A high-dimensional vector retrieval and semantic generalization system with cross-modal semantic alignment
By employing multi-level clustering and adaptive metric calibration techniques, the problem of retrieval imbalance caused by density differences in cross-modal semantic processing is solved, enabling efficient retrieval of heterogeneous modal data and improving retrieval accuracy and result diversity.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HANZHENG INFORMATION TECH CO LTD
- Filing Date
- 2026-01-05
- Publication Date
- 2026-06-02
Smart Images
Figure CN122132570A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a high-dimensional vector retrieval and semantic generalization system with cross-modal semantic alignment, belonging to the field of semantic processing technology. Background Technology
[0002] In current cross-modal semantic processing, mapping unstructured image and text data into high-dimensional feature vectors and storing and retrieving them using vector databases is the core path to information retrieval. Mainstream vector retrieval systems rely on approximate nearest neighbor search algorithms, such as inverted file indexes or hierarchical small-world graphs, to determine semantic similarity by calculating the geometric distance between the query vector and candidate vectors. These technologies are generally based on the assumption that feature vectors are uniformly distributed in the metric space and that a globally unified distance calculation standard applies. However, when processing heterogeneous modal data containing images and text, this assumption conflicts with the actual distribution characteristics of the data in the physical space. Differences in information entropy between different modalities lead to non-conservative semantic and density characteristics in the vector space. Image data, due to the continuity and high redundancy of pixel information, is prone to forming high-density clump-like distributions in space. When text data involves abstract concepts or long-tail semantics, it is prone to forming low-density filamentary or discrete manifold distributions, with fluctuating spatial topological characteristics, making it difficult for traditional indexing mechanisms based on a single rigid metric scale to take into account the retrieval efficiency of different regions.
[0003] While existing technologies attempt to optimize feature extraction and ranking algorithms, focusing on the depth of semantic understanding while neglecting the heterogeneity of the measurement space, for example, Chinese invention patent CN110879834B discloses an opinion retrieval system and method based on recurrent convolutional networks. The scheme introduces recurrent neural networks and convolutional networks to extract deep features of text context and combines a ranking learning framework to improve the accuracy of retrieval relevance. Although such methods enhance the semantic expressive power of the vectors themselves, the underlying logic of retrieval and ranking is still limited by globally unified measurement rules. It does not establish an adaptive calibration mechanism for the uneven distribution density of feature vectors in space. Regardless of whether the data falls into a high-density homogeneous region or a low-density sparse region, the system uses the same fixed distance or similarity calculation scale, lacking a rigid measurement method with environmental awareness. As a result, when processing cross-modal heterogeneous data, it is impossible to fundamentally solve the problem of retrieval imbalance caused by density differences.
[0004] Therefore, the technical problem to be solved by this invention is how to construct a retrieval mechanism that automatically calibrates the metric based on local data density and has the ability to generalize to sparse regions by improving the physical storage structure of the index and the online retrieval operation logic without relying on external model retraining. Summary of the Invention
[0005] To address the problems mentioned in the background art, the technical solution of this invention is as follows: A high-dimensional vector retrieval and semantic generalization system with cross-modal semantic alignment, comprising: The index building unit is used to obtain the heterogeneous modal feature vector set, divide the vector space into several vector clusters through a multi-level clustering algorithm, calculate the average distance statistical features of the feature vector distribution within each vector cluster to generate a density weighting factor, and store the density weighting factor as metadata in the index node corresponding to the vector cluster. The query positioning unit is used to respond to a retrieval request, map the query vector to the index space, and determine the target vector cluster into which the query vector falls or the sparse region in which it is located based on the geometric distance. An adaptive metric calibration unit is used to read the density weighting factor stored in the index node of the target vector cluster when the query vector falls into the target vector cluster, generate a nonlinear distance correction coefficient based on the density weighting factor, and adjust the Euclidean distance weight between the query vector and the candidate vector using the nonlinear distance correction coefficient. Specifically, the sensitivity of the distance metric is increased in the high-density region indicated by the density weighting factor, and the sensitivity of the distance metric is decreased in the low-density region indicated by the density weighting factor. The semantic extension retrieval unit is used to identify several boundary vector clusters around the sparse region when the query vector falls into the sparse region, calculate the shortest path connecting the boundary vector clusters based on the connection relationship of the index nodes, and aggregate the vectors represented by the nodes on the path as the retrieval results according to the path weight.
[0006] Preferably, the index building unit is also used to calculate the variance of the eigenvalue distribution of the feature vector set within each vector cluster, and generate local information entropy based on the variance of the eigenvalue distribution. The local information entropy is stored in the index node as verification metadata. The adaptive metric calibration unit is also configured with a computation bypass strategy, which is used to determine that the region is a low signal-to-noise ratio dense region when the density weighting factor of the target vector cluster is higher than the first preset threshold and the local information entropy is lower than the second preset threshold. The nonlinear distance correction coefficient is locked to a preset fixed value or only the centroid vector of the target vector cluster is output to skip the fine-grained distance calculation of the candidate vectors in the region.
[0007] Preferably, when calculating the density weighting factor, the index building unit performs the following operation: calculates the average Euclidean distance between all feature vectors in the vector cluster and their nearest neighbor vectors, and maps the inverse of the average Euclidean distance to the density weighting factor, which is used to quantify the compactness of the data distribution in the local subspace.
[0008] Preferably, the calculation logic of the adaptive metric calibration unit adjusting the Euclidean distance weights using the nonlinear distance correction coefficient satisfies the following relationship: ,in, This is the corrected distance metric. For query vector, Candidate vectors, The density weighting factor for the target vector cluster. The original Euclidean distance, and The preset adjustment constant is defined; the relationship defines the nonlinear adjustment rule of the distance metric with local density.
[0009] Preferably, the index building unit is also used to establish the adjacency relationship between adjacent vector clusters and calculate the density gradient vector between adjacent vector clusters; the adaptive metric calibration unit is also used to perform boundary smoothing processing. When it is determined that the query vector is located in the boundary transition region of the target vector cluster, the reference cluster adjacent to the boundary transition region is identified, and based on the position ratio of the query vector relative to the centroid of the target vector cluster and the centroid of the reference cluster, the density weighting factor of the target vector cluster and the density weighting factor of the reference cluster are weighted and interpolated. The smoothing factor obtained by the calculation is used as the basis for generating the nonlinear distance correction coefficient.
[0010] Preferably, when calculating the shortest path, the semantic expansion retrieval unit constructs an index adjacency graph based on the connection relationship between index nodes, takes the projection position of the query vector in the index adjacency graph as the starting point, and performs a multi-hop breadth-first search to determine the minimum cost path to the high-density vector cluster.
[0011] Preferably, the index building unit uses a hierarchical K-means clustering algorithm to build vector clusters, and only retains the centroid vector and density weighting factor of each vector cluster as a coarse index resident in memory, while storing the specific feature vectors within the vector clusters in an inverted file on disk.
[0012] Preferably, the heterogeneous modal feature vector set includes image feature vectors generated by the image encoder and text feature vectors generated by the text encoder; the mean value of the density weighting factor in the image feature vector clustering region is greater than the mean value in the text feature vector distribution region.
[0013] Preferably, the second preset threshold in the bypass strategy is determined based on the statistical histogram entropy of the category to which the feature vectors belong within the vector cluster, and is used to identify duplicate image data clusters of a single object.
[0014] Preferably, the nonlinear distance correction coefficient is a scalar value. The adaptive metric calibration unit adjusts the distance weight by multiplying the scalar value with the original Euclidean distance, and this adjustment process does not involve dimensional transformation of the vector space or matrix operations.
[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. By associating local subspace density potential energy factors with hierarchical quantization index nodes, a nonlinear distance correction logic is constructed. The retrieval operation utilizes the density potential energy factors of the microcavity where the query vector falls to participate in geometric distance weighting. Without external model intervention, it has the ability to perceive the distribution environment. This logic enables the distance measurement scale to dynamically deform: the scale is refined in high-density areas such as images to distinguish subtle differences, and the scale is coarsened in low-density areas such as text to tolerate semantic spans. Through the collaboration of storage structure and operation rules, the rigid binding of vector geometric distance and semantic similarity is decoupled from the bottom layer of the index, ensuring that the retrieval ranking reflects the true semantic relevance under the condition of extremely uneven distribution of heterogeneous modal data.
[0016] 2. By utilizing hierarchical quantization of semantic microcavities and connectivity relationships to construct a graph-based semantic inference path, when the localization unit detects that the query vector is in a sparse region lacking direct reference anchors, it activates the shortest path search logic based on the skeleton graph, identifies the microcavities surrounding the void boundary, and synthesizes the results based on the path weights. This process transforms traditional point-to-point geometric matching into graph theory-based path combination calculation, without relying on external knowledge bases or retraining neural networks. It uses the existing index structure to store knowledge boundaries for logical interpolation, providing a deterministic algorithm-based recall response for long-tail, low-frequency, or unseen semantic queries.
[0017] 3. Local semantic entropy is introduced as a density potential energy verification dimension to identify high-density, low-information pseudo-dense data clusters. Structural collapse marking is applied to this region in the index. During the online retrieval stage, the measurement and calibration unit automatically bypasses high-precision distance correction calculations based on the markings and directly outputs the microcavity centroid. This mechanism uses the principle of information entropy to physically block the system from over-computing homogeneous redundant data, reducing the average computational load of online retrieval. It also improves the semantic category diversity within the limited window of the retrieval result set by suppressing the dense arrangement of duplicate data. Attached Figure Description
[0018] Figure 1 This is a flowchart of the density-aware cross-modal retrieval adaptive processing of the present invention; Figure 2 This is a comparison chart of the accuracy and recall performance of the experimental group and the benchmark group in this invention; Figure 3 This is a diagram showing the core functional architecture and data interaction topology of the cross-modal retrieval system of this invention. Detailed Implementation
[0019] This specific embodiment is intended to provide a detailed description of the technical solution of the present invention, but should not be construed as limiting the scope of protection of the present invention. Any modifications or equivalent substitutions made by those skilled in the art based on the teachings of the present invention should be included within the scope of the claims of the present invention.
[0020] This invention discloses a high-dimensional vector retrieval and semantic generalization system with cross-modal semantic alignment, comprising an index building unit, a query positioning unit, an adaptive metric calibration unit, and a semantic expansion retrieval unit. These units interact via a data bus and shared memory. The index building unit maps heterogeneous modal data into a physical index structure with local density metadata. The query positioning unit maps received query requests to specific topological locations within this index structure. The adaptive metric calibration unit dynamically adjusts distance metric rules based on local topological features. The semantic expansion retrieval unit performs graph-based path deduction in sparse regions covered by the index. Furthermore, it addresses the issue of uneven distribution density of heterogeneous modal data in the vector space, which can cause a single distance metric to fail. For this engineering problem, the system employs a nonlinear metric calibration mechanism based on local density potential. The index construction unit acquires a heterogeneous modal feature vector set, which includes image feature vectors generated by the image encoder and text feature vectors generated by the text encoder. The system executes a multi-level clustering algorithm, using a hierarchical K-means algorithm to divide the vector space into several vector clusters. Each vector cluster represents a local semantic subspace. During the construction process, the system calculates the average distance statistics of the feature vector distribution within each vector cluster. The system traverses each vector within a cluster, calculates its Euclidean distance to its nearest neighbor vector within the cluster, calculates the average nearest neighbor Euclidean distance of all vectors within the cluster, and maps the reciprocal of this average value to a density weighting factor. This density weighting factor To quantify the compactness of the data distribution in this local subspace, after completing the calculation, the system combines the centroid vector of each vector cluster with the density weighting factor. It serves as a coarse index resident in memory, and stores the specific feature vectors within the vector clusters in an inverted file on disk, along with density weighting factors. The metadata is permanently stored in the index node structure corresponding to the vector cluster.
[0021] The query positioning unit responds to the retrieval request by receiving the query vector. This is then mapped to the index space, and the unit computes the query vector. The geometric distance between the query vector and the centroids of each vector cluster in the coarse index is used to determine the target vector cluster to which the query vector falls, based on the minimum distance principle. Simultaneously, this unit executes sparse region determination logic. The distance to the nearest centroid vector exceeds a preset sparsity threshold. When the query vector falls into a sparse region, it is determined that it falls into a target vector cluster; otherwise, it is determined that it falls into a target vector cluster. Furthermore, to address the issues of low discrimination in high-density regions and poor recall in low-density regions caused by traditional rigid metrics, the adaptive metric calibration unit is configured to perform dynamic distance weighting. When a cell falls into a target vector cluster, it reads the density weighting factor stored in the index node of that target vector cluster. Based on this factor, the system generates a non-linear distance correction coefficient and uses this coefficient to adjust the Euclidean distance weights between the query vector and the candidate vectors in the inverted file. This adjustment process follows the following defined calculation relationship: ,in, This is the corrected distance metric. For query vector, Candidate vectors, The density weighting factor for the target vector cluster. This is the original Euclidean distance between the query vector and the candidate vectors. and The preset adjustment constant is where Used to control the slope of the correction function's sensitivity to density changes. The calculation logic for setting the density reference offset is achieved through a nonlinear transformation.
[0022] exist High-density regions with larger values, i.e., areas where image features are clustered, generate larger correction coefficients, thereby amplifying the original distance. To improve the sensitivity of measurements to distinguish subtle semantic differences; In low-density regions with smaller values, i.e., long-tail text distribution areas, smaller correction coefficients are generated, thereby compressing the original distance and reducing metric sensitivity to tolerate larger semantic spans. This process only involves scalar multiplication operations and does not involve dimensionality transformations or matrix operations in the vector space. To address the time bottleneck of exponential function calculation in high-concurrency scenarios, a nonlinear correction coefficient lookup table (LUT) based on SRAM is established to incorporate density-weighted factors. The range of values is linearly mapped to a 4096-level discrete address index, and the Sigmoid function value corresponding to each index bit is pre-calculated. It resides in the L3 cache; at runtime, it is directly based on... Quantization index addressing read correction factor By replacing the hundred-clock-cycle floating-point exponentiation and division operations with single-clock-cycle memory read instructions, the time complexity of the distance correction logic remains constant. To address the issue of invalid computation caused by data duplication, the system integrates a computation bypass strategy. During the offline phase, the index building unit calculates the variance of the eigenvalue distribution of the feature vector set within each vector cluster and generates a local information entropy based on this variance. This entropy characterizes the semantic diversity of the data within the cluster and is stored as verification metadata in the index node. During online retrieval, the adaptive metric calibration unit reads the density weighting factor and local information entropy of the target vector cluster. When the density weighting factor is detected to be higher than a first preset threshold and the local information entropy is lower than a second preset threshold, the system determines that the region is a low signal-to-noise ratio dense region, i.e., a pseudo-dense region with a large amount of duplicate data. At this point, the system activates bypass logic, locking the nonlinear distance correction coefficient to a preset fixed value or directly outputting the centroid vector of the target vector cluster as a representative retrieval result, skipping the one-to-one fine-grained distance calculation of the massive number of candidate vectors in the region, thereby reducing the computational load and improving the diversity of results.
[0023] To address the potential metric jump issue caused by query vectors located at the boundaries of different density regions, the system incorporates boundary smoothing logic. The index building unit pre-establishes adjacency relationships between adjacent vector clusters and calculates the density gradient vectors between them. During the online retrieval phase, when the query positioning unit determines that the query vector lies within the boundary transition region of the target vector cluster (i.e., the distance from the query vector to its centroid is within a preset edge buffer range), the system identifies the reference cluster adjacent to this boundary transition region. The system calculates the ratio of the distance between the query vector and the centroids of the target and reference vector clusters, and applies this ratio to the density weighting factor of the target vector cluster. Density weighting factor of the reference cluster The system performs linear weighted interpolation and replaces the original density weighting factor with the smoothing factor obtained from the calculation. This smoothing factor is then substituted into the aforementioned nonlinear distance correction formula to achieve a smooth transition of the metric standard between different density regions. Furthermore, to address the zero recall problem caused by long-tail data or unseen semantics, the semantic extension retrieval unit provides graph topology-based generalization capabilities. When the query positioning unit determines that the query vector falls into a sparse region, the system activates the semantic extension retrieval logic. This unit identifies several boundary vector clusters surrounding the sparse region based on geometric distance. Utilizing the pre-constructed connection relationships between index nodes—i.e., the index adjacency graph—the system performs a multi-hop breadth-first search, starting from the projection position of the query vector in the graph. It calculates the shortest path connecting each boundary vector cluster. The system calculates the path weight based on the path length and the density weighting factor of the nodes on the path, and aggregates the vectors represented by the nodes on the path as the retrieval result. This process transforms point-to-point geometric matching into a path combination based on a graph structure, achieving effective recall of the implicit semantics in sparse regions.
[0024] Example 1: In large-scale e-commerce cross-modal retrieval applications, the system needs to process a heterogeneous vector space containing hundreds of millions of product images and long-tail text descriptions. This presents a challenge of uneven distribution, with high-density clustering due to image pixel redundancy and low-density sparsity due to text semantic discretization. When the query positioning unit receives an image query vector pointing to a specific athletic shoe style... When mapping it to the index space, the system determines that the vector falls into a target vector cluster containing a large number of similar product images from similar perspectives. Based on the fact that the index building unit has already calculated the region with an extremely high density weighting factor in the offline stage based on the average distance statistical characteristics of the feature vector distribution within the cluster, the system determines that the vector falls into a target vector cluster containing a large number of similar product images from similar perspectives. The adaptive measurement calibration unit reads the factor in real time and adjusts accordingly. The calculation relationship generates a non-linear distance correction coefficient with a value greater than 1, and adjusts the original Euclidean distance between the query vector and the candidate vectors in the inverted file. A magnification process is performed to stretch feature vectors that are extremely similar in the original geometric space or differ only slightly in the texture and stitching details of the shoe upper to a distinguishable metric scale, thereby enabling fine-grained and accurate retrieval in dense regions without increasing the feature dimension.
[0025] When a query vector falls into a cluster of target vectors consisting of repeated uploads of a single promotional poster, the system detects that although this region has a density weighting factor higher than the first preset threshold, its local information entropy pre-calculated and stored by the index building unit is lower than the second preset threshold. For this low signal-to-noise ratio dense region, the adaptive metric calibration unit activates a computation bypass strategy and forcibly locks the nonlinear distance correction coefficient to a preset fixed value or directly outputs the centroid vector of the cluster. This logic automatically filters semantically highly redundant duplicate data while maintaining retrieval recall, solving the problem of invalid occupation of online computing resources and crowding out the diversity of retrieval results by massive duplicate samples. The underlying logic executes computation graph collapse logic, calls the mask register setting operation in the SIMD instruction set AVX-512, and constructs an all-zero computation mask vector. The floating-point unit (FPU) instruction sequence for calculating candidate vectors within the target microcavity is mapped to a no-operation (NOP), physically blocking pipeline scheduling of the Euclidean distance matrix and directly extracting the centroid coordinates via the DMA channel. To the result register, zero-power bypass is achieved at the hardware level when the system processes text query vectors containing abstract design concepts. When the query positioning unit determines that the geometric distance between the target vector and the nearest centroid vector exceeds a preset sparsity threshold, the query positioning unit determines that the target vector's geometric distance exceeds a preset sparsity threshold. When a sparse region without a direct reference anchor is encountered, the semantic expansion retrieval unit is triggered and no longer performs nearest neighbor search based on geometric distance. Instead, it identifies several boundary vector clusters around the region and performs a multi-hop breadth-first search using the connection relationships between index nodes. The system calculates weights based on path length and node density weighting factors and synthesizes the shortest path connecting these boundary clusters, aggregating the vectors represented by the nodes on the path into retrieval results.
[0026] When processing edge query requests located at the boundary between dense image feature regions and sparse text feature regions, the system automatically activates a boundary smoothing procedure to eliminate metric abrupt changes, assuming the query vector... Falling into high-density target clusters (its density weighting factor) Set as Within the edge buffer zone and adjacent to the low-density reference cluster. (its density weighting factor) Set as ), system calculation Relative to the centroids of the two clusters and The ratio of the Euclidean distances, if measured arrive The distance between them is the ratio of the distance between their centroids to the distance between their centroids. This indicates that the vector is more likely to move away from high-density regions and towards low-density regions in terms of topological location. The adaptive metric calibration unit then substitutes this positional proportion into the linear interpolation formula to calculate the smoothed density weighting factor. The system utilizes this smoothing factor. Replace the original Substituting into the nonlinear distance correction formula In this way, a transitional correction coefficient between the two is generated, ensuring that when the query vector crosses the modal density fault, the ranking of the search results exhibits mathematical continuity and a smooth transition in business, effectively avoiding oscillations in search results caused by hard threshold cutting.
[0027] Example 2: This example is based on a simulation verification platform built on a high-performance server cluster and is equipped with a distributed vector retrieval engine capable of simulating tens of millions of vectors. It integrates a heterogeneous data generator that conforms to industry standards to synthesize a hybrid dataset containing high-density image clusters, low-density text manifolds, and long-tailed sparse semantics. The experiment aims to verify the effectiveness and engineering value of the cross-modal semantic alignment high-dimensional vector retrieval and semantic generalization system proposed in this invention when facing large-scale, heterogeneous, and extremely unbalanced datasets compared to traditional retrieval schemes.
[0028] The experiment verifies the effectiveness of the density-adaptive metric calibration mechanism. In the data preprocessing stage, a heterogeneous data generator is used to construct a basic dataset containing 10 million high-dimensional feature vectors. 80% of the data is set as high-density image feature vectors, simulating popular product display images in e-commerce scenarios; these vectors form compact spherical clusters in the feature space. The remaining 20% of the data is set as low-density text feature vectors, simulating long-tail product descriptions or user reviews; these vectors exhibit a sparse, filamentary, or discrete distribution. The system executes the index construction process, calculating the density weighting factor for each vector cluster. Based on this, an index structure was generated. To simulate the query load in a real industrial environment, a test set containing 10,000 query requests was generated, with image queries and text queries each accounting for 50%. Some queries were intentionally designed to fall into the boundary region of different modalities or extremely sparse semantic gaps.
[0029] To quantify the performance advantages of the proposed solution, a traditional benchmark group was introduced as a control. This control group adopted a standard inverted file index (IVF) structure and a globally unified Euclidean distance metric, without any density-aware or adaptive calibration mechanism. Under the same hardware environment and test dataset, the recall rate (Recall@K) and retrieval precision rate (Precision@K) of the proposed solution (experimental group) and the traditional benchmark group were recorded in different query scenarios. For queries in sparse regions, the zero-recall rate was also recorded to evaluate the system's generalization ability.
[0030] Table 1: Comparison of Search Performance under Different Schemes
[0031] Referring to Table 1, the data demonstrates the advantages of the present invention. In high-density image query scenarios, traditional benchmarks struggle to distinguish highly similar vectors due to insufficient resolution of the metric scale, resulting in an accuracy of only 68.5%. The present invention, by amplifying the metric scale in dense regions, significantly improves the accuracy to 92.3%, effectively solving the discrimination problem. In low-density text query scenarios, traditional benchmarks suffer from rigid distance truncation, leading to the missed detection of numerous related but distant long-tail semantics, resulting in a recall rate of only 55.2%. The present invention, by compressing the metric scale in sparse regions, improves the recall rate to 88.7%, enhancing the ability to capture long-tail content. Regarding the effectiveness verification of the computational bypass strategy, an experiment was conducted using a specific subset containing a large amount of repetitive data. In this region, the system detected extremely high density weighting factors and extremely low local information entropy, triggering bypass logic and directly outputting the centroid vector. Data shows that the retrieval throughput (QPS) on this specific subset increased by 4.5 times compared to when the bypass strategy was not enabled, and the diversity score of the retrieval results increased by 60%, demonstrating the value of this strategy in processing industrial-grade redundant data.
[0032] Example 3: This example combines Figures 1 to 3 This section describes a high-dimensional vector retrieval and semantic generalization system with cross-modal semantic alignment, such as... Figure 1 As shown, the process begins with the input of a heterogeneous modal feature vector set. The index building unit receives the feature set and calculates the density weighting factor of the vector clusters. This fixed density factor is stored as metadata in the index node. When the system receives an externally input query vector Query, it reads the index structure and maps the query vector to the index space through the query positioning unit. This determines whether the query vector falls into the target vector cluster or the sparse region. If the result is that it falls into the target vector cluster, the process leads to the left branch, where the adaptive metric calibration unit reads the density weighting factor and generates a nonlinear correction coefficient. This dynamically adjusts the distance weight between the query vector and the candidate vectors to output the retrieval result. If the result is that it falls into the sparse region, the process leads to the right branch, where the semantic expansion retrieval unit calculates the shortest path based on the index connection relationship and aggregates the node vectors on the path as the retrieval result.
[0033] like Figure 2 As shown, the horizontal axis lists three key evaluation indicators: image query precision (Precision@10), text query recall (Recall@100), and sparse region zero recall. The vertical axis represents the percentage values corresponding to each indicator. The data in the figure shows that the experimental group of this invention has higher image query precision and text query recall than the traditional benchmark group, while the experimental group of this invention has significantly lower values for sparse region zero recall, which represents missed detections. Figure 3As shown in the diagram, the system architecture diagram illustrates the complete data link from the user end to the underlying storage. Query requests issued by users or clients undergo preliminary processing via the query routing gateway and enter the core index area residing in memory through a positioning mapping mechanism. This area stores cluster centroids, dynamic indexes, and density weighting factors. Using this core area as a hub, data flows to three functional domains: upwards to the adaptive metric calibration engine located in the high-performance vector computing domain; to the right, through sparse routing, to the semantic graph expansion engine to generate the final results; and downwards, through a high-speed bus I / O interface, to the underlying high-speed disk storage pool, which is responsible for persistently storing inverted index files and adjacency graph data.
[0034] Example 4: In high-throughput data processing scenarios for real-time online public opinion monitoring and emergency event tracking, the system faces the challenge of drastic non-steady-state evolution of the vector space topology over time. Specifically, the data density of a specific semantic region can jump from a sparse state to an extremely high density state instantaneously due to the outbreak of a sudden event. This causes traditional retrieval systems based on static indexes to experience lag and failure in metric scales when dealing with such pulse-like data streams. To address this challenge, this example constructs a dynamic index maintenance mechanism based on incremental density awareness. This mechanism is executed collaboratively by the streaming update module and the adaptive metric calibration unit within the index construction unit. When real-time data streams are continuously written to the index, the streaming update module no longer performs a full reconstruction but instead performs a refactoring for each vector cluster that has undergone data changes. The density weighting factor is updated in real time using the exponentially weighted moving average (EWMA) algorithm. The update logic follows the following dynamic equation: ,in, For the current moment density weighting factor The factor from the previous moment, The preset time decay coefficient is as follows: To adjust the degree of memory for historical density, This represents the average nearest neighbor distance within the cluster for newly written vectors in the current batch.
[0035] Under this mechanism, when reports of a certain type of sudden event, such as an earthquake in a certain area, flood in within a short period of time, the corresponding semantic microcavity... Rapidly shrinking, driving The rapid increase was immediately detected by the adaptive metric calibration unit. The value jumps, and the instantaneous correction formula for nonlinear distance. The adjustment of the correction coefficient allows the system to automatically refine the measurement scale when the semantic region becomes extremely crowded. This enables it to accurately distinguish unique information with subtle differences, such as specific rescue progress and casualty figures, from a massive amount of highly similar reports, preventing search results from being overwhelmed by homogeneous content. For semantic regions where popularity declines over time and data inflow becomes depleted, the system continuously monitors and identifies these regions. As the value gradually falls back to a low level, the measurement scale is automatically relaxed, and the path deduction logic of the semantic expansion retrieval unit is activated instead.
[0036] Example 5: Before the system is officially put into online service, in order to ensure that the nonlinear correction model in the adaptive metric calibration unit can accurately adapt to the heterogeneous data distribution characteristics under specific application scenarios, the system executes a standardized offline parameter calibration and optimization procedure, and uses a hierarchical sampling algorithm to extract data from the full index containing no less than the total amount of data. A representative subset of the verification data, and based on the density weighting factor in this subset. Determine the adjustment constant based on the probability density function distribution. The initial search anchor point is The median of the distribution, the system is composed of Range of values and Range of values Within the constructed two-dimensional parameter space, a fine-grained grid search is performed. For each coordinate point in the grid representing a parameter combination, the system runs batch retrieval tests and calculates a weighted composite score of the mean precision and recall. Finally, the global extreme point corresponding to this score surface is selected. and The parameters are combined and stored in the read-only storage area of the configuration file as baseline operating parameters.
[0037] In addition, to address the potential data distribution drift that may occur during long-term operation, the system incorporates a dynamic baseline maintenance procedure based on statistical process control principles. This procedure continuously monitors the changing trends of the density statistics of feature vectors newly written to the index in the background and periodically calculates the density weighting factor of the data within the current sliding window. The system measures the relative entropy between the current calibration model and the initial calibration baseline distribution. When this relative entropy continuously exceeds a preset drift threshold, the system determines that the current calibration model has deviated from the optimal working range and automatically triggers an incremental parameter recalibration task. It then re-executes the aforementioned parameter optimization logic using the latest data snapshot to generate an updated calibration model. and The value is then seamlessly injected into the memory of the running retrieval service through atomic operations.
[0038] Example 6: This example constructs and executes a standardized engineering procedure to perform parameter calibration and model initialization for a high-dimensional vector retrieval system in a specific application scenario before deployment. This ensures the stability and reproducibility of the system when facing heterogeneous data distributions. The procedure targets the core physical carrier of the system, namely the computing cluster, and performs benchmark performance calibration. By running a set of standardized matrix operation loads, it measures and records the cluster's floating-point operation capability (FLOPS) and memory bandwidth limits under different concurrency pressures. This serves as the physical constraint boundary for subsequent parameter optimization. The procedure also addresses key process control parameters in the system, namely the adjustment constant in the nonlinear distance correction formula. and It executes an offline optimization procedure based on grid search and extracts a statistically representative validation dataset from historical data of the target application scenario, covering the expected distribution range of image and text features. Range of values and Range of values Within the constructed two-dimensional parameter space, the system divides the grid with a preset step size. For each parameter combination point in the grid, the system runs a batch retrieval task on the validation dataset and calculates the corresponding average accuracy (mAP) and average retrieval latency. By constructing the Pareto front of accuracy-latency, the system automatically identifies and selects the parameter combination that can achieve the highest accuracy under the maximum allowable latency constraint as the optimal operating point for this application scenario.
[0039] For the vector clustering algorithms involved in the system, a model architecture establishment and hyperparameter adaptation procedure were executed, explicitly selecting the hierarchical K-means algorithm as the clustering core and setting the number of clustering layers. Number of clusters per layer The optimization logic, specifically, involves the system calculating the total number of vectors in the dataset. And based on empirical formulas Set an initial number of clusters, and iteratively calculate different... For the contour coefficient under a given value, select the value that maximizes the contour coefficient. As the final configuration, the procedure specifies the use of the maximum number of iterations and the cluster center convergence threshold as dual termination conditions during the training process to ensure that the model reaches a stable state within a finite time. In addition, for abnormal situations during system operation, the procedure has a built-in set of deterministic boundary handling logic. When the real-time query traffic of a certain cluster exceeds three times the standard deviation of its historical mean during online retrieval, the system determines that the cluster is a hot spot abnormal area and automatically triggers the dynamic replica expansion mechanism to copy the index data of the cluster to the backup computing node to share the load. At the same time, for boundary situations of abnormal input vector dimensions or incorrect data format, the system is configured with a pre-validation filter. Any vector that does not conform to the preset dimension standard, such as 512 dimensions or 768 dimensions, will be intercepted immediately and a standardized error code will be returned.
[0040] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.
[0041] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims
1. A high-dimensional vector retrieval and semantic generalization system with cross-modal semantic alignment, characterized in that, include: The index building unit is used to obtain the heterogeneous modal feature vector set, divide the vector space into several vector clusters through a multi-level clustering algorithm, calculate the average distance statistical features of the feature vector distribution within each vector cluster to generate a density weighting factor, and store the density weighting factor as metadata in the index node corresponding to the vector cluster. The query positioning unit is used to respond to a retrieval request, map the query vector to the index space, and determine the target vector cluster into which the query vector falls or the sparse region in which it is located based on the geometric distance. An adaptive metric calibration unit is used to read the density weighting factor stored in the index node of the target vector cluster when the query vector falls into the target vector cluster, generate a nonlinear distance correction coefficient based on the density weighting factor, and adjust the Euclidean distance weight between the query vector and the candidate vector using the nonlinear distance correction coefficient. Specifically, the sensitivity of the distance metric is increased in the high-density region indicated by the density weighting factor, and the sensitivity of the distance metric is decreased in the low-density region indicated by the density weighting factor. The semantic extension retrieval unit is used to identify several boundary vector clusters around the region when the query vector falls into a sparse region, calculate the shortest path connecting the boundary vector clusters based on the connection relationship of the index nodes, and aggregate the vectors represented by the nodes on the path as the retrieval results according to the path weight.
2. The high-dimensional vector retrieval and semantic generalization system with cross-modal semantic alignment according to claim 1, characterized in that, The index building unit is also used to calculate the variance of the feature value distribution of the feature vector set within each vector cluster, and to generate local information entropy based on the variance of the feature value distribution. The local information entropy is then stored in the index node as verification metadata. The adaptive metric calibration unit is also equipped with a computation bypass strategy, which is used to determine that the region is a low signal-to-noise ratio dense region when the density weighting factor of the target vector cluster is higher than the first preset threshold and the local information entropy is lower than the second preset threshold. The nonlinear distance correction coefficient is locked to a preset fixed value or only the centroid vector of the target vector cluster is output, so as to skip the fine-grained distance calculation of candidate vectors in the region.
3. The high-dimensional vector retrieval and semantic generalization system with cross-modal semantic alignment according to claim 1, characterized in that, When calculating the density weighting factor, the index building unit performs the following operation: it calculates the average Euclidean distance between all feature vectors in the vector cluster and their nearest neighbor vectors, and maps the inverse of the average Euclidean distance to the density weighting factor, which is used to quantify the compactness of the data distribution in the local subspace.
4. The high-dimensional vector retrieval and semantic generalization system with cross-modal semantic alignment according to claim 3, characterized in that, The adaptive metric calibration unit uses a nonlinear distance correction coefficient to adjust the Euclidean distance weights. The calculation logic satisfies the following relationship: ,in, This is the corrected distance metric. For query vector, Candidate vectors, The density weighting factor for the target vector cluster. The original Euclidean distance, and The relationship defines a nonlinear adjustment rule for the distance metric as a function of local density, with the set of preset adjustment constants.
5. A high-dimensional vector retrieval and semantic generalization system with cross-modal semantic alignment according to claim 1, characterized in that, The index building unit is also used to establish the adjacency relationship between adjacent vector clusters and calculate the density gradient vector between adjacent vector clusters. The adaptive metric calibration unit is also used to perform boundary smoothing processing. When it is determined that the query vector is located in the boundary transition region of the target vector cluster, the reference cluster adjacent to the boundary transition region is identified. Based on the position ratio of the query vector relative to the centroid of the target vector cluster and the centroid of the reference cluster, the density weighting factor of the target vector cluster and the density weighting factor of the reference cluster are weighted and interpolated. The smoothing factor obtained by the operation is used as the basis for generating the nonlinear distance correction coefficient.
6. The high-dimensional vector retrieval and semantic generalization system with cross-modal semantic alignment according to claim 1, characterized in that, When calculating the shortest path, the semantic extension retrieval unit constructs an index adjacency graph based on the connection relationship between index nodes. It takes the projection position of the query vector in the index adjacency graph as the starting point and performs a multi-hop breadth-first search to determine the minimum cost path to the high-density vector cluster.
7. A high-dimensional vector retrieval and semantic generalization system with cross-modal semantic alignment according to claim 1, characterized in that, The index building unit uses a hierarchical K-means clustering algorithm to construct vector clusters, and retains only the centroid vector and density weighting factor of each vector cluster as a coarse index resident in memory, while storing the specific feature vectors within the vector clusters in an inverted file on disk.
8. A high-dimensional vector retrieval and semantic generalization system with cross-modal semantic alignment according to claim 1, characterized in that, The heterogeneous modal feature vector set includes image feature vectors generated by the image encoder and text feature vectors generated by the text encoder; the mean value of the density weighting factor in the image feature vector clustering region is greater than the mean value in the text feature vector distribution region.
9. A high-dimensional vector retrieval and semantic generalization system with cross-modal semantic alignment according to claim 2, characterized in that, The second preset threshold in the bypass strategy is determined based on the statistical histogram entropy of the category to which the feature vectors belong within the vector cluster, and is used to identify duplicate image data clusters of a single object.
10. A high-dimensional vector retrieval and semantic generalization system with cross-modal semantic alignment according to claim 1, characterized in that, The nonlinear distance correction coefficient is a scalar value. The adaptive metric calibration unit adjusts the distance weight by multiplying this scalar value with the original Euclidean distance. This adjustment process does not involve dimensional transformation of the vector space or matrix operations.