Job application matching method and system based on distributed architecture

Abstract

The invention relates to a job hunting matching method and system based on a distributed architecture, and belongs to the field of data processing. Job hunters and recruiters data are collected and preprocessed in parallel through multiple nodes, and high-precision data extraction is realized by adopting a distributed crawler cluster and a double-engine verification mechanism; privacy desensitization and feature vectorization are completed in combination with a dynamic cleaning pipeline; constructing a dual-channel deep residual network to perform cross-modal matching, dynamically fusing skills and position features by using a gating attention mechanism, and optimizing model robustness in combination with adversarial training and comparative learning; generating a dynamic job-seeking relation graph based on a matching result, and realizing weight attenuation and increment updating by adopting a time sequence graph convolutional network; designing a load awareness mechanism to trigger ant colony optimization task migration, and dynamically allocating calculation tasks through an improved consistent Hash algorithm; an online learning module is integrated, an incremental sample is generated through implicit feedback data, and model parameters are updated in real time.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a job matching method and system based on a distributed architecture. Background Technology

[0002] Current job matching systems generally employ centralized architectures to process massive amounts of data, resulting in bottlenecks such as poor scalability of computing resources and high real-time response latency. Traditional methods rely on keyword matching and static rule engines, making it difficult to capture the deep semantic relationships between skills and job requirements. Furthermore, the lag in updating the relationship graph leads to recommendations that deviate from the actual dynamics of the talent market. Existing distributed systems often use fixed sharding strategies for load balancing, which cannot adapt to sudden traffic surges and heterogeneous computing tasks. They also lack real-time learning mechanisms for implicit user feedback, resulting in long model iteration cycles and limited personalized matching accuracy. With the exponential growth of job market data and the increasing complexity of user behavior, existing technologies face severe challenges in terms of data processing efficiency, matching accuracy, and system resilience. Summary of the Invention

[0003] The main objective of this invention is to provide a job matching method and system based on a distributed architecture.

[0004] To achieve the above objectives, the present invention provides a job matching method based on a distributed architecture, comprising the following steps:

[0005] The raw data uploaded by job seekers and recruiters is collected and preprocessed by multiple parallel computing nodes. The preprocessing includes data cleaning based on preset rules and vectorization transformation of structured fields.

[0006] Based on a deep learning model, cross-modal matching degree is calculated between the vectorized job seeker skill vector and the job requirement vector. The deep learning model adopts a phased training strategy, first generating a general semantic representation layer through unsupervised learning, and then fine-tuning it by combining domain-labeled data with transfer learning.

[0007] A dynamically updated job-seeking relationship graph is constructed based on the matching degree calculation results. The connection weights between nodes in the graph are generated by the fusion calculation of historical successful matching cases, real-time behavioral data, and third-party evaluation data.

[0008] Based on the graph, the task load among distributed nodes is dynamically allocated. When the load of a local node exceeds the threshold, a cross-node collaborative computing mechanism is triggered, and the feature calculation task is reallocated through a consistent hashing algorithm.

[0009] After the matching results are output, incremental training samples are generated based on the implicit feedback data from the user's end. The model parameters are updated in real time through the online learning module. The implicit feedback data includes resume submission conversion rate, job browsing time, and interview invitation response rate.

[0010] Furthermore, the steps of collecting and preprocessing the raw data uploaded by job seekers and recruiters through multiple parallel computing nodes include:

[0011] Multi-node concurrent data collection is implemented through a distributed crawler cluster. Each node is assigned a target website based on its geographical location and an anti-crawler strategy simulator is embedded. A dual-engine redundancy verification mechanism using XPath and CSS selectors is adopted in the data extraction stage.

[0012] During preprocessing, a multi-level cleaning pipeline is implemented for unstructured text, including privacy information desensitization based on regular expressions, skill entity extraction based on dependency parsing, and resume update time alignment based on sliding time windows.

[0013] After the cleaned data stream is sharded through a message queue, the pre-trained BERT model is called to generate skill semantic vectors. At the same time, the improved TF-IDF algorithm is used to quantify the weighted feature words in the job description. The two types of feature vectors are stored in a distributed storage system using consistent hashing based on user ID.

[0014] Furthermore, the steps for calculating the cross-modal matching degree between the vectorized job seeker skill vector and the job requirement vector based on a deep learning model include:

[0015] The job seeker's skill vector and the job requirement vector are input into a dual-channel deep residual network. Dynamic feature projection is established between channels through a gating attention mechanism. The skill channel embeds an adversarial perturbation enhancement module to generate robust representations, while the job channel uses multi-head self-attention to capture long-distance dependencies.

[0016] The feature space alignment is optimized through cross-modal contrastive learning. A triplet training set containing explicit matching pairs, soft negative samples, and adversarial generated samples is constructed. A dynamic marginal loss function is used to automatically adjust the penalty intensity according to the sample density.

[0017] A lightweight matching engine is deployed during the inference phase. After screening the candidate set through the feature space approximation retrieval algorithm, an interpretable matching degree decomposition model is used to output multi-dimensional evaluation indicators, which include the weighted geometric mean of skill coverage, cultural fit, and growth fit.

[0018] Furthermore, the steps of inputting job seeker skill vectors and job requirement vectors into a dual-channel deep residual network, and establishing dynamic feature projections between channels through a gating attention mechanism, include:

[0019] In a dual-channel architecture, a skill feature encoder and a job feature encoder are deployed. The skill channel uses a dilated convolutional network with skip connections to extract multi-scale contextual features and superimposes adversarial training-generated perturbation noise for representation enhancement.

[0020] The job channel injects topological information into the text sequence through hierarchical positional encoding and uses a sparse attention matrix to filter non-key descriptive words;

[0021] The gated attention mechanism calculates cross-channel feature affinity using a differentiable routing algorithm, dynamically fuses the convolution kernel weight parameters of the two channels using a threshold function, and forms a feature projection matrix with domain adaptability. Finally, it generates a joint representation vector through element-wise multiplication of cross-channel feature maps, and its dimensionality compression ratio is dynamically adjusted according to the real-time calculated inter-channel mutual information entropy value.

[0022] Furthermore, the steps for constructing a dynamically updated job-seeking relationship graph based on the matching degree calculation results include:

[0023] Initial graph nodes are constructed based on heterogeneous data sources, where job seeker nodes embed skill vectors and behavioral temporal features, and job nodes integrate enterprise profile data and industry prosperity indicators.

[0024] A multi-hop neighbor sampling strategy is used to initialize edge relationships in the Neo4j graph database. The edge weight values ​​are then Bayesian weighted fusion based on the matching degree coefficient, historical interview conversion rate, and inter-industry talent mobility probability.

[0025] A dynamic update mechanism based on temporal graph convolutional networks is established. The relational event stream is extracted by a sliding time window, and an exponential weight decay is applied to old connections by combining a dynamic decay factor.

[0026] When new job-seeking relationship data arrives, the node representation is updated using an incremental graph embedding algorithm, which simultaneously triggers a subgraph partitioning operation based on node betweenness centrality, thus limiting the update scope to the local topology.

[0027] Furthermore, the step of dynamically allocating task load among distributed nodes based on the graph includes:

[0028] Load awareness is implemented based on the topological features stored in the graph database. The request throughput and feature vector dimension change rate of each computing node are statistically analyzed by sliding time window. A load prediction model based on gradient boosting decision tree is used to generate the node overload probability distribution.

[0029] When the betweenness centrality increase of a specific subgraph exceeds three consecutive sampling periods, the task migration strategy based on the dynamic community detection algorithm is activated, and the subgraph segments associated with high-load nodes are split into heterogeneous computing task packages according to the enterprise industry clustering results.

[0030] An improved ant colony optimization algorithm is used to find the optimal task migration path in a distributed cluster. The pheromone concentration is determined by the network latency between nodes, the GPU memory availability, and the density of graph relationships. During the migration process, a dual-buffer asynchronous write mechanism is used to maintain service continuity. At the same time, the sharded replica rebalancing operation of the graph storage nodes is triggered to ensure that the locality of the subgraph data after load balancing is not lower than a preset threshold.

[0031] Furthermore, the steps for finding the optimal task migration path in a distributed cluster using the improved ant colony optimization algorithm include:

[0032] In the ant colony initialization phase, the distributed topology is encoded as a weighted complete graph. The initial pheromone concentration of the edges between nodes is determined by the weighted harmonic mean of the inverse of network latency, the proportion of GPU memory remaining, and the density of graph relationships.

[0033] During the path construction process, a dynamic probability selection strategy is adopted. When the load balancing benefit gain of the candidate path exceeds the set threshold, the roulette wheel selection mechanism combined with the ε-greedy exploration strategy is activated to switch nodes.

[0034] A multi-objective fitness function is introduced when updating pheromones, while optimizing task migration latency, computing resource utilization and data locality preservation. The global update amount is dynamically scaled according to the real-time load change rate of the nodes traversed by the path.

[0035] A dynamic evaporation coefficient adjustment mechanism is set up so that when the frequency of cluster topology changes exceeds the preset value, the pheromone evaporation rate is automatically increased to accelerate algorithm convergence.

[0036] After each iteration, a parallel path exploration arbitration mechanism is initiated. A timestamp-based priority determination strategy is adopted for the generated conflict migration paths, and the path optimization effect is verified through a dimensionality reduction visualization module.

[0037] Furthermore, when the load on a local node exceeds a threshold, a cross-node collaborative computing mechanism is triggered, and the feature computation task is reallocated using a consistent hashing algorithm. This includes the following steps:

[0038] When the monitoring module detects that the CPU utilization of any node has exceeded the first threshold for three sampling cycles, it automatically triggers the hash ring reconstruction protocol to build a dynamic consistent hash ring based on the real-time computing capabilities of the nodes, where the number of virtual nodes is positively correlated with the physical resources.

[0039] During the task migration phase, a bidirectional hash slot partitioning strategy is adopted to generate multi-level migration queues for the feature calculation tasks of overloaded nodes according to timestamps and task priorities, and incremental data synchronization is achieved by comparing version numbers. During the migration process, a cross-node verification lock mechanism is initiated, and a Merkle tree based on blockchain is used to verify the integrity of the migration data. A shadow task queue is also established on the target node to maintain service availability.

[0040] After the migration is completed, an atomic update of the global hash map is triggered, synchronously updating the routing cache replicas of all nodes. At the same time, an anti-entropy protocol across data centers is started to eliminate replica differences. For node replicas that fail after the migration, a fast backfill mechanism based on historical operation logs is used to rebuild hash shards.

[0041] Furthermore, the steps of generating incremental training samples based on implicit feedback data from the user end and updating the model parameters in real time through the online learning module include:

[0042] The user behavior tracking system deploys an implicit feedback collection pipeline to capture real-time data streams of resume submission conversion rates, job details page dwell time, and interview result tracking. It uses a sliding time window to sample event sequences and adds dynamic weight allocation coefficients.

[0043] An incremental training sample pool is constructed through a negative feedback amplification mechanism, and feature space enhancement processing is performed on the delivery records that have not been successfully converted to generate adversarial pseudo-positive samples.

[0044] The online learning module adopts a dual-channel parameter update architecture. The main channel performs gradient descent through an adaptive learning rate optimizer, while the auxiliary channel uses knowledge distillation to constrain the drift of the model output distribution.

[0045] Establish a real-time data distribution offset detection mechanism. When the KL divergence between the incremental sample and the historical distribution exceeds the threshold, trigger the soft label re-labeling process with a temperature coefficient.

[0046] This invention also proposes a job matching system based on a distributed architecture, comprising:

[0047] The data acquisition unit is used to collect and preprocess the raw data uploaded by job seekers and recruiters through multiple parallel computing nodes. The preprocessing includes data cleaning based on preset rules and vectorization transformation of structured fields.

[0048] The computing unit is used to calculate the cross-modal matching degree between the vectorized job seeker skill vector and the job requirement vector based on a deep learning model. The deep learning model adopts a phased training strategy, first generating a general semantic representation layer through unsupervised learning, and then fine-tuning it by combining domain-labeled data with transfer learning.

[0049] The graph unit is used to construct a dynamically updated job-seeking relationship graph based on the matching degree calculation results. The connection weights between nodes in the graph are generated by the fusion calculation of historical successful matching cases, real-time behavioral data and third-party evaluation data.

[0050] The allocation unit is used to dynamically allocate task load among distributed nodes based on the graph. When the load of a local node exceeds a threshold, a cross-node collaborative computing mechanism is triggered to reallocate feature calculation tasks through a consistent hashing algorithm.

[0051] The optimization unit is used to generate incremental training samples based on implicit feedback data from the user after the matching results are output. The model parameters are updated in real time through the online learning module. The implicit feedback data includes resume submission conversion rate, job browsing time and interview invitation response rate.

[0052] The job matching method and system based on a distributed architecture provided by this invention have the following beneficial effects: By combining a distributed crawler cluster with a dynamic consistent hashing mechanism, it achieves rapid response under a throughput of hundreds of millions of data points, improving data processing efficiency by 3 times. Simultaneously, it employs cross-node collaborative computing and ant colony optimization task migration strategies to reduce hardware investment costs. Furthermore, the innovatively designed dual-channel deep residual network and dynamic relationship graph work synergistically to improve cross-modal matching accuracy. Combined with the real-time feedback mechanism of the online learning module, the system can quickly complete model iteration updates, significantly enhancing its adaptability to fluctuations in the talent market. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating a job matching method based on a distributed architecture in one embodiment of the present invention.

[0054] Figure 2 This is a structural block diagram of a job matching system based on a distributed architecture in one embodiment of the present invention;

[0055] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0057] Reference Figure 1 A job matching method based on a distributed architecture includes the following steps:

[0058] S1, the raw data uploaded by job seekers and recruiters are collected and preprocessed by multiple parallel computing nodes. The preprocessing includes data cleaning based on preset rules and vectorization transformation of structured fields.

[0059] S2, based on a deep learning model, calculates the cross-modal matching degree between the vectorized job seeker skill vector and the job requirement vector. The deep learning model adopts a phased training strategy, first generating a general semantic representation layer through unsupervised learning, and then fine-tuning it by combining transfer learning with domain-labeled data.

[0060] S3. Construct a dynamically updated job-seeking relationship graph based on the matching degree calculation results. The connection weights between nodes in the graph are generated by the fusion calculation of historical successful matching cases, real-time behavioral data and third-party evaluation data.

[0061] S4. Based on the graph, the task load among the distributed nodes is dynamically allocated. When the load of a local node exceeds the threshold, a cross-node collaborative computing mechanism is triggered, and the feature calculation task is reallocated through a consistent hashing algorithm.

[0062] S5. After the matching results are output, incremental training samples are generated based on the implicit feedback data from the user end. The model parameters are updated in real time through the online learning module. The implicit feedback data includes resume submission conversion rate, job browsing time and interview invitation response rate.

[0063] In the embodiment of step S1, the step of collecting and preprocessing the raw data uploaded by job seekers and recruiters through multiple parallel computing nodes includes:

[0064] Multi-node concurrent data collection is implemented through a distributed crawler cluster. Each node is assigned a target website based on its geographical location and an anti-crawler strategy simulator is embedded. A dual-engine redundancy verification mechanism using XPath and CSS selectors is adopted in the data extraction stage.

[0065] During preprocessing, a multi-level cleaning pipeline is implemented for unstructured text, including privacy information desensitization based on regular expressions, skill entity extraction based on dependency parsing, and resume update time alignment based on sliding time windows.

[0066] After the cleaned data stream is sharded through a message queue, the pre-trained BERT model is called to generate skill semantic vectors. At the same time, the improved TF-IDF algorithm is used to quantify the weighted feature words in the job description. The two types of feature vectors are stored in a distributed storage system using consistent hashing based on user ID.

[0067] In the specific implementation process, during the data collection phase, the system deploys an intelligent crawler cluster consisting of 50 geographically distributed physical nodes. Each node dynamically allocates collection tasks based on the target website's geographic attributes (e.g., North American nodes connect to Indeed, Asia-Pacific nodes connect to Liepin). Each node integrates an anti-crawler adaptive module, which uses randomized request intervals (satisfying a Poisson distribution λ = 0.2) and browser fingerprint obfuscation technology (generating a virtual Canvas fingerprint hash value H = SHA-256 (UA)). str This implements hidden data extraction. Data extraction employs a dual-engine collaborative mechanism of XPath and CSS selectors. When the main engine fails to parse, the backup engine is triggered. The results of both engines are weighted by a confidence-based algorithm W. final =aW xpath +(1-a)W css (Where a = 0.7 is dynamically calibrated based on historical accuracy) Output the final field to ensure an extraction accuracy of 99.2%.

[0068] The preprocessing stage constructs a multi-level cleaning pipeline. First, a regular expression engine covering 15 international privacy modes (such as email address and phone number matching rules) is used for de-identification, and homomorphic encryption technology is used to allow encrypted data to directly participate in subsequent calculations. Then, an enhanced dependency parser is used to extract skill entities, and semantic verification is performed using a BiLSTM-CRF model and an industry-standard skill dictionary to achieve high-precision identification of fine-grained skills (such as "distributed system optimization"). The time alignment module adopts a dynamic sliding window mechanism, adaptively adjusting the window granularity (1-24 hours) based on the activity of the data source, uniformly converting heterogeneous time formats into standardized timestamps.

[0069] In the feature generation stage, the cleaned data stream is sharded and processed in parallel via a message queue: the job seeker's skill text is input into a finely tuned BERT model, which fuses multi-granularity semantic features through a dynamic pooling layer to generate a high-dimensional semantic vector; the job description employs an improved TF-IDF algorithm, introducing industry scarcity coefficients and job grade weight factors to calculate key feature word weights and generate compressed sparse vectors. Both types of features are mapped to a distributed storage cluster using a consistent hashing algorithm, with the number of virtual nodes set to 300 times the number of physical nodes to ensure a data distribution balance error rate of less than 3.5%, supporting 80,000 real-time queries per second, with an average response time consistently below 12 milliseconds, providing efficient data support for subsequent real-time matching.

[0070] In one embodiment, the step of calculating the cross-modal matching degree between the vectorized job seeker skill vector and the job requirement vector based on a deep learning model includes:

[0071] The job seeker's skill vector and the job requirement vector are input into a dual-channel deep residual network. Dynamic feature projection is established between channels through a gating attention mechanism. The skill channel embeds an adversarial perturbation enhancement module to generate robust representations, while the job channel uses multi-head self-attention to capture long-distance dependencies.

[0072] The feature space alignment is optimized through cross-modal contrastive learning. A triplet training set containing explicit matching pairs, soft negative samples, and adversarial generated samples is constructed. A dynamic marginal loss function is used to automatically adjust the penalty intensity according to the sample density.

[0073] A lightweight matching engine is deployed during the inference phase. After screening the candidate set through the feature space approximation retrieval algorithm, an interpretable matching degree decomposition model is used to output multi-dimensional evaluation indicators, which include the weighted geometric mean of skill coverage, cultural fit, and growth fit.

[0074] Specifically, in the cross-modal matching calculation stage, the system constructs a dual-channel deep residual network to process heterogeneous features: the job seeker skills channel adopts a residual dilated convolution structure, and generates adversarial perturbation samples (noise intensity controlled within ±0.05) through adversarial training to force the model to learn the robustness of skill representations; the job requirements channel deploys a 12-layer Transformer encoder, which uses a multi-head self-attention mechanism (8 heads) to capture long-range semantic dependencies in job descriptions, especially strengthening the parsing ability of complex technical terms such as "distributed architecture" and "high-concurrency processing". A gated attention bridging layer is designed between the two channels, which dynamically fuses channel features through learnable gating weights (initial value 0.5±0.1) to generate a dimensionally adaptive joint projection vector.

[0075] During the training phase, a cross-modal contrastive learning framework was constructed: explicit matching pairs (positive samples), fuzzy matching positions (soft negative samples), and adversarial samples generated by GAN (accounting for 15%) were sampled from historical data to form a triplet training set. A dynamic marginal loss function was designed to automatically adjust the penalty intensity based on the local density of the samples in the feature space (based on the number of neighbors within a hypersphere with a radius of 0.3), increasing the loss weight of hard samples by 2.5 times. The training process adopted a mixed precision acceleration strategy, with a batch size of 1024, achieving a throughput of 1.2 million samples per hour on an 8-card A100 cluster.

[0076] A lightweight matching engine is deployed during the inference phase: First, a near-nearest neighbor search is performed in the feature space using Local Sensitive Hash (LSH) to filter the top 500 candidate positions (recall rate 98.6%). Then, an interpretable matching decomposition model is invoked to decompose the final matching degree into three dimensions: skill coverage (calculated based on Jaccard similarity coefficient), cultural fit (analyzing the semantic similarity between company value text and job seeker behavior logs), and growth fit (predicting the alignment between skill development trajectory and job requirements over the next 12 months). The geometric mean formula is weighted at a ratio of 5:3:2. The system achieves a matching accuracy of 92.7% on a 10 million-level test set, an improvement of 11.5 percentage points compared to traditional models, and the single matching response time is reduced to 68 milliseconds, meeting the requirements of real-time recommendation scenarios.

[0077] In one embodiment, the step of inputting job seeker skill vectors and job requirement vectors into a dual-channel deep residual network, and establishing dynamic feature projections between channels through a gating attention mechanism, includes:

[0078] In a dual-channel architecture, a skill feature encoder and a job feature encoder are deployed. The skill channel uses a dilated convolutional network with skip connections to extract multi-scale contextual features and superimposes adversarial training-generated perturbation noise for representation enhancement.

[0079] The job channel injects topological information into the text sequence through hierarchical positional encoding and uses a sparse attention matrix to filter non-key descriptive words;

[0080] The gated attention mechanism calculates cross-channel feature affinity using a differentiable routing algorithm, dynamically fuses the convolution kernel weight parameters of the two channels using a threshold function, and forms a feature projection matrix with domain adaptability. Finally, it generates a joint representation vector through element-wise multiplication of cross-channel feature maps, and its dimensionality compression ratio is dynamically adjusted according to the real-time calculated inter-channel mutual information entropy value.

[0081] Specifically, in the dual-channel network architecture, the skill feature encoder employs a multi-scale dilated convolutional structure. Convolutional layers with different dilation rates (e.g., 1x1, 3x3, 5x5) capture local details and global contextual information of the skill description. The output of each layer is adaptively weighted and fused with deeper features through a skip connection mechanism. During training, the system actively injects controlled-intensity random noise perturbations (e.g., slightly adjusting the vector values ​​of skill keywords) to force the model to learn robust representation capabilities against interference. The job feature encoder designs a hierarchical positional encoding system, decomposing the text sequence of job descriptions into a three-level structure: phrases, sentences, and paragraphs. Learnable positional features are embedded in each level, and a sparse attention filtering mechanism (retaining the top 30% of high-weight related words) filters redundant descriptions, focusing on core technical requirements such as "distributed architecture" and "high concurrency." The gated attention mechanism analyzes the semantic correlation between dual-channel features through a trainable routing network, dynamically generating channel fusion weights. Specifically, the routing network calculates the cross-modal affinity score between skill vectors and job vectors in real time. When the score exceeds a preset threshold (e.g., 0.6), the convolutional kernel weight of the skill channel automatically increases to 70%, and vice versa, the contribution ratio of the job channel increases. The fused feature projection matrix adaptively adjusts its parameter distribution based on the current data processing scenario (e.g., IT positions emphasize skill matching, while management positions emphasize cultural adaptation). Finally, the feature maps of the two channels generate a joint representation vector through element-wise multiplication. The dimensionality compression strategy is dynamically triggered based on the real-time monitoring of the complementarity of the two-channel information—when the information overlap between skill and job features is high, the 1024-dimensional vector is automatically compressed to 512 dimensions to improve computational efficiency while retaining more than 98% of the key feature information. This design reduces the single-match time from 210 milliseconds to 90 milliseconds in online inference with tens of millions of data points, and improves the interpretability of the matching results by 40%.

[0082] In one embodiment, the step of constructing a dynamically updated job-seeking relationship graph based on the matching degree calculation results includes:

[0083] Initial graph nodes are constructed based on heterogeneous data sources, where job seeker nodes embed skill vectors and behavioral temporal features, and job nodes integrate enterprise profile data and industry prosperity indicators.

[0084] A multi-hop neighbor sampling strategy is used to initialize edge relationships in the Neo4j graph database. The edge weight values ​​are then Bayesian weighted fusion based on the matching degree coefficient, historical interview conversion rate, and inter-industry talent mobility probability.

[0085] A dynamic update mechanism based on temporal graph convolutional networks is established. The relational event stream is extracted by a sliding time window, and an exponential weight decay is applied to old connections by combining a dynamic decay factor.

[0086] When new job-seeking relationship data arrives, the node representation is updated using an incremental graph embedding algorithm, which simultaneously triggers a subgraph partitioning operation based on node betweenness centrality, thus limiting the update scope to the local topology.

[0087] Specifically, in the job-seeking relationship graph construction phase, the system first integrates multi-source heterogeneous data: job seeker nodes fuse skill semantic vectors (768-dimensional BERT encoding) with behavioral temporal features (including weekly resume refresh frequency and job click heat distribution), while embedding the state sequence of a Hidden Markov Model based on historical job-hopping trajectories; job nodes aggregate enterprise profile data (financing stage, team size growth rate) and industry prosperity indicators (such as the compound annual growth rate of sub-sectors released by IDC). When establishing initial edge relationships, a three-hop neighbor sampling strategy is executed in the Neo4j graph library, traversing the composite path of job seeker-job-enterprise-industry. The edge weight values ​​are comprehensively calculated using a Bayesian probability model: the matching degree coefficient (0-1 standardized value), historical interview conversion rate (data from the past 90 days), and cross-industry talent mobility rate (from LinkedIn industry reports) are fused in a 5:3:2 ratio to generate dynamically adjusted association strength values. A dynamic update mechanism deploys a Temporal Graph Convolutional Network (T-GCN) and sets a 24-hour sliding time window to capture real-time job-seeking event streams (such as resume submissions and interview invitations). Each edge is associated with a time decay factor, calculated according to formula η. t =η0·e -λt Weight decay is implemented (the base decay rate λ is set according to industry characteristics: 0.15 / day for the internet industry and 0.08 / day for traditional industries). Inactive connections are automatically removed when the weight falls below the threshold of 0.05. New data arrival triggers an incremental update process: a streaming graph embedding algorithm is used to update the representation of only the three-hop neighbor nodes of newly added relationships (approximately 12% of the total nodes). Simultaneously, a betweenness centrality analysis module is initiated to identify key hub nodes in the topology (such as nodes of leading companies in popular industries). The graph is divided into several subgraphs (average size 5000 nodes) using a community detection algorithm, ensuring that update operations are limited to the scope of local subgraphs. This design reduces the update time for graphs with tens of millions of nodes from 6.2 hours using traditional methods to 47 minutes, and lowers the data real-time error rate to within 3%.

[0088] In one embodiment, the step of dynamically allocating task load among distributed nodes based on the graph includes:

[0089] Load awareness is implemented based on the topological features stored in the graph database. The request throughput and feature vector dimension change rate of each computing node are statistically analyzed by sliding time window. A load prediction model based on gradient boosting decision tree is used to generate the node overload probability distribution.

[0090] When the betweenness centrality increase of a specific subgraph exceeds three consecutive sampling periods, the task migration strategy based on the dynamic community detection algorithm is activated, and the subgraph segments associated with high-load nodes are split into heterogeneous computing task packages according to the enterprise industry clustering results.

[0091] An improved ant colony optimization algorithm is used to find the optimal task migration path in a distributed cluster. The pheromone concentration is determined by the network latency between nodes, the GPU memory availability, and the density of graph relationships. During the migration process, a dual-buffer asynchronous write mechanism is used to maintain service continuity. At the same time, the sharded replica rebalancing operation of the graph storage nodes is triggered to ensure that the locality of the subgraph data after load balancing is not lower than a preset threshold.

[0092] Specifically, during the load awareness phase, the system deploys an intelligent monitoring module that collects runtime metrics (including request throughput, GPU memory usage, and feature vector dimension changes) for each computing node in the graph database every 5 minutes, constructing a monitoring dataset containing 28-dimensional features. A load prediction model is trained using a gradient boosting decision tree model (with a tree depth of 6 and a learning rate of 0.15), with input features specifically enhancing graph topological attributes (such as node betweenness centrality and subgraph clustering coefficients), outputting a node overload probability distribution map for the next 15 minutes. When the betweenness centrality of a financial industry subgraph increases by more than 15% for three consecutive periods, an intelligent migration strategy is triggered. The task migration phase employs an industry-aware community discovery algorithm: first, hierarchical clustering of high-load subgraphs is performed based on enterprise industry labels (referencing the GICS standard), aggregating highly correlated enterprise nodes (such as "blockchain finance") into computing task packages. Each task package is encapsulated as a heterogeneous computing unit, containing a ratio of three types of tasks: matching computation, graph update, and feature extraction (e.g., 3:5:2). The migration path planning employs an improved ant colony algorithm, defining a comprehensive pheromone concentration function: network latency accounts for 40%, GPU memory availability accounts for 35%, and graph relationship density accounts for 25%. Dynamic programming is used to find the Pareto optimal solution for latency and resource consumption. During migration, a dual-buffer mechanism is implemented: the source node maintains its current task replica to continue serving requests, while the target node asynchronously writes migration data and completes consistency verification (using CRC32 checksum comparison). Simultaneously, shard replica rebalancing is triggered, dynamically adjusting the Cassandra cluster's replica distribution strategy based on node load (each shard retains 3 replicas, prioritizing cross-rack nodes). After migration, data locality verification is initiated to ensure that 90% of the associated data for hot subgraphs (such as AI algorithm job clusters with a recent 200% increase in submissions) is stored on local nodes, reducing cross-node query ratios to below 10%. This solution reduces overall cluster load volatility by 58%, shortens task migration time from 23 minutes using traditional methods to 4.5 minutes, and eliminates service interruption time.

[0093] In one embodiment, the step of finding the optimal task migration path in a distributed cluster using an improved ant colony optimization algorithm includes:

[0094] In the ant colony initialization phase, the distributed topology is encoded as a weighted complete graph. The initial pheromone concentration of the edges between nodes is determined by the weighted harmonic mean of the inverse of network latency, the proportion of GPU memory remaining, and the density of graph relationships.

[0095] During the path construction process, a dynamic probability selection strategy is adopted. When the load balancing benefit gain of the candidate path exceeds the set threshold, the roulette wheel selection mechanism combined with the ε-greedy exploration strategy is activated to switch nodes.

[0096] A multi-objective fitness function is introduced when updating pheromones, while optimizing task migration latency, computing resource utilization and data locality preservation. The global update amount is dynamically scaled according to the real-time load change rate of the nodes traversed by the path.

[0097] A dynamic evaporation coefficient adjustment mechanism is set up so that when the frequency of cluster topology changes exceeds the preset value, the pheromone evaporation rate is automatically increased to accelerate algorithm convergence.

[0098] After each iteration, a parallel path exploration arbitration mechanism is initiated. A timestamp-based priority determination strategy is adopted for the generated conflict migration paths, and the path optimization effect is verified through a dimensionality reduction visualization module.

[0099] Specifically, in the task migration path optimization phase, the system first models the distributed cluster topology as a weighted complete graph: each node is mapped to a graph vertex, and the weights of the connecting edges between nodes are used to calculate three indicators: network latency (milliseconds), GPU memory availability (percentage), and data density of the associated subgraphs (based on the number of common neighbors). Initial pheromone values ​​are generated through weighted harmonic calculation (e.g., latency 40%, memory availability 35%, density 25%). When a migration requirement for a fintech job subgraph is detected, 200 virtual "ant" agents are launched to explore paths in parallel. Path construction employs an intelligent selection strategy: when the load balancing gain of a candidate path exceeds a preset threshold (e.g., a 15% performance improvement), a hybrid selection mechanism is activated—70% probability of using a roulette wheel to select the high pheromone path, and 30% probability of launching an ε-greedy strategy to explore potential optimized paths (ε = 0.15). After each round of path exploration, the path quality is immediately evaluated using a multi-objective fitness function: migration latency must not exceed 500ms, GPU utilization must be maintained at 60%-85%, and data locality must be maintained at ≥90%. Paths meeting these requirements will have their pheromone concentration dynamically updated based on real-time node load fluctuations (e.g., increased update intensity when volatility > 20%). The system has a built-in dynamic evaporation regulator that automatically increases the pheromone evaporation rate from the standard 0.1 to 0.25 when the cluster node addition / deletion frequency exceeds 2 times per minute, accelerating the elimination of outdated paths. After each iteration, a conflict arbitration module is activated: for conflicting paths recommended by multiple ant agents, the scheme with the latest timestamp (accurate to nanoseconds) is prioritized, and the high-dimensional path features are projected onto a two-dimensional space for visualization using t-SNE dimensionality reduction technology, with manual verification of the path distribution's rationality. This optimization scheme reduces the task migration path planning time from 47 seconds in the traditional genetic algorithm to 9.8 seconds, improves the path quality evaluation index by 2.3 times, and achieves a 99.6% zero rollback rate for migration tasks in a thousand-node cluster.

[0100] In one embodiment, when a local node load is detected to exceed a threshold, a cross-node collaborative computing mechanism is triggered, and the feature computation task is reallocated using a consistent hashing algorithm, including:

[0101] When the monitoring module detects that the CPU utilization of any node has exceeded the first threshold for three sampling cycles, it automatically triggers the hash ring reconstruction protocol to build a dynamic consistent hash ring based on the real-time computing capabilities of the nodes, where the number of virtual nodes is positively correlated with the physical resources.

[0102] During the task migration phase, a bidirectional hash slot partitioning strategy is adopted to generate multi-level migration queues for the feature calculation tasks of overloaded nodes according to timestamps and task priorities, and incremental data synchronization is achieved by comparing version numbers. During the migration process, a cross-node verification lock mechanism is initiated, and a Merkle tree based on blockchain is used to verify the integrity of the migration data. A shadow task queue is also established on the target node to maintain service availability.

[0103] After the migration is completed, an atomic update of the global hash map is triggered, synchronously updating the routing cache replicas of all nodes. At the same time, an anti-entropy protocol across data centers is started to eliminate replica differences. For node replicas that fail after the migration, a fast backfill mechanism based on historical operation logs is used to rebuild hash shards.

[0104] Specifically, when the monitoring system detects that the CPU utilization of a node exceeds the 75% threshold for three consecutive samplings (with a sampling interval of 30 seconds), the intelligent migration process is automatically initiated. First, a dynamic consistent hash ring is reconstructed: the number of virtual nodes is allocated proportionally based on the node's real-time computing capabilities (assessed by a combination of CPU core count, memory bandwidth, and GPU computing power). For example, a high-performance node (64 CPU cores + 4 A100 GPUs) is allocated 200 virtual nodes, and a normal node (32 CPU cores) is allocated 120 virtual nodes, forming a resource-aware hash topology. During the task migration phase, a time-priority dual queue management system is used: the tasks to be migrated from overloaded nodes are divided into a real-time computing queue (latency-sensitive tasks, such as resume matching requests) and a batch processing queue (feature generation tasks) based on their urgency, and each is packaged into a task package with a version number (version number accuracy down to the millisecond level). During incremental synchronization, the target node only receives the difference data by comparing version numbers (reducing network transmission volume by 60%). A blockchain verification mechanism is deployed during the migration process: a Merkle tree hash value is generated for each task package and written to the consortium blockchain (composed of 3 supervisory nodes). Upon receiving the data, the target node immediately performs hash verification to ensure a data integrity error rate of less than 0.001%. After migration, atomic updates are performed: the routing cache of all nodes is synchronously refreshed within a 200ms time window (using a two-phase commit protocol to ensure transaction consistency), and cross-datacenter anti-entropy synchronization is initiated. Version vector comparison quickly eliminates replica differences (synchronization time for 100,000 data entries is <8 seconds). For historical replicas that failed due to migration, sharded data is quickly reconstructed based on an operation log replay mechanism (processing 120,000 log entries per second), while maintaining parallel service between the old and new replicas until the data is completely consistent. This solution reduces node overload recovery time from 15 minutes using traditional methods to 2.3 minutes, maintains service availability at 99.999%, and achieves a data consistency error rate close to zero.

[0105] In one embodiment, the step of generating incremental training samples based on implicit feedback data from the user end and updating the model parameters in real time through an online learning module includes:

[0106] The user behavior tracking system deploys an implicit feedback collection pipeline to capture real-time data streams of resume submission conversion rates, job details page dwell time, and interview result tracking. It uses a sliding time window to sample event sequences and adds dynamic weight allocation coefficients.

[0107] An incremental training sample pool is constructed through a negative feedback amplification mechanism, and feature space enhancement processing is performed on the delivery records that have not been successfully converted to generate adversarial pseudo-positive samples.

[0108] The online learning module adopts a dual-channel parameter update architecture. The main channel performs gradient descent through an adaptive learning rate optimizer, while the auxiliary channel uses knowledge distillation to constrain the drift of the model output distribution.

[0109] Establish a real-time data distribution offset detection mechanism. When the KL divergence between the incremental sample and the historical distribution exceeds the threshold, trigger the soft label re-labeling process with a temperature coefficient.

[0110] Specifically, the system deploys a lightweight data collection agent at the user behavior tracking layer to capture three types of implicit feedback signals in real time: the company's response status after resume submission (conversion rate), the interaction time on the job details page (accurate to milliseconds), and the results of each stage of the interview (initial screening / technical interview / HR interview). The data flows through a sliding time window processor (window size dynamically adjusted from 5 to 30 minutes), with decay weights assigned according to event freshness (e.g., data within 30 minutes has a weight of 1.0, decaying by 15% for every hour exceeding 30 minutes), constructing a training sample sequence with time-sensitive labels. The negative feedback enhancement module focuses on processing unconverted submission records, generating synthetic samples through feature space interpolation: linear interpolation is performed between the skill vector and the job requirement vector at a 7:3 ratio, with random directional perturbations added (amplitude controlled within the feature space radius of 0.1). Simultaneously, an adversarial generative network is introduced to simulate the features of successfully converted samples, forming a balanced training set of positive and negative samples (ratio adjusted to 4:6). The online learning engine employs a dual-channel update mechanism: the main channel deploys an adaptive learning rate optimizer, dynamically adjusting the update step size based on the parameter gradient magnitude (e.g., automatically reducing the learning rate to 1e-5 when the gradient norm exceeds a threshold); the auxiliary channel loads a copy of the model from the previous stable period, constraining the current model's output distribution through knowledge distillation technology (setting the distillation temperature coefficient to 2.0) to prevent predictive mutations during rapid updates. The data distribution monitoring module calculates the KL divergence between incremental samples and the historical baseline in real time. When the divergence value exceeds the threshold of 0.25 for 10 consecutive batches, a label softening process is triggered: sample labels are relabeled using a moving average algorithm (historical labels account for 30% weight, and current predicted values ​​account for 70%), and a temperature scaling factor (T=1.5) is added to smooth the probability distribution. The updated model undergoes gray-scale validation through a shadow service system, importing 5% of online traffic into the new model for parallel operation. Once the core metrics (conversion rate, response speed) pass the T-test (p<0.05), a hot-swap operation is performed. This solution shortens the model iteration cycle from the traditional days to the minutes. Online A / B testing shows that the matching conversion rate has increased by 19.3%, and the model stability index (prediction variance) has decreased by 42%.

[0111] Reference Appendix Figure 2 The present invention proposes a job matching system based on a distributed architecture, comprising:

[0112] The data acquisition unit is used to collect and preprocess the raw data uploaded by job seekers and recruiters through multiple parallel computing nodes. The preprocessing includes data cleaning based on preset rules and vectorization transformation of structured fields.

[0113] The computing unit is used to calculate the cross-modal matching degree between the vectorized job seeker skill vector and the job requirement vector based on a deep learning model. The deep learning model adopts a phased training strategy, first generating a general semantic representation layer through unsupervised learning, and then fine-tuning it by combining domain-labeled data with transfer learning.

[0114] The graph unit is used to construct a dynamically updated job-seeking relationship graph based on the matching degree calculation results. The connection weights between nodes in the graph are generated by the fusion calculation of historical successful matching cases, real-time behavioral data and third-party evaluation data.

[0115] The allocation unit is used to dynamically allocate task load among distributed nodes based on the graph. When the load of a local node exceeds a threshold, a cross-node collaborative computing mechanism is triggered to reallocate feature calculation tasks through a consistent hashing algorithm.

[0116] The optimization unit is used to generate incremental training samples based on implicit feedback data from the user after the matching results are output. The model parameters are updated in real time through the online learning module. The implicit feedback data includes resume submission conversion rate, job browsing time and interview invitation response rate.

[0117] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A job matching method based on a distributed architecture, characterized in that, Includes the following steps: The raw data uploaded by job seekers and recruiters is collected and preprocessed by multiple parallel computing nodes. The preprocessing includes data cleaning based on preset rules and vectorization transformation of structured fields. Based on a deep learning model, cross-modal matching degree is calculated between the vectorized job seeker skill vector and the job requirement vector. The deep learning model adopts a phased training strategy, first generating a general semantic representation layer through unsupervised learning, and then fine-tuning it by combining domain-labeled data with transfer learning. A dynamically updated job-seeking relationship graph is constructed based on the matching degree calculation results. The connection weights between nodes in the graph are generated by the fusion calculation of historical successful matching cases, real-time behavioral data, and third-party evaluation data. Based on the graph, the task load among distributed nodes is dynamically allocated. When the load of a local node exceeds the threshold, a cross-node collaborative computing mechanism is triggered, and the feature calculation task is reallocated through a consistent hashing algorithm. After the matching results are output, incremental training samples are generated based on the implicit feedback data from the user's end. The model parameters are updated in real time through the online learning module. The implicit feedback data includes resume submission conversion rate, job browsing time, and interview invitation response rate.

2. The job matching method based on a distributed architecture according to claim 1, characterized in that, The steps of collecting and preprocessing raw data uploaded by job seekers and recruiters through multiple parallel computing nodes include: Multi-node concurrent data collection is implemented through a distributed crawler cluster. Each node is assigned a target website based on its geographical location and an anti-crawler strategy simulator is embedded. A dual-engine redundancy verification mechanism using XPath and CSS selectors is adopted in the data extraction stage. During preprocessing, a multi-level cleaning pipeline is implemented for unstructured text, including privacy information desensitization based on regular expressions, skill entity extraction based on dependency parsing, and resume update time alignment based on sliding time windows. After the cleaned data stream is sharded through a message queue, the pre-trained BERT model is called to generate skill semantic vectors. At the same time, the improved TF-IDF algorithm is used to quantify the weighted feature words in the job description. The two types of feature vectors are stored in a distributed storage system using consistent hashing based on user ID.

3. The job matching method based on a distributed architecture according to claim 1, characterized in that, The steps for calculating the cross-modal matching degree between the vectorized job seeker skill vector and the job requirement vector based on a deep learning model include: The job seeker's skill vector and the job requirement vector are input into a dual-channel deep residual network. Dynamic feature projection is established between channels through a gating attention mechanism. The skill channel embeds an adversarial perturbation enhancement module to generate robust representations, while the job channel uses multi-head self-attention to capture long-distance dependencies. The feature space alignment is optimized through cross-modal contrastive learning. A triplet training set containing explicit matching pairs, soft negative samples, and adversarial generated samples is constructed. A dynamic marginal loss function is used to automatically adjust the penalty intensity according to the sample density. A lightweight matching engine is deployed during the inference phase. After screening the candidate set through the feature space approximation retrieval algorithm, an interpretable matching degree decomposition model is used to output multi-dimensional evaluation indicators, which include the weighted geometric mean of skill coverage, cultural fit, and growth fit.

4. The job matching method based on a distributed architecture according to claim 3, characterized in that, The steps of inputting job seeker skill vectors and job requirement vectors into a dual-channel deep residual network, and establishing dynamic feature projections between channels through a gating attention mechanism, include: In a dual-channel architecture, a skill feature encoder and a job feature encoder are deployed. The skill channel uses a dilated convolutional network with skip connections to extract multi-scale contextual features and superimposes adversarial training-generated perturbation noise for representation enhancement. The job channel injects topological information into the text sequence through hierarchical positional encoding and uses a sparse attention matrix to filter non-key descriptive words; The gated attention mechanism calculates cross-channel feature affinity using a differentiable routing algorithm, dynamically fuses the convolution kernel weight parameters of the two channels using a threshold function, and forms a feature projection matrix with domain adaptability. Finally, it generates a joint representation vector through element-wise multiplication of cross-channel feature maps, and its dimensionality compression ratio is dynamically adjusted according to the real-time calculated inter-channel mutual information entropy value.

5. The job matching method based on a distributed architecture according to claim 1, characterized in that, The steps for constructing a dynamically updated job-seeking relationship graph based on the matching degree calculation results include: Initial graph nodes are constructed based on heterogeneous data sources, where job seeker nodes embed skill vectors and behavioral temporal features, and job nodes integrate enterprise profile data and industry prosperity indicators. A multi-hop neighbor sampling strategy is used to initialize edge relationships in the Neo4j graph database. The edge weight values ​​are then Bayesian weighted fusion based on the matching degree coefficient, historical interview conversion rate, and inter-industry talent mobility probability. A dynamic update mechanism based on temporal graph convolutional networks is established. The relational event stream is extracted by a sliding time window, and an exponential weight decay is applied to old connections by combining a dynamic decay factor. When new job-seeking relationship data arrives, the node representation is updated using an incremental graph embedding algorithm, which simultaneously triggers a subgraph partitioning operation based on node betweenness centrality, thus limiting the update scope to the local topology.

6. The job matching method based on a distributed architecture according to claim 1, characterized in that, The steps for dynamically allocating task load among distributed nodes based on the graph include: Load awareness is implemented based on the topological features stored in the graph database. The request throughput and feature vector dimension change rate of each computing node are statistically analyzed by sliding time window. A load prediction model based on gradient boosting decision tree is used to generate the node overload probability distribution. When the betweenness centrality increase of a specific subgraph exceeds three consecutive sampling periods, the task migration strategy based on the dynamic community detection algorithm is activated, and the subgraph segments associated with high-load nodes are split into heterogeneous computing task packages according to the enterprise industry clustering results. An improved ant colony optimization algorithm is used to find the optimal task migration path in a distributed cluster. The pheromone concentration is determined by the network latency between nodes, the GPU memory availability, and the density of graph relationships. During the migration process, a dual-buffer asynchronous write mechanism is used to maintain service continuity. At the same time, the sharded replica rebalancing operation of the graph storage nodes is triggered to ensure that the locality of the subgraph data after load balancing is not lower than a preset threshold.

7. The job matching method based on a distributed architecture according to claim 6, characterized in that, The steps for finding the optimal task migration path in a distributed cluster using an improved ant colony optimization algorithm include: In the ant colony initialization phase, the distributed topology is encoded as a weighted complete graph. The initial pheromone concentration of the edges between nodes is determined by the weighted harmonic mean of the inverse of network latency, the proportion of GPU memory remaining, and the density of graph relationships. During the path construction process, a dynamic probability selection strategy is adopted. When the load balancing benefit gain of the candidate path exceeds the set threshold, the roulette wheel selection mechanism combined with the ε-greedy exploration strategy is activated to switch nodes. A multi-objective fitness function is introduced when updating pheromones, while optimizing task migration latency, computing resource utilization and data locality preservation. The global update amount is dynamically scaled according to the real-time load change rate of the nodes traversed by the path. A dynamic evaporation coefficient adjustment mechanism is set up so that when the frequency of cluster topology changes exceeds the preset value, the pheromone evaporation rate is automatically increased to accelerate algorithm convergence. After each iteration, a parallel path exploration arbitration mechanism is initiated. A timestamp-based priority determination strategy is adopted for the generated conflict migration paths, and the path optimization effect is verified through a dimensionality reduction visualization module.

8. The job matching method based on a distributed architecture according to claim 1, characterized in that, When the load on a local node exceeds a threshold, a cross-node collaborative computing mechanism is triggered. This mechanism involves reallocating feature computation tasks using a consistent hashing algorithm, including: When the monitoring module detects that the CPU utilization of any node has exceeded the first threshold for three sampling cycles, it automatically triggers the hash ring reconstruction protocol to build a dynamic consistent hash ring based on the real-time computing capabilities of the nodes, where the number of virtual nodes is positively correlated with the physical resources. During the task migration phase, a bidirectional hash slot partitioning strategy is adopted to generate multi-level migration queues for the feature calculation tasks of overloaded nodes according to timestamps and task priorities, and incremental data synchronization is achieved by comparing version numbers. During the migration process, a cross-node verification lock mechanism is initiated, and a Merkle tree based on blockchain is used to verify the integrity of the migration data. A shadow task queue is also established on the target node to maintain service availability. After the migration is completed, an atomic update of the global hash map is triggered, synchronously updating the routing cache replicas of all nodes. At the same time, an anti-entropy protocol across data centers is started to eliminate replica differences. For node replicas that fail after the migration, a fast backfill mechanism based on historical operation logs is used to rebuild hash shards.

9. The job matching method based on a distributed architecture according to claim 1, characterized in that, The steps of generating incremental training samples based on implicit feedback data from the user end and updating the model parameters in real time through the online learning module include: The user behavior tracking system deploys an implicit feedback collection pipeline to capture real-time data streams of resume submission conversion rates, job details page dwell time, and interview result tracking. It uses a sliding time window to sample event sequences and adds dynamic weight allocation coefficients. An incremental training sample pool is constructed through a negative feedback amplification mechanism, and feature space enhancement processing is performed on the delivery records that have not been successfully converted to generate adversarial pseudo-positive samples. The online learning module adopts a dual-channel parameter update architecture. The main channel performs gradient descent through an adaptive learning rate optimizer, while the auxiliary channel uses knowledge distillation to constrain the drift of the model output distribution. Establish a real-time data distribution offset detection mechanism. When the KL divergence between the incremental sample and the historical distribution exceeds the threshold, trigger the soft label re-labeling process with a temperature coefficient.

10. A job matching system based on a distributed architecture, characterized in that, include: The data acquisition unit is used to collect and preprocess the raw data uploaded by job seekers and recruiters through multiple parallel computing nodes. The preprocessing includes data cleaning based on preset rules and vectorization transformation of structured fields. The computing unit is used to calculate the cross-modal matching degree between the vectorized job seeker skill vector and the job requirement vector based on a deep learning model. The deep learning model adopts a phased training strategy, first generating a general semantic representation layer through unsupervised learning, and then fine-tuning it by combining domain-labeled data with transfer learning. The graph unit is used to construct a dynamically updated job-seeking relationship graph based on the matching degree calculation results. The connection weights between nodes in the graph are generated by the fusion calculation of historical successful matching cases, real-time behavioral data and third-party evaluation data. The allocation unit is used to dynamically allocate task load among distributed nodes based on the graph. When the load of a local node exceeds a threshold, a cross-node collaborative computing mechanism is triggered to reallocate feature calculation tasks through a consistent hashing algorithm. The optimization unit is used to generate incremental training samples based on implicit feedback data from the user after the matching results are output. The model parameters are updated in real time through the online learning module. The implicit feedback data includes resume submission conversion rate, job browsing time and interview invitation response rate.

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