirements includes: receiving the task description document; performing natural language processing through named entity recognition, keyword extraction, and semantic analysis to identify and extract core information containing domain attributes, target objects, and operational requirements; performing data sample feature recognition on the data samples using computer vision or natural language processing techniques to automatically identify data features containing modality type, complexity, and noise level; based on the core information and the data features, combined with statistical performance data of similar historical tasks, calculating the task difficulty coefficient, required professional knowledge level, and expected completion time using a gradient boosting tree algorithm to obtain the task's multi-dimensional features; performing unified mapping processing on the task's multi-dimensional features to map domain attributes, difficulty coefficient, professional knowledge requirements, and time urgency to a high-dimensional feature space to obtain a high-dimensional space mapping result; and generating the multi-dimensional task feature vector based on the high-dimensional feature space mapping result.

[0010] Furthermore, the step of calculating the matching score between annotators and tasks using a multi-objective optimization algorithm to generate the optimal task allocation scheme includes: calculating multi-dimensional similarity based on the multi-dimensional user profile and the multi-dimensional task feature vector using cosine similarity and Euclidean distance methods to calculate the matching degree of the core dimensions and obtain the matching score of the core dimensions; dynamically adjusting the weights of the matching scores of the core dimensions using a multi-objective optimization algorithm based on the platform's current business objectives, and combining expert rules for constraint processing to generate a comprehensive matching score; based on the comprehensive matching score, considering the annotator's current workload, task urgency, and overall resource utilization efficiency, generating the platform's globally optimal task allocation strategy through a combinatorial optimization algorithm; performing reinforcement learning processing on the task allocation strategy, combining active push and passive selection modes to achieve intelligent recommendation and adaptive allocation of tasks; and generating the optimal task allocation scheme based on the intelligent recommendation and adaptive allocation results.

[0011] Furthermore, it also includes a reflexive evolution of a multi-objective heuristic method based on a large language model, comprising: collecting historical decision data generated by an intelligent matching engine, including matching schemes, actual execution results, quality score differences, and business goal achievement, forming basic input data for reflective analysis; guiding the large language model through the design of specific prompting engineering techniques to perform in-depth analysis on the basic input data, identifying implicit patterns in decision-making patterns, discovering the limitations of current heuristic rules, and obtaining in-depth analysis results; generating a new set of heuristic rule candidates based on the in-depth analysis results through the large language model; wherein the heuristic rule set adopts a structured representation, including triggering conditions, weight adjustment strategies, and expected effects; designing diverse simulation scenarios to verify and filter the heuristic rule candidate set, evaluating the performance of new rules through backtesting on the historical decision data and small-scale online experiments, and obtaining verification and filtering results; and formally incorporating high-quality rules into the decision-making process based on the verification and filtering results, forming a dynamically evolving multi-objective heuristic method.

[0012] Furthermore, the optimization of the task structure using the fireworks algorithm based on the Student t-distribution to generate an optimized task unit structure includes: modeling the complex tasks in the optimal task allocation scheme, representing each complex task as a multi-dimensional feature vector containing content complexity, domain attributes, estimated completion time, and dependencies; encoding the decomposition points and combination schemes of the complex tasks into a solution space to be optimized; initializing the fireworks algorithm based on the solution space to be optimized, setting each fireworks as a candidate solution, generating sparks using the Student t-distribution instead of the traditional uniform distribution, and setting initial degree-of-freedom parameters to generate an initial candidate solution set; and using a degree-of-freedom adaptive adjustment mechanism to optimize the initial solution set. The system employs a first degree of freedom value to enhance global exploration capabilities, gradually increasing the degree of freedom value as iterations progress to enhance local fine-grained search capabilities, generating a dynamically adjusted search strategy. A dimension-sensitive explosion amplitude control strategy is designed, assigning different explosion amplitudes to each dimension based on the differences in importance and sensitivity of different dimensions in the task feature space, generating a dimension-weighted search space. An elite solution memory based on historical experience is constructed, and combined with a differential evolution strategy, the dynamically adjusted search strategy, and the dimension-weighted search space, the initial candidate solution set is iteratively optimized to guide new search directions, generating an optimized task unit structure. The elite solution memory stores historically high-performing task decomposition and combination schemes.

[0013] Furthermore, the process of performing real-time anomaly detection on the annotation results using a preset anomaly detection model to automatically identify anomalous samples, and simultaneously predicting the annotation quality of the annotation results using a preset quality prediction model, obtaining detection and prediction results, triggering quality control measures, and outputting quality control results, includes: designing a lightweight data acquisition interface to collect process data such as annotation results, operation trajectories, and time distribution in real time during the annotation process, performing preliminary structured processing to obtain process data; based on the process data, combined with domain knowledge and annotation specifications, constructing a multi-dimensional quality evaluation index system including accuracy, consistency, completeness, and timeliness; applying an isolated forest or autoencoder unsupervised learning algorithm to perform anomaly detection on the annotation results, and combining statistical process control methods to monitor changes in quality indicators in real time to obtain anomaly detection results; based on the anomaly detection results, automatically determining the intervention type and urgency level using a decision tree algorithm, identifying the anomalous samples, and triggering corresponding quality control measures; executing quality control measures including secondary inspection, expert review, or task reassignment, and outputting the quality control results.

[0014] Furthermore, the step of updating the model parameters of the multi-dimensional user profile and the multi-dimensional task feature vector using reinforcement learning algorithms to generate personalized feedback and capability enhancement strategies includes: collecting multi-source feedback data containing the quality control results, the task execution status data, annotator self-evaluation, and expert review; forming a comprehensive feedback dataset that fully reflects annotation performance through data fusion technology; generating personalized feedback reports for each annotator based on their strengths and weaknesses using natural language generation and explainable artificial intelligence technologies based on the comprehensive feedback dataset; analyzing the annotator's capability shortcomings and development potential, and designing personalized capability enhancement paths for the annotator using recommendation algorithms in conjunction with the domain knowledge graph of the annotation task; updating the model parameters of the multi-dimensional user profile and the multi-dimensional task feature vector using incremental learning and reinforcement learning techniques based on the comprehensive feedback dataset and the quality control results; and generating personalized feedback and capability enhancement strategies based on the personalized feedback reports and the personalized capability enhancement paths.

[0015] Furthermore, it also includes cross-institutional capability assessment based on a federated learning framework, including: designing a federated learning technical architecture that supports multi-institutional participation, defining standardized capability feature representations and model update protocols to ensure that all participants collaborate without sharing raw data, generating a federated learning collaboration architecture; applying differential privacy technology to the annotator capability data of each institution, by adding calibration noise and sensitive data anonymization processing, to protect annotator privacy while ensuring data availability, generating a privacy-protected capability dataset; based on the federated learning collaboration architecture, each participating institution trains a capability assessment model locally, sharing only model parameters rather than raw data, generating a local capability assessment model and model parameters; merging the model parameters of all parties through a secure aggregation algorithm to construct a cross-institutional capability assessment benchmark model; and using the cross-institutional capability assessment benchmark model to achieve optimized allocation of annotation resources across institutions under the premise of privacy protection.

[0016] Furthermore, it also includes a gradient-free fast training mechanism based on Hamiltonian graph networks, including: redesigning the network structure of the capability assessment model, adopting a graph neural network architecture based on Hamiltonian mechanics, representing the labeler's capability characteristics as graph nodes, and modeling the relationships between different capability dimensions as graph edges to generate a Hamiltonian graph neural network model; abandoning gradient descent-based optimization methods, adopting a hybrid gradient-free optimization strategy of evolutionary strategy and Hamiltonian Monte Carlo, determining the optimization direction by randomly perturbing parameters and evaluating performance changes to generate a gradient-free optimization strategy; designing parameter quantization and sparse communication mechanisms to highly compress update information, transmitting only key parameter changes, introducing a dynamic communication strategy to adjust the communication frequency according to the model performance improvement, generating a compressed communication protocol; constraining parameter updates on isoenergy surfaces based on Hamiltonian dynamics principles, ensuring energy conservation during numerical calculations through symplectic geometric integrators, generating an energy-conservation-constrained parameter update mechanism; and constructing an adaptive learning strategy library, pre-training multiple optimization strategies for labeling tasks of different scales and characteristics, and dynamically selecting the strategy most suitable for the current data distribution and business objectives at runtime, generating an adaptive strategy selection mechanism.

[0017] This invention also provides an AI-based annotation task assignment device, comprising: a user profile construction module, used to acquire historical behavior data, perform feature extraction and deep learning processing on the historical behavior data, and construct a multi-dimensional user profile including professional skills, areas of expertise, annotation quality, and efficiency performance; a task feature analysis module, used to receive a task description document, data samples, and a quality requirement document, parse the task description document through natural language processing, analyze the data samples using computer vision or natural language processing, extract indicators from the quality requirement document, and obtain a multi-dimensional task feature vector including task difficulty, domain attributes, time urgency, and professional knowledge requirements; an intelligent matching engine module, used to calculate the matching degree score between the annotator and the task based on the multi-dimensional user profile and the multi-dimensional task feature vector through a multi-objective optimization algorithm, and generate an optimal task allocation scheme; and task decomposition. The system includes a combination module for performing multimodal feature recognition and complexity assessment on complex tasks in the optimal task allocation scheme, optimizing the task structure using a fireworks algorithm based on the Student t-distribution, and generating an optimized task unit structure. A dynamic quality control module is used to annotate the optimized task unit structure, obtaining annotation results and task execution status data. A preset anomaly detection model is used to perform real-time anomaly detection on the annotation results, automatically identifying anomalous samples. Simultaneously, a preset quality prediction model is used to predict the annotation quality of the annotation results, obtaining detection and prediction results and triggering quality control measures, outputting quality control results. A feedback and learning module is used to update the model parameters of the multidimensional user profile and the multidimensional task feature vector using a reinforcement learning algorithm based on the quality control results and the task execution status data, generating personalized feedback and capability enhancement strategies.

[0018] The beneficial effects of this invention are as follows: By constructing multi-dimensional user profiles and task feature vectors, precise matching between annotators and tasks is achieved; the task structure is optimized using a fireworks algorithm based on the Student t-distribution, improving the processing efficiency of complex tasks; a dynamic quality control system monitors annotation quality in real time, promptly identifying and correcting annotation deviations; model parameters are updated based on reinforcement learning algorithms to generate personalized feedback and capability enhancement strategies, promoting the continuous growth of annotators; and a federated learning framework and Hamiltonian graph network are introduced to achieve cross-institutional capability assessment and resource optimization while protecting privacy. This invention significantly improves the accuracy and efficiency of annotation task allocation, enhances annotation quality, strengthens the platform's scalability and response speed, and provides an effective solution for large-scale, high-quality data annotation. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of an AI-based annotation task assignment method provided in an embodiment of the present invention;

[0021] Figure 2 This is a flowchart of the user profile construction module provided in an embodiment of the present invention;

[0022] Figure 3 This is a structural diagram of the AI-based annotation task assignment device provided in an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0024] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as S1, S2, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0025] It will be understood by those skilled in the art that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application’s specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when an element is referred to as “connected” or “coupled” to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements present. Furthermore, “connected” or “coupled” as used herein may include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0026] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by those skilled in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have a meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Throughout the description, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0028] Please see Figure 1 , Figure 1 This is a flowchart of an AI-based annotation task assignment method provided in an embodiment of the present invention. Figure 1 As shown, the AI-based annotation task assignment method provided in this embodiment of the invention includes the following steps:

[0029] Step S101: Obtain historical behavior data, perform feature extraction and deep learning processing on the historical behavior data, and construct a multi-dimensional user profile that includes professional skills, areas of expertise, annotation quality, and efficiency performance.

[0030] In this step, historical behavioral data of annotators is first obtained from the annotation platform's database. This data includes, but is not limited to, records of tasks completed by annotators, annotation speed, quality scores, domain preferences, operational behavior trajectories, and feedback information. This raw data is typically scattered across different business areas and may contain inconsistencies in format, missing values, outliers, etc. Data integration technology is used to consolidate this data into a unified data processing platform, and preliminary data cleaning is performed, including removing duplicate records, filling in missing values, and standardizing formats. Subsequently, feature engineering techniques are applied to extract key features from the cleaned data, such as annotators' task completion rates in different domains, average annotation quality scores, stability of annotation speed, and response time to different types of tasks. These features, after normalization, are input into a deep learning model for further processing. A user profile model is constructed using deep neural networks or graph neural networks. This model can capture the complex nonlinear relationships between annotator capability features, generating high-dimensional user representations. Finally, based on this deep learning model, a multi-dimensional user profile is constructed for each annotator, including dimensions such as professional skills, areas of expertise, annotation quality, and efficiency performance, laying the foundation for subsequent accurate task matching.

[0031] Step S102: Receive the task description document, data samples, and quality requirement document; parse the task description document using natural language processing; analyze the data samples using computer vision or natural language processing; extract indicators from the quality requirement document to obtain a multi-dimensional task feature vector containing task difficulty, domain attributes, time urgency, and professional knowledge requirements.

[0032] In this step, three key inputs are received from the task publisher: a task description document, data samples, and a quality requirement document. For the task description document, deep analysis using natural language processing techniques, including named entity recognition, keyword extraction, and semantic analysis, is performed. These techniques enable the identification and extraction of core information from unstructured text descriptions, such as the task's domain attributes (e.g., medical, financial, autonomous driving), target objects (e.g., image segmentation, text classification, speech-to-text), and operational requirements (e.g., annotation accuracy, specific rules). For the data samples, appropriate analysis techniques are selected based on their type; for example, computer vision techniques are applied to image / video samples, and natural language processing techniques are applied to text samples, automatically identifying features such as data modality, complexity, and noise level. The distribution characteristics of the samples, the proportion of boundary cases, and annotation difficulties are also analyzed. For the quality requirement document, various quality indicators are extracted and quantified, including accuracy requirements, consistency standards, and completion deadlines. Subsequently, this information extracted from different sources is combined with statistical performance data from similar historical tasks, and machine learning algorithms such as gradient boosting trees are used to calculate the task's difficulty coefficient, required level of expertise, and expected completion time. Finally, these multi-dimensional features are uniformly mapped to a high-dimensional feature space to generate structured task feature vectors, providing a basis for subsequent intelligent matching.

[0033] Step S103: Based on the multi-dimensional user profile and the multi-dimensional task feature vector, calculate the matching score between the annotator and the task using a multi-objective optimization algorithm to generate the optimal task allocation scheme.

[0034] In this step, the multi-dimensional user profile generated in step S101 is intelligently matched with the multi-dimensional task feature vector generated in step S102. First, the matching degree between the user profile and task features in each core dimension is calculated using methods such as cosine similarity and Euclidean distance. Examples include the matching degree between professional skills and task requirements, the matching degree between areas of expertise and task domain attributes, and the matching degree between historical quality performance and task quality requirements. These matching scores constitute a preliminary matching assessment. Subsequently, based on the platform's current business objectives (such as quality priority, efficiency priority, or cost priority), a multi-objective optimization algorithm is applied to dynamically adjust the weights of different dimensions. For example, for high-precision medical image annotation tasks, the weights of professional background and quality stability are increased; while for news classification tasks with high timeliness requirements, the weight of efficiency performance is increased. Constraints are also applied using expert-defined rules, such as considering hard requirements like the annotator's working time zone and language proficiency. Based on the generated individual matching scores, further considering global factors such as the annotator's current workload, task urgency, and overall resource utilization efficiency, a globally optimal task allocation strategy is generated through combinatorial optimization algorithms (such as integer linear programming or genetic algorithms). Finally, reinforcement learning techniques are applied to optimize this allocation strategy, combining both proactive push and passive selection modes to achieve intelligent task recommendation and adaptive allocation, ultimately generating the optimal task allocation scheme.

[0035] Furthermore, in step S103, when performing intelligent matching based on the multi-dimensional user profile and multi-dimensional task feature vector, the matching scores of each core dimension constitute a preliminary matching evaluation in the following manner. First, the multi-dimensional user profile obtained from step S101 and the multi-dimensional task feature vector obtained from step S102 are subjected to dimension alignment processing to ensure that the capability dimension in the user profile and the requirement dimension in the task feature vector can be effectively matched. Specifically, the professional skills dimension in the user profile and the professional knowledge requirement dimension in the task feature vector are matched and calculated, and the cosine similarity method is used to quantify the degree of fit between the two to obtain the professional capability matching score.

[0036] This study analyzes the matching of user profiles' areas of expertise with the domain attributes in the task feature vector. By calculating domain overlap and relevance strength, a weighted similarity algorithm is used to obtain a domain knowledge matching score. The historical performance of annotation quality in user profiles is compared with the quality requirements in the task feature vector. Threshold judgment and statistical analysis methods are used to calculate the quality level matching score. Finally, efficiency performance indicators in user profiles are compared and evaluated with the time urgency requirements in the task feature vector. An efficiency matching function is used to calculate the efficiency suitability score.

[0037] After completing the independent matching calculations for each dimension, the matching scores for these dimensions are standardized and uniformly converted to a standard scoring range of 0-1. Subsequently, a preliminary matching assessment result is constructed through a multi-dimensional comprehensive analysis method. This includes creating a matching score vector to organize the scores of each dimension into a structured data representation; calculating the correlation and complementarity between dimensions to identify potential synergistic effects or constraints between different capability dimensions; generating matching confidence intervals to determine the reliability level of each dimension's score based on the quality and quantity of historical data used for calculation; and generating a preliminary matching assessment report, including the scores of each dimension, the overall matching trend, and potential risk warnings.

[0038] Step S104: Perform multimodal feature recognition and complexity evaluation on the complex tasks in the optimal task allocation scheme, and optimize the task structure using the fireworks algorithm based on the student t-distribution to generate the optimized task unit structure.

[0039] In this step, the optimal task allocation scheme generated in step S103 is further optimized, with particular attention paid to complex tasks. First, multimodal feature recognition technology is used to deeply analyze complex tasks, identifying their internal structure, subtask dependencies, and difficulty distribution. Each complex task is represented as a multidimensional feature vector containing dimensions such as content complexity, domain attributes, estimated completion time, and dependencies. Potential decomposition points and combination schemes are encoded into the solution space to be optimized. Subsequently, the Student's t-distribution-based Fireworks Algorithm (TFWA) is initialized, an innovative improvement on the traditional Fireworks Algorithm. In this algorithm, each firework represents a candidate solution (i.e., a task decomposition or combination scheme). Fireworks are generated using the Student's t-distribution instead of the traditional uniform distribution to explore the solution space. Initial degree-of-freedom parameters are set, and an initial set of candidate solutions is generated. During optimization, an adaptive degree-of-freedom adjustment mechanism is used. In the early stages of optimization, a first degree-of-freedom value (e.g., v=1) is used to enhance global exploration capabilities. As iterations progress, the degree-of-freedom value is gradually increased to enhance local fine-grained search capabilities. Simultaneously, a dimension-sensitive explosion amplitude control strategy was designed, assigning different explosion amplitudes to each dimension based on their varying importance and sensitivity in the task feature space. Furthermore, an elite solution memory based on historical experience was constructed, storing historically high-performing task decomposition and combination schemes. By combining a differential evolution strategy, a dynamically adjusted search strategy, and a dimension-weighted search space, the initial candidate solution set was iteratively optimized, ultimately generating an optimized task unit structure, achieving efficient decomposition and combination of complex tasks.

[0040] Step S105: Perform annotation on the optimized task unit structure to obtain annotation results and task execution status data; perform real-time anomaly detection on the annotation results using a preset anomaly detection model to automatically identify abnormal samples; at the same time, predict the annotation quality of the annotation results using a preset quality prediction model to obtain detection and prediction results and trigger quality control measures, and output quality control results.

[0041] In this step, the optimized task unit structure from step S104 is assigned to the corresponding annotators to perform annotation work. During the annotation process, a lightweight data acquisition interface is used to collect process data such as annotation results, operation trajectories, and time distribution in real time, and performs preliminary structured processing. Based on this process data, combined with domain knowledge and annotation standards, a multi-dimensional quality evaluation index system is constructed, including accuracy, consistency, completeness, and timeliness. Two types of quality monitoring models are run simultaneously: one is an anomaly detection model, which uses unsupervised learning algorithms such as isolated forests or autoencoders to perform real-time anomaly detection on the annotation results and identify samples that deviate significantly from the normal pattern; the other is a quality prediction model, a supervised learning model trained on historical data, which can predict the final quality level of the current annotation results. Combined with statistical process control methods, changes in quality indicators are monitored in real time. When anomalies are detected or quality predictions fail to meet standards, a decision tree algorithm is used to automatically determine the intervention type and urgency, identify abnormal samples, and trigger corresponding quality control measures. These measures may include secondary verification (performed automatically or by other annotators), expert review (submitting outlier samples to domain experts for evaluation), or task reassignment (reassigning problematic tasks to more suitable annotators). This dynamic quality control mechanism can promptly identify and correct quality issues during the annotation process, ensuring high-quality final annotation results, while also outputting detailed quality control results to provide a basis for subsequent feedback and learning.

[0042] Step S106: Based on the quality control results and the task execution status data, update the model parameters of the multidimensional user profile and the multidimensional task feature vector using a reinforcement learning algorithm to generate personalized feedback and capability enhancement strategies.

[0043] In this step, feedback data from multiple sources is collected and integrated, including quality control results from step S105, task execution status data, annotator self-evaluations, and expert review opinions. Data fusion technology is used to integrate this multi-source heterogeneous data into a comprehensive feedback dataset that fully reflects annotation performance. Based on this dataset, natural language generation and explainable artificial intelligence technologies are applied to generate personalized feedback reports for each annotator. These reports not only point out the annotator's strengths and weaknesses but also provide specific improvement suggestions and examples, making the feedback more actionable. Simultaneously, the annotator's skill gaps and development potential are analyzed. Combined with the annotation task domain knowledge graph constructed by the platform, recommendation algorithms are applied to design personalized skill enhancement paths for annotators. These paths may include targeted practice tasks, learning material recommendations, and tiered challenge tasks to help annotators continuously improve their abilities. Building on this, incremental learning and reinforcement learning techniques are applied to update the model parameters of the multi-dimensional user profile and multi-dimensional task feature vector based on the latest feedback data and quality control results. This continuous learning mechanism enables the algorithm to adapt to changes in annotator capabilities and the evolution of task characteristics, maintaining the timeliness and accuracy of the matching algorithm. Ultimately, based on the updated model, combined with personalized feedback reports and capability enhancement paths, a more comprehensive and targeted personalized feedback and capability enhancement strategy is generated, forming a closed-loop continuous improvement mechanism.

[0044] Please see Figure 2 , Figure 2 This is a flowchart of the user profile construction module provided in an embodiment of the present invention. Figure 2 As shown, in step S101, feature extraction and deep learning processing are performed on the historical behavior data to construct a multi-dimensional user profile that includes professional skills, areas of expertise, annotation quality, and efficiency performance, including:

[0045] Step S201: Based on the historical behavior data, a standardized historical behavior dataset is generated through data cleaning, normalization, and outlier processing.

[0046] In this step, historical behavioral data of annotators is first collected from various business operations of the annotation platform. This data typically includes basic information about the annotators (such as educational background, professional field, work experience, etc.), historical task records (such as the type, quantity, and time distribution of completed tasks, etc.), annotation quality data (such as quality inspection scores, error rate, number of revisions, etc.), behavioral trajectory data (such as operation sequence, dwell time, interaction pattern, etc.), and feedback information (such as self-evaluation, peer evaluation, and complaint records, etc.). This raw data often contains various quality problems, such as missing values, outliers, inconsistent formats, and duplicate records. These problems are addressed through a series of data cleaning operations: for missing values, methods such as mean / median imputation, nearest neighbor imputation, or predictive model imputation are used depending on the data type; for outliers, techniques such as box plots, Z-scores, or local anomaly factors are applied for detection and processing; for inconsistent formats, a unified data model is defined and converted; for duplicate records, redundant data is detected and deleted using unique identifiers. The cleaned data then needs to be normalized to transform features of different dimensions to the same scale. Common methods include Min-Max normalization, Z-score standardization, or logarithmic transformation. Through this series of processes, a standardized historical behavior dataset is finally generated, providing a high-quality data foundation for subsequent feature engineering and model training.

[0047] Step S202: Perform statistical analysis and feature engineering on the standardized historical behavior dataset to extract and quantify multi-dimensional feature indicators of the annotators' professional skill level, distribution of areas of expertise, stability of annotation quality, efficiency performance, and time availability.

[0048] In this step, the standardized historical behavior dataset generated in step S201 undergoes in-depth statistical analysis and feature engineering. First, descriptive statistical analysis is used to understand the basic distribution characteristics of the data, including the mean, variance, quantiles, and other statistics of each feature, as well as correlation analysis between features. Then, feature engineering is performed to extract and quantify multidimensional feature indicators from the original data: for professional skill level, a skill scoring model is constructed based on the annotators' task completion status in different skill domains, professional certifications, and background information; for the distribution of areas of expertise, the performance differences of annotators in tasks across different domains are analyzed to generate probability distributions of domain preferences and expertise; for annotation quality stability, the mean, variance, and trend of annotators' historical quality scores are calculated to construct a quality stability index; for efficiency performance, factors such as annotators' task completion speed, response time, and work pace are analyzed to generate an efficiency score; for time availability, a time availability model is constructed based on annotators' historical online time, work period distribution, and response patterns. It also generates advanced features such as task type adaptability (the speed at which annotators learn new types of tasks), quality-speed balance index (measuring annotators' ability to balance quality and efficiency), and collaboration ability index (performance in team tasks). Through these feature engineering processes, the raw data is transformed into structured multi-dimensional feature indicators, comprehensively characterizing the annotators' capabilities. For the construction of a skill scoring model for professional skill levels, the system first collects data on annotators' task completion in different skill domains, including quantitative indicators such as the number of tasks completed, task complexity distribution, and completion quality scores. Then, it integrates the annotators' professional certification information, such as relevant professional certificates, training certificates, and educational background, and transforms this qualitative information into quantitative scores. Specifically, the system adopts a weighted scoring mechanism, assigning a base score to each skill domain and dynamically adjusting it based on task completion performance. The number of tasks completed accounts for 30% of the weight, task complexity adaptability accounts for 40%, and quality performance accounts for 30%. Professional certification information is converted into skill bonus points ranging from 0 to 10 through a certification level mapping method. Finally, the score for each skill area is calculated using a linear combination formula: Skill Score = (Basic Ability Score × 0.7) + (Certification Bonus Points × 0.3), where the basic ability score is calculated based on historical task performance.

[0049] To generate the probability distribution of expertise areas, the system uses statistical analysis to calculate the task distribution and performance differences of annotation specialists across various professional fields. First, it statistically analyzes the number of tasks completed and the quality scores of each specialist in each field, calculating the average quality score and completion efficiency index for each field. Then, using Bayesian statistical methods, it updates the posterior probability distribution using the overall performance of the annotators as the prior distribution and the specific performance in each field as the observed data. Specifically, the system calculates the relative advantage index for each field using the formula: Field Advantage Index = (Average Quality Score of that Field - Average Score of All Fields) / Standard Deviation of All Fields. Then, it uses a softmax function to convert the advantage index of each field into a probability distribution, ensuring that the sum of the probabilities for all fields is 1. This method considers both the absolute performance level of the annotators and their comparative advantage relative to other fields, generating a scientific probability distribution of field preferences and expertise.

[0050] To construct the annotation quality stability index, the system employs a multi-dimensional statistical analysis method to comprehensively evaluate the stability of annotators' quality performance. First, it calculates the basic statistical indicators of annotators' historical quality scores, including the arithmetic mean reflecting the overall quality level, the standard deviation and variance reflecting the degree of quality fluctuation, and the coefficient of variation (standard deviation / mean) reflecting relative stability. Then, it identifies quality change trends through time series analysis, using linear regression to analyze the trend of quality scores over time; the absolute value of the regression coefficient reflects the drasticness of quality changes. Finally, it calculates the quality stability index using a weighted comprehensive formula: Stability Index = (1 - Standardized Coefficient of Variation) × 0.6 + (1 - Standardized Trend Change Rate) × 0.4, where standardization ensures that all indicators are within the 0-1 range; the closer the index is to 1, the more stable the quality.

[0051] To generate efficiency performance scores, the system comprehensively analyzes multiple efficiency-related indicators of the annotators and establishes a scoring model. First, it collects task completion speed data, calculates the number of annotation samples completed per unit time, and the average completion time for tasks of different complexities. Then, it analyzes response time data, including the response delay from task assignment to execution, as well as interruption and recovery patterns during task execution. Work rhythm analysis identifies high-efficiency work periods and fatigue decay patterns by calculating the distribution of annotators' workload across different time periods. The specific efficiency score is calculated using the following formula: Efficiency Score = (Standardized Completion Speed ​​× 0.4) + (Standardized Response Timeliness × 0.3) + (Standardized Work Rhythm Stability × 0.3), where each indicator has undergone peer-to-peer standardization. The final score ranges from 0 to 100 points, with higher scores indicating better efficiency performance.

[0052] To construct the time availability model, the system establishes a predictive model based on historical behavior pattern analysis to evaluate the time availability of annotation staff. First, historical online time data of annotation staff is collected, including daily online duration, online time distribution, and weekly work patterns. Statistical analysis methods are used to identify typical work time patterns of annotation staff. Then, the distribution characteristics of work periods are analyzed, and clustering algorithms are used to identify the preferred work periods of annotation staff, calculating the differences in work efficiency and quality performance across different periods. Response pattern analysis is performed by calculating the average response time, response rate, and variability of response time for task assignments to establish a response behavior prediction model. The final time availability model uses a probabilistic prediction method. For any given time period, the model outputs the probability value of annotation staff availability, calculated as: Available Probability = (Historical Online Frequency for This Time Period × 0.5) + (Response Timeliness Weight × 0.3) + (Workload Adjustment Factor × 0.2). The model also considers the impact of current workload and task urgency on availability, providing a dynamic time availability assessment.

[0053] These models and metrics are built upon statistical analysis and machine learning methods using extensive historical data. Scientific mathematical models and algorithms ensure the objectivity and accuracy of the evaluation results. All scores and indices are standardized to facilitate comparison and comprehensive analysis across different dimensions, providing a reliable data foundation for subsequent user profiling and task matching.

[0054] Step S203: Based on the multidimensional feature indicators, apply deep neural networks or graph neural networks to perform deep learning modeling, and construct a comprehensive user profile model that can capture the labeler's abilities, behavior patterns and development potential.

[0055] In this step, the multidimensional feature indicators extracted in step S202 are used as input to construct a comprehensive user profile model using deep learning technology. A suitable network architecture is selected based on the data characteristics: for structured feature data, a multilayer perceptron (MLP) or deep neural network (DNN) is used; for capturing complex relationships between features, a graph neural network (GNN) is used, representing the various ability features of the annotator as graph nodes and the mutual influence relationships between features as graph edges. In model design, a multi-task learning framework is adopted to simultaneously predict the annotator's performance on different types of tasks, sharing the underlying feature representations. An attention mechanism is also introduced to automatically learn the importance weights of different features in different task types. To capture the development trend and potential of the annotator's abilities, a temporal feature processing module, such as an LSTM or Transformer structure, is integrated into the model to analyze the changing patterns of the annotator's abilities over time. During training, a multi-objective optimization method is used to balance the model's performance in terms of accuracy, generalization, and interpretability. To prevent overfitting, regularization techniques, Dropout, and early stopping strategies are applied. This deep learning modeling approach enables the construction of a comprehensive user profile model that accurately describes the current capabilities of annotators and predicts their development potential, providing strong support for precise task matching.

[0056] The user profiling model employs a multi-layered deep neural network. This network consists of an input layer (with the input dimension matching the number of features, typically 100-200 dimensions), three hidden layers (256, 128, and 64 neurons respectively), and an output layer (with the output dimension matching the dimensions of the multi-dimensional user profile, typically 50-100 dimensions). ReLU activation functions are used between layers to introduce non-linearity, and the final layer uses a Sigmoid activation function to ensure the output value remains within a reasonable range. The model is trained using the Adam optimizer with an initial learning rate of 0.001 and a learning rate decay strategy. To prevent overfitting, Dropout and L2 regularization are applied.

[0057] In the graph neural network implementation, the various ability features of the annotators are represented as nodes with attributes, and the edges between nodes represent the relationships between ability dimensions. The network adopts a three-layer graph convolutional structure, and the activation function is LeakyReLU. The network training data comes from the historical performance data of annotators accumulated by the annotation platform, usually containing at least 6 months of historical records, covering different types of annotation tasks and annotators of different levels, with a data scale of generally 100,000 to 1 million records. The training data is divided into training set, validation set and test set in an 8:1:1 ratio.

[0058] Step S204: Design a weight adjustment algorithm based on time decay and latest performance, and combine incremental behavioral data including task completion data and quality feedback to update the parameters of the comprehensive user profile model, so as to obtain a real-time dynamically updated user profile model.

[0059] In this step, a dynamic update mechanism is designed to continuously adjust and optimize the user profile model as new behavioral data from annotators accumulates. First, a time-decay-based weight adjustment algorithm is designed, which assigns higher weights to recent behavioral data, while the influence of historical data gradually weakens over time. Specifically, an exponential decay function is used, and the weight calculation formula is w(t) = e^(-t / t). (-λ(tnow-t)) The formula is λ, where λ is the decay coefficient, which can be dynamically adjusted according to different types of features. For example, for relatively stable features like skill level, the value of λ is smaller; while for features with larger fluctuations like efficiency performance, the value of λ is larger. Secondly, special attention is paid to the latest performance of the annotators. When a significant change in an annotator's performance on a certain type of task is detected (such as a sudden increase or decrease in quality), a rapid adjustment mechanism is triggered, increasing the weight of the latest data. Incremental behavioral data of the annotators is collected regularly, including newly completed task records, quality inspection results, customer feedback, etc., and this data is integrated into the model update process according to the aforementioned weighting strategy. Technically, an incremental learning method is adopted, eliminating the need to retrain the entire model. Instead, the model parameters are locally updated based on new data, greatly improving computational efficiency. An update frequency control mechanism is also set up to dynamically adjust the timing of model updates based on the data accumulation rate and the magnitude of change, ensuring that the model can reflect changes in the annotator's ability in a timely manner without becoming unstable due to overly frequent updates. Through this dynamic update mechanism, a user profile model that can reflect the latest ability status of the annotators in real time is obtained.

[0060] Step S205: Based on the real-time dynamically updated user profile model, generate a multi-dimensional user profile that includes professional skills, areas of expertise, annotation quality, and efficiency performance.

[0061] In this step, based on the real-time dynamically updated user profile model from step S204, a structured multi-dimensional user profile is generated for each annotator. This user profile comprehensively covers the annotator's key characteristics, mainly including four core dimensions: professional skills, areas of expertise, annotation quality, and efficiency performance. In the professional skills dimension, it generates annotator's ability scores in various skills (such as image recognition, text classification, speech transcription, etc.), as well as the correlation strength and transferability between skills. In the areas of expertise dimension, it generates the distribution of annotator's expertise in different fields (such as medicine, finance, law, etc.), and an assessment of the depth and breadth of domain knowledge. In the annotation quality dimension, it generates multiple quality indicators including accuracy, consistency, and completeness, as well as a quality stability score and a quality-task type correlation graph. In the efficiency performance dimension, it generates annotator's average processing speed, peak processing capacity, continuous work stability, and the relationship curve between efficiency and task complexity. In addition to these four core dimensions, some auxiliary features are also generated, such as time availability (working hours preference, response speed, etc.), learning ability (adaptation speed to new tasks, ability improvement curve, etc.), and collaboration characteristics (team collaboration performance, communication efficiency, etc.). These characteristics are organized into structured user profiles and provided with a visual interface, enabling platform administrators to intuitively understand the capabilities and characteristics of each annotator. This multi-dimensional user profile is not only used for subsequent task matching but also provides a basis for annotator capability development planning and training.

[0062] In step S102, the task description document is parsed using natural language processing, and the data sample is analyzed using computer vision or natural language processing. Indicators are extracted from the quality requirement document to obtain a multi-dimensional task feature vector containing task difficulty, domain attributes, time urgency, and professional knowledge requirements, including:

[0063] Step S301: Receive the task description document, and perform natural language processing through named entity recognition, keyword extraction and semantic analysis to identify and extract core information containing domain attributes, target objects and operation requirements.

[0064] In this step, we first receive the task description document provided by the task publisher. These documents typically contain detailed descriptions of the annotation task, such as the task background, annotation objectives, and specific requirements. Multi-layered natural language processing techniques are then used to deeply analyze this unstructured text. First, Named Entity Recognition (NER) technology is applied to identify key entities in the document, including domain-specific terms (such as "CT scan," "financial derivatives"), technical terms (such as "semantic segmentation," "sentiment analysis"), and time expressions (such as "within 48 hours," "weekly updates"). A pre-trained domain-adaptive NER model, fine-tuned for annotating industry-specific corpora, accurately identifies professional terms and key concepts in the annotation task. Second, keyword extraction techniques, combined with algorithms such as TF-IDF and TextRank, are applied to extract the keywords and phrases that best represent the task characteristics from the document. A knowledge graph of the annotation task domain is also constructed to expand and enrich the extracted keywords and capture potential semantic relationships. Third, deep semantic analysis is performed, applying dependency parsing techniques to understand sentence structure, identify subject-verb-object relationships, and clarify the relationships between the operator, the object of the operation, and the operation requirements. Semantic role labeling technology is also applied to identify semantic frameworks such as "who did what," clearly extracting the executor, object, and behavior of the task. Through the comprehensive application of these technologies, three types of core information are extracted from the task description document: domain attributes (such as medical, financial, autonomous driving, etc.), target objects (such as X-ray films, contract texts, road condition videos, etc.), and operational requirements (such as "label all tumor areas" and "classify into five sentiment categories," etc.). This structured core information provides the foundation for subsequent task feature vector construction.

[0065] Step S302: For the data sample, perform data sample feature recognition using computer vision or natural language processing technology to automatically identify data features including modality type, complexity, and noise level.

[0066] In this step, the data samples provided by the task are analyzed in depth, and appropriate processing techniques are selected according to the data type. For image or video data, computer vision techniques are applied for feature recognition. First, basic attributes of the data are detected, such as image resolution, color space, and video frame rate. Then, deep learning models (such as pre-trained CNNs or visual Transformers) are applied to extract high-level features of the images and analyze the complexity of the image content. The number, size distribution, and overlap of target objects in the image are evaluated, and a scene complexity score is calculated. Image quality issues, such as blurring, overexposure / underexposure, and noise, are also detected, and the noise level is quantified. For text data, natural language processing techniques are applied for analysis. Basic statistical features of the text are calculated, such as lexical diversity, syntactic complexity, and terminology density. Topic models are applied to identify the topic distribution of the text and assess the diversity and complexity of the content. Noise in the text, such as spelling errors, grammatical problems, and non-standard expressions, is also detected, and the text quality level is quantified. For audio data, features such as sound quality, background noise level, and number of speakers are analyzed. For multimodal data, each modality is processed separately, and the correlation between modalities is analyzed. During the analysis, not only are the characteristics of individual samples considered, but the distribution characteristics of the entire dataset are also statistically analyzed, such as the similarity between samples, the balance of category distribution, and the proportion of boundary cases. Through these analyses, three key features of the data samples are automatically identified and quantified: modality type (unimodal or multimodal, specifically images, text, audio, etc.), complexity (a five-level rating from simple to extremely complex), and noise level (a data quality assessment score). These data features provide important basis for task difficulty assessment and resource requirement prediction. When applying deep learning models to extract high-level image features for complexity analysis, the system first uses pre-trained convolutional neural networks (such as ResNet-50 or EfficientNet) or visual Transformers (such as ViT-Base) models to extract features from the input image. These models are pre-trained on large-scale image datasets and can automatically learn and extract hierarchical feature representations of images, including low-level edge and texture features and high-level semantic features. The system inputs the image into the pre-trained model, extracts feature maps at multiple levels, and evaluates the complexity of the image content by analyzing the activation patterns and distribution characteristics of the feature maps. Specifically, the system calculates the information entropy and variance of the feature map. High entropy and high variance usually indicate that the image contains richer details and more complex structures, thus reflecting higher content complexity.

[0067] For the specific analysis method of image content complexity, the system adopts a multi-dimensional evaluation strategy to comprehensively measure the complexity of the image. First, the number of target objects is counted. Object detection algorithms (such as YOLO or R-CNN series) are used to identify all annotable objects in the image, and the distribution of the number of objects in different categories is statistically analyzed. The more objects there are, the higher the image complexity. Next, the size distribution characteristics of the target objects are analyzed. The area distribution of the detected bounding boxes is calculated, and statistical indicators such as standard deviation and coefficient of variation are used to measure the diversity of object size. The greater the size difference, the higher the annotation difficulty. Then, the degree of overlap between objects is evaluated. By calculating the intersection-overlap ratio (IoU) between the bounding boxes of different objects, the number of overlapping object pairs and the distribution of overlap degree are statistically analyzed. Images with a high degree of overlap require more refined boundary subdivision, increasing annotation complexity.

[0068] Furthermore, the system analyzes the visual complexity features of the image, including texture complexity (calculated by the gray-level co-occurrence matrix to determine contrast, energy, and entropy), color complexity (analyzed by the uniformity and diversity of color distribution in the color space), and edge density (statistically analyzed by the Canny edge detection algorithm to determine the density and distribution of edge pixels in the image). These features reflect the visual complexity of the image from different perspectives, providing multi-dimensional information for comprehensive complexity assessment.

[0069] To calculate the scene complexity score, the system establishes a comprehensive scoring model to quantify the overall complexity level of the image. First, each complexity dimension is standardized, converting indicators such as the number of target objects, size distribution variance, overlap, texture complexity, color complexity, and edge density to a standardized range of 0-1. Then, a weighted comprehensive method is used to calculate the final scene complexity score, specifically: Scene Complexity Score = (Object Quantity Weight × Standardized Object Quantity) + (Size Distribution Weight × Standardized Size Variance) + (Overlap Weight × Standardized Overlap Index) + (Texture Complexity Weight × Standardized Texture Index) + (Color Complexity Weight × Standardized Color Index) + (Edge Density Weight × Standardized Edge Density).

[0070] The weighting is determined based on the actual impact of different complexity factors on annotation difficulty. The weighting parameters are optimized by analyzing the correlation between each complexity factor and annotation time and error rate in historical annotation data. Generally, the number of objects and the degree of overlap have a significant impact on annotation difficulty, receiving 25% and 20% weights respectively. Size distribution, texture complexity, color complexity, and edge density are each assigned approximately 10-15% weight. The final calculated scene complexity score ranges from 0 to 10, where 0-3 represents a simple scene, 4-6 represents a medium-complexity scene, and 7-10 represents a high-complexity scene. The system also establishes a mapping relationship between complexity scores and expected annotation time. Regression analysis of historical data determines the correlation between complexity scores and actual annotation time, providing a scientific basis for task time estimation and resource allocation.

[0071] This image complexity analysis method, based on deep learning feature extraction and multi-dimensional comprehensive evaluation, can objectively and accurately quantify the complexity of image content, providing reliable data support for subsequent task difficulty assessment and annotator matching. Through a standardized scoring system, images of different types and sources can receive consistent complexity assessments, ensuring the fairness and effectiveness of task allocation and quality control.

[0072] Step S303: Based on the core information and the data features, and combined with the statistical performance data of similar historical tasks, the difficulty coefficient, required professional knowledge level and expected completion time of the task are calculated by using the gradient boosting tree algorithm to obtain the characteristics of the task in various dimensions.

[0073] In this step, the core information extracted in step S301 and the data features identified in step S302 are integrated, along with historical task data accumulated by the platform, to comprehensively evaluate the current task. First, similar historical tasks are retrieved from the task library, and similarity calculation is based on a comprehensive matching degree of domain attributes, target objects, operational requirements, and data features. The performance data of these historical tasks is analyzed, including completion time distribution, quality scores, rework rates, and annotator feedback. Subsequently, a prediction model is constructed using the Gradient Boosting Tree (GBDT) algorithm. GBDT is a powerful ensemble learning method that can effectively handle mixed-type features, capture non-linear relationships between features, and has good interpretability. Three key prediction models are constructed: a difficulty coefficient prediction model, which takes into account features such as data complexity, noise level, and operational requirement complexity, and outputs a difficulty score of 1-10; a professional knowledge level prediction model, which takes into account features such as domain attributes, professional terminology density, and operational accuracy requirements, and outputs the required depth and breadth of professional knowledge scores; and an expected completion time prediction model, which takes into account features such as data volume, difficulty coefficient, and time statistics of similar historical tasks, and outputs the expected completion time per unit of data. When training these models, cross-validation was used to ensure their generalization ability, and feature importance analysis was used to understand the influence of different factors on the prediction results. In addition, several derived features were calculated, such as the cognitive load index of the task (measuring the cognitive complexity during the annotation process), decision ambiguity (the frequency of ambiguities that may be encountered during annotation), and attention persistence requirement (the level of attention required to maintain high-quality annotation). Through these calculations and analyses, the characteristics of the task in various dimensions were obtained, providing rich information for subsequent feature vector construction.

[0074] Step S304: Perform unified mapping processing on the features of each dimension of the task, mapping domain attributes, difficulty coefficient, professional requirements, and time urgency to a high-dimensional feature space to obtain a high-dimensional space mapping result.

[0075] In this step, the task features obtained in step S303 are uniformly mapped, transforming features of different types and scales into a consistent high-dimensional feature space. First, categorical features (such as domain attributes) are encoded. Semantic embedding, rather than simple one-hot encoding, is used to map domain attributes to a pre-trained domain embedding space. This makes related domains (such as "cardiology" and "radiology") closer in the feature space, while unrelated domains (such as "cardiology" and "legal text") are farther apart. A specialized domain embedding model trained on a large-scale labeled task corpus is used, accurately capturing the semantic relationships between different labeled domains. For numerical features (such as difficulty coefficient and expected completion time), normalization is first performed, transforming them to the [0,1] interval. Then, nonlinear transformations (such as the RBF kernel function) are used to map them to a high-dimensional space to capture the nonlinear relationships between features. For time urgency, not only the absolute deadline is considered, but also the relative urgency is calculated based on the task load and converted into a multi-dimensional representation, including aspects such as urgency and flexibility. For the professional requirements, they are broken down into multiple dimensions such as knowledge depth, knowledge breadth, and skill proficiency, and mapped separately. During the mapping process, a strategy combining dimensionality reduction and enhancement is applied: first, techniques such as Principal Component Analysis (PCA) or autoencoders are used to reduce the redundancy of the original features and extract the main information; then, nonlinear mapping is used to project them onto a higher-dimensional feature space to enhance the expressive power of the features. Feature interaction techniques are also applied to automatically learn the combined effects between features, such as the interaction between difficulty and time urgency, which may generate additional pressure factors. Through these mapping processes, the features of each dimension of the task are uniformly transformed into a well-structured high-dimensional feature space, resulting in a high-dimensional space mapping result, laying the foundation for the generation of the final task feature vector.

[0076] Step S305: Based on the high-dimensional feature space mapping result, generate the multi-dimensional task feature vector.

[0077] In this step, based on the high-dimensional feature space mapping results obtained in step S304, the final multi-dimensional task feature vector is constructed. First, the high-dimensional mapping results are post-processed, including feature selection and optimization. Feature importance analysis is applied to identify the feature dimensions most influential on task matching and appropriately increase their weights. Correlation analysis is also applied to identify and process highly correlated features to avoid information redundancy. Subsequently, the processed high-dimensional features are organized into a structured task feature vector, which contains four core parts: a domain attribute vector, representing the professional domain to which the task belongs and its relevance strength, such as medical imaging (0.9), radiology (0.8), oncology (0.6), etc.; a difficulty coefficient vector, which multidimensionally represents the complexity of the task, including cognitive complexity, operational precision, judgment difficulty, etc.; a professional knowledge requirement vector, representing the various knowledge and skill levels required to complete the task, such as medical terminology comprehension (4.5 / 5), anatomical knowledge (4 / 5), image annotation experience (3.5 / 5), etc.; and a time urgency vector, representing the time constraints of the task, including deadline urgency, duration requirements, and pace requirements. In addition to these four core components, the task feature vector also includes auxiliary information such as quality requirement level (a five-level standard from basic to expert), collaboration requirements (whether team collaboration is needed and the mode of collaboration), and contextual dependency (the degree to which the task depends on background information). The feature vector generated for each task typically contains 50-200 dimensions, dynamically adjusted according to the task type and complexity. These feature vectors are stored using a sparse representation to improve computational efficiency. The final multi-dimensional task feature vector comprehensively describes all aspects of the task's characteristics, providing a precise mathematical representation for subsequent intelligent matching.

[0078] In step S103, a multi-objective optimization algorithm is used to calculate the matching score between the annotator and the task, generating an optimal task allocation scheme, including:

[0079] Step S401: Based on the multidimensional user profile and the multidimensional task feature vector, multidimensional similarity is calculated using cosine similarity and Euclidean distance methods to calculate the matching degree of the core dimensions and obtain the matching degree score of the core dimensions.

[0080] In this step, the multi-dimensional user profile generated in step S101 is precisely matched with the multi-dimensional task feature vector generated in step S102. Multiple similarity calculation methods are employed, selecting the most suitable metric for different types of features. For vectorized features (such as domain embedding vectors), cosine similarity is primarily used. This method focuses on the consistency of vector directions and effectively measures the degree of similarity in the semantic space. For example, the cosine similarity between the annotator's domain expertise vector and the task's domain attribute vector is calculated to obtain a domain matching score. For numerical features (such as difficulty level and ability level), normalized Euclidean distance or Manhattan distance is used to measure the absolute difference between feature values. Special matching functions are also designed; for example, a custom time overlap calculation function is used for matching time availability with task time requirements. The matching calculation is broken down into several core dimensions: professional competence matching, which assesses the fit between the annotator's professional skills and the task requirements; domain knowledge matching, which assesses the relevance of the annotator's domain experience to the task domain; quality level matching, which compares the annotator's historical quality performance with the task quality requirements; efficiency adaptability, which assesses whether the annotator's work efficiency can meet the task time requirements; and cognitive style matching, which assesses the coordination between the annotator's work style and the characteristics of the task. A matching score is calculated separately for each core dimension, typically using a standardized score of 0-1, where 1 represents a perfect match and 0 represents a complete mismatch. During the calculation, the uncertainty of features is considered. For features with limited data or high variability, confidence intervals are introduced for evaluation to avoid over-reliance on unstable features. Context-aware matching calculation is also applied, considering the mutual influence between features, such as the higher requirements for professional skills for more challenging tasks. Through these multi-dimensional similarity calculations, the matching scores between the annotator and the task in each core dimension are obtained, laying the foundation for subsequent comprehensive scoring.

[0081] Step S402: Based on the platform's current business objectives, apply a multi-objective optimization algorithm to dynamically adjust the weights of the matching scores of the core dimensions, combine expert rules for constraint processing, and generate a comprehensive matching score.

[0082] In this step, based on the platform's current business goals and strategies, the matching scores of each core dimension calculated in step S401 are intelligently weighted and comprehensively scored. First, the platform's current business goals are identified. These goals may include quality priority (pursuing the highest annotation quality), efficiency priority (pursuing the fastest delivery speed), cost priority (optimizing resource utilization efficiency), or balanced development (seeking the best balance between quality, efficiency, and cost). These business goals are then transformed into mathematical objective functions, such as quality objective functions, efficiency objective functions, and cost objective functions. Subsequently, multi-objective optimization algorithms, such as Pareto optimization, weighted sum methods, or analytic hierarchy process (AHP), are applied to dynamically adjust the weights of each core dimension according to the current business goals. For example, in a quality-first mode, the weights of professional capability matching and quality level matching are increased; in an efficiency-first mode, the weight of efficiency adaptability is increased. Specific adjustments are also made based on the characteristics of the task. For example, for highly specialized tasks, regardless of the current business goals, professional capability matching is guaranteed to have a sufficiently high weight. In addition to dynamic weight adjustment, constraints are also applied using expert-defined rules. These rules include hard constraints (such as language proficiency requirements, confidentiality level requirements, and other mandatory conditions) and soft constraints (such as prioritizing experienced annotators). The rule engine enforces these constraints, directly excluding matches that do not meet hard constraints and awarding bonus points to matches that meet soft constraints. The process also considers the annotator's personal preferences and historical task acceptance patterns; if an annotator shows a clear preference for a particular type of task, the score for that match will be appropriately increased. Finally, by weighting and synthesizing the matching scores across all dimensions and applying adjustments to the constraint rules, a comprehensive matching score on a 0-100 scale is generated, fully reflecting the overall degree of matching between the annotator and the task.

[0083] Step S403: Based on the comprehensive matching score, considering the current workload of the annotator, the urgency of the task, and the overall resource utilization efficiency, a globally optimal task allocation strategy is generated through a combination optimization algorithm.

[0084] This step elevates the perspective from matching individual annotators with tasks to the overall resource optimization and allocation of the platform. First, global platform status information is collected, including a list of all pending tasks and their characteristics (e.g., priority, deadline, estimated workload), the status of all available annotators (e.g., current workload, available time, skill expertise), and overall platform performance metrics (e.g., resource utilization, task completion rate). Then, the task allocation problem is modeled as a combinatorial optimization problem, aiming to maximize overall matching quality and resource utilization efficiency while satisfying various constraints. Advanced combinatorial optimization algorithms, such as integer linear programming, genetic algorithms, or simulated annealing, are employed to solve this complex optimization problem. During optimization, several key factors are considered: first, the current workload of the annotators, avoiding assigning too many tasks to already heavily loaded annotators and ensuring a balanced workload distribution; second, the urgency of the tasks, prioritizing tasks with approaching deadlines or high customer priority to ensure timely completion of important tasks; and third, resource utilization efficiency, minimizing resource idleness and improving the overall throughput of the platform. The system also considers the dependencies and continuity requirements between tasks, such as the possibility that some tasks may require the same annotators to maintain consistency. In terms of technical implementation, a layered optimization strategy is adopted: first, high-priority tasks are precisely optimized; then, regular tasks are processed based on this foundation; and finally, long-term planning tasks are considered. A dynamic adjustment mechanism is also designed to quickly adjust the allocation strategy based on real-time feedback (such as task completion status and new tasks). Through this global optimization method, an optimal task allocation strategy for the platform as a whole is generated, considering not only the quality of individual matching but also overall efficiency and balance.

[0085] Step S404: Perform reinforcement learning processing on the task allocation strategy, combining active push and passive selection modes to achieve intelligent recommendation and adaptive allocation of tasks.

[0086] In this step, the task allocation strategy generated in step S403 is further optimized and implemented. Reinforcement learning is employed, treating task allocation as a sequential decision problem. Through continuous interaction with the environment (annotators and tasks), the allocation strategy is continuously optimized. A reinforcement learning model based on a deep Q-network (DQN) or policy gradient is constructed. The state space includes information such as currently available annotators, tasks to be assigned, and platform operating status; the action space represents possible allocation decisions; and the reward function incorporates factors such as matching quality, task completion status, and annotator feedback. Through pre-training on historical data and online learning, it can adapt to constantly changing environments and optimize long-term returns. When implementing task allocation, both proactive push and passive selection modes are combined to provide flexible solutions for different scenarios. In proactive push mode, tasks are directly assigned to the most suitable annotator based on the optimized allocation strategy. An intelligent notification mechanism is designed to push task information through channels preferred by annotators (such as in-app messages, emails, and SMS), providing information such as task summaries, matching reasons, and expected returns, increasing acceptance rates. It also implements task packaging technology, combining multiple similar or related small tasks into task packages to reduce switching costs and improve work efficiency. In passive selection mode, it provides annotators with a personalized task recommendation list, allowing them to choose tasks based on their actual situation and preferences. A recommendation algorithm is applied to rank the tasks, placing the most matching tasks in a prominent position and providing detailed task information and matching degree explanations to help annotators make informed choices. An intelligent guidance mechanism is also designed, encouraging annotators to try new areas and promoting long-term development by highlighting tasks that help improve their skills. Through this intelligent recommendation and adaptive allocation mechanism combining reinforcement learning, it can provide a more flexible and user-friendly task allocation experience while ensuring matching quality, improving annotator satisfaction and the overall efficiency of the platform.

[0087] Step S405: Based on the intelligent recommendation and adaptive allocation results, generate the optimal task allocation scheme.

[0088] In this step, based on the intelligent recommendation and adaptive allocation results of step S404, the optimal task allocation scheme is finally determined and generated. First, the actual execution of task allocation is collected and integrated, including feedback data such as the acceptance rate of proactive pushes, the selection mode of passive selections, and task startup speed. This data is analyzed to identify the effective parts and those requiring adjustment of the current allocation strategy. Allocations with high acceptance rates and rapid startup are confirmed as effective matches; for allocations that are rejected or do not respond for a long time, the reasons are analyzed and alternative solutions are prepared. Subsequently, the final allocation scheme is optimized. A decision tree or rule engine is applied to select the most suitable processing strategy based on different situations: for accepted tasks, the allocation is confirmed and the relevant resource status is updated; for rejected tasks, a suboptimal matching annotator is quickly found and a new allocation is initiated; for tasks without a clear response, a decision is made based on the urgency to either wait for the original annotator to respond or initiate an alternative solution. Special attention is paid to high-priority or time-sensitive tasks, and a rapid response mechanism is designed for these tasks to ensure timely allocation. In terms of technical implementation, a parallel processing and event-driven architecture is adopted, enabling real-time response to various allocation events, such as task acceptance, rejection, and completion, maintaining the dynamic optimization of the allocation scheme. An exception handling mechanism is also designed to handle unexpected situations, such as a large number of annotators going offline simultaneously or a sudden surge in tasks. Finally, a detailed task allocation scheme document is generated, including the allocation recipients for each task, expected start and completion times, quality requirements, and monitoring plans. This scheme is simultaneously sent to task management and quality control to initiate subsequent execution and monitoring processes. Through this comprehensive approach that considers actual execution conditions, the generated optimal task allocation scheme is not only theoretically optimal but also achieves good results in actual execution, effectively combining theory and practice.

[0089] Step S406: Reflexive evolution of a multi-objective heuristic method based on a large-scale language model, including: collecting historical decision data generated by the intelligent matching engine, including matching schemes, actual execution results, quality score differences, and business goal achievement, forming basic input data for reflective analysis; guiding the large-scale language model through the design of specific prompting engineering techniques to perform in-depth analysis on the basic input data, identifying implicit patterns in decision patterns, discovering the limitations of current heuristic rules, and obtaining in-depth analysis results; generating a new set of heuristic rule candidates based on the in-depth analysis results through the large-scale language model; wherein the heuristic rule set adopts a structured representation, including triggering conditions, weight adjustment strategies, and expected effects; designing diverse simulation scenarios to verify and screen the heuristic rule candidate set, evaluating the performance of new rules through backtesting on the historical decision data and small-scale online experiments, and obtaining verification and screening results; based on the verification and screening results, formally incorporating high-quality rules into the decision-making process, forming a dynamically evolving multi-objective heuristic method.

[0090] This step innovatively combines the powerful cognitive and reasoning capabilities of a large language model (LLM) with a multi-objective optimization algorithm to construct an intelligent matching decision capable of self-reflection and continuous evolution. First, a comprehensive historical decision data collection mechanism was established, extracting key information from the intelligent matching engine's operation logs. This includes matching schemes (annotator-task pairs and their matching scores), actual execution results (completion quality, time efficiency, annotator satisfaction, etc.), quality score discrepancies (deviation between predicted and actual quality), and the achievement of business objectives (such as the accuracy rate of high-quality tasks and the on-time completion rate of urgent tasks). This data is then structured and preliminarily analyzed to identify exceptionally high-performing or significantly deficient matching cases as the focus of reflective analysis. Subsequently, a carefully designed prompt engineering technique is applied to guide the large language model in in-depth analysis. Multi-level analysis prompt templates are constructed, including pattern recognition prompts ("Analyze the common features of these high-quality matching cases"), difference analysis prompts ("Compare the key differences between successful and failed cases"), and rule evaluation prompts ("Evaluate the limitations of the current rules in these cases"). A domain-specific knowledge enhancement mechanism was also designed, incorporating professional knowledge and terminology from the annotation industry into the prompts to improve the accuracy of LLM analysis. Through these carefully designed prompts, LLM can conduct multi-angle and multi-level in-depth analysis of matching decision data, identifying implicit patterns and regularities that are difficult for humans to discover. For example, LLM might find that: "When the complexity of a medical image annotation task exceeds 0.8 and includes rare cases, annotators with relevant clinical backgrounds, although slower, have significantly higher accuracy than annotators with purely technical backgrounds." Based on these in-depth analysis results, LLM is guided to generate a new set of heuristic rule candidates. These rules are represented in a structured manner, including explicit triggering conditions (such as task type, complexity threshold, etc.), weight adjustment strategies (such as increasing or decreasing the weight value of a certain dimension), and expected effects (such as improving quality, increasing speed, etc.). For example, a rule might be expressed as: "IF task_domain='medical_imaging' AND complexity>0.8 AND contains_rare_cases=TRUE THEN increase_weight(clinical_background, 0.3) AND decrease_weight(annotation_speed, 0.2) EXPECT quality_improvement=15%". To ensure the reliability of the generated rules, diverse simulation scenarios were designed to verify and filter the rule candidate set.First, backtesting is conducted on historical data to assess the matching results of applying the new rules. Then, experiments are carried out in a controlled, small-scale online environment to collect data on practical application effects. Evaluation metrics include multiple dimensions such as improvement in matching quality, changes in resource utilization efficiency, and achievement rate of business goals. Through these rigorous validation processes, truly effective rules are selected and formally incorporated into the decision-making process. A rule base management mechanism is designed, including rule version control, conflict detection, and priority management, to ensure that new rules can coexist harmoniously with the existing rule system. As business continues to develop and data accumulates, periodic reflective analysis and rule evolution processes are triggered, forming a dynamically evolving multi-objective heuristic approach that allows matching decisions to continuously improve and adapt to new business needs and challenges.

[0091] In the actual implementation, the large-scale language model used is based on the Transformer architecture, with at least 12 attention layers, 16 attention heads, 1024 hidden layer dimensions, and a total of over 1 billion parameters. The model is pre-trained on a large-scale text corpus containing annotated industry-specific linguistic data. The training data includes professional texts such as annotation guidelines, quality standard documents, expert review comments, and annotation error analysis reports, with a data volume exceeding 10GB.

[0092] The prompting design employs a multi-turn dialogue format, comprising three key components: (1) a task description, clearly stating the analysis objectives and requirements; (2) context provision, including a structured description of historical decision data and key statistical features; and (3) specific instructions, guiding the model to perform specific analysis tasks. A typical prompt template would require the model to analyze the common features of successfully matched cases, the common patterns of failed matches, the potential limitations of the current matching rules, and suggestions for new rules that could improve matching performance.

[0093] The model output is structured and parsed to extract candidate rules, which are then converted into an executable rule format. To verify the quality of the model output, the effectiveness of the model-generated rules was evaluated on a labeled test set containing 1000 pairs of annotators-task matches. The results show that, on average, the matching accuracy is improved by 15% and the task completion quality is improved by 12% compared to manually designed rules.

[0094] In step S104, the task structure is optimized using the fireworks algorithm based on the student t-distribution to generate an optimized task unit structure, including:

[0095] Step S501: Model the complex tasks in the optimal task allocation scheme, and represent each complex task as a multi-dimensional feature vector containing content complexity, domain attributes, estimated completion time, and dependencies. Encode the decomposition points and combination schemes of the complex tasks into a solution space to be optimized.

[0096] In this step, complex tasks in the optimal task allocation scheme generated in step S103 are analyzed and modeled in depth, laying the foundation for subsequent structural optimization. First, complex tasks requiring structural optimization are identified. These tasks typically have the following characteristics: large task scale, complex internal structure, multiple operation types, or the need for diverse professional knowledge. Multimodal feature extraction techniques are used to analyze these complex tasks, extracting key features from task descriptions, data samples, and operational requirements. For each complex task, a high-dimensional feature vector is constructed to comprehensively describe all aspects of the task. In terms of content complexity, factors such as cognitive complexity, operational difficulty, and decision complexity are quantified; in terms of domain attributes, the professional fields involved in the task and their weight distribution are represented; in terms of estimated completion time, the completion time of each part of the task is predicted based on historical data and task characteristics; and in terms of dependency relationships, the dependencies, resource sharing, and information flow relationships between the components within the task are identified and represented. Some special features are also extracted, such as task divisibility (the degree to which the task can be decomposed), consistency requirements (the degree to which different parts need to maintain consistency), and professional cross-disciplinary nature (the degree to which multiple professional fields are involved). Subsequently, the potential decomposition points and combination schemes of the complex task are encoded into a solution space to be optimized. Possible decomposition points are identified through task graph analysis; these points are typically natural boundaries in the task flow, transition points between different operation types, or boundaries between different datasets. Possible combination schemes are also identified, i.e., which subtasks can be combined into a single task unit to improve efficiency. These decomposition points and combination schemes are encoded into a high-dimensional solution space, where each point represents a possible task structure scheme. Evaluation functions are defined for this solution space, including balance metrics (the degree of balance in workload among subtasks), efficiency metrics (expected overall completion efficiency), quality metrics (expected annotation quality), and resource utilization metrics (the degree of matching between annotator skills and task requirements). This complex task modeling and solution space encoding provides a clear problem definition and evaluation criteria for subsequent optimization of the Fireworks algorithm.

[0097] Step S502: Based on the solution space to be optimized, initialize the fireworks algorithm based on the Student t-distribution, set each firework as a candidate solution, generate sparks through the Student t-distribution instead of the traditional uniform distribution, set the initial degree of freedom parameters, and generate an initial candidate solution set.

[0098] In this step, based on the solution space to be optimized defined in step S501, an innovative Student t-distribution-based Fireworks Algorithm (TFWA) is initialized. The Fireworks Algorithm is a swarm intelligence optimization algorithm inspired by fireworks explosions. This TFWA significantly improves the search efficiency in high-dimensional task spaces by introducing the Student t-distribution instead of the traditional uniform distribution. First, a set of fireworks is initialized in the solution space, each representing a candidate task structure scheme. The positions of the initial fireworks are generated using various strategies: some generate targeted initial solutions based on heuristic rules (such as even distribution by data volume, grouping by operation type, etc.); some generate experience-oriented initial solutions based on patterns from historical success cases; and some are generated randomly to increase the diversity of initial solutions. A fitness value is calculated for each initial fireworks, i.e., its quality as a task structure scheme is evaluated according to the evaluation function defined in step S501. Subsequently, the key parameters of TFWA are set. Unlike the traditional Fireworks Algorithm, TFWA uses the Student t-distribution to generate sparks (i.e., new candidate solutions generated by fireworks explosions). The Student t-distribution is a heavy-tailed distribution with the following probability density function:

[0099]

[0100] Here, v is the degree of freedom parameter, and Γ is the gamma function. An initial degree of freedom parameter v (usually starting from 1) is set. This parameter controls the tail weight of the distribution, thus affecting the global and local nature of the search. When v is small (e.g., v=1, which is a Cauchy distribution), the distribution has a heavy tail, generating sparks farther from the current solution, enhancing the algorithm's global exploration capability. As v increases, the distribution gradually approaches a normal distribution, which is more conducive to fine-grained local searches. Other TFWA parameters are also set, such as explosion amplitude (controlling the spark generation range), number of sparks (the number of candidate solutions generated in each iteration), and maximum number of iterations. These parameter settings generate an initial set of candidate solutions, preparing for subsequent iterative optimization. This initialization strategy based on the Student's t-distribution gives the algorithm good global exploration capability from the beginning, enabling it to search high-dimensional task structure spaces more effectively.

[0101] Step S503: Through the degree-of-freedom adaptive adjustment mechanism, the first degree-of-freedom value is used to enhance the global exploration capability in the early stage of optimization, and the degree-of-freedom value is gradually increased to enhance the local fine search capability as the iteration progresses, thereby generating a dynamically adjusted search strategy.

[0102] In this step, one of the core innovations of the TFWA algorithm—the adaptive adjustment mechanism of degrees of freedom—is implemented, enabling the algorithm to dynamically balance global exploration and local exploitation capabilities during the optimization process. An adaptive control framework is designed to automatically adjust the degree of freedom parameter v of the Student t-distribution based on the optimization progress and search state. In the early stages of optimization, the first degree of freedom value (usually v=1 or close to 1) is used. At this time, the t-distribution has a heavy tail, and the probability density function still has a large value in regions far from the center. This means that the algorithm has a high probability of generating sparks far from the current solution, thus enabling it to explore different regions of the solution space extensively and avoid getting trapped in local optima too early. As the iteration progresses, the degree of freedom value is gradually increased, making the shape of the t-distribution gradually approach a normal distribution. This change allows the algorithm to gradually reduce the exploration of distant regions and instead perform more refined searches in currently promising regions, enhancing its local exploitation capabilities. Several metrics are employed to guide the dynamic adjustment of the degree-of-freedom (DOF) parameters: first, an iteration progress metric, which simply adjusts the v value based on the ratio of the current iteration count to the maximum iteration count; second, a search stagnation metric, which temporarily reduces the v value to re-enhance global exploration when multiple iterations fail to significantly improve the optimal solution; and third, a solution set diversity metric, which also reduces the v value to increase diversity when the diversity of the candidate solution set falls below a threshold. A nonlinear strategy for DDF adjustment is also designed, employing different adjustment rates at different stages, such as rapidly increasing the v value in the mid-stage and slowly increasing it in the later stage, to achieve finer control. Furthermore, a regional DDF adjustment mechanism is implemented, allowing different DDF parameters to be applied to different promising regions in the solution space, further improving search efficiency. This adaptive DDF adjustment mechanism generates a dynamically adjusted search strategy that automatically balances global exploration and local development based on actual conditions during the search process, significantly improving performance on complex task structure optimization problems. Experiments show that compared to methods with fixed DDF parameters, this adaptive mechanism can reduce the number of iterations by an average of 30% while improving solution quality by 15%.

[0103] Step S504: Design a dimension-sensitive explosion amplitude control strategy. Based on the differences in importance and sensitivity of different dimensions in the task feature space, assign different explosion amplitudes to each dimension to generate a dimension-weighted search space.

[0104] This step implements another important innovation of the TFWA algorithm—a dimension-sensitive explosion amplitude control strategy, which solves the problem of varying importance and sensitivity of different dimensions in high-dimensional task feature spaces. Traditional fireworks algorithms use the same explosion amplitude for all dimensions, which is inefficient when dealing with high-dimensional heterogeneous problems such as task structure optimization. First, the importance and sensitivity of each dimension in the task feature space are evaluated using multiple methods: 1) Feature importance analysis, using models such as random forests or gradient boosting trees to assess the impact of each dimension on the final task structure quality; 2) Sensitivity analysis, calculating the partial derivatives or rates of change of the objective function with respect to each dimension to identify the dimensions most sensitive to small changes; 3) Historical data analysis, statistically analyzing the range and distribution characteristics of different dimensions in historical high-quality solutions. Based on these analyses, different explosion amplitudes are assigned to each dimension. For dimensions with high importance or sensitivity (such as key decomposition points affecting task quality), larger explosion amplitudes are assigned, allowing for broader exploration in these dimensions; for dimensions with low importance or sensitivity, smaller amplitudes are assigned to reduce unnecessary search space. The interaction between dimensions was also considered. For strongly correlated dimension groups, a collaborative amplitude control strategy was adopted to ensure that the changes in these dimensions maintained a certain degree of coordination. In implementation, an amplitude matrix A was designed, where each element a_ij represents the explosion amplitude of the i-th firework in the j-th dimension. This matrix not only considers the importance and sensitivity of dimensions but also the fitness of the fireworks (better-performing fireworks receive more refined local search amplitudes) and the search phase (the overall amplitude gradually decreases as the search progresses). A dynamic amplitude adjustment mechanism was also implemented, updating the amplitude allocation strategy in real time based on the actual contribution of each dimension during the search process. Through this dimension-sensitive explosion amplitude control strategy, a dimension-weighted search space was generated, enabling the algorithm to concentrate computational resources on the most influential dimensions, significantly improving search efficiency. Experiments show that compared with the uniform amplitude strategy, this dimension-sensitive strategy can reduce the computational load by an average of 40% and improve the solution quality by 20% in complex task structure optimization problems.

[0105] Step S505: Construct an elite solution memory based on historical experience, and combine the differential evolution strategy, the dynamically adjusted search strategy, and the dimension-weighted search space to iteratively optimize the initial candidate solution set and guide the new search direction, generating an optimized task unit structure; wherein, the elite solution memory stores historically excellent task decomposition and combination schemes.

[0106] In this step, the overall implementation of the Student t-distribution-based Fireworks Algorithm (TFWA) was completed and applied to the task structure optimization problem. First, an elite solution memory based on historical experience was constructed. This is a dynamically maintained knowledge base that stores historically high-performing task decomposition and combination schemes. Each scheme in the memory contains not only the solution vector itself but also its contextual information (such as task type, scale, domain, etc.) and performance evaluation (such as quality improvement, efficiency improvement, etc.). A similarity-based retrieval mechanism was designed, enabling new optimization tasks to retrieve similar cases from the memory for reference. The memory adopts a hierarchical storage structure, with the top layer storing general patterns and the bottom layer storing domain-specific professional solutions, improving retrieval efficiency and application accuracy. Subsequently, the differential evolution strategy was combined with TFWA to form a hybrid optimization algorithm. Differential evolution is a powerful global optimization method that generates new candidate solutions through vector differencing operations. Differential evolution operations are introduced within the TFWA framework, utilizing elite solutions in the memory to guide new search directions. Specifically, in each iteration, in addition to generating sparks through the Student t-distribution, the differential evolution formula v = x is also used. r1 + F * (x r2 - x r3 Generate a subset of candidate solutions, where x r1 x r2 x r3The solution vector is selected from the current population and memory, and F is the scaling factor. This hybrid strategy combines the adaptive exploration capability of TFWA with the directional search capability of differential evolution, significantly improving the algorithm performance. During iterative optimization, the dynamic adjustment search strategy in step 503 and the dimension-weighted search space in step 504 are applied in combination. In each iteration, the degree-of-freedom parameters of the student t-distribution are automatically adjusted according to the current stage, and different explosion amplitudes are assigned according to the importance of each dimension. Multiple selection strategies are also implemented, such as the elite preservation strategy (ensuring the optimal solution is not lost in iterations), the diversity maintenance strategy (maintaining population diversity and avoiding premature convergence), and the tabu search strategy (avoiding repeated exploration of known low-quality regions). Multiple termination conditions are set, including reaching the maximum number of iterations, no significant improvement after multiple consecutive iterations, or reaching the preset target quality. When the termination conditions are met, the current optimal solution is output as the final task structure optimization scheme. This scheme details how complex tasks should be decomposed into subtasks or how small tasks should be combined into task packages, including information such as the boundaries, dependencies, expected workload, and recommended annotator types for each task unit. This comprehensive optimization method generates a high-quality optimized task unit structure, significantly improving the processing efficiency and quality of complex annotation tasks. Practical applications show that, compared to traditional methods, this optimized task structure can reduce task completion time by an average of 23%, improve annotation quality by 9%, and achieve a more balanced workload distribution among different annotators.

[0107] Furthermore, in the specific implementation of the Student t-distribution-based Fireworks Algorithm (TFWA), the following key parameters are set: the initial population size is 50, each firework generates 30-50 sparks in each iteration, and the maximum number of iterations is 100. The initial degree of freedom parameter v of the Student t-distribution is set to 1, and the value of v gradually increases with iteration. The initial value of the explosion amplitude is set to 30% of the diameter of the task feature space, and gradually decreases with iteration.

[0108] The core algorithm implementation of TFWA includes key steps such as initializing the firework position, calculating fitness, adjusting the degree of freedom parameters, adjusting the amplitude of each dimension according to the importance of the dimension, generating random displacements using the Student t-distribution, generating new sparks, evaluating the fitness of sparks, selecting solutions from the elite solution memory for differential operations, selecting the best individual as the next generation of firework, and updating the elite solution memory.

[0109] The algorithm's training data comes from the platform's historical task decomposition records, containing over 5000 decomposition cases of complex tasks, covering labeled tasks of different domains, scales, and complexities. Each case includes the task's original features, decomposition scheme, and performance evaluation. Five-fold cross-validation is used during training to ensure the model's generalization performance. In experimental evaluations, compared to the traditional Fireworks algorithm, TFWA improves convergence speed by 45% and the quality of the final solution by 18% in task decomposition optimization problems.

[0110] In step S105, a preset anomaly detection model is used to perform real-time anomaly detection on the annotation results, automatically identifying abnormal samples. Simultaneously, a preset quality prediction model is used to predict the annotation quality of the annotation results, obtaining the detection and prediction results and triggering quality control measures. The quality control results are then output, including:

[0111] Step S601: Design a lightweight data acquisition interface to collect process data such as annotation results, operation trajectory, and time distribution in real time during the annotation process, and perform preliminary structured processing to obtain process data.

[0112] In this step, a high-efficiency, lightweight data acquisition interface was designed and implemented to capture key data in real time during the annotation process without significantly interfering with the annotation work. This interface adopts a layered architecture, including a front-end acquisition layer, a data transmission layer, and a back-end processing layer. In the front-end acquisition layer, a low-latency data acquisition component is embedded in the annotation tool, capable of capturing three types of key data: annotation result data, including the annotation content itself (such as bounding box coordinates, category labels, text annotations, etc.) and its version history; operation trajectory data, recording the sequence of the annotator's interaction behaviors, such as mouse movement paths, click events, keyboard input, tool switching, etc., reflecting the cognitive and operational patterns of the annotation process; and time distribution data, accurately recording the start time, completion time, interruption time, and time allocation of each annotation sample for different operation stages. An event-driven acquisition strategy is adopted, triggering data acquisition only when key operations occur, minimizing performance overhead. In the data transmission layer, an efficient data compression and batch transmission mechanism is implemented, employing an incremental update strategy to transmit only the changed data portions, significantly reducing network load. Local caching and network interruption resumption mechanisms are also designed to ensure that data is not lost in the event of network instability. In the backend processing layer, the received raw data undergoes preliminary structuring processing. First, data cleaning is performed to handle missing values, outliers, and duplicate records. Then, data standardization is performed, converting data from different sources and formats into a unified structure. Finally, feature extraction is performed, calculating a series of derived features from the raw data, such as annotation speed, modification frequency, and hesitation patterns. Real-time data quality monitoring is also implemented to ensure the reliability of the collected data. This lightweight data acquisition interface continuously acquires rich process data during annotation, providing a solid data foundation for subsequent quality monitoring and anomaly detection, while minimizing interference with the annotation work; annotators are almost unaware of the data acquisition process. Practical application shows that the interface's CPU utilization is typically below 3%, memory usage increases by no more than 50MB, and network bandwidth consumption averages no more than 2MB per hour, fully meeting the lightweight design goals.

[0113] Step S602: Based on the process data, combined with domain knowledge and annotation standards, construct a multi-dimensional quality evaluation index system including accuracy, consistency, completeness, and timeliness.

[0114] In this step, based on the process data collected in step S601, and combined with domain-specific professional knowledge and annotation specification requirements, a comprehensive multi-dimensional quality evaluation index system was constructed. This index system not only focuses on the quality of the final annotation results but also considers various aspects of the annotation process, forming a three-dimensional evaluation framework for annotation quality. First, evaluation indicators for the accuracy dimension were designed. These indicators measure the degree of conformity between the annotation results and the real situation, including: annotation accuracy indicators, such as the IoU (Intersection over Union) value in object recognition tasks and the label accuracy in classification tasks; boundary precision, evaluating the accuracy of bounding boxes or segmentation masks; attribute completeness, checking whether required attributes are complete and accurate; and reference consistency, the degree of consistency with reference standards or the gold standard. The calculation method of the accuracy indicators is dynamically adjusted according to different task types, such as using the Dice coefficient for image segmentation tasks and the F1 score for text classification tasks. Second, evaluation indicators for the consistency dimension were designed. These metrics measure the internal and cross-sample consistency of annotations, including: internal consistency (whether the same annotator's annotations on similar samples are consistent); cross-annotator consistency (the degree of consistency among different annotators' annotations of the same sample); temporal consistency (whether annotators maintain consistent annotation standards across different time periods); and rule compliance (whether annotations follow predefined annotation rules and conventions). Consistency issues, such as sudden changes in annotation style or deviations from team standards, are automatically detected using statistical methods and pattern recognition techniques. Third, evaluation metrics for the completeness dimension were designed. These metrics ensure that annotations cover all elements that should be annotated, including: coverage (whether all objects that should be annotated are annotated); detail completeness (whether detailed features are fully captured); contextual information completeness (whether relevant contextual information is appropriately recorded); and metadata completeness (whether necessary metadata is fully filled in). Completeness is assessed by comparing the expected number of objects, checking for blank areas, and validating metadata fields. Finally, evaluation metrics for the timeliness dimension were designed. These indicators assess the time efficiency and schedule compliance of annotation, including: annotation volume per unit time (measuring annotation efficiency); schedule achievement rate (comparing actual progress with planned progress); response timeliness (time from task allocation to start annotation); and consistent time distribution (whether the distribution of annotation time across different samples is reasonable). Time series analysis identifies efficiency anomalies and schedule risks. In addition to these four core dimensions, professional indicators (such as anatomical accuracy in medical annotation), innovation indicators (innovative insights in annotation), and user experience indicators (user experience of the annotation interface) are designed according to specific domain needs. This multi-dimensional quality evaluation indicator system provides a scientific evaluation standard for subsequent anomaly detection and quality prediction, ensuring that quality control is based on objectivity and comprehensiveness.

[0115] Step S603: Apply an isolated forest or autoencoder unsupervised learning algorithm to perform anomaly detection on the labeled results, and combine statistical process control methods to monitor changes in quality indicators in real time to obtain anomaly detection results.

[0116] In this step, a two-layer anomaly detection mechanism is implemented. Advanced unsupervised learning algorithms and statistical process control methods are used to monitor annotation quality in real time and promptly identify potential problems. First, unsupervised learning algorithms are applied to detect anomalies in the annotation results. Two main algorithms are used: Isolation Forest and Autoencoder. Isolation Forest is an anomaly detection algorithm based on random forests. Its core idea is that anomalous data points are usually easier to "isolate." The multidimensional quality index constructed from step S602 is used as features to train the Isolation Forest model. This model can effectively identify annotation results that deviate from normal patterns in the multidimensional feature space. A context-aware variant of Isolation Forest is also implemented, considering the relationships between samples and task characteristics to improve detection accuracy. For high-dimensional features or complex patterns, Autoencoder is applied for anomaly detection. Autoencoders learn the process of compressing and reconstructing input data, capturing the inherent structure of the data. The Autoencoder model is trained to learn the feature patterns of normal annotation results. When encountering anomalous annotations, the reconstruction error increases significantly, thus being identified as an anomaly. Advanced variants such as Variational Autoencoders (VAEs) and Adversarial Autoencoders (AAEs) were implemented to improve the detection capability for complex anomalies. An ensemble strategy was adopted to integrate the detection results of multiple models, reducing false positives and false negatives. Secondly, Statistical Process Control (SPC) methods were combined to monitor changes in quality indicators in real time. Multiple SPC techniques were implemented: control chart techniques, such as X-bar charts, R-charts, and CUSUM charts, were used to monitor the mean and variability of quality indicators; process capability analysis was used to assess the ability of the annotation process to meet quality requirements; and Multivariate Statistical Process Control (MSPC) was used to simultaneously monitor multiple related quality indicators. Adaptive control limits were designed to dynamically adjust warning and action limits based on task characteristics and historical performance, improving detection sensitivity. Trend analysis was also implemented to identify gradual trends in quality indicators and provide early warnings of potential quality declines. In actual operation, unsupervised learning and SPC methods were combined to form a multi-layered anomaly detection network. For each annotation result, an anomaly score and confidence level were calculated, and anomaly status was determined based on preset thresholds. The annotator's historical performance and task characteristics were also considered to personalize anomaly judgments. This comprehensive approach can accurately identify various types of anomalies, including obvious errors (such as labeling errors and severe boundary deviations), subtle deviations (such as slight boundary inaccuracies and incomplete attributes), and behavioral anomalies (such as abrupt changes in annotation patterns and abnormal fluctuations in efficiency). Practical applications show that this two-layer anomaly detection mechanism achieves an accuracy of 92%, a recall of 89%, and an F1 score of 90.5%, significantly outperforming the performance of single-method approaches.

[0117] Step S604: Based on the anomaly detection results, the intervention type and urgency are automatically determined using a decision tree algorithm, the abnormal sample is identified, and corresponding quality control measures are triggered.

[0118] In this step, based on the anomaly detection results of step S603, an intelligent decision-making mechanism is implemented to automatically determine the intervention type and urgency of each anomaly and trigger corresponding quality control measures. First, an intervention decision model is constructed using a decision tree algorithm. A large number of historical anomaly cases and their optimal intervention methods are collected and labeled as training data for the decision tree. Each case includes input features such as anomaly type, anomaly severity, annotator characteristics, and task characteristics, as well as the final intervention measures and their effects as output labels. The model is trained using decision tree algorithms such as C4.5 or CART, and the optimal splitting features are selected through information gain or Gini impurity to construct interpretable decision rules. Ensemble methods, such as random forests or gradient boosting decision trees, are also applied to improve decision accuracy. A hierarchical decision structure is designed: the first layer determines the type of anomaly, such as accuracy problems, consistency problems, completeness problems, or efficiency problems; the second layer determines the urgency of intervention, typically divided into three levels: high (requiring immediate intervention), medium (requiring intervention before the current batch is completed), and low (can be handled in subsequent quality improvements); the third layer determines the specific intervention type, such as secondary verification, expert review, task reassignment, and annotator guidance. The system considers multiple factors for decision-making: the nature and severity of the anomalies, such as the type of error and the degree of deviation from the normal range; the characteristics and historical performance of the annotators, such as experience level, professional background, and past quality records; the characteristics and importance of the task, such as task complexity, domain sensitivity, and client priority; and available resources and time constraints, such as expert availability and deadline urgency. Based on the decision results, the system automatically identifies anomalous samples requiring intervention and assigns appropriate intervention measures to each sample. For high-urgency accuracy issues, real-time expert review may be triggered; for medium-urgency consistency issues, secondary verification may be arranged; and for low-urgency efficiency issues, training recommendations may be generated. An automated execution mechanism for intervention measures is also implemented: for samples requiring secondary verification, they are automatically assigned to other suitable annotators; for samples requiring expert review, review tasks are automatically created and relevant experts are notified; and for tasks requiring reassignment, the task reassignment process is automatically triggered. An intervention effect tracking mechanism is also designed to record the results and impact of each intervention for continuous optimization of the decision-making model. This intelligent decision-making mechanism enables the most suitable intervention measures to be taken based on the specific circumstances of anomalies, ensuring both labeling quality and optimized resource utilization, while avoiding over- or under-intervention. Practical applications show that compared to intervention strategies based on fixed rules, this intelligent decision-making mechanism reduces intervention costs by an average of 30% while improving intervention effectiveness by 15%.

[0119] Step S605: Implement quality control measures including secondary inspection, expert review, or task reassignment, and output the quality control results.

[0120] In this step, the quality control measures decided in step S604 are implemented, and a detailed quality control result report is generated. Three main types of quality control measures are implemented, and a dedicated execution process and feedback mechanism are designed for each type of measure. The first is the secondary verification measure. When it is determined that a labeled sample requires secondary verification, an intelligent allocation mechanism is activated to select the most suitable annotator for review. Multiple factors are considered in the allocation: the review annotator's professional background should match the sample domain; the review annotator's experience level should generally be higher than or equal to the original annotator; the review annotator and the original annotator should have a certain degree of independence to avoid team bias; the review annotator's current workload should allow for timely completion of the review task. A structured review interface is provided for the review annotator, displaying the original annotation results but not showing the specific anomalies detected to avoid confirming bias. The review annotator completes the annotation independently, automatically comparing the differences between the two annotations and identifying key inconsistencies. For parts with significant differences, third-party arbitration or expert review may be triggered. The second is the expert review measure. For complex or high-risk anomalies, an expert review process will be initiated. An expert resource database is maintained, recording each expert's area of ​​expertise, available time, and review history. The system intelligently matches the most suitable experts based on sample characteristics and balances workload across experts. An enhanced review interface is provided for experts, displaying not only the original annotations and detected anomalies but also relevant references and similar historical cases. Experts can directly correct annotations, add detailed explanations, or provide guidance. All expert actions and feedback are recorded for subsequent quality analysis and annotator training. Thirdly, a task reassignment mechanism is implemented. When an annotator is deemed severely mismatched with the current task, or when an annotator's performance consistently falls short of expectations, a task reassignment process is triggered. First, the scope of the reassignment is assessed—it could be a single sample, the current batch, or the entire task. An intelligent matching engine then searches for the most suitable annotator, considering task characteristics, urgency, and available resources. A smooth handover mechanism is designed to ensure that completed work is not lost or duplicated during the reassignment process. Constructive feedback is also provided to the original annotator, explaining the reasons for the reassignment and offering improvement suggestions. In addition to these three main measures, several auxiliary measures were implemented, such as real-time guidance (providing immediate feedback and suggestions during the annotation process), training recommendations (recommending targeted training based on identified problems), and rule optimization (adjusting annotation rules and guidelines based on frequently occurring issues). The execution process and results of all quality control measures were meticulously recorded, forming a structured quality control results report. This report includes: an anomaly detection summary, listing all detected anomalies and their types, severity, and distribution characteristics; intervention details, recording the specific measures taken for each anomaly, the execution time, and the responsible party; quality improvement effect, comparing changes in quality indicators before and after the intervention and quantifying the improvement effect; problem pattern analysis, identifying frequently occurring and potential problems; and improvement suggestions, proposing improvements to processes, rules, or training related to the identified problems.This quality control results report not only records the quality control status of the current batch, but also provides valuable data support for continuous quality improvement.

[0121] In step S106, the model parameters of the multi-dimensional user profile and the multi-dimensional task feature vector are updated using a reinforcement learning algorithm to generate personalized feedback and capability enhancement strategies, including:

[0122] Step S701: Collect multi-source feedback data including the quality control results, the task execution status data, annotators' self-evaluation, and expert review, and form a comprehensive feedback dataset that fully reflects the annotation performance through data fusion technology.

[0123] In this step, a comprehensive multi-source data collection and fusion mechanism was designed and implemented to integrate feedback information from different channels and construct a rich and comprehensive integrated feedback dataset. First, four types of key feedback data were collected: quality control results, from the quality control in step S605, including anomaly detection records, details of intervention measures, and information on quality improvement effects; task execution status data, recording information on task completion, time efficiency, resource consumption, and other execution-level information; annotator self-evaluation data, collected through structured self-evaluation questionnaires and open-ended feedback to gather annotators' assessments and feelings about their own performance; and expert review data, including evaluations of annotation quality, specific correction opinions, and professional guidance suggestions from domain experts. Standardized collection interfaces and data models were designed for each type of data to ensure data consistency and integrity. For quality control results, structured data was automatically extracted from the quality control module; for task execution status, updates were obtained in real time through the task management platform's API; for annotator self-evaluation, a user-friendly feedback interface was designed that automatically pops up after task completion, encouraging annotators to provide detailed feedback; and for expert review, a dedicated review tool was provided, supporting evaluation and annotation accurate to the sample level. Subsequently, advanced data fusion techniques were applied to integrate these heterogeneous data into a unified comprehensive feedback dataset. A multi-level fusion strategy was employed: feature-level fusion, aligning and standardizing identical or similar features from different sources; instance-level fusion, linking data from different sources concerning the same labeled sample or the same annotator; and decision-level fusion, synthesizing evaluations and judgments from different sources to form a more comprehensive quality assessment. Entity resolution technology was applied to ensure accurate matching of the same entity (such as annotator, task, or sample) across different data sources. Temporal alignment was also implemented to handle timestamp differences between different data sources and construct a coherent timeline. Special attention was paid to conflict resolution between data sources; for example, when expert evaluations differed from detection results, a credibility-based weighting strategy or evidence theory method was applied to resolve the conflict. A data quality assessment mechanism was also designed to evaluate the reliability, completeness, and timeliness of each data source, taking these factors into account during the fusion process. Through this multi-source data fusion, a comprehensive feedback dataset reflecting annotation performance was generated. This dataset not only includes objective quality and efficiency indicators, but also subjective self-assessments and expert opinions. It contains both quantitative scoring data and qualitative text feedback, providing a rich information foundation for subsequent personalized feedback generation and capacity building planning.

[0124] Step S702: Based on the comprehensive feedback dataset, apply natural language generation and interpretable artificial intelligence technologies to generate personalized feedback reports for each annotator, highlighting their strengths and weaknesses.

[0125] In this step, based on the comprehensive feedback dataset constructed in step S701, advanced Natural Language Generation (NLG) and Explainable Artificial Intelligence (XAI) technologies are applied to generate detailed, specific, and constructive personalized feedback reports for each annotator. First, a deep analysis of the comprehensive feedback dataset is performed to identify each annotator's key strengths and weaknesses. Multiple analytical techniques are applied: statistical analysis to calculate the mean, variance, and trend of various quality and efficiency indicators; comparative analysis to compare the annotator's performance with the team average, historical performance, or benchmarks; pattern recognition to identify specific patterns in the annotator's work, such as the types of tasks they excel at and the types of errors they are prone to; and text analysis to perform natural language processing on expert comments and self-evaluation texts to extract key viewpoints and suggestions. Through these analyses, 3-5 major strengths and 3-5 areas for improvement for each annotator are identified, and specific supporting evidence and improvement suggestions are collected for each. Subsequently, personalized feedback reports are constructed using natural language generation technology. A template-enhanced neural network generation method is employed, combining predefined high-quality feedback templates with flexible neural network generation capabilities. A multi-layered report structure was designed: the summary section concisely outlines overall performance and key findings; the strengths section details the annotator's main strengths and provides specific examples; the improvements section tactfully and specifically points out areas for improvement and provides practical suggestions; and the development plan section proposes targeted skill enhancement paths and specific learning resources. Special attention was paid to the language used in feedback, ensuring a positive and constructive tone and avoiding negative or accusatory statements. Sentiment analysis technology was applied to ensure that feedback maintained an encouraging and supportive tone while pointing out problems. Explainable artificial intelligence technology was also applied to make the feedback more persuasive and actionable. Clear explanations and specific evidence were provided for each evaluation, such as "In medical imaging tasks, your tumor boundary annotation accuracy reached 95%, higher than the team average of 88%, especially excelling in handling ambiguous boundary cases, such as the accurate annotations in samples #A237 and #B452." Visual explanations were also generated, such as performance radar charts, progress trend charts, and case comparison charts, intuitively demonstrating the annotator's performance characteristics. A personalized approach was also designed, adjusting the level of detail, terminology, and style of feedback based on the annotator's preferences and characteristics. Cultural differences and language habits were considered to ensure the feedback was well-received by annotators from diverse cultural backgrounds. This combination of NLG and XAI methods resulted in personalized feedback reports that not only accurately reflected annotators' performance but also conveyed improvement suggestions in a constructive and encouraging manner, providing effective guidance for their continued growth. User research showed that compared to traditional standardized feedback, this personalized feedback report had a 40% higher acceptance rate, a 35% higher actual application rate, and a 28% greater positive impact on annotator behavior.

[0126] Step S703: Analyze the shortcomings and development potential of the annotators, and combine the domain knowledge graph of the annotation task to design personalized ability improvement paths for the annotators using recommendation algorithms.

[0127] This step involves an in-depth analysis of the annotators' competency structure, identifying key weaknesses and development potential, and designing scientific and effective personalized competency improvement paths based on domain knowledge systems. First, a comprehensive competency gap analysis is conducted. An annotation competency model is constructed, decomposing annotation competency into multiple dimensions, such as domain knowledge (e.g., professional knowledge in medicine, law, finance), technical skills (e.g., operational skills in image annotation, text classification, speech transcription), cognitive abilities (e.g., attention span, detail discrimination, contextual understanding), and meta-capabilities (e.g., learning ability, adaptability, teamwork). Based on a comprehensive feedback dataset, the annotators' performance across each competency dimension is quantitatively evaluated, identifying competency dimensions where performance is significantly below expectations or significantly different from task requirements; these are marked as competency weaknesses. Simultaneously, the development potential of annotators is also identified, namely, competency dimensions that perform well and have room for further improvement, or those domains where current performance is average but the learning curve is steep. Second, a domain knowledge graph for the annotation task is constructed and applied. This knowledge graph is a structured representation of domain-specific knowledge and skills, containing concept nodes (such as technical terms, annotation techniques, quality standards, etc.) and relational edges (such as premise relations, inclusion relations, similarity relations, etc.). Specialized knowledge graphs have been constructed for different annotation domains (such as medical imaging, natural language processing, autonomous driving, etc.). The shortcomings and development potential of annotation personnel are mapped onto the knowledge graph, identifying the knowledge points and skills that need to be learned, and determining the dependencies and optimal learning order between these points through graph analysis algorithms. Subsequently, personalized recommendation algorithms are applied to design skill improvement paths for annotation personnel. A hybrid recommendation strategy is adopted, combining multiple recommendation techniques: content-based recommendation, recommending relevant learning resources based on the annotation personnel's ability characteristics and interests; collaborative filtering, recommending effective learning paths based on the learning processes and achievements of similar annotation personnel; knowledge graph reasoning, recommending scientific learning sequences based on the structure and dependencies of domain knowledge; and reinforcement learning, optimizing the recommendation strategy by simulating the long-term benefits of different learning paths. A multi-tiered skill enhancement path is generated for each annotator: short-term goals (small objectives achievable within 1-2 weeks, providing immediate satisfaction), mid-term plans (1-3 month learning plans), and long-term development directions (six months to one year of career development planning). Each level includes specific learning tasks, recommended resources, and expected outcomes. Special attention is paid to the feasibility and effectiveness of the learning path, considering the annotator's time constraints, learning preferences, and existing resources. A dynamic adjustment mechanism is also designed to continuously optimize the enhancement path based on the annotator's learning progress and feedback. Through this method based on deep analysis and personalized recommendations, a skill enhancement path is designed for each annotator that is both tailored to their specific needs and aligned with the domain's knowledge structure, effectively supporting the annotator's continuous growth and career development. Practice shows that compared to general training programs, this personalized skill enhancement path increases annotator learning engagement by an average of 45% and the speed of skill improvement by 32%.

[0128] Step S704: Based on the comprehensive feedback dataset and the quality control results, apply incremental learning and reinforcement learning techniques to update the model parameters of the multidimensional user profile and the multidimensional task feature vector.

[0129] In this step, advanced machine learning techniques are applied to dynamically update user profiles and task feature models based on the latest feedback data and quality control results, maintaining an accurate understanding of annotators' capabilities and task characteristics. First, incremental learning updates of the multi-dimensional user profile model are implemented. Incremental learning allows the model to update its knowledge using new data without retraining the entire model. An incremental learning framework based on elastic weight merging is designed to effectively integrate existing model knowledge and information from new data. For each annotator, key features are extracted from the comprehensive feedback dataset, including the latest quality performance, efficiency metrics, domain expertise performance, and behavioral patterns. Feature importance analysis is applied to identify the key features that best reflect changes in annotators' capabilities. Multiple incremental learning techniques are employed: for the linear model part, online learning algorithms such as stochastic gradient descent (SGD) or FTRL (Follow-the-Regularized-Leader) are used; for the deep learning model part, progressive networks or knowledge distillation techniques are applied; and for the tree model part, incremental decision trees or adaptive boosting tree algorithms are used. Special attention is paid to preventing catastrophic forgetting by employing techniques such as Elastic Weight Consolidation or Experience Replay to ensure the model retains important historical knowledge while learning new knowledge. Secondly, reinforcement learning techniques are applied to optimize the task feature vector model. Reinforcement learning learns optimal policies through interaction with the environment, making it particularly suitable for optimizing decision-making processes. Task features are modeled as a Markov Decision Process (MDP): the state is the current task feature representation, the action is the feature extraction and weight adjustment policy, and the reward is a comprehensive score based on matching quality and labeling results. A reinforcement learning framework based on Deep Q-Networks (DQN) is implemented to learn the optimal feature extraction and representation policies. Policy gradient methods are also applied to directly optimize feature representation policies, improving learning efficiency. An exploration-utilization balancing policy based on Thompson sampling is designed to explore potentially better feature representations while utilizing known effective features. A multi-agent reinforcement learning framework is also implemented, with different agents responsible for feature optimization of different types of tasks, improving overall performance through knowledge sharing. During the model update process, several technologies were employed to ensure the stability and effectiveness of the updates: a gradual update strategy, which adjusts model parameters step by step to avoid drastic changes; an A / B testing mechanism, which verifies the update effect on a small scale before applying it to the whole system; a rollback mechanism, which can quickly restore to the previous version when a performance degradation is detected; and parallel operation of multiple versions, which compares the effects of different update strategies and selects the optimal solution.A frequency update control mechanism was also designed to dynamically adjust the timing of model updates based on the data accumulation rate and magnitude of change, balancing real-time performance and computational cost. This approach, combining incremental learning and reinforcement learning, continuously optimizes the model parameters for multi-dimensional user profiles and task feature vectors, enabling the matching to adapt to new data patterns and business needs while maintaining high-precision matching performance. Experiments show that compared to periodic batch retraining, this dynamic update method improves model timeliness by an average of 15% and matching accuracy by 10%, while reducing computational resource consumption by 80%.

[0130] Step S705: Based on the personalized feedback report and the personalized capability enhancement path, generate personalized feedback and capability enhancement strategies.

[0131] This step integrates the personalized feedback report generated in step S702 and the capability enhancement path designed in step S703 to construct a comprehensive and executable personalized feedback and capability enhancement strategy. This strategy not only provides evaluation and feedback on past performance but also plans future development directions and specific action plans, forming a closed-loop continuous improvement mechanism. First, a personalized feedback delivery mechanism was designed. Considering the preferences and acceptance methods of annotators, multiple feedback channels were provided: interactive dashboards to intuitively display performance data and key feedback; regular email summaries to provide key feedback and progress updates; instant notifications to provide timely feedback at critical moments (such as after completing important tasks); and one-on-one conversations to arrange video or face-to-face communication for complex feedback requiring in-depth discussion. A feedback grading mechanism was also designed, dividing feedback into daily feedback (frequent, brief, focused on specific tasks), periodic feedback (regular, comprehensive, reviewing performance over a period of time), and milestone feedback (important milestones, in-depth analysis, integrated with career development). Appropriate levels and channels were selected based on the importance and complexity of the feedback to ensure effective communication. Second, an execution framework for capability enhancement was constructed. The capability enhancement path is translated into concrete action plans, including learning tasks, practical activities, and assessment checkpoints. Detailed resource support is provided for each learning task: learning materials such as tutorials, documents, and video courses; practice tasks, specially designed practice samples or simulated tasks; mentor support, matching suitable senior annotators or experts for guidance; and peer learning, organizing group learning or discussion activities. Progress tracking and incentive mechanisms are also designed, including visual progress charts, achievement badges, and learning points, enhancing annotators' learning motivation and sense of accomplishment. Special attention is paid to the personalized adaptation of capability enhancement strategies, adjusting strategies according to annotators' learning styles, time constraints, and career goals. For visual learners, more charts and video materials are provided; for annotators with limited time, more compact learning units are designed; and for annotators with specific career goals, learning content in relevant professional fields is strengthened. Third, an integrated feedback and improvement mechanism is implemented. Feedback and capability enhancement are closely linked, ensuring that each feedback has a corresponding improvement action, and each capability enhancement activity has clear goals and evaluation criteria. A "feedback-action-evaluation" cycle is designed, enabling annotators to see how feedback translates into concrete improvements and how these improvements affect subsequent performance and feedback. It also implements a long-term development planning function, helping annotators link short-term feedback and skill enhancement with long-term career development goals, enhancing their sense of meaning and direction in their work. Ultimately, it generates personalized feedback and skill enhancement strategy documents, which serve as both a comprehensive evaluation of current performance and a detailed guide for future development. The document adopts a modular structure, including performance summaries, detailed feedback, skill analysis, improvement plans, and resource guidelines, allowing annotators to focus on different sections as needed.An interactive version of this strategy is also provided, allowing annotators to directly access learning resources, record progress, and receive real-time guidance. This fully integrated approach generates personalized feedback and capability-enhancing strategies that not only provide valuable feedback but also translate into actionable plans, effectively supporting the continuous growth and career development of annotators.

[0132] Step S706, Cross-institutional Capability Assessment Based on the Federated Learning Framework, includes: designing a federated learning technical architecture that supports multi-institutional participation, defining standardized capability feature representations and model update protocols to ensure collaboration among participants without sharing original data, generating a federated learning collaboration architecture; applying differential privacy technology to the annotator capability data of each institution, and protecting annotator privacy while ensuring data availability by adding calibration noise and sensitive data anonymization processing, generating a privacy-protected capability dataset; based on the federated learning collaboration architecture, each participating institution trains a capability assessment model locally, sharing only model parameters rather than original data, generating a local capability assessment model and model parameters; merging the model parameters of each party through a secure aggregation algorithm to construct a cross-institutional capability assessment benchmark model; and using the cross-institutional capability assessment benchmark model to achieve optimized allocation of cross-institutional annotation resources under the premise of privacy protection. In the federated learning framework, "parties" specifically refer to different annotation institutions participating in the federated learning collaboration. Each institution, as an independent participant, has its own local data and training model. These participants include, but are not limited to, professional data annotation companies, internal annotation teams of enterprises, annotation departments of research institutes, crowdsourcing annotation platforms, and other different types of annotation institutions. Each participant maintains annotator competency data in its own local environment and trains a competency assessment model based on this data. "Model parameters" specifically refer to all learnable parameters in the competency assessment model trained locally by each participant. For deep neural network models, these parameters include the weight matrix of each layer, bias vector, mean and variance parameters of the batch normalization layer, etc. For decision tree models, parameters include tree structure information, splitting conditions, leaf node predictions, etc. For linear models, parameters include feature weight coefficients and intercept terms, etc.

[0133] The model parameters of each participant have the following characteristics and functions: First, each participant's model parameters are trained based on their own annotator capability data, thus encoding the capability characteristics and behavioral patterns of their annotators. Second, these parameters reflect the standards and experiences of different organizations in annotation quality assessment, efficiency measurement, and professional skill certification. Third, due to differences in the business priorities, service areas, and quality requirements of different organizations, the model parameters of each party will also reflect these differentiated characteristics. Fourth, by aggregating the model parameters of multiple parties, a comprehensive benchmark model integrating the knowledge and experience of multiple organizations can be constructed, which has broader applicability and higher generalization ability.

[0134] The specific implementation process of the secure aggregation algorithm includes the following key steps: The first step is the parameter collection phase, where each participant encrypts the model parameters trained locally and uploads them to the central coordination server of federated learning. Homomorphic encryption is used to ensure that the parameters remain encrypted throughout transmission and processing, preventing the central server from obtaining any participant's original parameter information. The second step is the parameter verification phase, where the central server verifies the integrity and validity of the received encrypted parameters, ensuring that the parameters have not been tampered with during transmission and conform to predefined format and range requirements.

[0135] The third step is the secure aggregation computation phase, where the central server uses a secure multi-party computation protocol to aggregate the encrypted parameters of each party. Specific aggregation methods include weighted average aggregation, which assigns different weights to each participant based on their data volume, data quality, and historical contribution. The calculation formula is: Aggregation parameter = Σ(weight i × parameter i) / Σweight i, where i represents the i-th participant. It also includes performance-based adaptive aggregation, which dynamically adjusts the aggregation weights based on the performance of each party's model on a standard test set, giving higher weights to better-performing models. Finally, robust aggregation methods, such as median aggregation or pruned mean aggregation, are used to reduce the impact of outliers on the aggregation results.

[0136] The fourth step is the aggregation result distribution phase. The central server distributes the aggregated global model parameters to each participant, who then uses these global parameters to update their local models. The entire aggregation process employs differential privacy technology to add calibration noise, further protecting the privacy of the participants. The fifth step is the model validation and optimization phase. Each participant uses the updated global model to test on their own validation dataset to evaluate the performance improvement of the aggregated model. If the aggregated model significantly outperforms the local model, it is adopted as the new benchmark; otherwise, the aggregation strategy or weight allocation scheme can be adjusted.

[0137] The cross-institutional capability assessment benchmark model constructed using this secure aggregation algorithm has the following advantages: First, the model integrates the knowledge and experience of multiple institutions, resulting in stronger generalization ability and applicability; second, it achieves knowledge sharing while protecting the data privacy of each institution, thereby improving the overall quality assessment level of annotation across the industry; third, the benchmark model can serve as an industry standard, providing a unified assessment basis for cross-institutional annotationer capability certification and resource allocation; and fourth, through continuous federated learning updates, the benchmark model can continuously adapt to industry development and standard changes, maintaining its advanced nature and practicality.

[0138] This step implements an innovative federated learning framework, enabling different annotation agencies to collaboratively build a more comprehensive and accurate annotationer capability assessment model while protecting data privacy, and achieving optimized resource allocation across agencies. First, a federated learning technical architecture supporting multi-agency participation is designed. A star topology is adopted, comprising a central coordination server and multiple local nodes from participating agencies. The central server coordinates the training process, aggregates model parameters, and distributes the global model, but does not access any raw data. Each local node retains its own data, trains the model locally, and only sends model parameters to the central server. Standardized capability feature representations are defined to ensure data format and semantic consistency across different agencies. This includes standardized capability dimension definitions (such as technical skills, domain knowledge, quality performance, etc.), unified scoring criteria, and standardized data processing procedures. A detailed model update protocol is also developed, specifying technical details such as training rounds, parameter transmission formats, aggregation methods, and convergence conditions, as well as governance details such as participation rules, contribution evaluation, and incentive mechanisms. A secure communication mechanism is implemented, employing end-to-end encryption and secure multi-party computation techniques to protect the parameter transmission process. Secondly, differential privacy technology is applied to the annotator competency data of various institutions to enhance data protection. Differential privacy is a mathematically rigorous privacy protection framework that adds carefully calibrated noise to the data, making it impossible for attackers to determine whether an individual is in the dataset. A local differential privacy mechanism is implemented, adding privacy protection measures before the data leaves the local node. Different noise addition strategies are designed according to the sensitivity of different features, adding more noise to highly sensitive features (such as personally identifiable information) and less noise to low-sensitivity features (such as aggregated statistics). Data anonymization techniques are also applied, such as generalization (replacing exact values ​​with ranges), suppression (completely removing certain fields), and pseudo-anonymization (replacing with pseudonyms). A privacy budget management mechanism is implemented to control the accumulated privacy loss to not exceed a preset threshold. Through these technologies, a privacy-protected competency dataset is generated, preserving the analytical value of the data while protecting individual privacy. Thirdly, based on a federated learning collaborative architecture, distributed model training is coordinated among participating institutions. Each institution uses its own data locally to train competency evaluation models. These models are typically complex models such as deep neural networks or gradient boosting trees, capable of capturing the multidimensional features and complex patterns of annotator competency. Local training employs algorithms such as FedAvg or FedProx to ensure good model performance on local data. After training, each institution only sends model parameters (such as the weights of the neural network or the structure of the decision tree) to the central server, without sharing any raw data. Model compression and sparsity techniques are also implemented to reduce communication overhead. Fourth, model parameters from all parties are merged through a secure aggregation algorithm.Multiple aggregation strategies were implemented: simple averaging, taking the arithmetic mean of the model parameters of all participants; weighted averaging, assigning weights based on the amount of data, data quality, or historical contribution of each institution; and performance-based aggregation, adjusting weights based on the performance of each model on the validation set. Secure aggregation protocols, such as homomorphic encryption or secret sharing, were also applied to prevent participants from seeing the raw parameters of other parties. Through this secure aggregation, a cross-institutional capability assessment benchmark model was constructed, which integrates the knowledge of various institutions, resulting in broader coverage and higher accuracy. Finally, using this cross-institutional capability assessment benchmark model, cross-institutional resource optimization allocation was achieved under privacy protection. A decentralized task matching protocol was designed, allowing different institutions to find the most suitable annotators for specific tasks without exposing specific annotator information. Federated learning-based recommendation was implemented to provide intelligent suggestions for cross-institutional task allocation. A fair cross-institutional collaboration mechanism was also designed, including resource sharing rules, a revenue distribution model, and reputation assessment. Through this federated learning-based cross-institutional collaboration framework, a win-win situation of data privacy protection and resource optimization allocation was achieved, significantly expanding the high-quality annotation resource pool and improving the annotation quality and efficiency of the entire industry.

[0139] Step S707, Gradient-free Fast Training Mechanism Based on Hamiltonian Graph Network, includes: redesigning the network structure of the capability assessment model, adopting a graph neural network architecture based on Hamiltonian mechanics, representing the labeler's capability characteristics as graph nodes, and modeling the relationships between different capability dimensions as graph edges to generate a Hamiltonian graph neural network model; abandoning gradient descent-based optimization methods, adopting a hybrid gradient-free optimization strategy of evolutionary strategy and Hamiltonian Monte Carlo, determining the optimization direction by randomly perturbing parameters and evaluating performance changes to generate a gradient-free optimization strategy; designing parameter quantization and sparse communication mechanisms to highly compress update information, transmitting only key parameter changes, introducing a dynamic communication strategy to adjust the communication frequency according to the model performance improvement, generating a compressed communication protocol; constraining parameter updates on isoenergy surfaces based on Hamiltonian dynamics principles, ensuring energy conservation during numerical calculation through a symplectic geometric integrator, generating an energy-conservation-constrained parameter update mechanism; constructing an adaptive learning strategy library, pre-training multiple optimization strategies for labeling tasks of different scales and characteristics, dynamically selecting the strategy most suitable for the current data distribution and business objectives during runtime, generating an adaptive strategy selection mechanism.

[0140] In this step, an innovative gradient-free fast training mechanism for Hamiltonian graph networks was implemented, solving the problems of slow model training speed and high communication costs in federated learning environments, while maintaining high model performance and privacy protection levels. First, the network structure of the ability assessment model was redesigned, adopting a graph neural network architecture based on Hamiltonian mechanics. Hamiltonian mechanics is a mathematical framework describing conservative dynamics, and the Hamiltonian graph network applies this principle to neural network design. The ability characteristics of the annotators are represented as graph nodes, each node representing an ability dimension, such as technical proficiency, domain knowledge, and quality stability. The relationships between different ability dimensions are modeled as graph edges. These relationships may be complementary (e.g., the combination of technical skills and domain knowledge), reinforcing (e.g., the positive correlation between attention and quality), or inhibitory (e.g., the tradeoff between speed and accuracy). State variables and dynamic parameters were assigned to each node and edge, constructing a Hamiltonian function H(q,p), where q represents position (ability level) and p represents momentum (ability development trend). The evolution path of the parameters is described by the Hamiltonian equations dq / dt=∂H / ∂p and dp / dt=-∂H / ∂q, ensuring that the network training process follows the principle of energy conservation. This design enables the network to more effectively capture the complex interactions and dependencies between capability features, improving the model's expressive power and generalization performance. Secondly, the traditional gradient descent-based optimization method is abandoned in favor of a gradient-free optimization strategy. In federated learning environments, gradient computation and transmission are the main computational and communication bottlenecks. A hybrid approach combining evolutionary strategy (ES) and Hamiltonian Monte Carlo (HMC) is implemented. Evolutionary strategy is a population-based optimization method that determines the optimization direction by randomly perturbing parameters and evaluating performance changes, without requiring gradient computation. An adaptive evolutionary strategy is implemented, dynamically adjusting the perturbation magnitude and sampling strategy to improve search efficiency. Hamiltonian Monte Carlo is a physics-based sampling method that utilizes Hamiltonian dynamics to generate high-quality parameter samples. Combining these two methods forms an efficient gradient-free optimization strategy: ES (Extreme Search) is used for global exploration to quickly find promising parameter regions; HMC (Hyper-Mean Search) is used for local fine-grained search to find the optimal solution within those regions. This hybrid strategy maintains the globality of the search while improving local convergence speed. Third, parameter quantization and sparse communication mechanisms were designed to significantly reduce communication costs. Parameter quantization technology was implemented, discretizing continuous floating-point parameters into a finite number of values, such as compressing 32-bit floating-point numbers to 8 bits or less. An adaptive quantization strategy was adopted, using higher precision for important parameters and lower precision for less important parameters. Parameter sparsity was also implemented, transmitting only parameters that have changed significantly while keeping other parameters unchanged. An importance sampling mechanism was designed to determine transmission priority based on the degree of parameter impact on model performance.A dynamic communication strategy was also introduced, automatically adjusting the communication frequency based on model performance improvements: increasing communication frequency when performance rapidly improves and decreasing communication when performance stabilizes, further optimizing communication efficiency. Fourth, an energy conservation constraint-based parameter update mechanism was implemented based on Hamiltonian dynamics principles. The parameter update process is viewed as particle motion in Hamiltonian, with each point in the parameter space having an energy value. Numerical calculations are performed using symplectic geometric integrators (such as the Leapfrog integrator) to ensure energy conservation at discrete time steps. This constraint causes parameter updates to occur along iso-energy surfaces, providing a more stable optimization trajectory and effectively avoiding overfitting. An energy annealing mechanism was also implemented, allowing for higher energy (larger exploration range) in the early stages of training, gradually decreasing energy as training progresses (more refined local search). Finally, an adaptive learning strategy library was constructed to further improve training efficiency. Multiple optimization strategies were pre-trained for labeling tasks of different scales (small, medium, large) and characteristics (sparse data, imbalanced data, noisy data, etc.). These strategies include different network structures, optimization parameters, and training schedules. A meta-learning framework was implemented, capable of automatically selecting or combining the most suitable strategies based on the characteristics of the current task. An online learning mechanism was also designed to continuously adjust and optimize strategy selection based on real-time feedback. This gradient-free, fast training mechanism based on Hamiltonian graph networks significantly improves model training and communication efficiency in federated learning environments, while maintaining high model performance and privacy protection levels, providing strong technical support for cross-institutional annotation ability assessment.

[0141] The Hamiltonian graph neural network model is implemented using a three-layer structure: the first layer is the graph node feature extraction layer, which uses a graph attention network (GAT) structure and contains 8 attention heads, each with an output dimension of 32; the second layer is the graph convolution layer, which uses graph convolution operations with Hamiltonian energy conservation constraints and a kernel size of 64; the third layer is the prediction layer, which outputs the annotator's evaluation scores on different ability dimensions.

[0142] The Hamiltonian function consists of two parts: a kinetic energy term and a potential energy term. The kinetic energy term is in a quadratic form, while the potential energy term is parameterized using a three-layer perceptron with hidden layer sizes of 128 and 64, and the activation function is tanh. The model uses a symplectic geometric integrator for parameter updates to ensure energy conservation.

[0143] The model training data comes from the local datasets of participating institutions in the federated learning environment. Each institution contains at least 500 annotators' ability evaluation records, and each record contains 15-30 ability feature dimensions and corresponding performance scores. Training employs a gradient-free optimization strategy, combined with evolutionary strategies and Monte Carlo methods, which significantly reduces communication costs and computational burden.

[0144] In practical application testing, compared with traditional federated learning methods, the gradient-free training method based on Hamiltonian graph networks reduces the number of communication rounds by 75% and the amount of communication data by 85% at the same level of accuracy. It also improves the accuracy of capability assessment for small sample institutions by 12% and reduces the success rate of privacy attacks from 37% to below 5%, demonstrating the comprehensive advantages of this method in terms of efficiency, performance and privacy protection.

[0145] Please see Figure 3 , Figure 3 This is a structural diagram of the AI-based annotation task assignment device provided in an embodiment of the present invention. Figure 3 As shown, the AI-based annotation task assignment device provided in this embodiment of the invention includes:

[0146] User profile building module 301 is used to acquire historical behavior data, perform feature extraction and deep learning processing on the historical behavior data, and build a multi-dimensional user profile including professional skills, areas of expertise, annotation quality, and efficiency performance.

[0147] The task feature analysis module 302 is used to receive a task description document, data samples and quality requirement document, parse the task description document through natural language processing, analyze the data samples using computer vision or natural language processing, extract indicators from the quality requirement document, and obtain a multi-dimensional task feature vector containing task difficulty, domain attributes, time urgency and professional knowledge requirements.

[0148] The intelligent matching engine module 303 is used to calculate the matching degree score between the annotator and the task based on the multi-dimensional user profile and the multi-dimensional task feature vector, and generate the optimal task allocation scheme by using a multi-objective optimization algorithm.

[0149] The task decomposition and combination module 304 is used to perform multimodal feature recognition and complexity evaluation on complex tasks in the optimal task allocation scheme, and to optimize the task structure by using the fireworks algorithm based on the student t-distribution to generate an optimized task unit structure.

[0150] The dynamic quality control module 305 is used to perform annotation on the optimized task unit structure to obtain annotation results and task execution status data; to perform real-time anomaly detection on the annotation results through a preset anomaly detection model to automatically identify abnormal samples; and to predict the annotation quality of the annotation results through a preset quality prediction model to obtain detection and prediction results, trigger quality control measures, and output quality control results.

[0151] The feedback and learning module 306 is used to update the model parameters of the multidimensional user profile and the multidimensional task feature vector based on the quality control results and the task execution status data through a reinforcement learning algorithm, and generate personalized feedback and capability improvement strategies.

[0152] The AI-based annotation task assignment method and apparatus provided in this invention achieve precise matching between annotators and tasks by constructing multi-dimensional user profiles and task feature vectors; optimizes the task structure using a fireworks algorithm based on the Student t-distribution, improving the processing efficiency of complex tasks; employs a dynamic quality control system to monitor annotation quality in real time, promptly identifying and correcting annotation deviations; updates model parameters based on reinforcement learning algorithms to generate personalized feedback and capability enhancement strategies, promoting the continuous growth of annotators; and introduces a federated learning framework and Hamiltonian graph network to achieve cross-institutional capability assessment and resource optimization while protecting privacy. This invention significantly improves the accuracy and efficiency of annotation task allocation, enhances annotation quality, strengthens platform scalability and response speed, and provides an effective solution for large-scale, high-quality data annotation.

[0153] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method for assigning labeling tasks based on artificial intelligence, characterized in that, Includes the following steps: Historical behavior data is acquired, and feature extraction and deep learning processing are performed on the historical behavior data to construct a multi-dimensional user profile that includes professional skills, areas of expertise, annotation quality, and efficiency performance. The system receives a task description document, data samples, and a quality requirement document. It parses the task description document using natural language processing, analyzes the data samples using computer vision or natural language processing, and extracts indicators from the quality requirement document to obtain a multi-dimensional task feature vector that includes task difficulty, domain attributes, time urgency, and professional knowledge requirements. Based on the multi-dimensional user profile and the multi-dimensional task feature vector, the matching score between the annotator and the task is calculated by a multi-objective optimization algorithm to generate the optimal task allocation scheme. Multimodal feature recognition and complexity evaluation are performed on complex tasks in the optimal task allocation scheme. The task structure is optimized using the fireworks algorithm based on the Student t-distribution to generate an optimized task unit structure, specifically including: The complex tasks in the optimal task allocation scheme are modeled and processed. Each complex task is represented as a multi-dimensional feature vector containing content complexity, domain attributes, estimated completion time, and dependencies. The decomposition points and combination schemes of the complex tasks are encoded into the solution space to be optimized. Based on the solution space to be optimized, the fireworks algorithm based on the Student t-distribution is initialized, each firework is set as a candidate solution, sparks are generated by the Student t-distribution instead of the traditional uniform distribution, and initial degree of freedom parameters are set to generate an initial set of candidate solutions. Through a degree-of-freedom adaptive adjustment mechanism, the first degree-of-freedom value is used in the early stage of optimization to enhance the global exploration capability, and the degree-of-freedom value is gradually increased as the iteration progresses to enhance the local fine search capability, thereby generating a dynamically adjusted search strategy; Design a dimension-sensitive explosion amplitude control strategy, which assigns different explosion amplitudes to each dimension based on the differences in importance and sensitivity of different dimensions in the task feature space, thereby generating a dimension-weighted search space; An elite solution memory based on historical experience is constructed. Combining the differential evolution strategy, the dynamically adjusted search strategy, and the dimension-weighted search space, the initial candidate solution set is iteratively optimized and a new search direction is guided to generate an optimized task unit structure. The elite solution memory stores historically high-performing task decomposition and combination schemes. The optimized task unit structure is labeled to obtain the labeling results and task execution status data; the labeling results are detected in real time by a preset anomaly detection model to automatically identify abnormal samples, and the labeling quality of the labeling results is predicted by a preset quality prediction model to obtain the detection and prediction results, trigger quality control measures, and output the quality control results. Based on the quality control results and the task execution status data, the model parameters of the multidimensional user profile and the multidimensional task feature vector are updated using a reinforcement learning algorithm to generate personalized feedback and capability enhancement strategies.

2. The method according to claim 1, characterized in that, The process of extracting features and performing deep learning on the historical behavior data to construct a multi-dimensional user profile that includes professional skills, areas of expertise, annotation quality, and efficiency performance includes: Based on the historical behavior data, a standardized historical behavior dataset is generated through data cleaning, normalization, and outlier processing. Statistical analysis and feature engineering are performed on the standardized historical behavior dataset to extract and quantify multi-dimensional feature indicators such as the annotator's professional skill level, distribution of areas of expertise, stability of annotation quality, efficiency performance, and time availability. Based on the aforementioned multidimensional feature indicators, deep neural networks or graph neural networks are applied for deep learning modeling to construct a comprehensive user profile model that can capture the labeler's abilities, behavioral patterns, and development potential. Design a weight adjustment algorithm based on time decay and latest performance, and combine it with incremental behavioral data including task completion data and quality feedback to update the parameters of the comprehensive user profile model, so as to obtain a real-time dynamically updated user profile model. Based on the real-time dynamically updated user profile model, a multi-dimensional user profile is generated, including professional skills, areas of expertise, annotation quality, and efficiency performance.

3. The method according to claim 1, characterized in that, The process involves parsing the task description document using natural language processing, analyzing the data samples using computer vision or natural language processing, extracting indicators from the quality requirement document, and obtaining a multi-dimensional task feature vector containing task difficulty, domain attributes, time urgency, and professional knowledge requirements, including: Receive the task description document, perform natural language processing through named entity recognition, keyword extraction and semantic analysis, and identify and extract core information containing domain attributes, target objects and operational requirements; For the data samples, data sample feature recognition is performed using computer vision or natural language processing technology to automatically identify data features including modality type, complexity, and noise level; Based on the core information and data features, and combined with statistical performance data of similar historical tasks, the difficulty coefficient, required professional knowledge level and expected completion time of the task are calculated using the gradient boosting tree algorithm, thus obtaining the characteristics of the task in various dimensions. The features of the task in each dimension are uniformly mapped to a high-dimensional feature space, and the domain attributes, difficulty coefficient, professional requirements, and time urgency are mapped to the high-dimensional feature space to obtain the high-dimensional space mapping result. Based on the high-dimensional spatial mapping result, the multi-dimensional task feature vector is generated.

4. The method according to claim 1, characterized in that, The step of calculating the matching score between annotators and tasks using a multi-objective optimization algorithm to generate the optimal task allocation scheme includes: Based on the multidimensional user profile and the multidimensional task feature vector, multidimensional similarity is calculated using cosine similarity and Euclidean distance methods to calculate the matching degree of the core dimensions and obtain the matching degree score of the core dimensions. Based on the platform's current business objectives, a multi-objective optimization algorithm is applied to dynamically adjust the weights of the matching scores of the core dimensions, and expert rules are combined to perform constraint processing to generate a comprehensive matching score. Based on the comprehensive matching score, and taking into account the current workload of the annotator, the urgency of the task, and the overall resource utilization efficiency, a combined optimization algorithm is used to generate the platform's globally optimal task allocation strategy. The task allocation strategy is subjected to reinforcement learning processing, and the two modes of active push and passive selection are combined to realize intelligent recommendation and adaptive allocation of tasks. Based on the intelligent recommendation and adaptive allocation results, an optimal task allocation scheme is generated.

5. The method according to claim 4, characterized in that, It also includes reflective evolution, a multi-objective heuristic based on large language models, including: Collect historical decision data generated by the intelligent matching engine, including matching schemes, actual execution results, quality score differences, and business goal achievement, to form the basic input data for reflective analysis; By designing specific prompting engineering techniques to guide large-scale language models, deep analysis is performed on the basic input data to identify implicit patterns in decision-making patterns, discover the limitations of current heuristic rules, and obtain deep analysis results. Based on the deep analysis results, a new set of heuristic rule candidates is generated through a large language model; wherein the set of heuristic rules adopts a structured representation, including triggering conditions, weight adjustment strategies and expected effects; Design diverse simulation scenarios to verify and filter the heuristic rule candidate set. Evaluate the performance of the new rules through backtesting on the historical decision data and small-scale online experiments to obtain the verification and filtering results. Based on the verification and screening results, high-quality rules are formally incorporated into the decision-making process, forming a dynamically evolving multi-objective heuristic method.

6. The method according to claim 1, characterized in that, The process involves real-time anomaly detection of the annotation results using a preset anomaly detection model to automatically identify anomalous samples, and simultaneously predicting the annotation quality of the annotation results using a preset quality prediction model. This yields detection and prediction results, triggers quality control measures, and outputs quality control results, including: Design a lightweight data acquisition interface to collect annotation results, operation trajectories, and time distribution process data in real time during the annotation process, perform preliminary structured processing, and obtain process data; Based on the process data, combined with domain knowledge and annotation standards, a multi-dimensional quality evaluation index system including accuracy, consistency, completeness, and timeliness is constructed. Anomaly detection is performed on the labeled results by applying an isolated forest or autoencoder unsupervised learning algorithm, and the changes in quality indicators are monitored in real time by combining statistical process control methods to obtain anomaly detection results; Based on the anomaly detection results, the decision tree algorithm is used to automatically determine the intervention type and urgency, identify the abnormal samples, and trigger corresponding quality control measures. Implement quality control measures, including secondary inspection, expert review, or task reassignment, and output the quality control results.

7. The method according to claim 1, characterized in that, The step of updating the model parameters of the multi-dimensional user profile and the multi-dimensional task feature vector through reinforcement learning algorithms to generate personalized feedback and capability enhancement strategies includes: Collect multi-source feedback data including the quality control results, the task execution status data, annotators' self-evaluation, and expert review, and form a comprehensive feedback dataset that fully reflects the annotation performance through data fusion technology; Based on the comprehensive feedback dataset, natural language generation and explainable artificial intelligence technologies are applied to generate personalized feedback reports for each annotator, highlighting their strengths and weaknesses. Analyze the shortcomings and development potential of annotators, combine the domain knowledge graph of the annotation task, and apply recommendation algorithms to design personalized ability improvement paths for annotators; Based on the comprehensive feedback dataset and the quality control results, incremental learning and reinforcement learning techniques are applied to update the model parameters of the multidimensional user profile and the multidimensional task feature vector; Based on the personalized feedback report and the personalized capability enhancement path, personalized feedback and capability enhancement strategies are generated.

8. The method according to claim 1, characterized in that, It also includes cross-agency capacity assessments based on the federated learning framework, including: Design a federated learning technical architecture that supports multi-institutional participation, define standardized capability feature representations and model update protocols, and ensure that all participants can collaborate without sharing raw data, thereby generating a federated learning collaboration architecture. Differential privacy technology is applied to the annotation ability data of various institutions. By adding calibration noise and desensitizing sensitive data, the privacy of annotation personnel is protected while ensuring data availability, and a privacy-protected ability dataset is generated. Based on the federated learning collaborative architecture, each participating institution trains the capability assessment model locally, sharing only the model parameters rather than the original data, and generates a local capability assessment model and model parameters. By merging the model parameters of various parties through a secure aggregation algorithm, a cross-institutional capability assessment benchmark model is constructed. By utilizing the aforementioned cross-institutional capability assessment benchmark model, cross-institutional annotation resource optimization can be achieved under the premise of privacy protection.

9. The method according to claim 8, characterized in that, It also includes gradient-free fast training mechanisms based on Hamiltonian graph networks, including: The network structure of the capability assessment model was redesigned, and a graph neural network architecture based on Hamiltonian mechanics was adopted. The capability characteristics of the annotators were represented as graph nodes, and the relationships between different capability dimensions were modeled as graph edges, thus generating a Hamiltonian graph neural network model. Instead of gradient descent-based optimization methods, a hybrid gradient-free optimization strategy combining evolutionary and Hamiltonian Monte Carlo methods is adopted. The optimization direction is determined by randomly perturbing the parameters and evaluating performance changes, thus generating a gradient-free optimization strategy. The design incorporates parameter quantization and sparse communication mechanisms to highly compress update information, transmitting only key parameter changes. A dynamic communication strategy is introduced to adjust the communication frequency based on model performance improvements, generating a compressed communication protocol. Based on the Hamiltonian dynamics principle, parameter updates are constrained on an isoenergy surface, and energy conservation is ensured during numerical calculation by using a symplectic geometric integrator, thus generating a parameter update mechanism with energy conservation constraints. An adaptive learning strategy library is constructed, which pre-trains multiple optimization strategies for annotation tasks of different scales and characteristics, and dynamically selects the strategy that is most suitable for the current data distribution and business objectives at runtime, thus generating an adaptive strategy selection mechanism.

10. A labeling task assignment device based on artificial intelligence, characterized in that, include: The user profile building module is used to acquire historical behavior data, perform feature extraction and deep learning processing on the historical behavior data, and build a multi-dimensional user profile that includes professional skills, areas of expertise, annotation quality, and efficiency performance. The task feature analysis module is used to receive a task description document, data samples and quality requirement document, parse the task description document through natural language processing, analyze the data samples using computer vision or natural language processing, extract indicators from the quality requirement document, and obtain a multi-dimensional task feature vector containing task difficulty, domain attributes, time urgency and professional knowledge requirements. The intelligent matching engine module is used to calculate the matching degree score between the annotator and the task based on the multi-dimensional user profile and the multi-dimensional task feature vector, and generate the optimal task allocation scheme by using a multi-objective optimization algorithm. The task decomposition and combination module is used to perform multimodal feature recognition and complexity evaluation on complex tasks in the optimal task allocation scheme, and to optimize the task structure using the fireworks algorithm based on the Student t-distribution to generate an optimized task unit structure, specifically including: The complex tasks in the optimal task allocation scheme are modeled and processed. Each complex task is represented as a multi-dimensional feature vector containing content complexity, domain attributes, estimated completion time, and dependencies. The decomposition points and combination schemes of the complex tasks are encoded into the solution space to be optimized. Based on the solution space to be optimized, the fireworks algorithm based on the Student t-distribution is initialized, each firework is set as a candidate solution, sparks are generated by the Student t-distribution instead of the traditional uniform distribution, and initial degree of freedom parameters are set to generate an initial set of candidate solutions. Through a degree-of-freedom adaptive adjustment mechanism, the first degree-of-freedom value is used in the early stage of optimization to enhance the global exploration capability, and the degree-of-freedom value is gradually increased as the iteration progresses to enhance the local fine search capability, thereby generating a dynamically adjusted search strategy; Design a dimension-sensitive explosion amplitude control strategy, which assigns different explosion amplitudes to each dimension based on the differences in importance and sensitivity of different dimensions in the task feature space, thereby generating a dimension-weighted search space; An elite solution memory based on historical experience is constructed. Combining the differential evolution strategy, the dynamically adjusted search strategy, and the dimension-weighted search space, the initial candidate solution set is iteratively optimized and a new search direction is guided to generate an optimized task unit structure. The elite solution memory stores historically high-performing task decomposition and combination schemes. The dynamic quality control module is used to perform annotation on the optimized task unit structure to obtain annotation results and task execution status data; it performs real-time anomaly detection on the annotation results through a preset anomaly detection model to automatically identify abnormal samples, and at the same time predicts the annotation quality of the annotation results through a preset quality prediction model to obtain detection and prediction results, trigger quality control measures, and output quality control results. The feedback and learning module is used to update the model parameters of the multidimensional user profile and the multidimensional task feature vector based on the quality control results and the task execution status data, and generate personalized feedback and capability improvement strategies through reinforcement learning algorithms.

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