Labeling task assignment method and device based on artificial intelligence

Abstract

The invention discloses a labeling task assignment method and device based on artificial intelligence, and the method comprises the steps: obtaining historical behavior data, and constructing a multi-dimensional user portrait; receiving a task description document, a data sample and a quality requirement document to obtain a multi-dimensional task feature vector; based on the multi-dimensional user portraits and the multi-dimensional task feature vectors, a matching degree score is calculated through a multi-objective optimization algorithm, and an optimal task allocation scheme is generated; optimizing the task structure through a fireworks algorithm based on student t distribution, and generating an optimized task unit structure; real-time monitoring is carried out through the anomaly detection model and the quality prediction model, and quality control measures are triggered; model parameters are updated through a reinforcement learning algorithm, and a personalized feedback and capability improvement strategy is generated. According to the method, accurate matching between the annotators and the tasks is realized, the processing efficiency of complex tasks is improved, the annotation quality is improved, the expansibility and the response speed of a platform are enhanced, and an effective solution is provided for large-scale and high-quality data annotation.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and in particular to an artificial intelligence-based labeling task assignment method and device for achieving efficient and accurate data labeling task allocation and quality control. Background Art

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

[0003] The current data annotation industry primarily employs two models: traditional crowdsourcing platforms, such as Amazon's Mechanical Turk, which distribute a large number of small tasks to a diverse workforce; and professional annotation teams, where highly trained professionals complete more complex annotation tasks. These models have achieved some success in practice, but with the increasing sophistication of AI applications and the increasing sophistication of annotation requirements, these traditional models are becoming increasingly inadequate.

[0004] Existing technologies primarily rely on manual judgment or simple rule-based engines to assign annotation tasks, lacking a deep understanding of and intelligent matching between annotator capabilities and task requirements. Under these mechanisms, tasks are often assigned on a first-come, first-served basis or randomly, failing to consider key factors such as annotators' professional background, historical performance, and areas of expertise. Furthermore, annotation quality assessment primarily relies on manual spot checks or simple consistency checks, making it difficult to detect and correct annotation deviations in a timely manner.

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

[0006] The purpose of the present invention is to provide an artificial intelligence-based labeling task assignment method and device, aiming to solve technical problems in the existing technology such as lack of precise matching in labeling task allocation, uneven labeling quality, limited platform scalability and response speed, and lack of personalized guidance.

[0007] To achieve the above-mentioned objectives, the present invention provides an artificial intelligence-based labeling task assignment method, comprising the following steps: acquiring historical behavior data, performing feature extraction and deep learning processing on the historical behavior data, and constructing a multidimensional user portrait including professional skills, areas of expertise, labeling quality, and efficiency performance; receiving a task description document, a data sample, and a quality requirement document, parsing the task description document through natural language processing, analyzing the data sample with computer vision or natural language processing, extracting indicators from the quality requirement document, and obtaining a multidimensional task feature vector including task difficulty, domain attributes, time urgency, and professional knowledge requirements; based on the multidimensional user portrait and the multidimensional task feature vector, calculating the matching score between the labeler and the task through a multi-objective optimization algorithm, and generating an optimal task assignment plan. ; Perform multimodal feature recognition and complexity evaluation on the complex tasks in the optimal task allocation scheme, optimize the task structure through the fireworks algorithm based on Student's t distribution, and generate an optimized task unit structure; perform labeling on the optimized task unit structure to obtain labeling results and task execution status data; perform real-time anomaly detection on the labeling results through a preset anomaly detection model, automatically identify abnormal samples, and at the same time predict the labeling quality of the labeling results through a preset quality prediction model, obtain detection and prediction results, trigger quality control measures, and output quality control results; based on the quality control results and the task execution status data, update the model parameters of the multidimensional user portrait and the multidimensional task feature vector through a reinforcement learning algorithm to generate personalized feedback and capability improvement strategies.

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

[0009] Furthermore, the method of parsing the task description document through natural language processing, analyzing the data sample through computer vision or natural language processing, extracting indicators from the quality requirement document, and obtaining a multidimensional task feature vector including 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, and identifying and extracting core information including domain attributes, target objects, and operation requirements; performing data sample feature recognition on the data sample through computer vision or natural language processing technology, and automatically identifying data features including modal type, complexity, and noise level; based on the core information and the data features, combined with statistical performance data of historical similar tasks, calculating the difficulty coefficient, required professional knowledge level, and expected completion time of the task through the gradient boosting tree algorithm, and obtaining each dimensional feature of the task; performing unified mapping processing on each dimensional feature of the task, mapping the domain attributes, difficulty coefficient, professional requirements, and time urgency to a high-dimensional feature space, and obtaining a high-dimensional space mapping result; and generating the multidimensional task feature vector based on the high-dimensional feature space mapping result.

[0010] Furthermore, the matching scores between labelers and tasks are calculated by a multi-objective optimization algorithm to generate an optimal task allocation plan, including: based on the multi-dimensional user portrait and the multi-dimensional task feature vector, multi-dimensional similarity is calculated by cosine similarity and Euclidean distance methods, the matching degree of the core dimension is calculated, and the matching score of the core dimension is obtained; based on the current business goals of the platform, the multi-objective optimization algorithm is applied to dynamically adjust the weights of the matching scores of the core dimensions, and constraint processing is performed in combination with expert rules to generate a comprehensive matching score; based on the comprehensive matching score, the current workload of the labeler, the urgency of the task and the overall resource utilization efficiency are considered, and the global optimal task allocation strategy of the platform is generated by a combinatorial optimization algorithm; reinforcement learning is performed on the task allocation strategy, 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 plan is generated.

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

[0012] Furthermore, the task structure is optimized by the fireworks algorithm based on Student's t distribution to generate an optimized task unit structure, including: modeling the complex tasks in the optimal task allocation scheme, representing each complex task as a multidimensional feature vector containing content complexity, domain attributes, estimated completion time, and dependency relationships, encoding the decomposition points and combination schemes of the complex tasks into a solution space to be optimized; based on the solution space to be optimized, initializing the fireworks algorithm based on Student's t distribution, setting each firework as a candidate solution, generating sparks through Student's t distribution instead of traditional uniform distribution, and setting initial degree of freedom parameters to generate an initial candidate solution set; through the degree of freedom adaptive adjustment mechanism, In the initial stage, the first degree of freedom value is used to enhance the global exploration capability, and the degree of freedom value is gradually increased as the iteration proceeds to enhance the local fine search capability, thereby generating a dynamically adjusted search strategy; a dimension-sensitive explosion amplitude control strategy is designed, and different explosion amplitudes are assigned to each dimension according to the importance and sensitivity differences of different dimensions in the task feature space, thereby generating a dimension-weighted search space; an elite solution memory library based on historical experience is constructed, and the initial candidate solution set is iteratively optimized and new search directions are guided by combining the differential evolution strategy, the dynamically adjusted search strategy and the dimension-weighted search space, thereby generating an optimized task unit structure; wherein, the elite solution memory library stores task decomposition and combination schemes that have performed well in history.

[0013] Furthermore, the labeling results are subjected to real-time anomaly detection by a preset anomaly detection model, abnormal samples are automatically identified, and the labeling quality of the labeling results is predicted by a preset quality prediction model, detection and prediction results are obtained, quality control measures are triggered, and quality control results are output, including: designing a lightweight data acquisition interface to collect process data such as labeling results, operation trajectories, and time distribution in real time during the labeling process, performing preliminary structured processing, and obtaining process data; based on the process data, combining domain knowledge and labeling specifications, constructing a multi-dimensional quality evaluation index system including accuracy, consistency, completeness, and timeliness; applying an isolation forest or autoencoder unsupervised learning algorithm to perform anomaly detection on the labeling results, combining statistical process control methods to monitor changes in quality indicators in real time, and obtaining anomaly detection results; based on the anomaly detection results, automatically judging the intervention type and urgency through a decision tree algorithm, identifying the abnormal samples and triggering corresponding quality control measures; executing quality control measures including secondary inspection, expert review, or task reallocation, and outputting the quality control results.

[0014] Furthermore, the method of updating the model parameters of the multidimensional user portrait and the multidimensional task feature vector through a reinforcement learning algorithm to generate personalized feedback and capability improvement strategies includes: collecting multi-source feedback data including the quality control results, the task execution status data, annotator self-evaluation and expert review, and forming a comprehensive feedback data set that comprehensively reflects the annotation performance through data fusion technology; based on the comprehensive feedback data set, applying natural language generation and explainable artificial intelligence technology to generate a personalized feedback report for each annotator based on their strengths and weaknesses; analyzing the annotator's capability shortcomings and development potential, combining the domain knowledge graph of the annotation task, and applying a recommendation algorithm to design a personalized capability improvement path for the annotator; based on the comprehensive feedback data set and the quality control results, applying incremental learning and reinforcement learning technologies to update the model parameters of the multidimensional user portrait and the multidimensional task feature vector; and generating personalized feedback and capability improvement strategies based on the personalized feedback report and the personalized capability improvement path.

[0015] Furthermore, it also includes cross-institutional capability assessment based on the federated learning framework, including: designing a federated learning technology architecture that supports the participation of multiple institutions, defining standardized capability feature representation and model update protocols, ensuring that all participants collaborate without sharing original data, and generating a federated learning collaboration architecture; applying differential privacy technology to the capability data of the labelers of each institution, and protecting the privacy of the labelers while ensuring data availability by adding calibration noise and desensitizing sensitive data, and generating a privacy-protected capability data set; based on the federated learning collaboration architecture, each participating institution trains a capability assessment model locally, only sharing model parameters instead of original data, and 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; using the cross-institutional capability assessment benchmark model, the optimal configuration of labeling resources across institutions is achieved under the premise of privacy protection.

[0016] Furthermore, it also includes a gradient-free fast training mechanism based on the Hamiltonian graph network, including: redesigning the network structure of the ability evaluation model, adopting a graph neural network architecture based on Hamiltonian mechanics, representing the ability characteristics of the labelers as graph nodes, modeling the relationships between different ability dimensions as graph edges, and generating a Hamiltonian graph neural network model; abandoning the optimization method based on gradient descent, 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, and generating a gradient-free optimization strategy; designing parameter quantization and sparse communication mechanisms, highly compressing the update information, only transmitting changes in key parameters, introducing a dynamic communication strategy to adjust the communication frequency according to the improvement of model performance, and generating a compressed communication protocol; based on the principle of Hamiltonian dynamics, constraining parameter updates on isoenergy surfaces, ensuring energy conservation in the numerical calculation process through a symplectic geometric integrator, and generating a parameter update mechanism with energy conservation constraints; building an adaptive learning strategy library, pre-training multiple optimization strategies for labeling tasks of different scales and characteristics, and dynamically selecting the strategy that best suits the current data distribution and business goals at runtime to generate an adaptive strategy selection mechanism.

[0017] The present invention also provides an artificial intelligence-based labeling task assignment device, comprising: a user portrait construction module for acquiring historical behavior data, performing feature extraction and deep learning processing on the historical behavior data, and constructing a multidimensional user portrait including professional skills, areas of expertise, labeling quality, and efficiency performance; a task feature analysis module for receiving a task description document, a data sample, and a quality requirement document, parsing the task description document through natural language processing, analyzing the data sample with computer vision or natural language processing, extracting indicators from the quality requirement document, and obtaining a multidimensional task feature vector including task difficulty, domain attributes, time urgency, and professional knowledge requirements; an intelligent matching engine module for calculating the matching score between the labeler and the task through a multi-objective optimization algorithm based on the multidimensional user portrait and the multidimensional task feature vector, and generating an optimal task allocation plan; task decomposition A combination module is used to perform multimodal feature recognition and complexity evaluation on the complex tasks in the optimal task allocation plan, optimize the task structure through the fireworks algorithm based on Student's t distribution, and generate an optimized task unit structure; a dynamic quality control module is used to perform labeling on the optimized task unit structure to obtain labeling results and task execution status data; a preset anomaly detection model is used to perform real-time anomaly detection on the labeling results, automatically identify abnormal samples, and at the same time predict the labeling quality of the labeling results through a preset quality prediction model, obtain detection and prediction results and trigger quality control measures, and output quality control results; a feedback and learning module is used to update the model parameters of the multidimensional user portrait and the multidimensional task feature vector based on the quality control results and the task execution status data through a reinforcement learning algorithm to generate personalized feedback and capability improvement strategies.

[0018] The beneficial effects of the present invention are as follows: by constructing multi-dimensional user portraits and task feature vectors, accurate matching between annotators and tasks is achieved; the task structure is optimized using the fireworks algorithm based on Student's t distribution, thereby improving the processing efficiency of complex tasks; a dynamic quality control system is used to monitor the annotation quality in real time, and annotation deviations are promptly discovered and corrected; model parameters are updated based on the reinforcement learning algorithm, personalized feedback and capability improvement strategies are generated, and the continuous growth of annotators is promoted; the federated learning framework and Hamiltonian graph network are introduced to achieve cross-institutional capability assessment and resource optimization under the premise of protecting privacy. The present invention significantly improves the accuracy and efficiency of annotation task allocation, improves annotation quality, enhances the scalability and response speed of the platform, and provides an effective solution for large-scale, high-quality data annotation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 This is a flow chart of an artificial intelligence-based labeling task assignment method provided by an embodiment of the present invention; Figure 2 This is a flow chart of a user portrait construction module provided by an embodiment of the present invention; Figure 3 It is a structural diagram of an artificial intelligence-based labeling task assignment device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0022] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The sequence numbers of the operations, such as S1, S2, etc., are only used to distinguish between different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do they limit "first" and "second" to be different types.

[0023] It will be understood by those skilled in the art that, unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the 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 said to be "connected" or "coupled" to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0024] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0025] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. Obviously, the described embodiments are only a part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0026] See also Figure 1 , Figure 1 This is a flow chart of the method for assigning labeling tasks based on artificial intelligence provided by an embodiment of the present invention. Figure 1 As shown, the method for assigning labeling tasks based on artificial intelligence provided by an embodiment of the present invention includes the following steps: Step S101: Acquire historical behavior data, perform feature extraction and deep learning processing on the historical behavior data, and construct a multi-dimensional user portrait including professional skills, areas of expertise, annotation quality, and efficiency performance.

[0027] In this step, historical data on annotators' behavior is first collected from the annotation platform's database. This data includes, but is not limited to, completed tasks, annotation speed, quality ratings, domain preferences, operational trajectories, and feedback information. This raw data is often scattered across different businesses and may contain inconsistent formats, missing values, and outliers. Data integration technologies are used to integrate this data into a unified data processing platform, and preliminary data cleansing is performed, including deduplication, imputation of missing values, and format standardization. Feature engineering techniques are then applied to extract key features from this cleaned data, such as the annotator's task completion rate across different domains, average annotation quality scores, stability of annotation speed, and response time for different task types. After normalization, these features are fed into a deep learning model for further processing. A user profile model is constructed using a deep neural network or graph neural network. This model captures the complex nonlinear relationships between annotator's ability characteristics and generates a high-dimensional user representation. Ultimately, based on this deep learning model, a multidimensional user profile is constructed for each annotator, encompassing dimensions such as professional skills, domain expertise, annotation quality, and efficiency, laying the foundation for subsequent precise task matching.

[0028] Step S102: Receive a task description document, a data sample, and a quality requirement document, parse the task description document through natural language processing, analyze the data sample using computer vision or natural language processing, extract indicators from the quality requirement document, and obtain a multidimensional task feature vector including task difficulty, domain attributes, time urgency, and professional knowledge requirements.

[0029] In this step, three key inputs are received from the task issuer: a task description document, data samples, and a quality requirements document. Natural language processing techniques are applied to the task description document for in-depth analysis, including named entity recognition, keyword extraction, and semantic analysis. These techniques can identify and extract core information from unstructured text descriptions, including the task's domain attributes (e.g., healthcare, finance, autonomous driving), target objects (e.g., image segmentation, text classification, speech transcription), and operational requirements (e.g., annotation accuracy and specific rules). For data samples, appropriate analysis techniques are selected based on their type, such as computer vision for image / video samples and natural language processing for text samples, to automatically identify data characteristics such as modality type, complexity, and noise level. Sample distribution characteristics, the proportion of edge cases, and annotation difficulties are also analyzed. For the quality requirements document, various quality indicators are extracted and quantified, including accuracy requirements, consistency standards, and completion time limits. This information extracted from various sources is then combined with historical statistical performance data on similar tasks to calculate the task's difficulty, required expertise, and expected completion time using machine learning algorithms such as gradient boosting trees. Finally, these multi-dimensional features are uniformly mapped into a high-dimensional feature space to generate a structured task feature vector, which provides a basis for subsequent intelligent matching.

[0030] Step S103: Based on the multi-dimensional user portrait and the multi-dimensional task feature vector, a multi-objective optimization algorithm is used to calculate the matching score between the annotator and the task, and an optimal task allocation plan is generated.

[0031] 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 degree of match between the user profile and the task features is calculated using methods such as cosine similarity and Euclidean distance. These measures include the degree of match between professional skills and task requirements, the degree of match between areas of expertise and task domain attributes, and the degree of match between historical quality performance and task quality requirements. The matching scores for these dimensions constitute a preliminary match assessment. Subsequently, a multi-objective optimization algorithm is applied to dynamically adjust the weights of different dimensions based on the platform's current business objectives (e.g., prioritizing quality, efficiency, or cost). For example, for high-precision medical image annotation tasks, professional background and quality consistency are weighted more heavily; for news classification tasks with high timeliness requirements, efficiency performance is weighted more heavily. Constraint processing is also implemented using expert-defined rules, such as considering hard requirements such as the annotator's work time zone and language proficiency. Based on the individual matching scores, the platform further considers global factors such as the annotator's current workload, task urgency, and overall resource utilization efficiency. A combinatorial optimization algorithm (such as integer linear programming or genetic algorithms) is used to generate the platform's globally optimal task allocation strategy. Finally, reinforcement learning techniques are applied to optimize this allocation strategy, combining active push and passive selection modes to achieve intelligent task recommendation and adaptive allocation, ultimately generating the optimal task allocation solution.

[0032] In addition, in step S103, when intelligent matching is performed based on the multi-dimensional user portrait and the multi-dimensional task feature vector, the matching scores of each core dimension constitute a preliminary matching assessment in the following manner. First, the multi-dimensional user portrait obtained from step S101 and the multi-dimensional task feature vector obtained from step S102 are dimensionally aligned to ensure that the ability dimension in the user portrait and the demand dimension in the task feature vector can be effectively matched. Specifically, the professional skill dimension in the user portrait is matched with the professional knowledge requirement dimension in the task feature vector, and the cosine similarity method is used to quantify the degree of fit between the two to obtain a professional ability matching score.

[0033] The distribution of expertise in the user profile is matched with the domain attributes in the task feature vector. A weighted similarity algorithm is used to calculate domain overlap and correlation strength, and a domain knowledge matching score is calculated. The historical performance of annotation quality in the user profile is compared with the quality requirements in the task feature vector. A quality level matching score is calculated using threshold judgment and statistical analysis. The efficiency performance indicators in the user profile are compared and evaluated with the time urgency requirements in the task feature vector. An efficiency matching function is used to calculate an efficiency adaptability score.

[0034] 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. Specifically, this includes creating a matching score vector and organizing the scores of each dimension into a structured data representation; calculating the correlation and complementarity between dimensions to identify possible synergies or constraints between different capability dimensions; generating matching confidence intervals to determine the reliability level of each dimension score based on the quality and quantity of the historical data used for the calculation; and producing a preliminary matching assessment report containing the scores of each dimension, comprehensive matching trends, and potential risk warnings.

[0035] Step S104: perform multimodal feature recognition and complexity evaluation on the complex tasks in the optimal task allocation scheme, optimize the task structure by using a fireworks algorithm based on Student's t distribution, and generate an optimized task unit structure.

[0036] 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 conduct an in-depth analysis of 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 for the task are encoded as the solution space to be optimized. Subsequently, a fireworks algorithm (TFWA) based on the Student's t distribution is initialized, an innovative improvement to the traditional fireworks algorithm. In this algorithm, each firework represents a candidate solution (i.e., a task decomposition or combination scheme). Sparks are generated using a Student's t distribution rather than a traditional uniform distribution, enabling exploration of the solution space. Initial degrees of freedom parameters are set, and an initial set of candidate solutions is generated. During the optimization process, an adaptive degree of freedom adjustment mechanism is used. Initially, 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, refined search capabilities. At the same time, a dimension-sensitive explosion amplitude control strategy was designed, assigning different explosion amplitudes to each dimension based on their importance and sensitivity within the task feature space. Furthermore, a historically validated elite solution memory library was constructed to store 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 set of candidate solutions was iteratively optimized, ultimately generating an optimized task unit structure and achieving efficient decomposition and combination of complex tasks.

[0037] Step S105: perform labeling on the optimized task unit structure to obtain labeling results and task execution status data; perform real-time anomaly detection on the labeling results through a preset anomaly detection model to automatically identify abnormal samples, and at the same time predict the labeling quality of the labeling results through a preset quality prediction model to obtain detection and prediction results, trigger quality control measures, and output quality control results.

[0038] In this step, the task unit structure optimized in step S104 is assigned to the corresponding labelers to perform the labeling work. During the labeling process, the designed lightweight data acquisition interface collects process data such as labeling results, operation trajectories, and time distribution in real time and performs preliminary structured processing. Based on these process data, combined with domain knowledge and labeling specifications, a multi-dimensional quality evaluation index system including accuracy, consistency, completeness, timeliness, etc. is constructed. Two types of quality monitoring models are run simultaneously: one is the anomaly detection model, which uses unsupervised learning algorithms such as isolation forests or autoencoders to perform real-time anomaly detection on the labeling results and identify samples that deviate significantly from the normal pattern; the other is the quality prediction model, which is a supervised learning model trained based on historical data and can predict the final quality level of the current labeling results. Combined with statistical process control methods, the quality indicator changes are monitored in real time. When an anomaly is detected or the quality prediction does not meet the standards, the 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 by automated or other annotators), expert review (submitting anomalous 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 the high quality of the final annotation results. Detailed quality control results are also output to provide a basis for subsequent feedback and learning.

[0039] Step S106: Based on the quality control results and the task execution status data, the model parameters of the multi-dimensional user portrait and the multi-dimensional task feature vector are updated through a reinforcement learning algorithm to generate personalized feedback and capability improvement strategies.

[0040] In this step, feedback data from multiple sources is collected and integrated, including the quality control results from step S105, task execution status data, annotator self-assessments, and expert review opinions. Data fusion techniques are used to integrate this multi-source, heterogeneous data into a comprehensive feedback dataset that comprehensively 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 identify the annotator's strengths and weaknesses but also provide specific improvement suggestions and examples, making the feedback more actionable. Furthermore, the platform analyzes the annotator's skills and potential for development. Based on the annotation task domain knowledge graph constructed by the platform, a recommendation algorithm is applied to design personalized skill improvement paths for the annotator. These paths may include targeted practice tasks, recommended learning materials, and step-by-step challenge tasks to help annotators continuously improve their skills. Furthermore, incremental learning and reinforcement learning techniques are applied to update the model parameters of the multidimensional user profile and multidimensional task feature vector based on the latest feedback data and quality control results. This continuous learning mechanism enables the matching algorithm to continuously adapt to changes in annotator's skills 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 improvement paths, more comprehensive and targeted personalized feedback and capability improvement strategies are generated, forming a closed-loop continuous improvement mechanism.

[0041] See also Figure 2 , Figure 2 This is a flowchart of the user portrait construction module provided by the 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 including professional skills, areas of expertise, annotation quality, and efficiency performance, including: Step S201: Based on the historical behavior data, generate a standardized historical behavior data set through data cleaning, normalization and outlier processing.

[0042] In this step, we first collect historical behavioral data from annotators across various businesses within the annotation platform. This data typically includes basic information about the annotators (such as educational background, professional field, and work experience), historical task records (such as the type, number, and time distribution of completed tasks), annotation quality data (such as quality inspection scores, error rates, and number of revisions), behavioral trajectory data (such as operation sequence, dwell time, and interaction patterns), and feedback information (such as self-evaluations, peer reviews, and complaint records). This raw data often contains various quality issues, such as missing values, outliers, inconsistent formats, and duplicate records. These issues are addressed through a series of data cleaning operations: For missing values, methods such as mean / median filling, nearest neighbor filling, or predictive model filling are used depending on the data type; for outliers, techniques such as boxplots, Z-scores, or local outlier factors are used to detect and address them; for format inconsistencies, a unified data schema is defined and converted; for duplicate records, redundant data is detected and removed using unique identifiers. The cleaned data also needs to be normalized to convert features of different dimensions to the same scale. Common methods include Min-Max normalization, Z-score normalization, or logarithmic transformation. This series of processes ultimately generates a standardized historical behavior dataset, providing a high-quality data foundation for subsequent feature engineering and model training.

[0043] Step S202: Statistical analysis and feature engineering are performed on the standardized historical behavior dataset to extract and quantify multi-dimensional feature indicators such as the professional skill level, distribution of areas of expertise, stability of annotation quality, efficiency performance, and time availability of the annotators.

[0044] In this step, the standardized historical behavior data set generated in step S201 is subjected to 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, quantile and other statistical quantities of each feature, as well as the correlation analysis between features. Subsequently, 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 task completion status, professional certification and background information of the annotators in different skill areas; for the distribution of areas of expertise, the performance differences of the annotators in tasks in different fields are analyzed to generate the probability distribution of field preferences and expertise; for the stability of annotation quality, the mean, variance, trend and other indicators of the annotators' historical quality scores are calculated to construct a quality stability index; for efficiency performance, the task completion speed, response time, work rhythm and other factors of the annotators are analyzed to generate an efficiency score; for time availability, a time availability model is constructed based on the annotators' historical online time, work period distribution and response mode. It also generates advanced features such as task type adaptability (how quickly annotators learn new types of tasks), a quality-speed balance index (which measures the balance between quality and efficiency), and a collaborative ability index (performance in team tasks). Through these feature engineering processes, raw data is transformed into structured, multi-dimensional feature indices that comprehensively characterize annotators' abilities. To construct a skill scoring model for professional skill levels, the system first collects data on annotators' task completion in different skill areas, including quantitative indicators such as the number of tasks completed, task complexity distribution, and completion quality scores. It then integrates the annotators' professional certification information, such as relevant qualifications, training certificates, and academic background, and converts this qualitative information into a quantitative score. Specifically, the system uses a weighted scoring mechanism, assigning a base score to each skill area and dynamically adjusting it based on task completion. The weighting is 30% for the number of completed tasks, 40% for adaptability to task complexity, and 30% for quality performance. Professional certification information is converted into skill bonus points on a scale of 0-10 through certification level mapping. The score for each skill area is finally 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.

[0045] For the probability distribution generation of the distribution of areas of expertise, the system calculates the task distribution and performance differences of annotators in various professional fields through statistical analysis methods. First, the number of tasks completed by the annotators in each field and the quality score are counted, and the average quality score and completion efficiency index of each field are calculated. Then, using the Bayesian statistical method, the overall performance of the annotators is used as the prior distribution, and the specific performance of each field is used as the observation data to update the posterior probability distribution. Specifically, the system calculates the relative advantage index of each field, with the formula: field advantage index = (average quality score of the field - average score of all fields) / standard deviation of all fields, and then converts the advantage index of each field into a probability distribution through the softmax function to ensure that the sum of the probabilities of all fields is 1. This method takes into account both the absolute performance level of the annotators and the comparative advantages relative to other fields, and generates a scientific probability distribution of field preferences and expertise.

[0046] To construct the annotation quality stability index, the system uses a multi-dimensional statistical analysis method to comprehensively assess the stability of annotators' quality performance. First, basic statistical indicators of annotators' historical quality scores are calculated, including the arithmetic mean to reflect the overall quality level, the standard deviation and variance to reflect the degree of quality fluctuation, and the coefficient of variation (standard deviation / mean) to reflect relative stability. Then, time series analysis is used to identify quality trends, and linear regression is used to analyze the changes in quality scores over time. The absolute value of the regression coefficient reflects the severity of quality changes. Finally, the quality stability index is calculated using a weighted comprehensive formula: Stability Index = (1 - Standardized Coefficient of Variation) × 0.6 + (1 - Standardized Trend Change Rate) × 0.4. Normalization ensures that each indicator is within the range of 0-1, with the index closer to 1 indicating more stable quality.

[0047] To generate efficiency performance scores, the system comprehensively analyzes multiple efficiency-related indicators of annotators and establishes a scoring model. First, task completion speed data is collected, calculating the number of annotation samples completed per unit time and the average completion time for tasks of varying complexity. Response time data is then analyzed, including response delays from task assignment to start of execution, as well as interruption and recovery patterns during task execution. Work cadence analysis calculates the distribution of annotators' work intensity over different time periods to identify periods of high productivity and fatigue decay patterns. The specific efficiency score is calculated using the following formula: Efficiency score = (standardized completion speed × 0.4) + (standardized response timeliness × 0.3) + (standardized work cadence stability × 0.3). Each indicator is standardized based on peer comparison, resulting in a final score ranging from 0 to 100, with higher scores indicating superior efficiency performance.

[0048] To construct the time availability model, the system establishes a predictive model based on historical behavior pattern analysis to assess annotators' time availability. First, historical online time data for annotators is collected, including daily online time duration, online time distribution, and weekly work patterns. Statistical analysis methods are used to identify the annotators' typical work time patterns. Next, the distribution of work periods is analyzed, and clustering algorithms are used to identify the annotators' preferred work periods. Differences in work efficiency and quality performance across these periods are calculated. Response pattern analysis calculates the average response time, response rate, and response time variability of annotators to 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 of annotator's availability, calculated as: availability probability = (historical online frequency for that 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.

[0049] These models and indicators are based on 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 of indicators across different dimensions, providing a reliable data foundation for subsequent user profile construction and task matching.

[0050] Step S203: Based on the multi-dimensional feature indicators, a deep neural network or a graph neural network is applied to perform deep learning modeling to build a comprehensive user portrait model that can capture the capabilities, behavior patterns and development potential of the annotators.

[0051] 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. The appropriate network architecture is selected based on the data characteristics: for structured feature data, a multi-layer perceptron (MLP) or deep neural network (DNN) is used; to capture the complex relationships between features, a graph neural network (GNN) is used, representing the various capabilities of the annotator as graph nodes and the interactions 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 and share the underlying feature representation. An attention mechanism is also introduced to automatically learn the importance weights of different features in different task types. To capture the development trends and potential of annotators' capabilities, a time series feature processing module, such as an LSTM or Transformer structure, is integrated into the model to analyze the changes in annotator's capabilities 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. Through this deep learning modeling, we can build a comprehensive user portrait model that can not only accurately describe the current ability characteristics of the labeler, but also predict his or her development potential, providing strong support for precise task matching.

[0052] The user profile model uses a multi-layer deep neural network architecture. This network consists of an input layer (the input dimension matches the number of features, typically 100-200 dimensions), three hidden layers (with 256, 128, and 64 neurons, respectively), and an output layer (with the output dimension matching the multi-dimensional user profile, typically 50-100 dimensions). Reluctant linear unit (ReLU) activation functions are used between layers to introduce nonlinearity, and the final layer uses a sigmoid activation function to ensure output values ​​remain 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, the model employs dropout techniques and L2 regularization.

[0053] In the graph neural network implementation, the various capabilities of annotators are represented as nodes with attributes, and the edges between nodes represent the relationships between capability dimensions. The network uses a three-layer graph convolutional architecture, with the LeakyReLU activation function. The network training data is derived from the annotator performance data accumulated by the annotation platform. This data typically covers at least six months of history, covering different types of annotation tasks and annotators of varying skill levels, and typically ranges from 100,000 to 1 million records. The training data is divided into training, validation, and test sets in a ratio of 8:1:1.

[0054] Step S204: Design a weight adjustment algorithm based on time decay and latest performance, and update the parameters of the comprehensive user portrait model in combination with incremental behavioral data including task completion data and quality feedback to obtain a real-time dynamically updated user portrait model.

[0055] In this step, a dynamic update mechanism is designed to enable the user portrait model to be continuously adjusted and optimized as the annotators accumulate new behavioral data. First, a weight adjustment algorithm based on time decay is designed. This algorithm gives higher weight to recent behavioral data, while the influence of historical data gradually weakens over time. In the specific implementation, an exponential decay function is used, and the weight calculation formula is w(t)=e (-λ(tnow-t)) , where λ is the decay coefficient, which can be dynamically adjusted based on different types of features. For example, for relatively stable features like skill level, λ is smaller; for more volatile features like efficiency performance, λ is larger. Secondly, special attention is paid to the annotator's latest performance. When a significant change in their performance on a certain 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 from annotators is regularly collected, including newly completed task records, quality inspection results, and customer feedback. This data is integrated into the model update process according to the aforementioned weighting strategy. Technically, an incremental learning approach is employed, eliminating the need to retrain the entire model. Instead, model parameters are locally updated based on new data, significantly improving computational efficiency. An update frequency control mechanism is also implemented to dynamically adjust the timing of model updates based on the speed of data accumulation and the magnitude of change. This ensures that the model can promptly reflect changes in annotator's performance while avoiding instability caused by overly frequent updates. Through this dynamic update mechanism, a user profile model is developed that reflects the annotator's latest performance status in real time.

[0056] Step S205: Based on the real-time dynamically updated user portrait model, a multi-dimensional user portrait including professional skills, areas of expertise, annotation quality, and efficiency performance is generated.

[0057] In this step, a structured, multi-dimensional user profile is generated for each annotator based on the user profile model dynamically updated in real time in step S204. This user profile comprehensively covers all key characteristics of the annotator, primarily encompassing four core dimensions: professional skills, areas of expertise, annotation quality, and efficiency performance. In the professional skills dimension, the annotator's ability scores for various skills (such as image recognition, text classification, and speech transcription) are generated, as well as the strength of correlation and transferability between skills. In the areas of expertise dimension, the distribution of annotators' expertise in different fields (such as healthcare, finance, and law) is generated, along with an assessment of the depth and breadth of their domain knowledge. In the annotation quality dimension, multiple quality indicators are generated, including accuracy, consistency, and completeness, as well as a quality stability score and a quality-task type correlation diagram. In the efficiency performance dimension, indicators such as the annotator's average processing speed, peak processing capacity, and continuous work stability are generated, as well as a curve showing the relationship between efficiency and task complexity. In addition to these four core dimensions, several auxiliary features are also generated, such as time availability (work time preference, responsiveness, etc.), learning ability (speed of adapting to new tasks, ability improvement curve, etc.), and collaboration characteristics (teamwork performance, communication efficiency, etc.). These characteristics are organized into structured user profiles and provided with a visual interface, allowing platform managers 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.

[0058] In step S102, the task description document is parsed by natural language processing, the data sample is analyzed by computer vision or natural language processing, and indicators are extracted from the quality requirement document to obtain a multi-dimensional task feature vector including task difficulty, domain attributes, time urgency, and professional knowledge requirements, including: Step S301: Receive a task description document, perform natural language processing through named entity recognition, keyword extraction, and semantic analysis, and identify and extract core information including domain attributes, target objects, and operation requirements.

[0059] In this step, we first receive task description documents from the task publisher. These documents typically contain detailed descriptions of the annotation task, such as the task background, annotation objectives, and specific requirements. We then use multi-layered natural language processing techniques to deeply analyze this unstructured text. First, we apply named entity recognition (NER) technology to identify key entities in the document, including domain-specific terms (e.g., "CT scan" and "financial derivatives"), technical terms (e.g., "semantic segmentation" and "sentiment analysis"), and time expressions (e.g., "within 48 hours" and "weekly updates"). We use a pre-trained, domain-adaptive NER model, fine-tuned with industry-specific annotation corpus, to accurately identify the specialized terminology and key concepts in the annotation task. Second, we apply keyword extraction techniques, combining algorithms such as TF-IDF and TextRank, to extract keywords and phrases from the document that best represent the task's characteristics. We also construct a knowledge graph for the annotation task domain to expand and enrich the extracted keywords and capture potential semantic connections. Third, we perform deep semantic analysis, applying dependency parsing techniques to understand sentence structure, identify subject-verb-object relationships, and clarify the relationships between the subject, object, and requirements of the operation. Semantic role labeling technology is also applied to identify the semantic framework of "who does what to what" and clearly extract the task's performer, object, and behavior. Through the combined application of these technologies, three core pieces of information are extracted from task description documents: domain attributes (such as healthcare, finance, and autonomous driving), target objects (such as X-rays, contracts, and road condition videos), and operational requirements (such as "label all tumor areas" and "classify into five emotion categories"). This structured core information provides the foundation for the subsequent construction of task feature vectors.

[0060] Step S302: Perform data sample feature recognition on the data sample through computer vision or natural language processing technology to automatically identify data features including modality type, complexity, and noise level.

[0061] In this step, the data samples provided by the task are thoroughly analyzed, and appropriate processing techniques are selected based on the data type. For image or video data, computer vision techniques are applied for feature recognition. First, basic data attributes such as image resolution, color space, and video frame rate are detected. Next, deep learning models (such as pre-trained CNNs or visual transformers) are applied to extract high-level features from the image and analyze the complexity of the image content. The number, size distribution, and overlap of target objects in the image are assessed, and a scene complexity score is calculated. Image quality issues such as blur, 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 errors, and non-standard expressions, is also detected to quantify the text quality level. 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 correlations between them are analyzed. During the analysis process, the system not only focuses on the characteristics of individual samples but also analyzes the distribution characteristics of the entire dataset, such as inter-sample similarity, class distribution balance, and the proportion of edge cases. Through these analyses, the system automatically identifies and quantifies three key features of the data samples: modality type (unimodal or multimodal, specifically image, text, audio, etc.), complexity (rated on a five-level scale from simple to extremely complex), and noise level (a score assessing data quality). These data features provide important information 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 extracts features from the input image using pre-trained convolutional neural networks (such as ResNet-50 or EfficientNet) or visual transformers (such as ViT-Base). These models, pre-trained on large-scale image datasets, automatically learn and extract hierarchical feature representations of images, including low-level edge and texture features and high-level semantic features. The system then feeds the image into the pre-trained model, extracts feature maps at multiple levels, and assesses the complexity of the image content by analyzing the activation patterns and distribution characteristics of these feature maps. Specifically, the system calculates the information entropy and variance of the feature map. High entropy values ​​and high variance usually indicate that the image contains richer details and more complex structures, thus reflecting higher content complexity.

[0062] Regarding the specific analysis method for image content complexity, the system uses a multi-dimensional evaluation strategy to comprehensively measure the complexity of the image. First, the number of target objects is counted. An object detection algorithm (such as YOLO or the R-CNN series) is used to identify all labelable objects in the image. The number distribution of objects of different categories is counted. The greater the number of objects, the higher the image complexity. Then, the size distribution characteristics of the target objects are analyzed, and the area distribution of the detected bounding boxes is calculated. Statistical indicators such as standard deviation and coefficient of variation are used to measure the diversity of object sizes. The greater the size difference, the higher the labeling difficulty. Next, the degree of overlap between objects is evaluated. By calculating the intersection over union (IoU) between the bounding boxes of different objects, the number of overlapping object pairs and the distribution of the degree of overlap are counted. Images with a high degree of overlap require more refined boundary demarcation, which increases the complexity of labeling.

[0063] The system also analyzes visual complexity features of an image. These include texture complexity (calculated using the gray-level co-occurrence matrix to measure texture contrast, energy, and entropy), color complexity (analyzing the uniformity and diversity of color distribution in the color space), and edge density (calculating the density and distribution of edge pixels in the image using the Canny edge detection algorithm). These features reflect the visual complexity of an image from different perspectives, providing multi-dimensional information for comprehensive complexity assessment.

[0064] To calculate the scene complexity score, the system established 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. The final scene complexity score is then calculated using a weighted comprehensive method. The specific formula is: Scene Complexity Score = (Object Count Weight × Normalized Object Count) + (Size Distribution Weight × Normalized Size Variance) + (Overlap Weight × Normalized Overlap Index) + (Texture Complexity Weight × Normalized Texture Index) + (Color Complexity Weight × Normalized Color Index) + (Edge Density Weight × Normalized Edge Density).

[0065] The weight allocation is determined based on the actual impact of different complexity factors on the difficulty of annotation. The weight parameters are optimized by analyzing the correlation between each complexity factor and the annotation time and error rate in the historical annotation data. Generally speaking, the number of objects and the degree of overlap have a greater impact on the difficulty of annotation, and are assigned weights of 25% and 20% respectively. The size distribution, texture complexity, color complexity and edge density are each assigned a weight of approximately 10-15%. The final calculated scene complexity score is in the range of 0-10, where 0-3 points represent simple scenes, 4-6 points represent medium complexity scenes, and 7-10 points represent high complexity scenes. The system also establishes a mapping relationship between complexity score and expected annotation time, and determines the correlation between complexity score and actual annotation time through regression analysis of historical data, providing a scientific basis for task time estimation and resource allocation.

[0066] This image complexity analysis method, based on deep learning feature extraction and multi-dimensional comprehensive evaluation, objectively and accurately quantifies 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 receive consistent complexity assessments, ensuring fair and effective task allocation and quality control.

[0067] Step S303: Based on the core information and the data features, combined with the statistical performance data of historical similar tasks, the difficulty coefficient, required professional knowledge level and expected completion time of the task are calculated through the gradient boosting tree algorithm to obtain the dimensional features of the task.

[0068] In this step, the core information extracted in step S301 and the data features identified in step S302 are integrated, and combined with historical task data accumulated by the platform to conduct a comprehensive assessment of the current task. First, similar historical tasks are retrieved from the task library. Similarity is calculated based on the 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 ratings, 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 effectively handles mixed-type features, captures nonlinear relationships between features, and has good interpretability. Three key prediction models were constructed: a difficulty coefficient prediction model, which takes as input features such as data complexity, noise level, and operational requirement complexity, and outputs a difficulty score on a scale of 1-10; a professional knowledge level prediction model, which takes as input features such as domain attributes, professional term density, and operational precision requirements, and outputs a score of the required professional knowledge depth and breadth; and an expected completion time prediction model, which takes as input features such as data volume, difficulty coefficient, and time statistics for similar historical tasks, and outputs the expected completion time per unit of data. When training these models, cross-validation was used to ensure model generalization, and feature importance analysis was performed to understand the influence of different factors on prediction results. Furthermore, derived features were calculated, such as the task's cognitive load index (a measure of the cognitive complexity of the annotation process), decision ambiguity (the frequency of ambiguous situations encountered during annotation), and attention span (the level of attention required to maintain high-quality annotation). Through these calculations and analyses, we derived characteristics for each dimension of the task, providing rich information for subsequent feature vector construction.

[0069] Step S304: uniformly map the dimensional features of the task, map the domain attributes, difficulty coefficient, professional requirements, and time urgency to a high-dimensional feature space, and obtain a high-dimensional space mapping result.

[0070] In this step, the various dimensional 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. Using semantic embedding rather than simple one-hot encoding, domain attributes are mapped into a pre-trained domain embedding space. This results in related domains (such as "cardiology" and "radiology") being closer in the feature space, while unrelated domains (such as "cardiology" and "legal texts") are further apart. A specialized domain embedding model trained on a large-scale annotated task corpus is used to accurately capture the semantic connections between different annotated domains. Numerical features (such as difficulty coefficient and expected completion time) are first normalized and converted to the [0, 1] range. They are then mapped into a high-dimensional space using a nonlinear transformation (such as the RBF kernel function) to capture 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 volume. This is then converted into a multidimensional representation, encompassing aspects such as urgency and flexibility. Professional requirements are decomposed 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 dimensionality increase is applied: first, the redundancy of the original features is reduced through techniques such as principal component analysis (PCA) or autoencoders to extract the main information; then, nonlinear mapping is used to project it into a higher-dimensional feature space to enhance the expressive power of the features. Feature interaction technology is also applied to automatically learn the combined effects between features, such as the interaction between difficulty and time urgency, which may generate additional stress factors. Through these mapping processes, the various dimensional features of the task are uniformly converted into a well-structured high-dimensional feature space, and the high-dimensional space mapping result is obtained, laying the foundation for the generation of the final task feature vector.

[0071] Step S305: Generate the multi-dimensional task feature vector based on the high-dimensional feature space mapping result.

[0072] In this step, the final multidimensional task feature vector is constructed based on the high-dimensional feature space mapping results obtained in step S304. First, the high-dimensional mapping results are post-processed, including feature selection and optimization. Feature importance analysis techniques are applied to identify the feature dimensions that have the greatest impact 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 components: a domain attribute vector, which represents the professional field to which the task belongs and the strength of its correlation, such as medical imaging (0.9), radiology (0.8), oncology (0.6), etc.; a difficulty coefficient vector, which represents the complexity of the task in multiple dimensions, including cognitive complexity, operational sophistication, and judgment difficulty; a professional knowledge requirement vector, which represents the various levels of knowledge and skills required to complete the task, such as medical terminology comprehension (4.5 / 5), anatomical knowledge (4 / 5), and image annotation experience (3.5 / 5); and a time urgency vector, which represents the time constraints of the task, including deadline urgency, duration requirements, and pacing requirements. In addition to these four core components, the task feature vector also includes auxiliary information, such as the required quality level (a five-level standard ranging from basic to expert), collaboration requirements (whether teamwork is required and how), and contextual dependencies (the degree to which the task relies on background information). The feature vector generated for each task typically contains 50-200 dimensions, which are dynamically adjusted based on the task type and complexity. These feature vectors are stored using a sparse representation to improve computational efficiency. The resulting multidimensional task feature vector comprehensively describes all aspects of the task, providing a precise mathematical representation for subsequent intelligent matching.

[0073] In step S103, a multi-objective optimization algorithm is used to calculate the matching scores between the annotators and the tasks, and an optimal task allocation plan is generated, including: Step S401: Based on the multidimensional user portrait and the multidimensional task feature vector, multidimensional similarity calculation is performed using cosine similarity and Euclidean distance methods to calculate the matching degree of the core dimension and obtain the matching score of the core dimension.

[0074] In this step, the multi-dimensional user portrait generated in step S101 is precisely matched with the multi-dimensional task feature vector generated in step S102. A variety of similarity calculation methods are used to select the most suitable measurement method for different types of features. For vectorized features (such as domain embedding vectors), the cosine similarity calculation method is mainly used. This method focuses on the consistency of the vector direction and can effectively measure the degree of similarity in the semantic space. For example, the cosine similarity between the annotator's domain vector and the domain attribute vector of the task is calculated to obtain the domain matching score. For numerical features (such as difficulty level and ability level), the normalized Euclidean distance or Manhattan distance is used to measure the absolute difference between feature values. Special matching functions are also designed. For example, for matching time availability with task time requirements, a custom time overlap calculation function is used. The matching calculation is broken down into several core dimensions: professional competence matching, which assesses the fit between annotators' professional skills and task requirements; domain knowledge matching, which assesses the relevance of annotators' domain experience to the task domain; quality level matching, which compares annotators' historical quality performance with task quality requirements; efficiency adaptability, which assesses whether annotators' work efficiency meets task time requirements; and cognitive style matching, which assesses the compatibility of annotators' work style with task characteristics. A matching score is calculated for each core dimension, typically using a standardized scale of 0-1, with 1 indicating a perfect match and 0 indicating a complete mismatch. The calculation takes feature uncertainty into account. For features with limited data or high variability, confidence intervals are introduced to avoid over-reliance on unstable features. A context-aware matching calculation is also applied, accounting for the interplay between features, such as the fact that higher-difficulty tasks may require higher professional skills. Through these multi-dimensional similarity calculations, matching scores between annotators and tasks are obtained for each core dimension, laying the foundation for subsequent comprehensive scoring.

[0075] Step S402: Based on the current business objectives of the platform, a multi-objective optimization algorithm is applied to dynamically adjust the weights of the matching scores of the core dimensions, and constraint processing is performed in combination with expert rules to generate a comprehensive matching score.

[0076] In this step, the matching scores for each core dimension calculated in step S401 are intelligently weighted and comprehensively scored based on the platform's current business objectives and strategies. First, the platform's current business objectives are identified. These may include prioritizing quality (pursuing the highest annotation quality), efficiency (pursuing the fastest delivery speed), cost (optimizing resource utilization), or balanced development (seeking the optimal balance between quality, efficiency, and cost). These business objectives are converted into mathematically expressed objective functions, such as quality, efficiency, and cost. Subsequently, multi-objective optimization algorithms, such as Pareto optimization, weighted sum methods, or the analytic hierarchy process (AHP), are applied to dynamically adjust the weights of each core dimension based on the current business objectives. For example, in a quality-first mode, the weights for professional competence and quality level matching are increased; in an efficiency-first mode, the weight for efficiency adaptability is increased. Specific adjustments are also made based on the characteristics of the task. For example, for highly specialized tasks, professional competence matching is given a sufficiently high weight regardless of the current business objectives. In addition to dynamic weight adjustment, expert-defined rules are also incorporated for constraint processing. These rules include hard constraints (such as language proficiency requirements, confidentiality level requirements, and other mandatory conditions) and soft constraints (such as giving priority to experienced annotators). These constraints are enforced through a rule engine, with matches that don't meet the hard constraints directly eliminated and matches that meet the soft constraints awarded bonus points. The processing also considers the annotator's personal preferences and historical task acceptance patterns. If an annotator has a clear preference for a certain type of task, the score of that match will be appropriately increased. Ultimately, by weighting the match scores across all dimensions and applying adjustments to the constraint rules, a comprehensive match score (0-100) is generated, fully reflecting the overall fit between the annotator and the task.

[0077] Step S403: Based on the comprehensive matching score, taking into account the current workload of the annotator, the urgency of the task and the overall resource utilization efficiency, a global optimal task allocation strategy for the platform is generated through a combinatorial optimization algorithm.

[0078] In this step, the perspective shifts from matching individual annotators with tasks to optimizing resource allocation across the entire platform. First, global platform status information is collected, including a list of all pending tasks and their characteristics (such as priority, deadline, and estimated workload); the status of all available annotators (such as current workload, available time, and skills); and overall platform performance metrics (such as resource utilization and task completion rate). The task allocation problem is then modeled as a combinatorial optimization problem, with the goal of maximizing 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 the optimization process, several key factors are considered: first, the annotator's current workload, ensuring balanced workload distribution by avoiding overassigning tasks to already overburdened annotators; second, the urgency of the task, prioritizing tasks with approaching deadlines or high customer priority to ensure timely completion of important tasks; and third, resource utilization efficiency, minimizing idle resources and maximizing overall platform throughput. Inter-task dependencies and continuity requirements are also considered. For example, some tasks may require the same annotator to maintain consistency. In terms of technical implementation, a hierarchical optimization strategy is adopted: high-priority tasks are first precisely optimized, then routine 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, newly added tasks, etc.). Through this global optimization approach, the optimal task allocation strategy for the platform is generated, which not only considers the quality of individual matches but also takes into account overall efficiency and balance.

[0079] Step S404: Perform reinforcement learning processing on the task allocation strategy, combine the two modes of active push and passive selection, and realize intelligent recommendation and adaptive allocation of tasks.

[0080] In this step, the task allocation strategy generated in step S403 is further optimized and implemented. Reinforcement learning techniques are employed to treat task allocation as a sequential decision-making problem. The allocation strategy is continuously optimized through continuous interaction with the environment (annotators and tasks). 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, pending tasks, and platform operating status. The action space represents possible allocation decisions. The reward function incorporates factors such as matching quality, task completion status, and annotator feedback. Pre-training on historical data and online learning enable the system to adapt to changing environments and optimize long-term returns. Task allocation combines active push and passive selection modes to provide flexible solutions for different scenarios. In the active 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 the annotator's preferred channels (such as in-app messaging, email, and SMS), providing information such as a task summary, the reason for matching, and the expected return, to increase acceptance rates. We have also implemented task packaging technology to combine multiple similar or related small tasks into task packages, reducing switching costs and improving work efficiency. In the passive selection mode, we provide annotators with a personalized task recommendation list, and they can choose tasks according to their actual situation and preferences. We apply a recommendation algorithm to sort the tasks, place the most matching tasks in a prominent position, and provide detailed task information and matching explanations to help annotators make wise choices. We have also designed an intelligent guidance mechanism to encourage annotators to try new areas and promote long-term development by highlighting tasks that help improve their abilities. This intelligent recommendation and adaptive allocation mechanism combined with reinforcement learning can provide a more flexible and user-friendly task allocation experience while ensuring matching quality, thereby improving annotator satisfaction and the overall effectiveness of the platform.

[0081] Step S405: Generate an optimal task allocation solution based on the intelligent recommendation and adaptive allocation results.

[0082] In this step, the optimal task allocation plan is finally determined and generated based on the intelligent recommendation and adaptive allocation results of step S404. First, the actual execution of task allocation is collected and integrated, including feedback data such as the acceptance rate of proactive push notifications, the selection mode of passive selections, and the speed of task initiation. This data is analyzed to identify the effective parts of the current allocation strategy and those that need adjustment. Assignments with high acceptance rates and rapid initiation are confirmed as valid matches. For assignments that are rejected or have not been responded to for a long time, the reasons are analyzed and alternative plans are prepared. Subsequently, the final allocation plan is optimized. A decision tree or rule engine is applied to select the most appropriate processing strategy for each situation: for accepted tasks, the assignment is confirmed and the relevant resource status is updated; for rejected tasks, the suboptimal matching annotator is quickly found and a new assignment is initiated; for tasks with no clear response, the urgency of the task is determined whether to wait for the response of the original annotator or initiate an alternative plan. Special attention is paid to high-priority or time-sensitive tasks, and a rapid response mechanism is designed for these tasks to ensure their timely allocation. In terms of technical implementation, parallel processing and an event-driven architecture are employed, enabling real-time responses to various assignment events, such as task acceptance, rejection, and completion, to maintain a dynamically optimized assignment plan. An exception handling mechanism is also designed to address emergencies, such as a large number of labelers going offline simultaneously or a sudden surge of tasks. Ultimately, a detailed task assignment plan document is generated, including information such as the assigned recipients, expected start and completion times, quality requirements, and a monitoring plan for each task. This plan is also sent to task management and quality control, initiating the subsequent execution and monitoring processes. This comprehensive approach, which considers actual execution conditions, yields an optimal task assignment plan that is not only theoretically optimal but also achieves good results in actual execution, effectively integrating theory with practice.

[0083] Step S406, reflective evolution of a multi-objective heuristic method based on a large language model, including: collecting historical decision data generated by an intelligent matching engine, including matching plans, actual execution results, quality score differences, and business goal achievement, to form basic input data for reflective analysis; guiding a large language model through designing specific prompt engineering technology, conducting an in-depth analysis of the basic input data, identifying implicit rules in decision patterns, discovering limitations of current heuristic rules, and obtaining in-depth analysis results; based on the in-depth analysis results, generating a new heuristic rule candidate set through a large language model; wherein the heuristic rule set adopts a structured representation, including trigger conditions, weight adjustment strategies, and expected effects; designing a variety of simulation scenarios, verifying and screening the heuristic rule candidate set, and evaluating the performance of new rules through backtesting the historical decision data and small-scale online experiments to obtain verification and screening results; based on the verification and screening results, formally incorporating high-quality rules into the decision-making process to form a dynamically evolving multi-objective heuristic method.

[0084] In this step, the powerful cognitive and reasoning capabilities of a large language model (LLM) are innovatively combined with a multi-objective optimization algorithm to construct a self-reflective and continuously evolving intelligent matching decision-making system. First, a comprehensive historical decision data collection mechanism was established to extract key information from the intelligent matching engine's operation logs, including matching plans (annotator-task pairs and their matching scores), actual execution results (completion quality, time efficiency, annotator satisfaction, etc.), quality score discrepancies (the deviation between predicted and actual quality), and the achievement of business goals (such as the accuracy of high-quality tasks and the on-time completion rate of urgent tasks). This data was structured and initially analyzed to identify matching cases with particularly strong or weak performance, which served as the focus of reflective analysis. Subsequently, a carefully designed prompt engineering technique was applied to guide the large language model in in-depth analysis. Multi-level analysis prompt templates were constructed, including pattern recognition prompts ("Analyze the common characteristics of these high-quality matching cases"), difference analysis prompts ("Compare the key differences between successful and unsuccessful cases"), and rule evaluation prompts ("Evaluate the limitations of the current rules in these cases"). A domain-specific knowledge enhancement mechanism has also been designed to incorporate industry-specific knowledge and terminology into prompts, improving LLM's analytical accuracy. These carefully designed prompts enable LLM to conduct in-depth, multi-angle, and multi-level analysis of matching decision data, identifying implicit patterns and patterns that are difficult for humans to detect. For example, LLM might discover that, "When the complexity of a medical image annotation task exceeds 0.8 and includes rare cases, annotators with relevant clinical backgrounds, while slower, achieve significantly higher accuracy than those with purely technical backgrounds." Based on these in-depth analysis results, LLM is guided to generate a new set of candidate heuristic rules. These rules are structured, with clear triggering conditions (such as task type and complexity threshold), weight adjustment strategies (such as increasing or decreasing the weight of a dimension), and expected effects (such as improved quality or faster speed). For example, a rule might be expressed as: "IF task_domain='medical_imaging' AND complexity>0.8 ANDcontains_rare_cases=TRUE THEN increase_weight(clinical_background, 0.3) ANDdecrease_weight(annotation_speed, 0.2) EXPECT quality_improvement=15%." To ensure the reliability of the generated rules, a variety of simulation scenarios were designed to verify and screen the rule candidate set.First, backtesting is performed on historical data to evaluate the matching results that would be produced if the new rules were applied. Then, experiments are conducted in a controlled, small-scale online environment to collect data on the actual application effects. Evaluation metrics include multiple dimensions such as improvement in matching quality, changes in resource utilization efficiency, and achievement of business goals. Through this rigorous verification process, truly effective rules are screened and formally incorporated into the decision-making process. A rule base management mechanism is designed, including functions such as rule version control, conflict detection, and priority management, to ensure that new rules can coexist harmoniously with the existing rule system. As the business continues to develop and data continues to accumulate, reflective analysis and rule evolution processes are regularly triggered, forming a dynamically evolving multi-objective heuristic method that enables matching decisions to continuously improve themselves and adapt to new business needs and challenges.

[0085] 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, a hidden layer dimension of 1024, and a total parameter count of over 1 billion. The model was pre-trained on a large-scale text corpus containing professional annotation industry corpora. The training data includes professional text such as annotation guidelines, quality standard documents, expert review opinions, and annotation error analysis reports, totaling over 10GB.

[0086] Prompt engineering design uses a multi-round dialogue format and includes three key components: (1) task description, which clearly states the analysis goals and requirements; (2) context provision, including a structured description of historical decision data and key statistical features; and (3) specific instructions to guide the model to perform specific analysis tasks. A typical prompt template requires the model to analyze the common characteristics of successful matching cases, the common patterns of failed matching cases, the possible limitations of the current matching rules, and the suggestions for new rules that can improve matching results.

[0087] The model output is structured and parsed to extract rule candidates, which are then converted into an executable rule format. To validate the quality of the model output, the model-generated rules were evaluated on a test set of 1,000 annotator-task pairs. The results showed that compared to manually designed rules, the matching accuracy improved by 15% and the task completion quality improved by 12%.

[0088] In step S104, the task structure is optimized using a fireworks algorithm based on Student's t distribution to generate an optimized task unit structure, including: Step S501: Model the complex tasks in the optimal task allocation plan, represent each complex task as a multidimensional feature vector containing content complexity, domain attributes, estimated completion time, and dependencies, and encode the decomposition points and combination plans of the complex tasks into a solution space to be optimized.

[0089] In this step, the complex tasks in the optimal task allocation solution generated in step S103 are thoroughly analyzed and modeled, laying the foundation for subsequent structural optimization. First, complex tasks requiring structural optimization are identified. These tasks typically exhibit the following characteristics: large scale, complex internal structure, multiple types of operations, or the need for diverse expertise. 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, comprehensively describing all aspects of the task. In the dimension of content complexity, factors such as cognitive complexity, operational difficulty, and decision complexity are quantified. In the dimension of domain attributes, the professional domains involved in the task and their weight distribution are represented. In the dimension of estimated completion time, the completion time of each task component is predicted based on historical data and task characteristics. In the dimension of dependencies, the forward and backward dependencies, resource sharing, and information flow relationships between the various components of the task are identified and represented. 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 components must maintain consistency), and interdisciplinary nature (the degree to which multiple professional domains are involved). Subsequently, potential decomposition points and combination schemes for the complex task are encoded into a solution space to be optimized. Task graph analysis identifies possible decomposition points, which 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—subtasks that 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, with each point representing a possible task structure. Evaluation functions are defined for this solution space, including balance metrics (the degree of balance between the workload of each subtask), efficiency metrics (expected overall completion efficiency), quality metrics (expected annotation quality), and resource utilization metrics (the degree to which annotator skills match 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.

[0090] Step S502: Based on the solution space to be optimized, initialize the fireworks algorithm based on Student's t distribution, set each firework as a candidate solution, generate sparks through Student's t distribution instead of traditional uniform distribution, set initial degree of freedom parameters, and generate an initial candidate solution set.

[0091] In this step, the innovative Fireworks Algorithm (TFWA) based on the Student's t distribution is initialized based on the solution space to be optimized defined in step S501. The Fireworks Algorithm is a swarm intelligence optimization algorithm inspired by the explosion of fireworks. This TFWA significantly improves the algorithm's search efficiency in high-dimensional task spaces by introducing the Student's t distribution instead of the traditional uniform distribution. First, a set of fireworks is initialized in the solution space, with each firework representing a candidate task structure solution. The positions of the initial fireworks are generated using a variety of strategies: some are based on heuristic rules (such as equal distribution by data volume or grouping by operation type) to generate targeted initial solutions; some are based on empirically derived initial solutions based on historical success cases; and some are randomly generated to increase the diversity of initial solutions. A fitness value is calculated for each initial firework, evaluating its quality as a task structure solution based on the evaluation function defined in step S501. Subsequently, key parameters of the TFWA are set. Unlike the traditional Fireworks Algorithm, the TFWA uses the Student's t distribution to generate sparks (i.e., new candidate solutions generated by the fireworks explosion). The Student's t distribution is a heavy-tailed distribution with a probability density function of:

[0092] Where v is the degree of freedom parameter, and Γ is the gamma function. An initial degree of freedom parameter v is set (typically starting at 1). This parameter controls the tail weight of the distribution, thereby affecting the global and local nature of the search. When v is small (e.g., v = 1, which corresponds to a Cauchy distribution), the distribution has a heavier tail, generating sparks farther from the current solution and enhancing the algorithm's global exploration capabilities. As v increases, the distribution gradually approaches a normal distribution, facilitating more local, refined search. Other TFWA parameters are also set, such as the burst amplitude (controlling the range of spark generation), the number of sparks (the number of candidate solutions generated per iteration), and the maximum number of iterations. These parameters generate an initial set of candidate solutions, preparing the ground for subsequent iterative optimization. This initialization strategy, based on the Student's t distribution, ensures that the algorithm has strong global exploration capabilities from the outset, enabling a more efficient search of high-dimensional task structure spaces.

[0093] Step S503: Through the degree of freedom adaptive adjustment mechanism, the first degree of freedom value is used to enhance the global exploration capability at the initial optimization stage, and the degree of freedom value is gradually increased as the iteration proceeds to enhance the local fine search capability, thereby generating a dynamically adjusted search strategy.

[0094] This step implements one of the core innovations of the TFWA algorithm—an adaptive degree-of-freedom adjustment mechanism. This mechanism enables the algorithm to dynamically balance global exploration and local exploitation during the optimization process. An adaptive control framework is designed to automatically adjust the degree-of-freedom parameter v of the Student's t-distribution based on the optimization progress and search status. Initially, the algorithm uses the first degree-of-freedom value (typically v = 1 or close to 1). This value creates a heavier tail for the t-distribution, and the probability density function remains large in regions far from the center. This means the algorithm has a higher probability of generating sparks that are far from the current solution, allowing it to explore diverse regions of the solution space and avoid prematurely falling into local optima. As iterations proceed, the degree-of-freedom value is gradually increased, causing the shape of the t-distribution to approach a normal distribution. This shift allows the algorithm to reduce exploration of distant regions and instead conduct a more refined search in currently promising areas, enhancing its local exploitation capabilities. Multiple metrics are employed to guide the dynamic adjustment of the degree-of-freedom (DOF) parameters: first, an iteration progress metric, which simply adjusts the value v based on the ratio of the current iteration count to the maximum number of iterations; second, a search stagnation metric, which temporarily reduces the value v to re-enhance global exploration when multiple consecutive iterations fail to significantly improve the optimal solution; and third, a solution set diversity metric, which similarly reduces the value v to increase diversity when the diversity of the candidate solution set falls below a threshold. A nonlinear DOF adjustment strategy is also designed, employing different adjustment rates at different stages—for example, rapidly increasing the value v in the middle phase and slowly increasing it in the later stages—to achieve finer control. Furthermore, a region-specific DOF adjustment mechanism is implemented, applying different DOF parameters to different promising regions in the solution space, further improving search efficiency. This adaptive DOF adjustment mechanism generates a dynamically adjusted search strategy that automatically balances global exploration and local exploitation based on the actual situation during the search process, significantly improving performance on complex task structure optimization problems. Experiments show that compared to methods with fixed DOF parameters, this adaptive mechanism can reduce the number of iterations by an average of 30% while improving solution quality by 15%.

[0095] Step S504: Design a dimension-sensitive explosion amplitude control strategy. According to the importance and sensitivity differences of different dimensions in the task feature space, assign different explosion amplitudes to each dimension to generate a dimension-weighted search space.

[0096] This step implements another key innovation of the TFWA algorithm: a dimension-sensitive burst amplitude control strategy. This strategy addresses the issue of varying importance and sensitivity across dimensions in high-dimensional task feature spaces. Traditional fireworks algorithms use the same burst amplitude for all dimensions, which is inefficient for high-dimensional, heterogeneous problems like task structure optimization. First, the importance and sensitivity of each dimension in the task feature space are assessed using various methods: First, feature importance analysis uses models such as random forests or gradient boosting trees to assess the impact of each dimension on the quality of the final task structure. Second, sensitivity analysis calculates the partial derivatives or rates of change of the objective function with respect to each dimension to identify dimensions most sensitive to small changes. Third, historical data analysis analyzes the variation range and distribution characteristics of different dimensions in historically high-quality solutions. Based on these analyses, different burst amplitudes are assigned to each dimension. Dimensions of high importance or sensitivity (such as key decomposition points that affect task quality) are assigned larger burst amplitudes to allow for more extensive exploration in these dimensions. Dimensions of low importance or sensitivity are assigned smaller amplitudes to reduce unnecessary search space. Interactions between dimensions are also considered. For strongly correlated groups of dimensions, a coordinated amplitude control strategy is employed to ensure that changes in these dimensions maintain a certain degree of coordination. In implementation, an amplitude matrix A is designed, where each element a_ij represents the explosion amplitude of the i-th firework in the j-th dimension. This matrix not only accounts for the importance and sensitivity of the dimensions, but also the fitness of the firework (better-performing fireworks receive finer local search amplitudes) and the search phase (the overall amplitude gradually decreases as the search progresses). A dynamic amplitude adjustment mechanism is also implemented, updating the amplitude allocation strategy in real time based on the actual contribution of each dimension during the search. This dimension-sensitive explosion amplitude control strategy generates a dimension-weighted search space, enabling the algorithm to focus computational resources on the most influential dimensions, significantly improving search efficiency. Experiments show that compared to a uniform amplitude strategy, this dimension-sensitive strategy can reduce computational effort by an average of 40% while improving solution quality by 20% for complex task structure optimization problems.

[0097] Step S505: construct an elite solution memory library based on historical experience, combine the differential evolution strategy, the dynamically adjusted search strategy and the dimension-weighted search space, iteratively optimize the initial candidate solution set and guide new search directions to generate an optimized task unit structure; wherein, the elite solution memory library stores task decomposition combination solutions that have performed well in history.

[0098] In this step, the overall implementation of the Student's t-distribution-based fireworks algorithm (TFWA) was completed and applied to the task structure optimization problem. First, a memory of elite solutions based on historical experience was constructed. This dynamically maintained knowledge base stores historically high-performing task decomposition and combination solutions. Each solution in the memory contains not only the solution vector itself, but also its contextual information (such as task type, scale, and domain) and performance evaluation (such as quality improvement and efficiency improvement). A similarity-based retrieval mechanism was designed, allowing new optimization tasks to retrieve similar cases from the memory as reference. The memory adopts a hierarchical storage structure, with general patterns stored at the top level and specialized solutions for specific domains at the bottom level, improving retrieval efficiency and application accuracy. Subsequently, a 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. By introducing differential evolution operations within the TFWA framework, the elite solutions in the memory are used to guide new search directions. Specifically, in each iteration, in addition to generating sparks using the Student's t-distribution, the differential evolution formula v = x is also used. r1 + F * (x r2 -x r3 ) generates a part of candidate solutions, where x r1 、x r2 、x r3is the solution vector selected from the current population and the memory, and F is the scaling factor. This hybrid strategy combines the adaptive exploration capabilities of TFWA with the directional search capabilities of differential evolution, significantly improving algorithm performance. During the iterative optimization process, the dynamically adjusted search strategy of step 503 and the dimension-weighted search space of step 504 are combined. Each iteration automatically adjusts the degrees of freedom parameter of the Student's t distribution based on the current stage, and different explosion amplitudes are assigned based on the importance of each dimension. Various selection strategies are also implemented, including an elite retention strategy (to ensure that the optimal solution is not lost during iterations), a diversity maintenance strategy (to maintain population diversity and avoid premature convergence), and a tabu search strategy (to avoid repeated exploration of known low-quality areas). Various termination criteria are set, including reaching the maximum number of iterations, no significant improvement after multiple consecutive iterations, or reaching a preset target quality. When the termination criteria are met, the current optimal solution is output as the final task structure optimization plan. This plan details how a complex task should be decomposed into subtasks or how small tasks should be combined into task packages, including information such as the boundaries of each task unit, dependencies, expected workload, and recommended annotator types. This comprehensive optimization approach generates a high-quality, optimized task unit structure, significantly improving the efficiency and quality of complex labeling tasks. Practical applications have shown that, compared to traditional methods, this optimized task structure can reduce task completion time by an average of 23%, improve labeling quality by 9%, and achieve a more balanced workload distribution among different labelers.

[0099] In addition, the specific implementation of the Student's t-distribution fireworks algorithm (TFWA) sets the following key parameters: the initial population size is 50, each firework produces 30-50 sparks per iteration, and the maximum number of iterations is 100. The initial degree of freedom parameter v of the Student's t-distribution is set to 1 and gradually increases with each iteration. The explosion amplitude is initially set to 30% of the diameter of the task feature space and gradually decreases with each iteration.

[0100] The core algorithm implementation of TFWA includes key steps such as initializing the firework position, calculating fitness, adjusting the degree of freedom parameter, adjusting the amplitude of each dimension according to the dimension importance, generating random displacements using Student's t distribution, generating new sparks, evaluating the fitness of sparks, selecting solutions from the elite solution memory library for difference operations, selecting the best individual as the next generation of firework, and updating the elite solution memory library.

[0101] The algorithm training data is derived from the platform's historical task decomposition records, including over 5,000 complex task decomposition examples, covering labeling tasks of varying scales and complexity across diverse domains. Each example includes the original task features, the decomposition solution, and an evaluation of the execution performance. Five-fold cross-validation is used during training to ensure model generalization. In experimental evaluation, TFWA achieved a 45% faster convergence speed and an 18% improvement in the quality of the final solution for task decomposition optimization compared to the traditional Fireworks algorithm.

[0102] In step S105, the anomaly detection model is used to perform real-time anomaly detection on the annotation results, and abnormal samples are automatically identified. At the same time, the annotation quality of the annotation results is predicted by the preset quality prediction model, and the detection and prediction results are obtained, and quality control measures are triggered, and the quality control results are output, including: Step S601: Design a lightweight data acquisition interface to collect annotation results, operation trajectories, and time-distributed process data in real time during the annotation process, perform preliminary structured processing, and obtain process data.

[0103] In this step, an efficient and lightweight data collection interface was designed and implemented to capture critical data in real time during the annotation process without significantly disrupting the work. This interface utilizes a layered architecture, comprising a front-end collection layer, a data transmission layer, and a back-end processing layer. In the front-end collection layer, a low-latency data collection component is embedded within the annotation tool, capturing three key data types: annotation result data, including the annotation content itself (such as bounding box coordinates, classification labels, and text annotations) and its version history; operation trajectory data, which records the annotator's interaction sequences, such as mouse movement paths, click events, keyboard input, and tool switching, reflecting the cognitive and operational patterns of the annotation process; and time distribution data, which accurately records the start, completion, and interruption times of each annotation sample, as well as the time distribution of different operation phases. An event-driven collection strategy is employed, triggering data collection only when critical operations occur, minimizing performance overhead. In the data transmission layer, efficient data compression and batch transmission mechanisms are implemented, employing an incremental update strategy that transmits only the modified data, significantly reducing network load. Local caching and resumable transmission mechanisms are also designed to prevent data loss in the event of network instability. At the back-end processing layer, the received raw data undergoes preliminary structural processing. First, data cleaning is performed to address missing values, outliers, and duplicate records. Next, data standardization is performed to convert data from different sources and formats into a unified structure. Finally, feature extraction is performed to calculate 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 itself. This lightweight data acquisition interface enables continuous acquisition of rich process data during the annotation process, providing a solid data foundation for subsequent quality monitoring and anomaly detection. This minimizes disruption to the annotation process, making the data collection process virtually imperceptible to the annotator. Actual applications have shown that the interface's CPU usage is typically less than 3%, memory usage increases by no more than 50MB, and network bandwidth consumption averages no more than 2MB per hour, fully meeting its lightweight design goals.

[0104] Step S602: Based on the process data, combined with domain knowledge and annotation specifications, a multi-dimensional quality evaluation index system including accuracy, consistency, completeness, and timeliness is constructed.

[0105] In this step, based on the process data collected in step S601, combined with the professional knowledge and annotation specifications of specific fields, a comprehensive multi-dimensional quality evaluation index system is constructed. This index system not only focuses on the quality of the final annotation results, but also takes into account all aspects of the annotation process, forming a three-dimensional evaluation framework for annotation quality. First, evaluation indicators of the accuracy dimension are designed. These indicators measure the degree of conformity of the annotation results with the actual 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 accuracy, evaluating the accuracy of the bounding box or segmentation mask; attribute completeness, checking whether the required attributes are complete and accurate; reference consistency, the degree of consistency with the reference standard or gold standard. The calculation method of the accuracy indicator 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. Secondly, evaluation indicators of the consistency dimension are designed. These metrics measure the internal and cross-sample consistency of annotations, including: internal consistency (whether the annotations of similar samples by the same annotator are consistent); cross-annotator consistency (the degree of consistency between annotations of the same sample by different annotators); temporal consistency (whether annotators maintain consistent annotation standards over different time periods); and rule adherence (whether annotations follow predefined annotation rules and conventions). Statistical methods and pattern recognition techniques are used to automatically detect consistency issues, such as sudden changes in annotation style or deviations from team standards. 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 verifying metadata fields. Finally, evaluation metrics for the timeliness dimension were designed. These metrics assess annotation time efficiency and schedule compliance, including: annotation volume per unit time, which measures annotation efficiency; progress achievement rate, which compares actual progress against planned progress; responsiveness, which measures the time from task assignment to the start of annotation; and consistent time distribution, which determines whether the distribution of annotation time across different samples is reasonable. Time series analysis is used to identify efficiency anomalies and schedule risks. In addition to these four core metrics, specialized metrics (such as anatomical accuracy in medical annotation), innovation metrics (innovative insights in annotation), and user experience metrics (user experience with the annotation interface) are designed to meet specific domain needs. This multi-dimensional quality evaluation metric system provides scientific evaluation criteria for subsequent anomaly detection and quality prediction, ensuring that quality control is based on an objective and comprehensive foundation.

[0106] Step S603: Apply an isolation forest or autoencoder unsupervised learning algorithm to perform anomaly detection on the labeling results, and combine with a statistical process control method to monitor the changes in quality indicators in real time to obtain anomaly detection results.

[0107] In this step, a two-layer anomaly detection mechanism is implemented. Using advanced unsupervised learning algorithms and statistical process control methods, annotation quality is monitored in real time to promptly identify potential issues. First, an unsupervised learning algorithm is applied to the annotation results for anomaly detection. Two primary algorithms are employed: Isolation Forest and Autoencoder. Isolation Forest is a random forest-based anomaly detection algorithm whose core concept is that anomalous data points are generally more easily "isolated." Using the multidimensional quality metrics constructed in step S602 as features, the Isolation Forest model is trained. This model effectively identifies annotation results that deviate from normal patterns in the multidimensional feature space. A context-aware variant of Isolation Forest is also implemented, taking into account inter-sample relationships and task characteristics to improve detection accuracy. For high-dimensional features or complex patterns, autoencoders are applied for anomaly detection. By learning to compress and then reconstruct input data, autoencoders can capture the inherent structure of the data. The autoencoder model is trained to learn the characteristic patterns of normal annotations. When encountering anomalous annotations, the reconstruction error increases significantly, resulting in their identification as anomalies. Advanced variants such as variational autoencoders (VAEs) and adversarial autoencoders (AAEs) have been implemented to enhance detection capabilities for complex anomalies. An ensemble strategy is employed to combine the detection results of multiple models, reducing false positives and missed negatives. Secondly, statistical process control (SPC) methods are integrated to monitor changes in quality indicators in real time. Various SPC techniques have been implemented: control charts such as X-bar charts, R charts, and CUSUM charts are used to monitor the mean and variability of quality indicators; process capability analysis assesses the ability of the annotation process to meet quality requirements; and multivariate statistical process control (MSPC) simultaneously monitors multiple related quality indicators. Adaptive control limits have been designed to dynamically adjust warning and action limits based on task characteristics and historical performance, improving detection sensitivity. Trend analysis has also been implemented to identify gradual changes in quality indicators and provide early warning of potential quality declines. In actual operation, unsupervised learning and SPC methods are combined to form a multi-layered anomaly detection network. For each annotation result, an anomaly score and confidence level are calculated, and a pre-set threshold is used to determine whether it is an anomaly. Anomaly judgment is also personalized based on the annotator's historical performance and task characteristics. This integrated approach accurately identifies a wide range of anomalies, including obvious errors (such as incorrect labels and significant boundary deviations), subtle deviations (such as slightly inaccurate boundaries and incomplete attributes), and behavioral anomalies (such as sudden changes in labeling patterns and unusual fluctuations in efficiency). Practical applications have demonstrated that this dual-layer anomaly detection mechanism achieves 92% accuracy, 89% recall, and a 90.5% F1 score, significantly exceeding the performance of a single approach.

[0108] Step S604: Based on the abnormality detection results, the intervention type and urgency are automatically determined by a decision tree algorithm, the abnormal samples are identified, and corresponding quality control measures are triggered.

[0109] In this step, based on the anomaly detection results from step S603, an intelligent decision-making mechanism is implemented to automatically determine the intervention type and urgency for each anomaly and trigger corresponding quality control measures. First, a decision tree algorithm is used to construct an intervention decision model. A large number of historical anomaly cases and their optimal intervention methods are collected and annotated as training data for the decision tree. Each case contains input features such as anomaly type, severity, annotator characteristics, and task characteristics, as well as output labels for the final intervention measures taken and their effects. The model is trained using decision tree algorithms such as C4.5 or CART. The optimal splitting features are selected using information gain or Gini impurity, and interpretable decision rules are constructed. Ensemble methods such as random forests or gradient boosted decision trees are also applied to improve decision accuracy. A hierarchical decision structure is designed: the first layer determines the anomaly type, such as accuracy, consistency, completeness, or efficiency issues; the second layer determines the urgency of intervention, which is generally categorized into three levels: high (requiring immediate intervention), medium (requiring intervention before the current batch is completed), and low (can be addressed in subsequent quality improvement efforts); the third layer determines the specific intervention type, such as secondary inspection, expert review, task reassignment, or annotator guidance. Decisions are made based on a variety of factors: the nature and severity of the anomaly, such as the type of error and the degree of deviation from the normal range; the characteristics and historical performance of the annotator, such as their experience level, professional background, and past quality record; the nature and importance of the task, such as its complexity, domain sensitivity, and client priority; and available resources and time constraints, such as expert availability and deadline urgency. Based on the decision results, anomaly samples requiring intervention are automatically identified and appropriate intervention measures are assigned to each sample. High-urgency accuracy issues may trigger a real-time expert review; medium-urgency consistency issues may require a secondary inspection; and low-urgency efficiency issues may generate training recommendations. An automated execution mechanism for intervention measures has also been implemented: samples requiring secondary inspection are automatically assigned to other appropriate annotators; samples requiring expert review are automatically created and the relevant experts are notified; and tasks requiring reassignment are automatically triggered through the task reassignment process. An intervention effectiveness tracking mechanism has also been designed to record the results and impact of each intervention for continuous optimization of the decision model. This intelligent decision-making mechanism enables the most appropriate intervention measures to be taken based on the specific circumstances of the anomaly, ensuring both annotation quality and optimizing resource utilization, avoiding over- or under-intervention. Practical applications have shown that compared to fixed-rule intervention strategies, this intelligent decision-making mechanism reduces intervention costs by an average of 30% while improving intervention effectiveness by 15%.

[0110] Step S605: Execute quality control measures including secondary inspection, expert review or task reallocation, and output the quality control results.

[0111] In this step, the quality control measures decided in step S604 are executed, and a detailed quality control results report is generated. Three main quality control measures are implemented, each with a dedicated execution process and feedback mechanism. The first is the secondary verification measure. When a labeled sample is determined to require secondary verification, an intelligent allocation mechanism is activated to select the most suitable annotator for review. This allocation is based on various factors: the reviewer's professional background should match the sample domain; the reviewer's experience level should generally be higher than or equal to the original annotator; the reviewer should have a certain degree of independence from the original annotator to avoid team bias; and the reviewer's current workload should allow for timely completion of the review task. A structured review interface is provided for the reviewer, displaying the original annotation results but not the specific anomalies detected to avoid confirmation bias. The reviewer independently completes the annotations, automatically comparing the differences between the two annotations and identifying key inconsistencies. Significant discrepancies may trigger third-party arbitration or expert review. The second is the expert review measure. Complex or high-risk anomalies are subject to an expert review process. We maintain an expert resource library, documenting each expert's expertise, availability, and review history. We intelligently match the most suitable expert based on sample characteristics and balance their workload. We provide experts with an enhanced review interface that not only displays the original annotations and detected anomalies, but also provides relevant references and historical similar cases. Experts can directly revise annotations, add detailed explanations, or offer guidance. All expert actions and feedback are recorded for subsequent quality analysis and annotator training. Third, we implement task reassignment. When an annotator is deemed significantly mismatched for the task, or their performance consistently falls short of expectations, a task reassignment process is triggered. We first assess the scope of the reassignment, which could be for a single sample, the current batch, or the entire task. We then use an intelligent matching engine to find the most suitable annotator, taking into account task characteristics, urgency, and available resources. We have designed a smooth handover mechanism to ensure that the reassignment process avoids loss of completed work or duplication of effort. We also provide constructive feedback to the original annotator, explaining the reason for the reassignment and offering suggestions for improvement. In addition to these three main measures, a number of auxiliary measures have also been implemented, such as real-time guidance (providing immediate feedback and suggestions during the annotation process), training recommendations (recommending targeted training based on identified issues), and rule optimization (adjusting annotation rules and guidelines based on recurring issues). The implementation process and results of all quality control measures are recorded in detail to form a structured quality control results report. This report includes: an anomaly detection summary, which lists all detected anomalies and their types, severity, and distribution characteristics; intervention details, which record the specific measures taken for each anomaly, the implementation time, and the person responsible; quality improvement effects, which compare the changes in quality indicators before and after the intervention and quantify the improvement effects; problem pattern analysis, which identifies recurring and potential problems; and improvement suggestions, which propose improvements to processes, rules, or training for the problems discovered.This quality control result report not only records the quality control status of the current batch, but also provides valuable data support for continuous quality improvement.

[0112] In step S106, the model parameters of the multi-dimensional user portrait and the multi-dimensional task feature vector are updated by a reinforcement learning algorithm to generate personalized feedback and capability improvement strategies, including: Step S701: Collect multi-source feedback data including the quality control results, the task execution status data, annotator self-evaluation and expert review, and form a comprehensive feedback data set that fully reflects the annotation performance through data fusion technology.

[0113] In this step, a comprehensive multi-source data collection and fusion mechanism was designed and implemented to integrate feedback from various channels and construct a rich and comprehensive feedback dataset. First, four key types of feedback data were collected: quality control results (from step S605), which include anomaly detection records, intervention details, and quality improvement results; task execution status data, which records task completion, time efficiency, resource consumption, and other execution-level information; annotator self-assessment data, which collects annotators' assessments and feelings about their own performance through a structured self-assessment questionnaire and open-ended feedback; and expert review data, which includes domain experts' evaluations of annotation quality, specific corrections, and professional guidance. Standardized collection interfaces and data schemas were designed for each data type 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-assessment, a user-friendly feedback interface was designed that pops up automatically after task completion, encouraging annotators to provide detailed feedback; and for expert review, a dedicated review tool was provided, supporting accurate evaluation and annotation down to the sample level. Subsequently, advanced data fusion techniques were applied to integrate this heterogeneous data into a unified, comprehensive feedback dataset. A multi-level fusion strategy was employed: feature-level fusion aligns and standardizes identical or similar features from different sources; instance-level fusion links data from different sources related to the same annotated sample or the same annotator; and decision-level fusion integrates the evaluations and judgments of different sources to form a more comprehensive quality assessment. Entity resolution techniques were applied to ensure accurate matching of entities (such as annotators, tasks, and samples) across different data sources. Time series alignment was also implemented to address timestamp discrepancies between data sources and construct a coherent timeline. Particular attention was paid to conflict resolution between data sources. For example, when expert evaluations and detection results differ, a credibility-based weighting strategy or evidence-based approaches were applied to resolve the conflict. A data quality assessment mechanism was also designed to evaluate the reliability, completeness, and timeliness of each data source and factor these factors into the fusion process. Through this multi-source data fusion, a comprehensive feedback dataset was generated that comprehensively reflects annotation performance. This dataset not only contains objective quality and efficiency indicators, but also subjective self-assessments and expert opinions. It includes both quantitative scoring data and qualitative text feedback, providing a rich information basis for subsequent personalized feedback generation and capacity improvement planning.

[0114] Step S702: Based on the comprehensive feedback dataset, natural language generation and explainable artificial intelligence technologies are applied to generate a personalized feedback report for each annotator targeting his or her strengths and weaknesses.

[0115] 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, the comprehensive feedback dataset is deeply analyzed to identify each annotator's key strengths and weaknesses. Various analytical techniques are applied: statistical analysis calculates statistics such as mean, variance, and trend for various quality and efficiency indicators; comparative analysis compares the annotator's performance with the team average, historical performance, or benchmarks; pattern recognition identifies 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 performs natural language processing on expert comments and self-evaluations to extract key insights and suggestions. Through these analyses, 3-5 key strengths and 3-5 areas for improvement are identified for each annotator, and specific supporting evidence and improvement suggestions are collected for each. Subsequently, natural language generation technology is applied to construct the personalized feedback report. A template-enhanced neural network generation method is employed, combining predefined high-quality feedback templates with flexible neural network generation capabilities. A multi-layered reporting structure was designed: a summary section concisely summarizes overall performance and key findings; a strengths section details the annotator's key strengths and provides specific examples; an improvement section tactfully and specifically identifies areas for improvement and provides practical suggestions; and a development plan section proposes targeted capacity building paths and specific learning resources. Special attention was paid to the language of feedback, ensuring a positive and constructive tone and avoiding negative or accusatory language. Sentiment analysis techniques were applied to ensure that feedback, while identifying issues, maintained an encouraging and supportive tone. Explainable AI techniques were also applied to make feedback more persuasive and actionable. Each evaluation was provided with clear explanations and specific evidence, such as "In the medical imaging task, your tumor boundary annotation accuracy reached 95%, exceeding the team average of 88%. You performed particularly well in handling cases with ambiguous boundaries, such as the precise annotations in samples #A237 and #B452." Visual explanations were also generated, such as performance radar charts, progress trend charts, and case comparison charts, to intuitively demonstrate the characteristics of the annotator's performance. A personalized expression method was also designed to adjust the level of detail, use of professional terminology, and expression style of feedback based on the annotator's preferences and characteristics. Cultural differences and language habits were taken into account to ensure that feedback was positively accepted by annotators from different cultural backgrounds. Through this combined NLG and XAI approach, the generated personalized feedback report not only accurately reflects the annotator's performance but also conveys improvement suggestions in a constructive and encouraging manner, providing effective guidance for the annotator's continued growth. User research shows that compared to traditional standardized feedback, this personalized feedback report has a 40% higher acceptance rate, a 35% higher actual application rate, and a 28% higher positive impact on annotator behavior.

[0116] Step S703: Analyze the annotator's shortcomings and development potential, combine the domain knowledge graph of the annotation task, and apply the recommendation algorithm to design a personalized ability improvement path for the annotator.

[0117] In this step, we conduct an in-depth analysis of the annotator's competency structure, identifying key shortcomings and potential for development. By integrating domain knowledge, we design a scientific, effective, and personalized competency improvement path. First, we conduct a comprehensive competency gap analysis. We construct an annotation competency model that breaks down annotation competency into multiple dimensions: domain knowledge (e.g., specialized knowledge in medicine, law, finance, etc.), technical skills (e.g., operational skills such as image annotation, text classification, and speech transcription), cognitive abilities (e.g., attention span, detail discrimination, and contextual understanding), and meta-abilities (e.g., learning ability, adaptability, and teamwork). Based on a comprehensive feedback dataset, we quantitatively evaluate annotators' performance across each competency dimension. We identify competency dimensions where performance falls significantly below expectations or significantly falls short of task requirements. These are labeled as competency shortcomings. We also identify potential annotators for development: those in which performance is strong and has room for further improvement, or those in which current performance is average but has a steep learning curve. Second, we construct and apply a domain knowledge graph for the annotation task. This knowledge graph is a structured representation of knowledge and skills in the annotation domain, consisting of concept nodes (such as professional terminology, annotation techniques, and quality standards) and relationship edges (such as premise, inclusion, and similarity relationships). Specialized knowledge graphs have been constructed for different annotation domains (such as medical imaging, natural language processing, and autonomous driving). The annotators' skill gaps and development potential are mapped onto the knowledge graph, identifying the knowledge and skill points that need to be learned. A graph analysis algorithm is then used to determine the dependencies between these points and the optimal learning sequence. Subsequently, a personalized recommendation algorithm is applied to design a skill improvement path for the annotators. A hybrid recommendation strategy is employed, combining multiple recommendation techniques: content-based recommendation recommends relevant learning resources based on the annotator's skills and interests; collaborative filtering recommends effective learning paths based on the learning history and achievements of similar annotators; knowledge graph reasoning recommends a scientific learning sequence based on the structure and dependencies of domain knowledge; and reinforcement learning optimizes the recommendation strategy by simulating the long-term benefits of different learning paths. A multi-tiered competency development path is generated for each annotator: short-term goals (small goals achievable within 1-2 weeks to provide an immediate sense of accomplishment), medium-term plans (a 1-3 month learning plan), and long-term development paths (a six-month to one-year career development plan). Each level includes specific learning tasks, recommended resources, and expected outcomes. Special attention is paid to the feasibility and effectiveness of the learning path, taking into account the annotator's time constraints, learning preferences, and available resources. A dynamic adjustment mechanism is also designed to continuously optimize the development path based on the annotator's learning progress and feedback. This approach, based on in-depth analysis and personalized recommendations, provides each annotator with a competency development path that is both tailored to their specific needs and aligned with their domain knowledge structure, effectively supporting their continuous growth and career development. Practice has shown that this personalized development path increases annotators' learning engagement by an average of 45% and increases their competency development by 32% compared to a general training program.

[0118] Step S704: Based on the comprehensive feedback data set and the quality control result, apply incremental learning and reinforcement learning techniques to update the model parameters of the multi-dimensional user portrait and the multi-dimensional task feature vector.

[0119] In this step, advanced machine learning techniques are applied to dynamically update user profile and task feature models based on the latest feedback data and quality control results, maintaining an accurate understanding of annotator capabilities and task characteristics. First, incremental learning updates of the multidimensional user profile model are implemented. Incremental learning allows the model to update its knowledge with new data without retraining the entire model. An incremental learning framework based on elastic weight merging is designed to effectively integrate existing model knowledge with information from new data. For each annotator, key features are extracted from the comprehensive feedback dataset, including the latest quality performance, efficiency indicators, domain expertise, and behavioral patterns. Feature importance analysis is applied to identify the key features that best reflect changes in annotator capabilities. Multiple incremental learning techniques are employed: for linear models, online learning algorithms such as stochastic gradient descent (SGD) or FTRL (Follow-the-Regularized-Leader) are used; for deep learning models, progressive networks or knowledge distillation techniques are applied; and for tree models, incremental decision trees or adaptive boosted tree algorithms are used. Special attention is paid to preventing catastrophic forgetting. Techniques such as elastic weight consolidation and experience replay are used to ensure that 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 and is particularly well-suited 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 the matching quality and annotation results. A reinforcement learning framework based on a deep Q-network (DQN) is implemented to learn the optimal feature extraction and representation strategy. Policy gradient methods are also applied to directly optimize the feature representation strategy, improving learning efficiency. An exploration-exploitation balance strategy based on Thompson sampling is designed to leverage known effective features while exploring potentially better feature representations. A multi-agent reinforcement learning framework is also implemented, with different agents responsible for feature optimization for different types of tasks, improving overall performance through knowledge sharing. During the model update process, a variety of technologies are used to ensure the stability and effectiveness of the update: a progressive update strategy that gradually adjusts model parameters in small steps to avoid drastic changes; an A / B testing mechanism that verifies the update effect on a small scale before fully applying it; a rollback mechanism that can quickly restore to the previous version when performance degradation is detected; and multiple versions running in parallel to compare the effects of different update strategies and select the optimal solution.An update frequency control mechanism has also been designed to dynamically adjust the timing of model updates based on the speed of data accumulation and the magnitude of change, balancing real-time performance with computational cost. This combined approach of incremental learning and reinforcement learning continuously optimizes model parameters for multi-dimensional user profiles and task feature vectors, enabling matching to continuously adapt to new data patterns and business needs while maintaining high-precision matching performance. Experiments have shown that compared to periodic batch retraining, this dynamic update approach improves model timeliness by an average of 15% and matching accuracy by 10%, while reducing computing resource consumption by 80%.

[0120] Step S705: Generate personalized feedback and capability improvement strategies based on the personalized feedback report and the personalized capability improvement path.

[0121] In this step, the personalized feedback report generated in step S702 and the capability improvement path designed in step S703 are integrated to construct a comprehensive and actionable personalized feedback and capability improvement strategy. This strategy not only provides evaluation and feedback on past performance but also outlines future development directions and specific action plans, forming a closed-loop continuous improvement mechanism. First, a personalized feedback delivery mechanism was designed. Taking into account the annotators' preferences and acceptance methods, multiple feedback channels were provided: an interactive dashboard that visually displays performance data and key feedback; regular email summaries that provide focused feedback and progress updates; instant notifications that provide timely feedback at critical moments (such as after completing important tasks); and one-on-one conversations that schedule video or face-to-face meetings for complex feedback requiring in-depth discussion. A feedback grading mechanism was also designed, categorizing feedback into daily feedback (frequent, brief, and focused on specific tasks); periodic feedback (regular, comprehensive, and reviews performance over a period of time); and milestone feedback (important milestones, in-depth analysis, and integrated with career development). Appropriate levels and channels were selected based on the importance and complexity of the feedback to ensure effective delivery. Second, an implementation framework for capability improvement was established. The competency development path is translated into a specific action plan, 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, including specially designed practice samples or simulations; mentor support, matching appropriate 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, milestone achievement badges, and learning points, to enhance annotators' learning motivation and sense of accomplishment. Special attention is paid to personalizing competency development strategies, adjusting them to annotators' learning styles, schedules, and career goals. For visual learners, more diagrams and videos are provided; for time-constrained annotators, more compact learning units are designed; and for annotators with specific career goals, learning content in related professional areas is strengthened. Third, an integrated feedback and improvement mechanism is implemented. Feedback is closely linked to competency development, ensuring that every feedback action is matched with an improvement action, and each competency development activity has clear goals and assessment criteria. A "feedback-action-evaluation" cycle is designed to enable annotators to see how feedback translates into concrete improvements and how these improvements impact subsequent performance and feedback. A long-term development planning feature has also been implemented, helping annotators connect short-term feedback and competency development with long-term career goals, enhancing their sense of purpose and direction in their work. Ultimately, a personalized feedback and competency development strategy document is generated, serving as both a comprehensive assessment of current performance and a detailed guide for future development. The document adopts a modular structure, including sections such as performance summary, detailed feedback, competency analysis, improvement plan, and resource guide, allowing annotators to focus on different sections as needed.An interactive version of the strategy is also available, allowing annotators to directly access learning resources, record their progress, and receive real-time guidance. Through this fully integrated approach, the generated personalized feedback and capability improvement strategy not only provides valuable feedback information but also transforms it into an executable action plan, effectively supporting the annotators' continuous growth and career development.

[0122] Step S706, cross-institutional capability assessment based on a 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 participating parties without sharing original data, thereby generating a federated learning collaborative architecture; applying differential privacy techniques to the capability data of each institution's annotators, adding calibration noise and desensitizing sensitive data to ensure data availability while protecting the privacy of annotators, thereby generating a privacy-protected capability dataset; based on the federated learning collaborative architecture, each participating institution trains a capability assessment model locally, sharing only model parameters rather than original data, thereby 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 utilizing this cross-institutional capability assessment benchmark model to optimize the allocation of annotation resources across institutions while preserving privacy. In the federated learning framework, "parties" specifically refer to the different annotation institutions participating in the federated learning collaboration. Each institution, as an independent participant, possesses its own local data and training models. These participants include, but are not limited to, various types of annotation institutions, such as professional data annotation companies, internal enterprise annotation teams, annotation departments of research institutes, and crowdsourcing annotation platforms. Each participant maintains annotator performance data in their local environment and trains a performance assessment model based on this data. "Model parameters" specifically refer to all learnable parameters in the performance assessment model trained locally by each participant. For deep neural network models, these parameters include the weight matrix, bias vectors, mean and variance parameters of the batch normalization layer, etc. For decision tree models, these parameters include tree structure information, splitting conditions, leaf node prediction values, etc. For linear models, these parameters include feature weight coefficients and intercept terms.

[0123] The model parameters of each participant have the following characteristics and functions: First, each participant's model parameters are trained based on the institution's own annotator capability data. Therefore, these parameters encode the ability characteristics and behavioral patterns of the institution's annotators. Second, these parameters reflect the standards and experience of different institutions in annotation quality assessment, efficiency measurement, professional skills certification, and other aspects. Third, because different institutions may have different business priorities, service areas, and quality requirements, 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 can be constructed that integrates the knowledge and experience of multiple institutions. This model has wider applicability and higher generalization capabilities.

[0124] The implementation of the secure aggregation algorithm includes the following key steps: The first step is parameter collection, where each participant encrypts the locally trained model parameters and uploads them to the federated learning central coordination server. This encryption utilizes homomorphic encryption technology to ensure that the parameters remain encrypted during transmission and processing, preventing the central server from accessing any participant's original parameter information. The second step is parameter verification, where the central server verifies the integrity and validity of the received encrypted parameters to ensure they have not been tampered with during transmission and conform to predefined format and range requirements.

[0125] The third step is the secure aggregation calculation phase, where the central server aggregates the encryption parameters of each party using a secure multi-party computation protocol. 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. Other methods include performance-based adaptive aggregation, which dynamically adjusts the aggregation weights based on the performance of each party's model on a standard test set, with higher weights awarded to better-performing models. Robust aggregation methods, such as median aggregation or trimmed mean aggregation, mitigate the impact of outliers on the aggregation results.

[0126] The fourth step is the aggregation results distribution phase, where the central server distributes the aggregated global model parameters to all participants, who then use these global parameters to update their local models. Differential privacy techniques are employed throughout the aggregation process to add calibration noise, further protecting the privacy of the participants. The fifth step is the model validation and optimization phase, where each participant uses the updated global model to test its 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 baseline; otherwise, the aggregation strategy or weight distribution scheme can be adjusted.

[0127] Through this secure aggregation algorithm, the cross-institutional capability assessment benchmark model constructed has the following advantages: First, the model integrates the knowledge and experience of multiple institutions and has stronger generalization and applicability; second, it achieves knowledge sharing while protecting the data privacy of each institution, and improves the annotation quality assessment level of the entire industry; third, the benchmark model can serve as an industry standard, providing a unified evaluation basis for cross-institutional annotator capability certification and resource allocation; fourth, through continuous federated learning updates, the benchmark model can continuously adapt to industry development and standard changes, maintaining its advanced nature and practicality.

[0128] In this step, an innovative federated learning framework was implemented, enabling different annotation organizations to collaboratively build a more comprehensive and accurate annotator competency assessment model while protecting data privacy, and achieving optimized resource allocation across organizations. First, a federated learning technical architecture supporting multi-organizational participation was designed. This architecture utilizes a star-shaped topology, consisting of a central coordination server and local nodes from multiple participating organizations. 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, performs model training locally, and only transmits model parameters to the central server. A standardized competency profile representation was defined to ensure consistent data format and semantics across organizations. This includes standardized competency dimension definitions (such as technical skills, domain knowledge, and quality performance), unified scoring criteria, and standardized data processing procedures. A detailed model update protocol was 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 was implemented, using end-to-end encryption and secure multi-party computation to protect parameter transmission. Second, differential privacy techniques are applied to the annotator performance data of each institution to enhance data protection. Differential privacy is a mathematically rigorous privacy protection framework that adds carefully calibrated noise to 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 based on the sensitivity of different features, adding larger noise to highly sensitive features (such as personally identifiable information) and smaller noise to less sensitive features (such as aggregated statistical data). Data desensitization techniques such as generalization (replacing exact values ​​with ranges), suppression (completely removing certain fields), and pseudonymization (replacing them with pseudonyms) are also applied. A privacy budget management mechanism is implemented to control the cumulative privacy loss within a preset threshold. Through these techniques, a privacy-protected performance dataset is generated, protecting individual privacy while preserving the analytical value of the data. Third, based on a federated learning collaborative architecture, distributed model training is coordinated among participating institutions. Each institution uses its own data to train performance assessment models locally. These models are typically complex models such as deep neural networks or gradient boosting trees, capable of capturing the multidimensional characteristics and complex patterns of annotator performance. Local training uses algorithms such as Federated Averaging (FedAvg) and Federated Proximal Optimization (FedProx) to ensure optimal model performance on local data. After training, each institution sends only model parameters (such as neural network weights or decision tree structure) to the central server without sharing any raw data. Model compression and sparsification techniques are also implemented to reduce communication overhead. Fourth, model parameters from all parties are merged using a secure aggregation algorithm.Multiple aggregation strategies were implemented: simple averaging, which takes the arithmetic mean of all participating model parameters; weighted averaging, which assigns weights based on each institution's data volume, data quality, or historical contributions; and performance-based aggregation, which adjusts weights based on each model's performance on the validation set. Secure aggregation protocols, such as homomorphic encryption or secret sharing, were also applied to prevent participants from seeing each other's original parameters. Through this secure aggregation, a cross-institutional capability assessment benchmark model was constructed, incorporating knowledge from across institutions for broader coverage and higher accuracy. Finally, this cross-institutional capability assessment benchmark model was used to achieve optimal cross-institutional resource allocation under privacy protection. A decentralized task matching protocol was designed, allowing different institutions to find the most suitable annotators for specific tasks without exposing their specific annotators' information. Federated learning-based recommendations were implemented to provide intelligent advice 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. This federated learning-based cross-institutional collaboration framework achieves a win-win situation for data privacy protection and resource optimization, significantly expanding the pool of high-quality annotation resources and improving the quality and efficiency of annotation across the industry.

[0129] Step S707, a gradient-free fast training mechanism based on a Hamiltonian graph network, including: redesigning the network structure of the ability evaluation model, adopting a graph neural network architecture based on Hamiltonian mechanics, representing the ability characteristics of the annotators as graph nodes, modeling the relationships between different ability dimensions as graph edges, and generating a Hamiltonian graph neural network model; abandoning the optimization method based on gradient descent, 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, and generating a gradient-free optimization strategy; designing parameter quantization and sparse communication mechanisms, highly compressing update information, only transmitting changes in key parameters, introducing a dynamic communication strategy to adjust the communication frequency according to the improvement of model performance, and generating a compressed communication protocol; based on the principle of Hamiltonian dynamics, constraining parameter updates on an isoenergy surface, ensuring energy conservation in the numerical calculation process through a symplectic geometric integrator, and generating a parameter update mechanism with energy conservation constraints; building an adaptive learning strategy library, pre-training multiple optimization strategies for labeling tasks of different scales and characteristics, dynamically selecting the strategy that best suits the current data distribution and business objectives at runtime, and generating an adaptive strategy selection mechanism.

[0130] In this step, an innovative, gradient-free, fast training mechanism for Hamiltonian graph networks was implemented, addressing the issues of slow model training and high communication costs in federated learning environments while maintaining high model performance and privacy protection. First, the network structure of the competency assessment model was redesigned, adopting a graph neural network architecture based on Hamiltonian mechanics. Hamiltonian mechanics is a mathematical framework for describing conservative dynamics, and Hamiltonian graph networks apply this principle to neural network design. The ability profiles of annotators are represented as graph nodes, with each node representing a competency dimension, such as technical proficiency, domain knowledge, or quality stability. The relationships between different competency dimensions are modeled as graph edges. These relationships can be complementary (such as the combination of technical skills and domain knowledge), reinforcing (such as the positive correlation between attention and quality), or inhibitory (such as the trade-off between speed and accuracy). State variables and dynamical parameters were assigned to each node and edge, and the Hamiltonian function H(q,p) was constructed, where q represents position (competency level) and p represents momentum (competency development trend). The Hamiltonian equations dq / dt=∂H / ∂p and dp / dt=-∂H / ∂q are used to describe the parameter evolution path, ensuring that the network training process adheres to 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 expressiveness 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 calculation and transmission are major computational and communication bottlenecks. A hybrid approach combining evolutionary strategies (ES) and Hamiltonian Monte Carlo (HMC) is implemented. ES is a population-based optimization method that determines the optimization direction by randomly perturbing parameters and evaluating performance changes, without the need for gradient calculation. An adaptive ES is implemented to dynamically adjust the perturbation amplitude and sampling strategy to improve search efficiency. Hamiltonian Monte Carlo is a sampling method based on physics simulation that utilizes Hamiltonian dynamics to generate high-quality parameter samples. Combining these two methods creates an efficient gradient-free optimization strategy: ES performs global exploration to quickly identify promising parameter regions; HMC performs local, refined search to find the optimal solution within promising regions. This hybrid strategy maintains the global nature of the search while improving local convergence speed. Third, a parameter quantization and sparse communication mechanism were designed to significantly reduce communication costs. Parameter quantization technology was implemented to discretize continuous floating-point parameters into a limited number of values, such as compressing 32-bit floating-point numbers to 8 bits or less. An adaptive quantization strategy was employed, using higher precision for important parameters and lower precision for less important parameters. Parameter sparsification was also implemented, transmitting only parameters that have changed significantly while keeping other parameters unchanged. An importance sampling mechanism was designed to prioritize transmission based on the degree of impact of a parameter on model performance.A dynamic communication strategy is also introduced to automatically adjust the communication frequency based on model performance improvements: the communication frequency is increased when performance rapidly improves and reduced when performance stabilizes, further optimizing communication efficiency. Fourth, an energy conservation-constrained parameter update mechanism is implemented based on Hamiltonian dynamics. The parameter update process is viewed as the motion of particles in a Hamiltonian, with each point in the parameter space having an energy value. Numerical computation is performed using a symplectic integrator (such as the Leapfrog integrator) to ensure energy conservation even at discrete time steps. This constraint ensures that parameter updates follow an iso-energy surface, providing a more stable optimization trajectory and effectively avoiding overfitting. An energy annealing mechanism is also implemented, allowing for higher energy (larger exploration range) at the beginning of training and gradually reducing it (more refined local search) as training progresses. Finally, a library of adaptive learning strategies is constructed to further improve training efficiency. A variety of pre-trained optimization strategies are available for labeling tasks of varying scales (small, medium, and 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 has been implemented that automatically selects or combines the most appropriate strategies based on the characteristics of the task at hand. An online learning mechanism has also been 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, providing strong technical support for cross-institutional annotator competency assessment.

[0131] The specific implementation of the Hamiltonian graph neural network model adopts a three-layer structure: the first layer is the graph node feature extraction layer, which uses the 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 the Hamiltonian energy conservation constraint graph convolution operation with a convolution kernel size of 64; the third layer is the prediction layer, which outputs the evaluation scores of the annotators in different ability dimensions.

[0132] The Hamiltonian function consists of two parts: kinetic energy and potential energy. The kinetic energy term is expressed in quadratic form, while the potential energy term is parameterized using a three-layer perceptron with hidden layer sizes of 128 and 64, respectively, and the tanh activation function. The model uses a symplectic integrator for parameter updates to ensure energy conservation.

[0133] Model training data is derived from local datasets of participating institutions within the federated learning environment. Each institution contains performance assessment records of at least 500 annotators, with each record containing 15-30 performance characteristics and corresponding performance scores. Training utilizes a gradient-free optimization strategy, combining evolutionary strategies and Monte Carlo methods, significantly reducing communication costs and computational burdens.

[0134] In actual application tests, compared with traditional federated learning methods, the gradient-free training method based on Hamiltonian graph networks reduced communication rounds by 75% and the amount of communication data by 85% at the same accuracy level, and improved the accuracy of capability assessment of small sample institutions by 12%. At the same time, it reduced the success rate of privacy attacks from 37% to below 5%, proving the comprehensive advantages of this method in efficiency, performance and privacy protection.

[0135] See also Figure 3 , Figure 3 This is a structural diagram of an artificial intelligence-based labeling task assignment device provided by an embodiment of the present invention. Figure 3 As shown, the artificial intelligence-based labeling task assignment device provided by the embodiment of the present invention includes: User profile building module 301 is used to obtain 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; A task feature analysis module 302 is configured to receive a task description document, a data sample, and a quality requirement document, parse the task description document using natural language processing, analyze the data sample using computer vision or natural language processing, extract indicators from the quality requirement document, and obtain a multidimensional task feature vector including task difficulty, domain attributes, time urgency, and professional knowledge requirements; An intelligent matching engine module 303 is configured to calculate the matching scores between the annotators and the tasks using a multi-objective optimization algorithm based on the multi-dimensional user portraits and the multi-dimensional task feature vectors, and generate an optimal task allocation solution; The task decomposition and combination module 304 is used to perform multimodal feature recognition and complexity evaluation on the complex tasks in the optimal task allocation solution, optimize the task structure using a fireworks algorithm based on Student's t distribution, and generate an optimized task unit structure; The dynamic quality control module 305 is used to perform labeling on the optimized task unit structure to obtain labeling results and task execution status data; perform real-time anomaly detection on the labeling results using a preset anomaly detection model to automatically identify abnormal samples; and predict the labeling quality of the labeling results using 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 306 is used to update the model parameters of the multi-dimensional user portrait and the multi-dimensional task feature vector based on the quality control results and the task execution status data through a reinforcement learning algorithm to generate personalized feedback and capability improvement strategies.

[0136] The artificial intelligence-based labeling task assignment method and device provided by the embodiment of the present invention achieves a precise match between labelers and tasks by constructing multi-dimensional user portraits and task feature vectors; optimizes the task structure using the fireworks algorithm based on Student's t distribution, thereby improving the processing efficiency of complex tasks; adopts a dynamic quality control system to monitor labeling quality in real time, and promptly discovers and corrects labeling deviations; updates model parameters based on a reinforcement learning algorithm, generates personalized feedback and capability improvement strategies, and promotes the continuous growth of labelers; introduces a federated learning framework and Hamiltonian graph network, and realizes cross-institutional capability assessment and resource optimization allocation under the premise of protecting privacy. The present invention significantly improves the accuracy and efficiency of labeling task assignment, improves labeling quality, enhances the scalability and response speed of the platform, and provides an effective solution for large-scale, high-quality data labeling.

[0137] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made by using the contents of the present invention description and drawings under the inventive concept of the present invention, or direct / indirect application in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for assigning labeling tasks based on artificial intelligence, characterized in that: The following steps are involved: Obtain 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; Receive a task description document, a data sample, and a quality requirement document, parse the task description document through natural language processing, analyze the data sample using computer vision or natural language processing, extract indicators from the quality requirement document, and obtain a multidimensional task feature vector including task difficulty, domain attributes, time urgency, and professional knowledge requirements; Based on the multi-dimensional user portrait and the multi-dimensional task feature vector, a matching score between the annotator and the task is calculated by a multi-objective optimization algorithm to generate an optimal task allocation plan; Performing multimodal feature recognition and complexity evaluation on the complex tasks in the optimal task allocation scheme, optimizing the task structure using a fireworks algorithm based on Student's t distribution, and generating an optimized task unit structure; Perform labeling on the optimized task unit structure to obtain labeling results and task execution status data; perform real-time anomaly detection on the labeling results using a preset anomaly detection model to automatically identify abnormal samples; and simultaneously predict the labeling quality of the labeling results using a preset quality prediction model to obtain detection and prediction results, trigger quality control measures, and output quality control results; Based on the quality control results and the task execution status data, the model parameters of the multi-dimensional user portrait and the multi-dimensional task feature vector are updated through a reinforcement learning algorithm to generate personalized feedback and capability improvement strategies.

2. The method according to claim 1, characterized in that The feature extraction and deep learning processing of the historical behavior data to construct a multi-dimensional user profile including professional skills, areas of expertise, annotation quality, and efficiency performance includes: Based on the historical behavior data, generating a standardized historical behavior data set through data cleaning, normalization, and outlier processing; Perform statistical analysis and feature engineering on the standardized historical behavior dataset to extract and quantify multi-dimensional feature indicators of the annotators' professional skills, distribution of areas of expertise, stability of annotation quality, efficiency performance, and time availability; Based on the multi-dimensional feature indicators, deep neural networks or graph neural networks are used for deep learning modeling to build a comprehensive user portrait model that can capture the capabilities, behavior patterns, and development potential of the annotators; Designing a weight adjustment algorithm based on time decay and latest performance, combining incremental behavioral data including task completion data and quality feedback to update the parameters of the comprehensive user profile model, thereby obtaining a real-time dynamically updated user profile model; Based on the real-time dynamically updated user portrait model, a multi-dimensional user portrait including professional skills, areas of expertise, annotation quality, and efficiency performance is generated.

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

4. The method according to claim 1, wherein The multi-objective optimization algorithm is used to calculate the matching scores between the annotators and the tasks, and generate the optimal task allocation plan, including: Based on the multidimensional user portrait and the multidimensional task feature vector, multidimensional similarity calculation is performed using cosine similarity and Euclidean distance methods to calculate the matching degree of the core dimension and obtain the matching score of the core dimension; Based on the platform's current business goals, 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 for constraint processing to generate a comprehensive matching score; Based on the comprehensive matching score, taking into account the current workload of the annotator, the urgency of the task, and the overall resource utilization efficiency, a combinatorial optimization algorithm is used to generate the platform's globally optimal task allocation strategy; Reinforcement learning is performed on the task allocation strategy, combining active push and passive selection modes to achieve intelligent recommendation and adaptive allocation of tasks; Based on the intelligent recommendation and adaptive allocation results, an optimal task allocation solution is generated.

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

6. The method according to claim 1, characterized in that The task structure is optimized by the fireworks algorithm based on the Student's t distribution to generate an optimized task unit structure, including: Modeling the complex tasks in the optimal task allocation solution, representing each complex task as a multidimensional feature vector containing content complexity, domain attributes, estimated completion time, and dependencies, and encoding the decomposition points and combination solutions of the complex tasks into a solution space to be optimized; Based on the solution space to be optimized, a fireworks algorithm based on Student's t distribution is initialized, each firework is set as a candidate solution, sparks are generated by Student's t distribution instead of traditional uniform distribution, and initial degree of freedom parameters are set to generate an initial set of candidate solutions; Through the degree of freedom adaptive adjustment mechanism, the first degree of freedom value is used to enhance the global exploration ability in the early stage of optimization. As the iteration progresses, the degree of freedom value is gradually increased to enhance the local fine search ability, generating a dynamically adjusted search strategy. Design a dimension-sensitive explosion amplitude control strategy. Based on the importance and sensitivity differences of different dimensions in the task feature space, different explosion amplitudes are assigned to each dimension to generate a dimension-weighted search space. An elite solution memory library based on historical experience is constructed. By combining the differential evolution strategy, the dynamically adjusted search strategy, and the dimensionally weighted search space, the initial candidate solution set is iteratively optimized and new search directions are guided to generate an optimized task unit structure. The elite solution memory library stores task decomposition and combination schemes that have performed well in history.

7. The method according to claim 1, characterized in that The method performs real-time anomaly detection on the annotation results through a preset anomaly detection model, automatically identifies abnormal samples, and predicts the annotation quality of the annotation results through a preset quality prediction model, obtains 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 specifications, a multi-dimensional quality evaluation index system including accuracy, consistency, completeness and timeliness is constructed; Applying an isolation forest or autoencoder unsupervised learning algorithm to perform anomaly detection on the labeled 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, the decision tree algorithm is used to automatically determine the intervention type and urgency, identify the abnormal samples and trigger corresponding quality control measures; Perform quality control measures including secondary inspection, expert review, or task reallocation, and output the quality control results.

8. The method according to claim 1, characterized in that The method of updating the model parameters of the multi-dimensional user portrait and the multi-dimensional task feature vector by using a reinforcement learning algorithm to generate personalized feedback and capability improvement strategies includes: Collect multi-source feedback data including the quality control results, the task execution status data, annotator self-evaluation and expert review, and form a comprehensive feedback data set 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 based on 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 result, applying incremental learning and reinforcement learning techniques to update the model parameters of the multi-dimensional user portrait and the multi-dimensional task feature vector; Based on the personalized feedback report and the personalized capability improvement path, personalized feedback and capability improvement strategies are generated.

9. The method according to claim 1, characterized in that It also includes a cross-institutional capacity assessment based on the federated learning framework, including: Design a federated learning technology architecture that supports multi-institutional participation, define standardized capability feature representations and model update protocols, ensure that all participants can collaborate without sharing original data, and generate a federated learning collaborative architecture; Differential privacy technology is applied to the annotator capability data of each institution. By adding calibration noise and desensitizing sensitive data, the privacy of annotators is protected while ensuring data availability, thus generating a privacy-protected capability dataset. 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 combining the model parameters of all parties through a secure aggregation algorithm, a cross-institutional capability assessment benchmark model is constructed; By utilizing the cross-institutional capability assessment benchmark model, the optimal configuration of cross-institutional annotation resources can be achieved under the premise of privacy protection.

10. The method according to claim 9, characterized in that It also includes a fast gradient-free training mechanism based on Hamiltonian graph networks, including: The network structure of the competency assessment model was redesigned, using a graph neural network architecture based on Hamiltonian mechanics. This architecture represents the ability characteristics of annotators as graph nodes, and models the relationships between different competency dimensions as graph edges, generating a Hamiltonian graph neural network model. Abandoning the optimization method based on gradient descent, a hybrid gradient-free optimization strategy of evolutionary strategy and Hamiltonian Monte Carlo is adopted. By randomly perturbing parameters and evaluating performance changes, the optimization direction is determined and a gradient-free optimization strategy is generated. Design parameter quantization and sparse communication mechanisms to highly compress update information and transmit only key parameter changes. Introduce a dynamic communication strategy to adjust the communication frequency based on model performance improvements and generate a compressed communication protocol. Based on the Hamiltonian dynamics principle, the parameter update is constrained on the isoenergy surface. The energy conservation in the numerical calculation process is ensured by the symplectic geometric integrator, and the parameter update mechanism with energy conservation constraint is generated. Build an adaptive learning strategy library, pre-train multiple optimization strategies for labeling tasks of different scales and characteristics, dynamically select the strategy that best suits the current data distribution and business goals at runtime, and generate an adaptive strategy selection mechanism.

11. An artificial intelligence-based labeling task assignment device, characterized in that: include: A user profile building module is used to obtain 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; A task feature analysis module is configured to receive a task description document, a data sample, and a quality requirement document, parse the task description document through natural language processing, analyze the data sample using computer vision or natural language processing, extract indicators from the quality requirement document, and obtain a multidimensional task feature vector including task difficulty, domain attributes, time urgency, and professional knowledge requirements; An intelligent matching engine module is used to calculate the matching score between the annotator and the task based on the multi-dimensional user portrait and the multi-dimensional task feature vector through a multi-objective optimization algorithm to generate an optimal task allocation plan; A task decomposition and combination module is used to perform multimodal feature recognition and complexity evaluation on complex tasks in the optimal task allocation scheme, optimize the task structure using a fireworks algorithm based on Student's t distribution, and generate an optimized task unit structure; A dynamic quality control module is used to perform labeling on the optimized task unit structure to obtain labeling results and task execution status data; perform real-time anomaly detection on the labeling results using a preset anomaly detection model to automatically identify abnormal samples; and predict the labeling quality of the labeling results using a preset quality prediction model to obtain detection and prediction results, trigger quality control measures, and output quality control results; A feedback and learning module is used to update the model parameters of the multi-dimensional user portrait and the multi-dimensional task feature vector based on the quality control results and the task execution status data through a reinforcement learning algorithm to generate personalized feedback and capability improvement strategies.

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