Vocational ability gene map dynamic planning method based on multi-modal data and federal learning

Abstract

The invention relates to the technical field of intelligent vocational ability analysis, and discloses a dynamic planning method for a vocational ability genetic map based on multi-modal data and federal learning. The method comprises the steps that multi-source heterogeneous data in occupational activities is collected, and a multi-modal data set is formed through data cleaning and standardization processing; a graph neural network technology is utilized to construct a correlation topology between capability elements, spatio-temporal context information is fused, a dynamically evolved vocational capability gene map is established, and the accuracy and interpretability of vocational capability analysis are improved. Multi-node cooperative training is realized by adopting a federated learning framework, data privacy security is ensured in combination with a differential privacy technology, and the problem of data islands is solved. A capability evolution path is simulated based on a three-dimensional visualization and digital twinning technology, an occupational development scheme is generated through an intelligent recommendation algorithm, and a real-time feedback optimization mechanism is established. According to the method, dynamic, visual and personalized vocational ability analysis is realized, and accurate decision support is provided for vocational planning.

Description

Technical Field

[0001] This invention relates to the field of intelligent analysis technology of professional competence, specifically a dynamic programming method for professional competence gene maps based on multimodal data and federated learning. Background Technology

[0002] Career competency assessment and planning are core components of human resource management and career development. Existing technologies typically rely on structured resume data, single skill certificates, or periodic performance evaluation reports. When dealing with the complex components of an individual's career competency and their interrelationships, these methods often employ traditional statistical analysis or basic machine learning models. Such models treat competencies as isolated, static sets of labels, failing to effectively represent the complex, non-linear interactions between different skills, knowledge, and experiences, and also failing to capture the dynamic evolution of career competencies over time and across different career scenarios. Due to the lack of in-depth analysis of the topological structure between competency elements and modeling their spatiotemporal dynamics, existing methods struggle to generate predictive competency maps that reflect the real career ecosystem.

[0003] Occupational competency data is highly sensitive and is scattered across private databases of different companies or institutions, creating significant data silos. To build a macro-level occupational competency model, the conventional approach is to attempt to centralize the data on a single server. This approach faces substantial data privacy and security compliance risks. Another compromise is to train local models only on isolated datasets, but this results in a narrow model perspective, poor generalization ability, and an inability to reflect overall competency trends across organizations and industries. In the absence of an effective data collaboration framework, how to collaboratively train a high-quality, global competency model using multi-source data without aggregating the raw data is a major technological obstacle currently facing the field. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic programming method for vocational competence gene maps based on multimodal data and federated learning, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, this invention provides a dynamic programming method for vocational competence gene maps using multimodal data and federated learning, the method comprising: Acquire multi-source heterogeneous data from professional activities, and perform data purification and standardization preprocessing operations on the multi-source heterogeneous data to generate a multimodal data set in a unified format; The multimodal dataset is input into the multimodal feature fusion module, and graph neural network technology is used to analyze the ability elements and fuse spatiotemporal context information to construct a professional ability gene map model. The occupational ability gene map model is collaboratively optimized using a federated learning-based distributed training framework. Multiple local nodes participate in the training, and a differential privacy mechanism is applied to generate a privacy-preserving ability evolution model. Based on the capability evolution model, a dynamic map of capability distribution is generated using three-dimensional visualization technology, and the evolution path and change pattern of capability requirements are simulated using a digital twin system. Based on the simulated output of the dynamic diagram of the ability distribution, an intelligent recommendation strategy is used to generate career development paths, and the planning scheme is dynamically adjusted through a real-time feedback mechanism.

[0006] Preferably, the step of acquiring multi-source heterogeneous data from professional activities and performing data purification and standardization preprocessing operations on the multi-source heterogeneous data to generate a multimodal data set in a unified format includes: Occupation-related data were collected from multiple data sources, including text records, skill indicators, and job task descriptions. Data denoising and outlier detection were performed. Principal component analysis-based dimensionality reduction was used to eliminate data dimensionality differences and generate a dimensionality-reduced multimodal data sequence. Feature encoding techniques are applied to the dimensionality-reduced multimodal data sequence. A word embedding model is used to convert text data into numerical vectors and integrate unstructured data to obtain encoded multimodal feature representations. For the encoded multimodal feature representation, a feature importance evaluation algorithm is applied to calculate the weight values ​​of different features. Feature selection is performed based on the weight values ​​to highlight the influence of key features and generate a weighted multimodal feature representation. The weighted multimodal feature representation is input into the standardization processing unit, and the feature scale is adjusted by the min-max scaling method to generate a multimodal data set in a unified format.

[0007] Preferably, the step of inputting the multimodal dataset into the multimodal feature fusion module, using graph neural network technology to analyze ability elements, and fusing spatiotemporal context information to construct a professional ability gene map model includes: A unified format of multimodal data is input into a graph convolutional network, where a graph attention mechanism is used to capture the relationships between nodes, and a graph autoencoder extracts global features to obtain a preliminary representation of capability elements. Based on the preliminary representation of capability elements, a graph embedding algorithm is applied to construct a capability topology, where nodes represent capability units and edges represent the correlation strength between capabilities. A community detection algorithm is used to identify capability clusters, forming a capability relationship graph. Based on the aforementioned ability relationship diagram and combined with spatiotemporal context information, a time-series graph neural network model is used to project ability elements into a continuous space to construct an occupational ability gene map model, wherein the gene map model contains the hierarchical structure and dynamic evolution attributes of abilities.

[0008] Preferably, the step of collaboratively optimizing the vocational ability gene map model using a federated learning-based distributed training framework, involving multiple local nodes in training and applying differential privacy mechanisms to generate a privacy-preserving ability evolution model, includes: The professional ability gene mapping model is distributed to multiple local nodes. Each local node trains a personalized model using a local dataset and uses a Laplace mechanism to add noise to protect data privacy. The model updates from each local node are aggregated on the central server, and the model gradients are integrated using a secure aggregation algorithm. Through multiple rounds of iterative optimization of the global model, a preliminary collaborative capability model is obtained. For the preliminary collaborative capability model, the Bayesian inference method is applied to calculate the posterior distribution of the model parameters, and the uncertainty is estimated through variational inference to generate a probabilistic capability evolution model. For the probabilistic capability evolution model, an outlier detection algorithm is used to identify abnormal capability patterns and generate a privacy-preserving capability evolution model.

[0009] Preferably, the step of generating a dynamic map of capability distribution using 3D visualization technology based on the capability evolution model, and simulating the evolution path and change pattern of capability requirements using a digital twin system, includes: The output of the capability evolution model is mapped to a three-dimensional spatial coordinate system, and the discrete capability points are spatially interpolated using the inverse distance weighted interpolation method to create a preliminary three-dimensional capability distribution dynamic map. Based on historical capability evolution data, a time sliding window is introduced, and the Kalman filter algorithm is used to dynamically update the three-dimensional capability distribution dynamic map to reflect the real-time changes in capabilities. Input the dynamic map of three-dimensional capability distribution into the digital twin system to construct a virtual capability environment. Simulate the diffusion process of capability demand through a particle system and predict the evolution path. By using time series forecasting models to analyze historical evolution data, we can infer the changing patterns of future capability requirements and generate simulated outputs of capability evolution.

[0010] Preferably, the simulated output of the ability distribution dynamic map uses an intelligent recommendation strategy to generate career development paths and dynamically adjusts the planning scheme through a real-time feedback mechanism, including: Based on historical career path data, a sequence generation model is trained to generate candidate career paths, and a variational autoencoder is used to ensure the diversity and rationality of the paths. An evaluation model is constructed to calculate the matching degree between the current capability state and the candidate path. A convolutional neural network is used to calculate the similarity score and output the evaluation result. Based on the evaluation results, a hierarchical clustering algorithm is used to divide the path into multiple categories, and combined with the real-time update of the capability distribution dynamic graph, a multi-level recommendation threshold is generated. By comparing real-time capability characteristics with multi-level recommendation thresholds, a reinforcement learning model is applied to adjust path recommendations based on feedback signals, thereby achieving dynamic adjustment of career development paths.

[0011] Preferably, the step of collecting occupation-related data from multiple data sources, including text records, skill indicators, and job task descriptions, performing data denoising and outlier detection operations, and employing a data dimensionality reduction method based on principal component analysis to eliminate data dimensionality differences, generates a dimensionality-reduced multimodal data sequence, including: Occupational data is obtained from an online platform using an application programming interface (API), and the data format is converted and encoded uniformly. A median filtering algorithm is then used to remove noise. Missing data is filled using multiple imputation methods, and reasonable values ​​are estimated based on clustering algorithms to ensure data integrity. Principal component analysis is applied to reduce the dimensionality of high-dimensional data. Eigenvectors and eigenvalues ​​are calculated, and principal components are selected to form a low-dimensional representation, generating a multimodal data sequence after dimensionality reduction.

[0012] Preferably, the step of inputting a multimodal data set in a unified format into a graph convolutional network, wherein a graph attention mechanism is used to capture relationships between nodes, and a graph autoencoder extracts global features to obtain a preliminary representation of capability elements, including: A graph structure is constructed for the multimodal dataset, with each data point as a node and edges calculated based on similarity. The graph is then input into a convolutional network for feature propagation. In graph convolutional networks, a graph attention mechanism is applied to calculate the attention weights between nodes, aggregate neighbor node information, and generate attention-enhanced feature representations. A graph autoencoder is used to encode and decode the attention-enhanced feature representation, and the feature extraction is optimized by reconstruction loss to obtain a preliminary capability element representation.

[0013] Preferably, the process of aggregating model updates from local nodes on the central server, integrating model gradients using a secure aggregation algorithm, and optimizing the global model through multiple rounds of iteration to obtain a preliminary collaborative capability model includes: After each local node trains the model, it encrypts the model gradient and uploads it to the central server, using homomorphic encryption to protect the data transmission. After the central server decrypts the gradient, it calculates the weighted average gradient, updates the global model parameters, and uses an adaptive optimization algorithm to accelerate convergence. Repeat the iterative process until the model loss stabilizes, verify the performance of the global model on the preserved dataset, and obtain a preliminary collaborative capability model.

[0014] Preferably, after dynamically adjusting the planning scheme through a real-time feedback mechanism, the method further includes: Establish a user behavior log recording system to collect user interaction data on the recommended career development path; The interactive operation data is cleaned and its features are extracted to obtain a user behavior feature vector; The user behavior feature vector is compared with the prediction results of the capability evolution model to calculate the path execution deviation. By applying a reinforcement learning framework, the parameters of the policy network are adjusted based on the path execution bias to generate an optimized recommendation policy; Based on the optimized recommendation strategy, the intelligent recommendation model is updated, and a new career development path is generated.

[0015] Compared with the prior art, the beneficial effects of the present invention are: By employing graph neural network (Graph Neural Network) technology to analyze capability elements and integrate spatiotemporal context information, discrete capability units such as skills, knowledge, and project experience can be modeled as nodes in a graph structure, with logical or temporal relationships defined through edges in the graph. The message passing mechanism of Graph Neural Networks allows nodes to aggregate information from their neighborhoods, thereby capturing deep, indirect connections between capability elements. By introducing spatiotemporal context, the model can perceive the dynamic patterns of capability demand fluctuations with industry cycles, technological innovations, and regional economic changes. This modeling approach elevates professional capabilities from a one-dimensional list of labels to a multi-dimensional, interconnected, and dynamically evolving network graph. Its representational power surpasses the feature flattening processing of traditional machine learning models, enabling the generated genetic map to better reveal the intrinsic structure and evolutionary dynamics of professional capabilities.

[0016] The federated learning-based distributed training framework, combined with differential privacy mechanisms, eliminates the need to aggregate raw data from various data holders during model training. Participants only calculate model update gradients locally using their own data, and then upload only encrypted or noisy gradient parameters to a central server for aggregation. Differential privacy technology adds mathematically controlled random noise to local model updates, preventing outsiders from inferring any individual data point's information from the aggregated results. This mechanism provides quantifiable privacy guarantees mathematically, fundamentally addressing the privacy leakage risks associated with collaborative training of sensitive multi-source data. It breaks down data silos, enabling the use of broader, more macro-level data samples to optimize capability evolution models, while rigorously protecting the core data assets of individuals and organizations, meeting data security and compliance requirements. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the working principle of the dynamic programming method for vocational ability gene maps using multimodal data and federated learning as described in this invention. Figure 2 Flowchart for constructing a genetic map model of professional competence; Figure 3 A flowchart generated for the privacy protection capability evolution model; Figure 4 This is a dynamic analysis diagram of the three-dimensional capability distribution. Figure 5 Optimization process analysis diagram for real-time feedback. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 This invention provides a dynamic programming method for vocational ability gene maps using multimodal data and federated learning, and the specific implementation is as follows: This method acquires multi-source heterogeneous data from occupational activities and performs data purification and standardization preprocessing to generate a unified-format multimodal dataset. This dataset is input into a multimodal feature fusion module, which uses graph neural network technology to analyze capability elements and integrates spatiotemporal context information to construct a vocational capability gene map model. Subsequently, a distributed training framework based on federated learning is used to collaboratively optimize the vocational capability gene map model. Multiple local nodes participate in the training, and a differential privacy mechanism is applied to generate a privacy-preserving capability evolution model. Based on the capability evolution model, a dynamic capability distribution map is generated using 3D visualization technology. A digital twin system is then used to simulate the evolution path and change patterns of capability requirements. Finally, based on the simulated output of the capability distribution map, an intelligent recommendation strategy is used to generate career development paths, and a real-time feedback mechanism dynamically adjusts the planning scheme. The entire process ensures the dynamism and privacy security of vocational capability analysis.

[0020] Example 1: Data Acquisition and Preprocessing Stage. This stage involves collecting occupational-related data from multiple data sources and performing a series of operations to generate a unified-format multimodal dataset. In practice, the acquisition of heterogeneous multi-source data includes various types such as text records, skill indicators, and job task descriptions. This data is acquired in real-time from an online platform via an application programming interface (API), and undergoes data format conversion and encoding standardization to eliminate differences between source systems. Data denoising employs a median filtering algorithm, which calculates the neighborhood median based on a sliding window, effectively smoothing random noise. Outlier detection uses statistical methods such as Z-score or IQR to identify and remove data points deviating from the normal range, ensuring data quality. The Z-score calculation formula is as follows:

[0021] in: It is the first Z-score values ​​of each data point It is the first One original data point, It is the mean of the data set. It is the standard deviation of the data set, usually when If the data point is an outlier, it is considered an outlier.

[0022] In the IQR method, the formula for calculating the Interquartile Range (IQR) is:

[0023] in: It is the first quartile (25th percentile) of the dataset. It is the third quartile (75th quartile) of the dataset, and the outlier detection range is less than... or greater than Data points.

[0024] During data cleaning, multiple imputation is used to fill in missing data. This method generates multiple complete datasets and estimates reasonable values ​​based on clustering algorithms such as K-means. Clustering algorithms group similar data points and deduce missing values ​​from similar datasets, thus maintaining data integrity. Principal component analysis (PCA) is then applied for dimensionality reduction, calculating the covariance matrix of the data. The calculation formula is:

[0025] in: It is the th in the covariance matrix Line number Column elements, It refers to the number of data samples. It is the first The first sample 1 eigenvalue, It is the first The mean of each feature, It is the first The first sample 1 eigenvalue, It is the first The mean of each feature.

[0026] By solving the covariance matrix Characteristic equation Extracting feature vectors and eigenvalues (in (This represents the variance contribution along the corresponding eigenvector direction). Principal components with high cumulative contribution rates are selected to form a low-dimensional representation. (Cumulative contribution rate) The calculation formula is:

[0027] in: It is the number of selected principal components. It is the total number of features in the original data. It is the first A feature value, usually selected The smallest The value is used as the final principal component count, which in turn generates a dimensionality-reduced multimodal data sequence, serving as the basis for subsequent feature processing.

[0028] In some embodiments, the dimensionality-reduced multimodal data sequence enters the feature encoding stage. Feature encoding techniques use word embedding models such as Word2Vec or BERT to transform text data into numerical vectors. These word embedding models map words to a low-dimensional space through neural network training, capturing semantic relationships. For unstructured data such as images or audio, convolutional neural networks or autoencoders are used to extract feature tensors. The integration process aligns features from different modalities to a unified dimension through concatenation or weighted fusion, obtaining the encoded multimodal feature representation. In specific implementations, a feature importance evaluation algorithm is subsequently applied. This algorithm, based on information gain or random forest methods, calculates the weight of each feature to the target variable. Information gain measures the entropy reduction after feature partitioning, while random forests determine feature importance through decision tree voting. After normalization, the weights are selected based on a preset threshold, retaining high-weight features and highlighting the influence of key features, resulting in a weighted multimodal feature representation. The weighted multimodal feature representation is further input into a standardization unit, which uses a min-max scaling method to linearly transform feature values ​​to the range of zero to one. The formula is:

[0029] in: S is the standardized eigenvalue. These are the original eigenvalues. It is the minimum value of this feature. It is the maximum value of this feature.

[0030] Optionally, during the data acquisition phase, the application programming interface (API) can be configured to use periodic polling or event-triggered modes to acquire data from multiple online platforms, such as professional social networks or enterprise systems. Data format conversion involves parsing formats such as XML, JSON, or CSV, and encoding is uniformly based on the UTF-8 standard to ensure character consistency. After format conversion and encoding unification of multi-source heterogeneous data, a data consistency verification step needs to be added to compare related data of the same user or the same professional task in different data sources. For example, verify whether the skill level in the professional skills certification database is logically consistent with the skill assessment result of the same employee in the enterprise human resources system. If conflicts exist, they are marked and corrected through manual review or preset rules. The window size of the median filtering algorithm can be adjusted according to the data sampling frequency. For example, a larger window is used for high-frequency data to enhance the noise reduction effect. In outlier detection, the Z-score method sets a threshold such as ±3 standard deviations, and the IQR method uses 1.5 times the interquartile range to automatically mark outliers. The multiple interpolation method generates 5-10 interpolated datasets, and the K value in the clustering algorithm is determined by the elbow rule to optimize the interpolation accuracy. In principal component analysis, the selection criterion for principal components is a cumulative variance contribution rate exceeding 85%, ensuring that key information is retained after dimensionality reduction. When integrating unstructured data in the feature encoding stage, image data requires preprocessing, including image cropping and grayscale conversion, while audio data undergoes noise reduction and segmentation to ensure a unified basic format before feature extraction. This is then integrated with the numerical vectors of the text data. The word embedding model is trained using a large-scale corpus with dimensions set to 100-300. During unstructured data integration, an attention mechanism can be applied to weighted fusion to dynamically adjust modality contributions. In feature importance evaluation, the number of trees in the random forest is set to 100, and the information gain threshold is optimized through cross-validation. Before processing with the min-max scaling method, the feature range is statistically calculated, resulting in a uniform data distribution after scaling.

[0031] Understandably, the design of the entire preprocessing stage ensures the consistency and usability of multimodal data. Data denoising and outlier detection reduce noise interference, multiple imputation maintains data integrity, principal component analysis reduces computational complexity, feature encoding and importance assessment improve feature representativeness, and standardization promotes the convergence of subsequent models. In implementation, these operations are implemented through a pipeline architecture, automating data flow. The output of each step is validated, such as using statistical tests to check data distribution and ensure that the generated multimodal dataset conforms to the expected format. In some embodiments, a data validation step can be added to confirm the processing effect by comparing statistical indicators, such as mean and variance, of the raw and processed data. Optionally, a logging system records the parameters and results of each operation for debugging and auditing. Understandably, this preprocessing approach provides high-quality input for subsequent feature fusion and model building.

[0032] In practical implementation, the hardware deployment for the data acquisition and preprocessing stages involves distributed data storage systems, such as Hadoop or Spark clusters. Processing nodes perform denoising, imputation, and dimensionality reduction operations in parallel. Software implementation uses Python or Java libraries, such as Scikit-learn for principal component analysis and clustering, and NLTK or TensorFlow for word embeddings, ensuring efficient processing of large-scale data. Application programming interfaces (APIs) are implemented via RESTful APIs, authentication mechanisms such as OAuth2 ensure data security, and data streams are managed through message queues such as Kafka for asynchronous processing. In the feature encoding stage, word embedding models can be pre-trained or learned online. Unstructured data feature extraction uses pre-trained models such as ResNet or VGG to accelerate processing. The weight calculation for feature importance evaluation is visualized, displaying feature rankings through charts to aid decision-making. Standardized processing units are integrated into the data pipeline, automatically adapting to data changes, with dynamic updates to the minimum-maximum scaling parameters.

[0033] This implementation method supports scalability through modular design. For example, new data sources can be easily added by adapting the application programming interface (API), and preprocessing algorithms can be replaced, such as using wavelet transform for denoising or t-SNE for dimensionality reduction, maintaining flexibility. In practice, performance optimization measures include caching intermediate results and using incremental learning to process streaming data, reducing redundant computations. The entire stage outputs a multimodal data set in a uniform format, stored as tensors or data frames for use in the next stage.

[0034] Example 2: See Figure 2This embodiment details the construction of a multimodal feature fusion module. This module receives a unified-format multimodal dataset as input and uses graph neural network technology to analyze capability elements, fuse spatiotemporal context information, and finally construct a professional capability gene map model. In specific implementation, the unified-format multimodal dataset is first converted into a graph structure data form, where each data point is defined as a graph node. Edges between nodes are constructed based on pre-calculated similarity metrics, commonly including cosine similarity or Euclidean distance. A threshold is set to determine whether edges exist, thus constructing the initial graph data structure. This initially constructed graph is input into a graph convolutional network for feature learning. The graph convolutional network propagates node features on the graph through multiple convolutional operations. Each graph convolutional operation aggregates information about the node itself and its neighboring nodes, updating the node's feature representation. In the graph convolutional network, a graph attention mechanism is integrated into the convolutional layers. The graph attention mechanism calculates an attention coefficient for each node pair, which represents the importance of neighboring nodes to the central node. By weighted aggregation of neighboring node information, attention-enhanced node feature representations are generated. When using graph attention mechanisms to capture relationships between nodes, the initial priority of attention weights needs to be set according to the type of capability element. For example, the initial association weight between technical capability elements and tool operation capability elements can be set higher than that between technical capability elements and administrative office capability elements, and then dynamically adjusted through model training. Graph autoencoders are used for attention-enhanced feature representation. A graph autoencoder consists of an encoder and a decoder. The encoder maps node features to a low-dimensional latent space to obtain a compressed feature representation. The decoder attempts to reconstruct the original graph structure information from the latent space. By minimizing the reconstruction loss function, such as graph reconstruction loss or feature reconstruction loss, the parameters of the entire graph autoencoder are optimized so that the extracted latent features can retain the structural and attribute information of the original data to the greatest extent, and finally output a preliminary representation of capability elements.

[0035] In some embodiments, a graph embedding algorithm is applied to the initial capability element representation. This algorithm aims to map nodes in the graph to a low-dimensional vector space while preserving the graph's topology as much as possible. For example, Node2Vec or depth-walk algorithms are used to generate node sequences by simulating random walks and to learn the vector representations of nodes using a Skip-gram model, thereby constructing a capability topology. In this topology, each node represents a basic capability unit, and the edges between nodes represent the strength of the association between capability units. This strength can be represented by vector dot products or specific weight parameters. Based on this, community detection algorithms are used to analyze the capability topology. Community detection algorithms, such as the Louvain method or the Girvan-Newman algorithm, identify clusters of nodes with tightly connected internal connections and sparse external connections by optimizing metrics such as modularity. These clusters are identified as different capability clusters, ultimately forming a visualized capability relationship graph depicting the complex relationships between capabilities. After identifying capability clusters using the community detection algorithm, each capability cluster needs to be tagged. The tag content should be combined with the professional classification of the professional capability field. For example, clusters containing capability units such as programming, algorithm design, and system development are tagged as "technology development clusters", and clusters containing capability units such as project planning, resource coordination, and risk management are tagged as "project management clusters", which facilitates the subsequent interpretation and application of the gene map.

[0036] Optionally, during graph structure construction, the similarity threshold can be dynamically adjusted based on data distribution characteristics, such as using percentile methods to determine the threshold. The number of layers in the graph convolutional network can be selected to be 2 to 3 to avoid oversmoothing. The graph attention mechanism can adopt a multi-head attention mechanism, such as using 8 attention heads to capture node relationships from different subspaces, enhancing the model's expressive power. The latent space dimension of the graph autoencoder is usually set to a value much lower than the original feature dimension, such as 32 or 64 dimensions. The reconstruction loss function can be selected as mean squared error loss for continuous features and cross-entropy loss for discrete graph structures. In the graph embedding algorithm, the parameters p and q of Node2Vec can be adjusted to adjust the walk strategy, favoring breadth-first or depth-first search. The community detection algorithm can be set to iterative optimization until the modularity no longer significantly improves.

[0037] It is understandable that the initial representation of capability elements is the foundation for the subsequent construction of the gene map. It effectively aggregates local and global information through graph neural networks, while capability topology and capability relationship graph organize micro-capability units into macro-level, understandable structured patterns.

[0038] In practical implementation, based on the established capability relationship graph, it is necessary to further process it by incorporating spatiotemporal context information. This spatiotemporal context information mainly includes timestamp data and spatial location data related to capabilities. Timestamp data records the specific time points when capability elements are observed or measured, while spatial location data may be associated with geographical locations or organizational structures. A temporal graph neural network model is used to process this dynamic graph data. This model introduces the time dimension into the graph neural network, for example, using a graph recurrent neural network or a graph neural network architecture based on temporal convolution. This model can handle the evolution of the graph structure over time. It takes the capability relationship graph on each time slice as input, captures temporal dependencies through recurrent connections or temporal convolutional layers, and projects the dynamically changing capability elements into a continuous latent space to obtain their evolutionary trajectory. The construction of the occupational ability gene map model is based on the projection in this continuous space. This model not only includes the hierarchical structure of abilities, that is, the parent-child relationship or affiliation relationship between ability units of different granularities, but also includes the dynamic evolutionary attributes of abilities, such as the emergence, extinction, strengthening or weakening of abilities over time. Gene map models are usually stored and managed in graph databases or specific data structures, supporting complex map queries and evolutionary analysis operations.

[0039] In some embodiments, the fusion of spatiotemporal context information can be performed at different layers of the graph neural network. For example, temporal information can be input into the model as part of node features, or it can be used to modulate attention weights in the graph attention mechanism. Spatial information can enrich the original graph structure by constructing a spatial proximity graph. Training a temporal graph neural network model is a supervised or self-supervised learning process that requires historical sequence data as training samples and optimizes model parameters by predicting the graph state at future time steps. The hierarchical structure of the occupational ability gene mapping model can be constructed by adding hierarchical labels to graph nodes or by using a hierarchical graph neural network, and the dynamic evolutionary attributes are quantified by analyzing the change patterns of node and edge features on the time axis.

[0040] Optionally, the temporal graph neural network model can use gating mechanisms such as GRU or LSTM units to handle temporal dynamics. The dimension of the latent space needs to be weighed based on data complexity and computational resources. The occupational ability gene graph model can be stored using graph databases such as Neo4j, facilitating graph operations such as path lookup and community discovery. It can be understood that fusing spatiotemporal context information makes the final constructed occupational ability gene graph model no longer a static snapshot, but an active graph capable of depicting the dynamic evolution of abilities, providing a solid foundation for subsequent simulation and prediction. The implementation of the entire multimodal feature fusion module relies on deep learning frameworks, such as PyTorchGeometric or DeepGraphLibrary, which provide efficient implementations of graph neural network components.

[0041] Example 3: See Figure 3 This embodiment details the operation of a distributed training framework based on federated learning. This framework aims to collaboratively optimize the occupational ability gene map model constructed in the aforementioned stages. In specific implementation, the initialization stage of the federated learning process involves distributing the global occupational ability gene map model held on the central server to multiple local nodes participating in the training. After receiving a copy of the global occupational ability gene map model, each local node uses its own private local dataset, which is stored within its local scope, to train the model. The training process typically employs an optimization algorithm based on stochastic gradient descent. To meet privacy protection requirements, a differential privacy mechanism is integrated into the local model training process. Specifically, a Laplace mechanism is used to add random noise following a Laplace distribution to the model update. The noise scale of the Laplace mechanism is strictly controlled by the privacy budget parameter ε. The noise addition operation can be performed after calculating the model gradient or by directly perturbing the gradient, thereby ensuring that the information of a single data point is not inferred when the model update is uploaded to the server, effectively protecting the data privacy of each local node.

[0042] In some embodiments, after a local node completes a round of local training, it needs to process the generated model update for secure upload. The model update typically refers to the increment of model parameters or the calculated gradient vector. Before uploading, the local node encrypts the model update. Encryption methods can employ homomorphic encryption or secure multi-party computation (SMPC) techniques, such as using the Paillier homomorphic encryption algorithm to encrypt the gradient values. This allows the central server to aggregate the gradients in the encrypted state without decrypting the original gradient information from individual nodes. The encrypted model update is then transmitted to the central server via a secure communication channel. The central server is responsible for aggregating model updates from multiple local nodes. The aggregation process uses a secure aggregation algorithm. This algorithm first decrypts the received encrypted gradients (if threshold homomorphic encryption is used, multiple servers may need to collaborate), and then calculates the weighted average of all decrypted gradients. The weights are typically proportional to the size of each local node's dataset. The calculation formula can be expressed as:

[0043] in: This represents the average gradient after aggregation. This is the total number of local nodes participating in this round of aggregation. It is the first Number of data samples per local node It is the sum of data samples from all participating nodes. It comes from the first The gradients of each local node are then aggregated and averaged by the central server. To update the parameters of the global career ability gene map model, the parameter update rule is as follows: ,in, It is the first The global model parameters for each iteration. It's the learning rate. It is the first The global model parameters are iterated by +1. To accelerate the convergence of the training process, adaptive optimization algorithms such as Adam or AdaGrad can be used to dynamically adjust the learning rate of each parameter. This "local training - secure upload - secure aggregation - global update" loop will be iterated multiple times until the loss function value of the global model on a reserved validation dataset tends to stabilize or the maximum number of iterations is reached, ultimately resulting in a preliminary collaborative capability model that has undergone initial collaborative optimization.

[0044] In practical implementation, the preliminary collaborative capability model needs to be further quantified to generate a probabilistic output. Bayesian inference is used to achieve this goal. Bayesian inference treats model parameters as random variables and focuses on calculating the posterior probability distribution of these parameters given training data. However, for complex neural network models, accurately calculating the posterior distribution is often difficult. Therefore, variational inference is used as an approximation method. Variational inference approximates the true posterior distribution by choosing a simple parametric family of distributions (such as a Gaussian distribution) and minimizing the KL divergence between the approximate distribution and the true posterior distribution by optimizing the variational lower bound, thereby estimating the uncertainty of the model parameters. This process ultimately transforms the determined preliminary collaborative capability model into a probabilistic capability evolution model that can output a probability distribution of predicted values ​​for a given input, rather than just a point estimate. The core objective of variational inference is described: maximizing the lower bound of evidence.

[0045] in: It is the variational lower bound. These are the parameters of the variational distribution. It is used to approximate the true posterior. variational distribution, Expressing expectations, It is the likelihood of the data given the parameters. It is the KL divergence. It is the prior distribution of the model parameters.

[0046] In some embodiments, post-processing is required for probabilistic capability evolution models to enhance their robustness and practicality. Outlier detection algorithms are employed to identify potential anomalous capability patterns. These anomalous patterns may originate from noise in the training data, adversarial attacks, or rare edge cases. Outlier detection algorithms can be implemented based on the uncertainty of the model's predictions. For example, if the model's prediction variance for an input sample is significantly higher than average, that sample may be considered an outlier. Alternatively, distance- or density-based methods can be used, such as the local outlier factor algorithm, to detect anomalous patterns that differ significantly from other samples in the model's latent feature space. By filtering or specially labeling these anomalous capability patterns, a more robust capability evolution model with privacy protection capabilities is ultimately generated.

[0047] Optionally, during federated learning training, the privacy budget parameter ε needs to be set with a trade-off between privacy protection and model utility. A smaller ε provides stronger privacy protection but may add too much noise, affecting model accuracy. Laplace noise can be added after gradient calculation and before uploading. The homomorphic encryption scheme can choose to use the CKKS scheme to support approximate floating-point calculations. The secure aggregation protocol may need to be designed to handle the situation where nodes drop out midway. In variational inference, the choice of variational distribution and prior distribution will affect the quality of uncertainty estimation. The threshold for outlier detection can be dynamically determined using statistical quantile methods.

[0048] Understandably, federated learning frameworks enable collaborative training of models on distributed data without the need for centralized data. Differential privacy mechanisms provide strict mathematical privacy guarantees, Bayesian inference empowers models to quantify uncertainty, and outlier detection further enhances the model's reliability in the face of anomalies. The entire implementation process involves parallel local training executed by distributed computing nodes, with a central server coordinating global updates. The resulting privacy-preserving evolutionary model provides the core driver for subsequent visualization and recommendation.

[0049] Example 4: In a specific implementation, a dynamic programming method for vocational ability gene maps based on multimodal data and federated learning involves generating visual outputs based on an ability evolution model and performing intelligent recommendations. This process begins by mapping the output of the ability evolution model to a three-dimensional coordinate system. The output of the ability evolution model is typically a series of discrete ability points, each represented by a numerical vector containing ability attributes and evolutionary states. The mapping operation is achieved by defining the semantics of the three-dimensional coordinate axes; for example, the X-axis represents the technical ability dimension, the Y-axis represents the managerial ability dimension, and the Z-axis represents the innovation ability dimension. Each ability point is projected onto a specific location in three-dimensional space based on its vector value, forming a discrete point cloud. When mapping the output of the ability evolution model to the three-dimensional coordinate system, the discrete ability points output by the ability evolution model must first be deduplicated to remove duplicate ability point data and avoid data redundancy in subsequent interpolation processes. In practice, in order to generate a continuous visual surface from a discrete point cloud, the inverse distance weighted interpolation method is used to spatially interpolate discrete capability points. The inverse distance weighted interpolation method is based on the principle of spatial proximity and calculates a weighted average value for any unsampled point in three-dimensional space. The weight is inversely proportional to the p-th power of the distance from the point to be interpolated to the known point. By traversing the grid points in three-dimensional space and calculating the interpolation, a smooth and preliminary three-dimensional capability distribution dynamic map is created. This map presents the spatial distribution of capability density in the form of a surface or voxels.

[0050] Based on historical capability evolution data, a time-sliding window mechanism is introduced to capture the temporal changes in capabilities. The time-sliding window extracts a fixed-length continuous time segment from historical data, such as days or weeks. The window size can be adjusted according to business needs, and each time window corresponds to a snapshot of a dynamic 3D capability distribution map. The Kalman filter algorithm is used to dynamically update the dynamic 3D capability distribution map. The Kalman filter algorithm is a recursive state estimation algorithm that treats the capability distribution state as a dynamic system. By combining prediction and update steps with historical observations, the current state estimate is corrected in real time, thus reflecting real-time changes in capabilities. This allows the dynamic 3D capability distribution map to evolve dynamically, rather than being statically displayed. The updated dynamic 3D capability distribution map is input into a digital twin system. The digital twin system constructs a virtual capability environment, which is a digital mapping of the physical occupational world. A particle system is used to simulate the diffusion process of capability requirements. The particle system models capability requirements as a large number of virtual particles. When using the particle system to simulate the diffusion process of capability requirements, the diffusion rules of the particles need to be set according to the characteristics of different occupational fields. For example, the diffusion speed of capability requirements in the technology research and development field can be set to be faster than that in the administrative service field, simulating the differences in the propagation of capability requirements in different fields. Each particle possesses attributes such as position, velocity, and lifespan. Particle motion follows preset physical rules or behavioral models, simulating the propagation, aggregation, or dissipation of capability demand in virtual space, thereby predicting the evolution path of capability demand. Historical evolution data is analyzed using time series forecasting models, such as autoregressive integral moving average models or long short-term memory network models. These models extract trend, periodic, and seasonal components from historical capability distribution dynamics to infer future patterns of capability demand changes, generating a simulated output of capability evolution that includes predictions of capability distribution at future points in time.

[0051] For the simulated output of the dynamic graph of ability distribution, an intelligent recommendation strategy is adopted to generate career development paths. This strategy operates based on historical career path data, which records the career trajectories of different individuals, including job changes and skill improvement sequences. A sequence generation model is trained to produce candidate career development paths. This model employs an encoder-decoder architecture, such as a model based on a recurrent neural network or transformer. The encoder encodes historical paths into context vectors, and the decoder generates new path sequences. A variational autoencoder is used to ensure the diversity and rationality of the paths. The variational autoencoder introduces random latent variables during the encoding process, and through reconstruction loss and KL divergence constraints, the generated paths maintain rationality while possessing diversity, avoiding pattern collapse. An evaluation model is constructed to calculate the matching degree between the current ability state and the candidate career development paths. The evaluation model receives the current user's ability feature vector and candidate paths as input, and uses a convolutional neural network to calculate a similarity score. The convolutional neural network extracts local features through convolutional layers, and the fully connected layers output a matching score. A higher score indicates a better matching degree, and the evaluation result is output. Based on the evaluation results, hierarchical clustering algorithms are used to divide candidate career development paths into multiple categories. Hierarchical clustering algorithms, such as AGNES, calculate the distance between paths (such as Euclidean distance or dynamic time-warped distance), and merge or split them from bottom to top or top to form a path hierarchy tree. Combined with the real-time updates of the ability distribution dynamic graph, representative paths or statistical features are extracted from the clustering results to generate multi-level recommendation thresholds. The multi-level recommendation thresholds define recommendation strategies at different matching levels. For example, high thresholds are used for precise recommendations, and low thresholds are used for exploratory recommendations.

[0052] The system compares real-time capability features with multi-level recommendation thresholds. Real-time capability features are extracted from the user's current data stream. A reinforcement learning model is applied to adjust path recommendations based on feedback signals. The reinforcement learning model uses Q-learning or policy gradient methods to model the recommendation problem as a Markov decision process. The state is the user's capability state and environmental context, the action is the recommended path selection, and the reward signal is calculated based on user feedback (such as clicks and completion rate). By iteratively updating the policy network parameters, the career development path can be dynamically adjusted.

[0053] In some embodiments, the implementation of 3D visualization technology relies on a graphics rendering engine, such as WebGL or Unity. The parameter p in inverse distance weighted interpolation is selected through cross-validation. The state transition matrix and observation matrix of the Kalman filter algorithm need to be initialized according to the capability evolution characteristics. The virtual environment of the digital twin system can integrate geographic information system data to augment the spatial context. The parameters of the particle system, such as the number of particles and the initial velocity, need to be tuned to balance simulation accuracy and computational overhead. The training of the time series prediction model uses a sliding window to generate samples. The training of the sequence generation model requires a large amount of historical path data. The latent spatial dimension of the variational autoencoder affects the generation diversity. The convolutional neural network structure of the evaluation model can be designed to process different path features through multi-channel input. The linking criteria (such as single linking and full linking) of the hierarchical clustering algorithm affect the cluster shape. The exploration-exploitation strategy of the reinforcement learning model needs to be designed to adapt to the dynamic environment.

[0054] Optionally, inverse distance weighted interpolation can be combined with the Kriging method to improve interpolation accuracy; the size of the time sliding window can be adaptively adjusted based on data volatility; Kalman filtering can be extended to unscented Kalman filtering to handle nonlinear systems; digital twin systems can be connected to real-time data streams to achieve online simulation; particle systems can introduce collision detection to simulate competition for capabilities; time series prediction models can integrate external variables such as economic indicators; sequence generation models can use attention mechanisms to improve the quality of long sequence generation; variational autoencoders can conditionally generate based on user profiles; evaluation models can fuse multimodal features such as text descriptions; hierarchical clustering algorithms can visualize dendrograms to assist in threshold setting; and reinforcement learning models can use an actor-critic architecture to accelerate convergence.

[0055] Optionally, the rendering of the 3D capability distribution dynamic map can be enhanced with color mapping to represent capability strength; the step size of the time sliding window can be set to overlap for smooth transition; the covariance of process noise and observation noise in the Kalman filter needs to be estimated online; the interface of the digital twin system can support interactive queries; the rendering of the particle system can be optimized to level-detail control; the confidence interval of the time series prediction model can be visualized to represent uncertainty; the beam search width of the sequence generation model affects the number of candidate paths; the reconstruction loss weight of the variational autoencoder can be dynamically adjusted; the similarity score of the evaluation model can be normalized to a probability output; the distance metric of the hierarchical clustering algorithm can be cosine similarity to handle high-dimensional data; and the reward function of the reinforcement learning model can be designed as a multi-objective optimization.

[0056] Understandably, the entire implementation process transforms static capacity prediction into an interactive dynamic programming system. 3D visualization provides intuitive insights, digital twins enable virtual experiments, intelligent recommendations provide personalized guidance, and real-time feedback ensures system adaptability. Refer to Table 1, which illustrates the key parameters involved in generating the dynamic capacity distribution diagram and their typical settings.

[0057] Table 1: Parameter Configuration Table for Dynamic Chart of Capacity Distribution

[0058] In practical implementation, the generation of the dynamic map of 3D capability distribution requires high-performance computing resources, such as graphics processors to accelerate interpolation and rendering. Inverse distance weight interpolation is computed in parallel on the 3D mesh, with each mesh point interpolated independently. Data management of the time sliding window uses a circular buffer to avoid memory overflow. The matrix operation of the Kalman filter algorithm is optimized for batch processing. The particle system of the digital twin system uses shader programs to achieve hardware acceleration. The inference deployment of the time series prediction model is a microservice interface. The training of the sequence generation model adopts a teacher-forced strategy. The convolutional neural network for evaluating the model is initialized with pre-trained weights. The implementation of the hierarchical clustering algorithm uses an efficient algorithm library such as Scikit-learn. The training environment of the reinforcement learning model is encapsulated using an open-source framework such as OpenAIGym.

[0059] See Figure 4 This chart presents the three-dimensional distribution of professional competence gene maps based on multimodal data and federated learning. The chart uses a three-dimensional coordinate system, where the X-axis represents the technical competence dimension, the Y-axis represents the managerial competence dimension, and the Z-axis represents the innovation competence dimension, comprehensively showcasing the distribution characteristics of professional competence in multidimensional space. The surface in the chart is generated using an inverse distance weighted interpolation method, creating a continuous visual surface from discrete competence point cloud data, clearly showing the density distribution of different competence combinations. Color mapping represents competence intensity, presenting a gradient effect from low to high, with dark areas representing dense competence areas and light areas representing sparse competence areas. Scattered points represent specific individual competence points, and their color intensity reflects the overall strength of individual competences. This three-dimensional distribution map reflects the overall layout of professional competence across the three key dimensions of technology, management, and innovation, enabling intuitive identification of competence clusters and development gaps. The technical competence dimension primarily measures professional skill levels, the managerial competence dimension reflects organizational and coordination abilities, and the innovation competence dimension showcases creative thinking abilities. Through this three-dimensional visualization, the distribution patterns and interrelationships of different competence types can be clearly observed, providing an intuitive spatial cognitive foundation for professional competence planning.

[0060] Example 5: In a specific implementation, a dynamic programming method for vocational ability gene maps based on multimodal data and federated learning includes a continuously optimized closed-loop process after dynamically adjusting the planning scheme through a real-time feedback mechanism. In specific implementation, the first step is to establish a user behavior log recording system. The user behavior log recording system is a distributed data collection framework deployed at various touchpoints where users interact with the recommendation system, such as web front-ends, mobile application interfaces, or internal enterprise systems. This system continuously collects interactive operation data generated by users during the execution of recommended career development paths in a structured format. The types of interactive operation data include, but are not limited to, the duration of user clicks on and views of recommended paths, completion status markers of specific learning courses or training tasks, passing results of skills certification exams, job application submission behavior, and text feedback or rating data left at path nodes. Each log record typically includes a timestamp, user anonymous identifier, operation type, operation object identifier, and operation-related metadata. This data is transmitted to a central data repository for aggregation in real time or near real time.

[0061] In some embodiments, the user behavior logging system needs to be highly available and scalable to cope with large-scale concurrent user access. The system can be implemented using a technology stack based on Elasticsearch, Logstash, and Kibana, or use Apache Kafka as a message queue for asynchronous collection and buffering of log data to ensure that data is not lost under high load. During data transmission, HTTPS protocol and field-level encryption are used to protect user privacy. When storing, the user's direct identifier is de-identified or hashed. After generating user behavior log records, the system enters the data processing stage.

[0062] The collected raw interaction data undergoes cleaning and feature extraction. Data cleaning includes handling missing values, removing duplicate records, and correcting obvious logical errors. For example, interpolation methods are used to fill in missing timestamps in consecutive records, or impossible operation sequences are filtered out based on business rules. Feature extraction extracts meaningful numerical or vector representations from the cleaned interaction data. For example, it calculates the average dwell time of users at a certain recommended path node, the cumulative number of tasks completed, the slope of skill improvement, and the frequency pattern of operation sequences. These features are normalized or standardized and combined to form a fixed-dimensional user behavior feature vector. The user behavior feature vector serves as a quantitative representation of real-time user feedback and provides input for subsequent deviation analysis.

[0063] In practice, comparing user behavior feature vectors with the prediction results of the capability evolution model is the core analytical step. The capability evolution model generates predictions about user capability development in previous stages, such as predicting the capability level vector that a user should reach at a certain point in the future. The comparison process involves calculating the degree of difference between the user behavior feature vector extracted from actual behavior and the prediction result vector of the capability evolution model at the corresponding point in time. This degree of difference is quantified as path execution deviation. The path execution deviation can be calculated using various vector distance or similarity measurement methods. For example, Euclidean distance directly measures the absolute difference between vectors, cosine similarity focuses on the consistency of vector direction, or Mahalanobis distance is used to consider the correlation between features. The path execution deviation is a scalar value, and its magnitude intuitively reflects the degree of deviation between the user's actual development trajectory and the model's expected path.

[0064] By applying a reinforcement learning framework, the parameters of the policy network are adjusted based on the calculated path execution bias. The reinforcement learning framework models the path recommendation problem as a Markov decision process. In this framework, the state represents the current user capability and context, the action is the career development path recommendation chosen by the intelligent recommendation policy network, and the reward signal is mainly defined based on the path execution bias. For example, a small path execution bias results in a positive reward, indicating good user compliance; a large path execution bias results in a negative reward, indicating the recommendation may be inapplicable. Through a policy gradient algorithm or an Actor-Critic algorithm, the policy gradient is calculated using the current batch of user interaction data, and the weight parameters of the policy network are updated, thereby generating an optimized recommendation policy. The optimized recommendation policy can better adapt to the user's real behavior patterns and environmental changes.

[0065] Based on the optimized recommendation strategy, the intelligent recommendation model is updated, and a new career development path is generated. The intelligent recommendation model can be updated through online learning or periodic batch retraining. In online learning mode, the model parameters are fine-tuned in real time with each or each small batch of new feedback data. In batch retraining, all data within a certain period of time is collected periodically to retrain the model. The updated intelligent recommendation model incorporates the latest user feedback information, and its internal recommendation logic is adjusted. Finally, the updated intelligent recommendation model is used to recalculate and generate new, more personalized career development paths for users, completing a full feedback optimization cycle.

[0066] Optionally, the data collection frequency of the user behavior log recording system can be configured to be event-triggered or timed sampling. New data types can be added to the interactive operation data, such as eye-tracking trajectories or video interview analysis results. In the data cleaning process, an anomaly detection model can be introduced to automatically identify suspicious data patterns. In the feature extraction process, an autoencoder can be introduced to perform non-linear dimensionality reduction to obtain a more compact user behavior feature vector. The calculation of path execution deviation can be combined with multiple metrics for weighted summation. The reward function design of the reinforcement learning framework can introduce multi-objective optimization to balance short-term compliance with long-term development potential. The intelligent recommendation model can be updated using transfer learning technology to accelerate model adaptation by utilizing data from similar user groups.

[0067] Optionally, the dimension of the user behavior feature vector can be compressed through principal component analysis to reduce computational complexity. The threshold of path execution deviation can be dynamically adjusted to adapt to the differences among different user groups. The exploration strategy in the reinforcement learning framework can be designed as an ε-greedy strategy that decays over time to balance exploring new strategies and utilizing existing knowledge. The regeneration of the intelligent recommendation model can be set to be triggered by a specific event, such as starting immediately when the path execution deviation is continuously higher than the threshold.

[0068] Optionally, to handle concurrent user feedback, the reinforcement learning framework can be deployed in a distributed version, with multiple worker nodes processing feedback data from different users in parallel, periodically synchronizing policy network parameters, and establishing a data quality monitoring dashboard for the user behavior log recording system to monitor the integrity and accuracy of log data in real time. The calculation of path execution deviation can introduce a decay factor to give higher weight to recent feedback behaviors. A / B testing can be performed before and after the intelligent recommendation model is updated to evaluate the actual improvement in the recommendation effect of the new model.

[0069] Understandably, this closed-loop feedback mechanism transforms the occupational competence gene mapping dynamic programming method from a one-way prediction and recommendation into an intelligent system that can continuously learn and evolve from actual use. The user behavior log recording system captures real-world feedback, cleaning and feature extraction transform raw behavior into structured information, path execution deviation measures the gap between prediction and reality, reinforcement learning framework transforms the gap signal into the driving force for policy improvement, and finally model update and path regeneration complete the cycle from perception to action. The continuous iteration of this process ensures that the recommendation system can dynamically adapt to changes in the labor market and the unique development path of individual users.

[0070] See Figure 5This chart illustrates the operation of the real-time feedback optimization mechanism of a career development path recommendation system. The horizontal axis represents the number of feedback optimization iterations, and the vertical axis represents the path execution deviation, reflecting the degree of matching between the user's actual development trajectory and the system's recommended path. Multiple colored curves represent the optimization trajectories of different users, with the red dashed line indicating the overall average trend. The chart shows that as the number of feedback iterations increases, the path execution deviation for most users exhibits a significant downward trend. This indicates that the system continuously optimizes the recommendation strategy through reinforcement learning, making the recommended path more aligned with the user's actual situation and development needs. While the optimization trajectories of different users vary, reflecting the diversity of individual user characteristics, they all converge towards a reduction in deviation. The real-time feedback optimization mechanism is based on user behavior logs and the interaction data collected by the system. It quantifies the gap between prediction and reality by calculating the path execution deviation and dynamically adjusts the strategy network parameters using a reinforcement learning framework. This closed-loop optimization process ensures that the recommendation system can continuously learn from actual use, adapting to changes in the labor market and the unique development paths of individual users, achieving truly personalized career development planning.

[0071] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0072] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A dynamic programming method for vocational competence gene mapping based on multimodal data and federated learning, characterized in that, The method includes the following processing stages: Acquire multi-source heterogeneous data from professional activities, and perform data purification and standardization preprocessing operations on the multi-source heterogeneous data to generate a multimodal data set in a unified format; The multimodal dataset is input into the multimodal feature fusion module, and graph neural network technology is used to analyze the ability elements and fuse spatiotemporal context information to construct a professional ability gene map model. The occupational ability gene map model is collaboratively optimized using a federated learning-based distributed training framework. Multiple local nodes participate in the training, and a differential privacy mechanism is applied to generate a privacy-preserving ability evolution model. Based on the capability evolution model, a dynamic map of capability distribution is generated using three-dimensional visualization technology, and the evolution path and change pattern of capability requirements are simulated using a digital twin system. Based on the simulated output of the dynamic diagram of the ability distribution, an intelligent recommendation strategy is used to generate career development paths, and the planning scheme is dynamically adjusted through a real-time feedback mechanism.

2. The dynamic programming method for vocational ability gene maps based on multimodal data and federated learning according to claim 1, characterized in that, The process of acquiring multi-source heterogeneous data from professional activities and performing data purification and standardization preprocessing operations on the multi-source heterogeneous data to generate a unified format multimodal data set includes: Occupation-related data were collected from multiple data sources, including text records, skill indicators, and job task descriptions. Data denoising and outlier detection were performed. Principal component analysis-based dimensionality reduction was used to eliminate data dimensionality differences and generate a dimensionality-reduced multimodal data sequence. Feature encoding techniques are applied to the dimensionality-reduced multimodal data sequence. A word embedding model is used to convert text data into numerical vectors and integrate unstructured data to obtain encoded multimodal feature representations. For the encoded multimodal feature representation, a feature importance evaluation algorithm is applied to calculate the weight values ​​of different features. Feature selection is performed based on the weight values ​​to highlight the influence of key features and generate a weighted multimodal feature representation. The weighted multimodal feature representation is input into the standardization processing unit, and the feature scale is adjusted by the min-max scaling method to generate a multimodal data set in a unified format.

3. The dynamic programming method for vocational ability gene maps using multimodal data and federated learning according to claim 1, characterized in that, The step of inputting the multimodal dataset into the multimodal feature fusion module, using graph neural network technology to analyze ability elements, and fusing spatiotemporal context information to construct a professional ability gene map model includes: A unified format of multimodal data is input into a graph convolutional network, where a graph attention mechanism is used to capture the relationships between nodes, and a graph autoencoder extracts global features to obtain a preliminary representation of capability elements. Based on the preliminary representation of capability elements, a graph embedding algorithm is applied to construct a capability topology, where nodes represent capability units and edges represent the correlation strength between capabilities. A community detection algorithm is used to identify capability clusters, forming a capability relationship graph. Based on the aforementioned ability relationship diagram and combined with spatiotemporal context information, a time-series graph neural network model is used to project ability elements into a continuous space to construct an occupational ability gene map model, wherein the gene map model contains the hierarchical structure and dynamic evolution attributes of abilities.

4. The dynamic programming method for vocational ability gene maps using multimodal data and federated learning according to claim 1, characterized in that, The process of collaboratively optimizing the vocational ability gene map model using a federated learning-based distributed training framework, involving multiple local nodes in training and applying differential privacy mechanisms to generate a privacy-preserving ability evolution model, includes: The professional ability gene mapping model is distributed to multiple local nodes. Each local node trains a personalized model using a local dataset and uses a Laplace mechanism to add noise to protect data privacy. The model updates from each local node are aggregated on the central server, and the model gradients are integrated using a secure aggregation algorithm. Through multiple rounds of iterative optimization of the global model, a preliminary collaborative capability model is obtained. For the preliminary collaborative capability model, the Bayesian inference method is applied to calculate the posterior distribution of the model parameters, and the uncertainty is estimated through variational inference to generate a probabilistic capability evolution model. For the probabilistic capability evolution model, an outlier detection algorithm is used to identify abnormal capability patterns and generate a privacy-preserving capability evolution model.

5. The dynamic programming method for vocational ability gene maps based on multimodal data and federated learning according to claim 1, characterized in that, Based on the capability evolution model, a dynamic map of capability distribution is generated using 3D visualization technology, and combined with a digital twin system to simulate the evolution path and change patterns of capability requirements, including: The output of the capability evolution model is mapped to a three-dimensional spatial coordinate system, and the discrete capability points are spatially interpolated using the inverse distance weighted interpolation method to create a preliminary three-dimensional capability distribution dynamic map. Based on historical capability evolution data, a time sliding window is introduced, and the Kalman filter algorithm is used to dynamically update the three-dimensional capability distribution dynamic map to reflect the real-time changes in capabilities. Input the dynamic map of three-dimensional capability distribution into the digital twin system to construct a virtual capability environment. Simulate the diffusion process of capability demand through a particle system and predict the evolution path. By using time series forecasting models to analyze historical evolution data, we can infer the changing patterns of future capability requirements and generate simulated outputs of capability evolution.

6. The dynamic programming method for vocational ability gene maps based on multimodal data and federated learning according to claim 1, characterized in that, The simulated output of the dynamic map of ability distribution uses an intelligent recommendation strategy to generate career development paths and dynamically adjusts the planning scheme through a real-time feedback mechanism, including: Based on historical career path data, a sequence generation model is trained to generate candidate career paths, and a variational autoencoder is used to ensure the diversity and rationality of the paths. An evaluation model is constructed to calculate the matching degree between the current capability state and the candidate path. A convolutional neural network is used to calculate the similarity score and output the evaluation result. Based on the evaluation results, a hierarchical clustering algorithm is used to divide the path into multiple categories, and combined with the real-time update of the capability distribution dynamic graph, a multi-level recommendation threshold is generated. By comparing real-time capability characteristics with multi-level recommendation thresholds, a reinforcement learning model is applied to adjust path recommendations based on feedback signals, thereby achieving dynamic adjustment of career development paths.

7. The dynamic programming method for vocational ability gene maps based on multimodal data and federated learning according to claim 2, characterized in that, The process involves collecting occupation-related data from multiple data sources, including text records, skill indicators, and job task descriptions; performing data denoising and outlier detection operations; and employing a principal component analysis-based dimensionality reduction method to eliminate data dimensionality differences, generating a dimensionality-reduced multimodal data sequence, including: Occupational data is obtained from an online platform using an application programming interface (API), and the data format is converted and encoded uniformly. A median filtering algorithm is then used to remove noise. Missing data is filled using multiple imputation methods, and reasonable values ​​are estimated based on clustering algorithms to ensure data integrity. Principal component analysis is applied to reduce the dimensionality of high-dimensional data. Eigenvectors and eigenvalues ​​are calculated, and principal components are selected to form a low-dimensional representation, generating a multimodal data sequence after dimensionality reduction.

8. The dynamic programming method for vocational ability gene maps using multimodal data and federated learning according to claim 3, characterized in that, The process involves inputting a unified format of multimodal data sets into a graph convolutional network, where a graph attention mechanism is used to capture relationships between nodes, and a graph autoencoder extracts global features to obtain a preliminary representation of capability elements, including: A graph structure is constructed for the multimodal dataset, with each data point as a node and edges calculated based on similarity. The graph is then input into a convolutional network for feature propagation. In graph convolutional networks, a graph attention mechanism is applied to calculate the attention weights between nodes, aggregate neighbor node information, and generate attention-enhanced feature representations. A graph autoencoder is used to encode and decode the attention-enhanced feature representation, and the feature extraction is optimized by reconstruction loss to obtain a preliminary capability element representation.

9. The dynamic programming method for vocational ability gene maps using multimodal data and federated learning according to claim 4, characterized in that, The process involves aggregating model updates from local nodes on a central server, integrating model gradients using a secure aggregation algorithm, and iteratively optimizing the global model through multiple rounds to obtain a preliminary collaborative capability model, including: After each local node trains the model, it encrypts the model gradient and uploads it to the central server, using homomorphic encryption to protect the data transmission. After the central server decrypts the gradient, it calculates the weighted average gradient, updates the global model parameters, and uses an adaptive optimization algorithm to accelerate convergence. Repeat the iterative process until the model loss stabilizes, verify the performance of the global model on the preserved dataset, and obtain a preliminary collaborative capability model.

10. The dynamic programming method for vocational ability gene maps based on multimodal data and federated learning according to claim 1, characterized in that, The method of dynamically adjusting the planning scheme through a real-time feedback mechanism also includes: Establish a user behavior log recording system to collect user interaction data on the recommended career development path; The interactive operation data is cleaned and its features are extracted to obtain a user behavior feature vector; The user behavior feature vector is compared with the prediction results of the capability evolution model to calculate the path execution deviation. By applying a reinforcement learning framework, the parameters of the policy network are adjusted based on the path execution bias to generate an optimized recommendation policy; Based on the optimized recommendation strategy, the intelligent recommendation model is updated, and a new career development path is generated.

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