A computer vision large model-based urban governance massive data efficient processing method and system

Abstract

The application discloses a kind of based on computer vision large model's urban governance mass data efficient processing method and system, comprising: the pre-processing of urban governance multi-source data;Based on the scene feature library, the pre-trained SegFormer visual large model is fine-tuned using LoRA low-rank adaptive algorithm, and high-precision exclusive CV model is generated;Unified semantic mapping is constructed, and correlation analysis is carried out using GATv2 graph attention network, to realize the deep fusion and accurate research of cross-source data;Using the "hierarchical inference" strategy integrated with MobileViTv3 and TensorRT quantization, lightweight real-time inference is realized on the edge side;The result is analyzed in multidimension and visual decision support is provided;Based on feedback data, model iteration optimization is automatically triggered, to realize the adaptive evolution of system.The application significantly reduces the computing power and delay of mass data processing, improves the recognition accuracy and decision intelligence level in complex scenarios, and can quickly adapt to the new needs of dynamic urban governance.

Description

Technical Field

[0001] This invention relates to the fields of computer vision and artificial intelligence, and in particular to a method and system for efficient processing of massive amounts of data for urban governance based on a large computer vision model. Background Technology

[0002] As smart city governance enters a data-driven phase, the scale of multimodal data generated during city operations (including traffic video streams, public facility monitoring images, environmental sensor data, and structured government data) continues to expand, urgently requiring efficient processing and intelligent decision-making through computer vision and deep learning technologies. However, existing technological systems have the following key limitations in supporting urban governance needs:

[0003] First, for the real-time processing needs of massive amounts of data (TB / PB), existing solutions rely on traditional visual models (such as CNN and ResNet), which consume a lot of computing power and have high processing latency, making it difficult to meet the timeliness requirements of real-time traffic signal control and dynamic response to emergencies. For example, traffic congestion warnings need to be analyzed in a very short time, but the existing full-frame high-precision inference mode results in a response lag, which restricts the efficiency of governance.

[0004] Secondly, general-purpose large-scale visual models (such as ViT and Mask R-CNN) have insufficient generalization ability in complex urban scenarios. Especially in scenarios with severe weather (such as rain, fog, and backlight) or minor damage to facilities (such as cracked manhole covers or malfunctioning streetlights), the accuracy of target recognition drops significantly, requiring a large amount of manual review. This not only increases governance costs but also prolongs the problem response cycle.

[0005] Third, existing systems generally process visual data (such as video streams) and structured data (such as pipeline information and meteorological data) in isolation, lacking an effective cross-modal fusion mechanism. In scenarios requiring multi-source collaborative analysis, such as waterlogging monitoring, the inability to link key information such as pipeline status and rainfall leads to biased decision-making, making it difficult to achieve accurate risk warnings and scientific handling.

[0006] Fourth, urban governance needs are constantly changing (such as new types of illegal parking and public emergencies), but existing system models are rigid and lack adaptive iteration capabilities. Each adaptation to a new scenario requires manual re-labeling of data and adjustment of model parameters, which is time-consuming and cumbersome, seriously hindering the agility of the governance system in responding quickly to new problems.

[0007] The aforementioned shortcomings collectively limit the release of the value of urban governance data. Existing technologies are insufficient to support the upgrade of governance models from "passive response" to "proactive prediction." There is an urgent need to develop a technical solution that can efficiently process massive amounts of data, accurately identify complex scenarios, deeply integrate multi-source information, and support dynamic evolution. Summary of the Invention

[0008] This invention addresses the shortcomings of existing technologies by providing a method and system for efficient processing of massive amounts of data for urban governance based on large-scale computer vision models.

[0009] To achieve the above-mentioned objectives, the technical solution adopted by the present invention is as follows:

[0010] A method for efficiently processing massive amounts of urban governance data based on a large computer vision model, characterized by the following steps:

[0011] S1. Preprocess the collected multi-source urban governance data to obtain a standardized dataset; the multi-source data includes at least visual data and non-visual structured data;

[0012] S2. Based on a scenario-based feature library containing specific features for different urban governance scenarios, incremental fine-tuning is performed on the pre-trained large-scale computer vision model to generate a specific CV model corresponding to a particular governance scenario.

[0013] S3. Establish a unified semantic mapping relationship between visual features and the non-visual structured data, and perform correlation analysis on the mapped multi-source data based on graph neural networks to output a fusion analysis result that includes the causes of problems and risk predictions;

[0014] S4. Based on the hierarchical reasoning strategy, the preprocessed visual data is analyzed using the dedicated CV model to identify the target object, and governance decision suggestions are generated by combining the fusion analysis results.

[0015] S5. Mining and analyzing the governance decision-making recommendations and historical monitoring data to generate multi-dimensional analysis results, and providing visualization and decision support;

[0016] S6. Dynamically trigger and execute model iteration based on feedback data to update the dedicated CV model and / or the unified semantic mapping relationship.

[0017] Further, in step S1, the preprocessing includes: compressing and standardizing the resolution of visual data, and standardizing the format of non-visual structured data; wherein, the visual data is compressed using the FIPER factorized feature-based image compression algorithm, which encodes the input image into a structured implicit representation containing structural factors, texture factors, and semantic factors.

[0018] Further, in step S2, the pre-trained computer vision large model is a SegFormer model, and the incremental fine-tuning is implemented using the low-rank adaptive LoRA algorithm; wherein, the number of adaptation parameters of the LoRA algorithm does not exceed 0.2% of the total number of model parameters; the incremental fine-tuning uses the Lion optimizer for 10 to 20 rounds of iterative training.

[0019] Furthermore, in step S3, the unified semantic mapping relationship is achieved by constructing a "visual feature-structured data" mapping table; the "visual feature-structured data" mapping table binds visual features (such as the outline of the water accumulation area) with pipeline network data (pipeline network ID, pipe diameter) and meteorological data (rainfall). The graph neural network is a GATv2 graph attention network, which is used to calculate the association weights between each data node after mapping.

[0020] Further, in step S4, the hierarchical reasoning strategy includes:

[0021] S41. Utilize the lightweight backbone network MobileViTv3 to quickly analyze the preprocessed video stream and filter out target frames containing potential target objects;

[0022] S42. Input the target frame into the dedicated CV model after TensorRT quantization for accurate recognition.

[0023] Further, in step S42, the dedicated CV model is quantized to INT8 precision; in step S41, the inference speed of the lightweight backbone network MobileViTv3 is not less than 45 FPS.

[0024] Furthermore, in step S5, the multi-dimensional analysis results include: a risk distribution heatmap generated based on spatiotemporal aggregation and kernel density estimation, trend prediction results generated based on a lightweight time series model, and decision priority suggestions generated based on multi-attribute weighted sorting.

[0025] Furthermore, in step S6, the triggering condition for the model iteration is: the identification error rate in the collected feedback data is ≥5%, or the effective data volume of the newly added scene is ≥1000; the model iteration reuses the incremental fine-tuning process of step S2 and is completed within 24 hours.

[0026] This invention also discloses a high-efficiency system for processing massive amounts of urban governance data based on a large computer vision model, the system comprising:

[0027] The data acquisition module is used to collect multi-source data on urban governance and preprocess the multi-source data to output a standardized dataset; the multi-source data includes at least visual data and non-visual structured data.

[0028] The model fine-tuning module is connected to the scenario-based feature library and is used to incrementally fine-tune the pre-trained computer vision model based on the exclusive feature data in the scenario-based feature library, generate an exclusive CV model corresponding to a specific governance scenario and store it in the model library.

[0029] The data fusion module is used to establish a unified semantic mapping relationship between visual features and the non-visual structured data, and to perform correlation analysis on the mapped multi-source data based on graph neural networks, and output the fusion analysis results.

[0030] The reasoning and analysis module is connected to the model library and is used to load the dedicated CV model and perform reasoning and analysis on the visual data in the standardized dataset based on a hierarchical reasoning strategy to identify the target object.

[0031] The data analysis and application module is used to mine and analyze historical monitoring data and inference results, generate multi-dimensional analysis results, and provide visual decision support.

[0032] The model iteration optimization module is used to collect feedback data and automatically trigger the model fine-tuning module and data fusion module to perform iterative updates when preset conditions are met.

[0033] Furthermore, the preprocessing unit of the data acquisition module is used to compress the visual data using the FIPER image compression algorithm based on factorized features;

[0034] The model fine-tuning module is configured to perform incremental fine-tuning based on the SegFormer model, using the LoRA low-rank adaptive algorithm and the Lion optimizer.

[0035] The data fusion module includes a semantic mapping table for storing the correspondence between "visual features and structured data", and an association analysis model built based on the GATv2 graph attention network.

[0036] The inference analysis module includes a MobileViTv3 lightweight backbone network for quickly filtering target frames, and a TensorRT optimization unit for performing INT8 quantization on the dedicated CV model.

[0037] The model iteration and optimization module is configured to automatically trigger the iteration process when the recognition error rate is ≥5% or the amount of newly added scene data is ≥1000.

[0038] Compared with the prior art, the advantages of the present invention are as follows:

[0039] 1. Employing a collaborative architecture of "layered inference strategy + model quantization + edge computing," it achieves precise scheduling and efficient utilization of computing resources. A lightweight network rapidly filters target frames, performing high-precision analysis only on high-value data, avoiding resource waste from full-frame processing, significantly reducing end-to-end processing latency, and meeting the time-sensitive needs of urban governance scenarios such as real-time traffic signal control and dynamic fault detection.

[0040] 2. Based on the scenario-based feature library and LoRA low-rank adaptive fine-tuning technology, the general visual large model can be efficiently adapted to the subdivided scenarios of urban governance (such as target recognition under severe weather and detection of minor damage to facilities), which significantly improves the accurate perception capability of complex environments and urban scenarios, effectively reduces the reliance on manual review, reduces governance costs and response delays, and promotes the upgrade of governance model from "passive response" to "proactive prediction".

[0041] 3. Construct a unified semantic mapping mechanism for "visual features - structured data". Through graph neural networks, dynamically associate visual entities with cross-source data such as pipeline network and meteorology to achieve in-depth analysis of the causes of problems (such as "pipeline blockage + excessive rainfall") and risk spread, providing comprehensive decision support for "where, why, and what trend", and overcoming the problem of one-sided analysis caused by the fragmentation of multi-source data.

[0042] 4. Through an automatically triggered dynamic model iteration mechanism, the system can update the model without manual intervention based on feedback data (such as identification errors and new scenario requirements), quickly adapt to the dynamic changes in urban governance, break through the bottleneck of the rigidity of traditional system models, and significantly improve the response speed and flexibility of the governance system to new problems.

[0043] In summary, this invention, through its innovative design of efficient processing, accurate identification, deep integration, and dynamic evolution, fully unleashes the value of urban governance data, provides sustainable technological support for smart cities, and effectively promotes the development of urban governance towards intelligence, proactivity, and refinement. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is an overall flowchart of an embodiment of the present invention;

[0046] Figure 2 This is a flowchart illustrating the scenario-based fine-tuning process in an embodiment of the present invention;

[0047] Figure 3 This is a flowchart of the multi-source data fusion analysis in an embodiment of the present invention;

[0048] Figure 4 This is a flowchart of lightweight hierarchical inference in an embodiment of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] The present invention provides a method and system for efficient processing of massive amounts of urban governance data based on a large computer vision model, which mainly includes the following key components:

[0051] I. System Module Design

[0052] This invention focuses on the application of large-scale computer vision (CV) models in deep learning optimization of massive urban governance data. It follows the technical logic of "data access - feature processing - fusion analysis - inference optimization" to achieve efficient processing, accurate identification, deep fusion, and dynamic optimization of multimodal urban data. The specific technical solution is as follows:

[0053] The data acquisition module, composed of multi-source sensors and data interfaces, connects to edge computing nodes via API interfaces to collect various data sources for urban governance, including video streams from traffic cameras, images from public facility monitoring equipment, environmental sensor data, and government system ledger data. For this massive amount of data, the module employs a three-level preprocessing logic: "coarse screening-compression-standardization." First, invalid data is filtered using preset rules. Then, the video stream is compressed using the H.266 / VVC video coding standard, and image data is processed using a factorized feature (FIPER)-based image compression algorithm to retain key features. Finally, video frames are uniformly parsed and scaled to a standardized 640×640 pixel size, while non-visual structured data is converted to Apache Parquet columnar storage format to form a standardized dataset, reducing computational redundancy in subsequent processing.

[0054] Model Fine-tuning Module: Based on a scenario-based feature library, the pre-trained SegFormer visual model is incrementally fine-tuned using the LoRA low-rank adaptive algorithm to generate dedicated CV models for specific scenarios such as traffic, facilities, and environment. The feature library stores dedicated feature data for subdivided urban governance scenarios, such as "backlit license plate texture features" and "congested vehicle density features" for traffic scenarios, and "damaged manhole cover edge features" and "streetlight circuit fault light spot features" for facility operation and maintenance scenarios. The feature data is continuously expanded through manual annotation and automatic mining (extracting high-frequency features from historical valid data) to form a scenario-based feature benchmark. The fine-tuning process uses dedicated feature data extracted from the scenario-based feature library and performs 10 to 20 rounds of iterative training using the Lion optimizer to generate dedicated CV models adapted to specific scenarios such as traffic, facilities, and environment. These models are then stored in the model library for use by the inference and analysis layer.

[0055] The data fusion module constructs a unified semantic mapping table for "visual features - structured data". For example, it binds "the contour features of waterlogged areas identified by video" with "the network ID, pipe diameter, and historical number of blockages in underground pipe network data" and "real-time rainfall in meteorological data" to establish a one-to-one correspondence between features and data fields. It uses a graph neural network GATv2 to construct an association analysis model, which transforms the mapped multi-source data into graph nodes (such as "waterlogged area node", "pipe network node", and "rainfall node"). The module calculates the correlation degree through the edge weights between nodes (such as "spatial distance weight between waterlogged area and pipe network" and "correlation weight between rainfall and water depth") and outputs analysis results such as "causes of waterlogging (such as pipe network blockage + excessive rainfall)" and "spread risk (such as spreading to two surrounding intersections within 30 minutes)", providing comprehensive data support for decision-making.

[0056] Inference Analysis Module: Based on TensorRT, the fine-tuned CV large model is quantized and compressed (from FP32 to FP16), reducing the model parameter size by 30%-50%; a "layered inference" strategy is designed: for the preprocessed video stream, frames containing target areas (such as frames containing congested vehicles or broken manhole covers) are quickly filtered through the lightweight backbone network MobileViTv3, and then the target frames are input into the quantized CV large model for accurate identification. Non-target frames are only recorded with simple features to avoid wasting resources on full-frame high-precision inference; the inference process is deployed on edge computing nodes (such as intersection edge servers), and the end-to-end processing latency is controlled at the second level to meet the timeliness requirements of traffic congestion warning and real-time detection of facility failures.

[0057] Data Analysis and Application Module: Conducts in-depth mining and analysis of historical monitoring data, generates multi-dimensional analysis results, and provides customized services and decision support to the government through a visual interface.

[0058] Model Iteration and Optimization Module: A "feedback-update" mechanism is established to collect feedback (such as recognition errors discovered during manual review, and governance requirements for new scenarios). When the error rate is ≥5% or the amount of new scenario data is ≥1000 records, model iteration is automatically triggered. The iteration process requires no manual intervention, automatically calling new scenario feature data from the scenario-based feature library, repeating the incremental fine-tuning process of the feature processing layer, and simultaneously updating the semantic mapping table (the mapping relationship between new features and new structured data). The entire iteration cycle is controlled within 24 hours, quickly adapting to changes in governance requirements.

[0059] II. Core Algorithm Design

[0060] The core algorithm of this invention mainly includes the following parts:

[0061] 2.1 Data Compression Algorithm

[0062] The FIPER-based image compression algorithm is the core preprocessing algorithm of the data acquisition module. It is used to compress video streams and image data while retaining key semantic features, thereby reducing computational redundancy in subsequent processing.

[0063] Input image It is encoded as a structured implicit representation consisting of multiple factors.

[0064] The factorization process is defined as follows:

[0065] Let the implicit representation be .in, The structural factor is extracted through 4 layers of stride convolution downsampling and is responsible for encoding the macroscopic contours and geometric information in the image. The texture factor is extracted using a residual attention module and is responsible for encoding local details and surface features. As a semantic factor, it is encoded by Vision Transformer and is responsible for encoding high-level features related to scene classification and target recognition.

[0066] encoder Image Mapping to implicit representation :

[0067]

[0068] in These are encoder parameters.

[0069] The decoder Depending on the task requirements, the image or features can be selectively reconstructed:

[0070] Used for image reconstruction: ,in To reconstruct the image.

[0071] Used for feature output: This is to directly output feature maps for use in subsequent large-scale CV models.

[0072] Training objective function For weighted sum:

[0073]

[0074] in, Image distortion loss (using or ), For bitrate loss, For specific downstream tasks (tagged as) The loss of supervision on ) The parameters can be determined through hyperparameter search. This design allows the algorithm to retain semantic information crucial to urban governance tasks while maintaining a high compression rate.

[0075] 2.2 Model Fine-tuning Algorithm

[0076] A collaborative optimization framework based on the SegFormer large-scale visual model and the LoRA low-rank adaptive algorithm serves as the core algorithm of the feature processing module. Through scenario-based incremental fine-tuning of the pre-trained large-scale model, it effectively solves the core problems faced by traditional visual models in urban governance applications—insufficient accuracy in subdivided scene recognition and the high cost of full parameter fine-tuning. This combined architecture maintains the model's versatility while rapidly generating lightweight, high-precision CV models specifically for urban governance, achieving efficient adaptation and accurate perception of diverse governance scenarios.

[0077] The SegFormer multi-scale encoder employs a hierarchical Transformer design, effectively addressing the challenges of multi-scale object detection in urban governance. Through overlapping block embedding techniques and an efficient attention mechanism, it significantly reduces computational complexity while maintaining feature extraction quality.

[0078]

[0079]

[0080]

[0081] }

[0082] in Input image resolution OverlapPatchMerge is implemented using 3×3 convolutions with stride=2. Compared to the non-overlapping blocks of traditional ViT, it can better preserve edge information and solve the boundary blurring problem in small object detection.

[0083] To address the computational complexity challenge of high-resolution inputs in urban governance, an efficient attention mechanism is introduced to further improve model efficiency:

[0084]

[0085] in Dimensionality reduction matrices reduce computational complexity from... Down to This enables the model to process high-definition video streams in urban governance in real time, solving the memory bottleneck problem of traditional self-attention in long sequence processing.

[0086] Building upon this, the LoRA low-rank adaptive algorithm addresses the resource consumption problem of full parameter fine-tuning in large models through an efficient parameter fine-tuning strategy:

[0087]

[0088] in For a low-rank fitting matrix, the rank is... This algorithm only applies to the query projection matrix. Sum projection matrix Adaptation is performed, and the number of adaptation parameters is calculated:

[0089]

[0090] Only accounting for a small percentage of the total parameters in the model This solves the problem that traditional full-parameter fine-tuning requires a lot of computing resources and storage space, enabling the model to quickly adapt to new urban governance scenarios.

[0091] To ensure the stability of the training process, the Lion optimizer handles the parameter scaling differences between the pre-trained large model and the LoRA adapter through a sign function and momentum mechanism:

[0092]

[0093]

[0094] Among the hyperparameters: , , By employing sign functions and momentum mechanisms, the parameter scale differences between pre-trained large models and LoRA adapters are effectively addressed, resolving the gradient oscillation problem in mixed-parameter training of the traditional Adam optimizer and improving training stability and convergence speed.

[0095] 2.3 Data Fusion Algorithm

[0096] The GATv2 graph attention network model, as the core algorithm of the data fusion module, realizes deep correlation analysis between visual features and structured data through dynamic attention mechanism, and outputs accurate cross-source decision support results.

[0097] Given a by A graph consisting of nodes, each node having characteristics. GATv2 first assigns each pair of adjacent nodes... Calculate an attention coefficient The calculation formula is as follows:

[0098]

[0099] in, It is a learnable linear transformation matrix that maps node features to a common space; This represents a vector concatenation operation; It is a learnable weight vector; It is a non-linear activation function, with a negative slope set to... .

[0100] Then, the softmax function is used on the nodes. The attention coefficients of all neighbors are normalized to obtain standardized attention weights. :

[0101]

[0102] in For nodes The set of neighbors.

[0103] node The final output features The weighted sum of the features of all its neighboring nodes is then subjected to a nonlinear transformation:

[0104]

[0105] in For non-linear activation functions (using) : ).

[0106] Node features This is derived from the concatenation of visual entity feature vectors extracted by the feature processing layer and the embedding vectors of structured data. This is achieved by stacking three layers of GATv2 (hidden dimension). The model can capture multi-hop dependencies, thereby enabling deep reasoning on complex cross-modal associations such as "waterlogged area-pipeline network-rainfall".

[0107] 2.4 Inference Analysis Algorithm

[0108] The hierarchical inference algorithm based on the MobileViTv3 lightweight backbone network and TensorRT quantization technology serves as the core algorithm of the inference analysis module. Through dynamic resource scheduling and efficient feature extraction mechanisms, it achieves lightweight, high-precision real-time inference of massive video data for urban governance while balancing receptive field and computational cost, effectively solving the problem of computational resource bottlenecks in large-scale scenarios.

[0109] To address the dual challenges of limited computing power and insufficient receptive field in edge devices, the MobileViTv3 network architecture innovatively adopts a hybrid architecture design combining CNN and Transformer:

[0110]

[0111]

[0112]

[0113] MobileNetBlock, based on an inverse residual structure, significantly reduces the number of parameters through depthwise separable convolutions.

[0114]

[0115]

[0116]

[0117]

[0118] This design reduces computation by about 8-9 times compared to standard convolution, making it possible to deploy on edge devices.

[0119] MobileViTBlock enhances the model's global awareness through serialization processing:

[0120]

[0121]

[0122]

[0123] The Unfold operation transforms spatial features into a Patch sequence, and the TransformerLayer employs lightweight self-attention.

[0124]

[0125] The lightweight self-attention mechanism can effectively capture long-distance dependencies, significantly improving the performance of application scenarios that require global perception, such as traffic flow analysis and crowd gathering detection.

[0126] Based on the superior features of MobileViTv3, we further constructed a hierarchical inference strategy:

[0127] 1) Target frame filtering mechanism:

[0128]

[0129] Where the input resolution The output confidence map is downsampled by 8 times. This lightweight network achieves an inference speed of 45 FPS on JetsonOrin edge devices with only 2.5M parameters, solving the resource waste problem of full-frame high-precision inference.

[0130] 2) Smart frame selection criteria:

[0131] The frame selection mechanism combines confidence thresholds and information entropy analysis to ensure that only frames containing significant targets enter the subsequent fine-grained processing flow.

[0132]

[0133] in Information entropy calculation:

[0134]

[0135] Through intelligent filtering strategies, the system can concentrate computing resources on the most valuable data, significantly improving processing efficiency while ensuring detection accuracy.

[0136] 3) Precision inference link optimization:

[0137] The selected target frame will be fed into the quantized SegFormer model for detailed analysis:

[0138]

[0139] TensorRT INT8 quantization employs layer-by-layer calibration:

[0140]

[0141] in This is the scaling factor. This quantization strategy reduces the model size by 60%, increases inference speed by 2.8 times, and solves the memory bandwidth limitation problem of edge devices.

[0142] The hierarchical inference mechanism concentrates computing resources on high-value frames through a collaborative design of "lightweight screening + precise analysis". Compared with the traditional full-frame processing scheme, the computing efficiency is improved by 4-6 times. At the same time, the global perception capability of MobileViTv3 improves the recall rate of small target detection by 15%, effectively solving the dual technical bottlenecks of real-time processing of massive video data and accurate detection of small targets in urban governance.

[0143] 2.5 Data Analysis Algorithms

[0144] It provides technical support for data analysis and application modules, with the core objective of mining data value and providing real-time visualization and decision support for urban governance, meeting application needs without the need for complex calculations.

[0145] It mainly includes three types of lightweight functions:

[0146] 1) Multidimensional statistical analysis, using spatiotemporal aggregation formulas (in for time Regional statistics As the indicator weight, By integrating historical monitoring data and real-time results (using the original indicator values) to generate indicators such as fault frequency and risk distribution, and combining them with the kernel density estimation formula... ( For Gaussian kernel function, Generate a visual heatmap for adaptive bandwidth to intuitively present the governance situation;

[0147] 2) Time series trend prediction: A lightweight time series model is used, based on historical data. Key characteristics such as weather and time period Through the prediction formula ( (Based on historical conditions) Short-term forecasts of traffic flow, flooding risk, and other trends can support proactive prevention and control.

[0148] 3) Prioritization decision-making: using multi-attribute weighted sorting combined with a proximity formula. ( The distance between the decision object and the ideal solution. (Based on the distance from the negative ideal solution), and considering indicators such as the scope of the fault's impact and the cost of remediation, priority suggestions are provided for facility upgrades and resource allocation to support scientific decision-making.

[0149] 2.6 Model Iteration Algorithm

[0150] It provides core technical support for the model iteration and optimization module, aiming to achieve automated and low-cost model iteration, quickly adapt to dynamic urban governance scenarios, and avoid adaptation lag caused by manual intervention.

[0151] The core logic revolves around "feedback filtering - triggering iteration - adaptive fine-tuning - smooth update":

[0152] First, the collected manually reviewed error samples and newly added scene data were screened for quality, removing low-quality data such as blurry or conflicting annotations, and retaining the valid feedback dataset. Subsequently, dual triggering conditions were set, using the error rate formula. (in The number of erroneous samples. Calculate the current error of the model (for the number of valid samples). Or increase the amount of valid data When a condition is met, iteration is automatically initiated by triggering a decision function:

[0153]

[0154] The fine-tuning stage reuses the LoRA low-rank adaptive framework from Section 5.2.2, updating only the adaptation matrices A and B, and incorporating historical scene feature data. Building a hybrid fine-tuning dataset Through the dynamic round number formula The number of training epochs is controlled between 10 and 20 to balance convergence and efficiency; finally, the model hot update formula is used. ( For the original model, (To fine-tune incremental parameters) Replace the old model to ensure uninterrupted system operation. The entire iteration cycle is controlled within 24 hours, enabling rapid adaptation to new scenarios.

[0155] III. System Workflow

[0156] like Figure 1 As shown, the system workflow of this invention mainly includes the following steps:

[0157] Step 1: Multi-source data access and preprocessing

[0158] 1) Activate the multi-source data acquisition unit of the data acquisition module to collect traffic video streams, facility monitoring images, environmental sensor data and government ledger data in real time.

[0159] 2) Perform layered preprocessing on the collected data: filter invalid data (such as black frames and abnormal values), compress video / image data, unify data format, and output standardized dataset.

[0160] Step 2: Fine-tuning of the large CV model ( Figure 2 )

[0161] 1) The model fine-tuning module calls the scenario-based feature library to extract corresponding exclusive feature data according to the current governance scenario (such as traffic congestion monitoring and facility failure identification);

[0162] 2) Load the pre-trained CV model, input the specific feature data for incremental fine-tuning, generate a specific CV model adapted to the current scene, and store it in the model library.

[0163] Step 3: Multi-source data fusion analysis Figure 3 )

[0164] 1) The data fusion module reads the standardized dataset and associates visual data features with structured data through a semantic mapping table.

[0165] 2) The graph neural network association analysis model calculates the associated data and outputs a fusion analysis result containing "problem causes, scope of impact, and risk level".

[0166] Step 4: Lightweight Reasoning and Result Output ( Figure 4 )

[0167] 1) The inference analysis module loads a dedicated CV model and performs "hierarchical inference" on the standardized dataset through the TensorRT lightweight framework: first, the target frames are filtered, and then the recognition is accurate.

[0168] 2) Based on the results of the fusion analysis, generate governance decision-making suggestions (such as congestion warnings and fault repair instructions).

[0169] Step 5: Data Analysis and Application

[0170] 1) The data analysis and application module uses historical monitoring data and algorithms such as statistical analysis to perform in-depth mining and generate multi-dimensional analysis reports.

[0171] 2) Display analysis results through a visual interface to provide customized services and decision support.

[0172] Step Six: Dynamic Iterative Optimization of the Model

[0173] 1) The model iteration and optimization module collects feedback data (such as identification errors and new scenario requirements).

[0174] 2) When the feedback data meets the iteration trigger conditions (error rate ≥ 5% or new data ≥ 1000), the scenario-based feature library and dynamic fine-tuning module are automatically invoked to complete the incremental fine-tuning of the model and the update of the semantic mapping table, generating an optimized dedicated CV model to replace the old model in the original model library, so as to realize the continuous adaptation of the system to governance needs.

[0175] IV. Examples

[0176] A specific embodiment of the system of the present invention in practical application:

[0177] Step 1: Multi-source data access and preprocessing

[0178] 1) Real-time access to video streams from high-definition cameras deployed at flood-prone areas, municipal pipeline database (including pipeline ID, pipe diameter, and historical blockage records), and real-time rainfall data provided by the meteorological bureau.

[0179] 2) The preprocessing unit performs VVC encoding compression on the video stream and uses the FIPER algorithm to perform feature-preserving compression on keyframes. Simultaneously, all structured data is converted to Parquet format. The processed data is then transmitted to the edge computing nodes.

[0180] Step 2: Fine-tuning the large CV model

[0181] 1) Call the feature library of the "urban flooding" scene, which contains exclusive feature data such as "water reflection texture", "water surface ripple feature" and "submerged object outline".

[0182] 2) Load the pre-trained SegFormer model, utilize the feature data, and perform incremental fine-tuning using the LoRA algorithm and Lion optimizer to generate a dedicated "water accumulation recognition model".

[0183] Step 3: Multi-source data fusion analysis

[0184] 1) The “waterlogged area outline” identified by the fine-tuning model is associated with the “spatial location of the pipe network” in the pipe network data and the “rainfall intensity” in the meteorological data, and the association weight between each node is calculated by the GATv2 model.

[0185] 2) The model analysis outputs the following conclusion: "Cause of water accumulation point A: Pipeline No. 3 (diameter 300mm, historical blockage 3 times) is completely blocked, coupled with 50mm of rainfall in the past hour; it is expected that the water will spread to the main road B on the east side within 30 minutes, risk level: high."

[0186] Step 4: Lightweight Reasoning and Result Output

[0187] 1) On the Jetson Orin edge node, a dedicated model accelerated by TensorRT is used to perform hierarchical inference on the video stream and monitor water accumulation changes in real time.

[0188] 2) The analysis results and water accumulation images are pushed to the municipal command center, and a traffic diversion plan including suggested routes and traffic management schemes is automatically generated.

[0189] Step 5: Data Analysis and Application

[0190] 1) The data analysis module regularly mines historical monitoring data and generates multi-dimensional reports through statistical analysis: "Frequency statistics of water accumulation at flood-prone point A in the quarter (a total of 5 times, 3 of which were caused by blockage of pipe network No. 3)", "Heat map of blockage frequency of pipe network No. 3 (significantly higher than other surrounding pipe networks)", and "Trend of water accumulation risk".

[0191] 2) Display the above report through the visualization interface of the municipal command center, generate "Priority Recommendation for Pipeline Network No. 3 Renovation", and indicate that the renovation can reduce the risk of water accumulation at flood-prone point A.

[0192] Step Six: Dynamic Iterative Optimization of the Model

[0193] 1) After the system has been running for a period of time, the model iteration optimization layer collects false alarm feedback on water accumulation identification under new lighting conditions (such as night). When the error rate triggers the threshold (≥5%), the system automatically starts iteration.

[0194] 2) Utilizing the newly added nighttime water accumulation data, the system repeated the incremental fine-tuning process of the model fine-tuning layer, automatically executing the LoRA fine-tuning process and updating the model parameters. Within 24 hours, the system completed the adaptation to the nighttime scene, and the recognition accuracy was restored and improved.

[0195] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0196] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0197] The above-described 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for efficiently processing massive amounts of urban governance data based on a large computer vision model, characterized in that: The method includes the following steps: S1. Preprocess the collected multi-source urban governance data to obtain a standardized dataset; the multi-source data includes at least visual data and non-visual structured data; S2. Based on a scenario-based feature library containing specific features for different urban governance scenarios, incremental fine-tuning is performed on the pre-trained large-scale computer vision model to generate a specific CV model corresponding to a particular governance scenario. S3. Establish a unified semantic mapping relationship between visual features and the non-visual structured data, and perform correlation analysis on the mapped multi-source data based on graph neural networks to output a fusion analysis result that includes the causes of problems and risk predictions; S4. Based on the hierarchical reasoning strategy, the preprocessed visual data is analyzed using the dedicated CV model to identify the target object, and governance decision suggestions are generated by combining the fusion analysis results. S5. Mining and analyzing the governance decision-making recommendations and historical monitoring data to generate multi-dimensional analysis results, and providing visualization and decision support; S6. Dynamically trigger and execute model iteration based on feedback data to update the dedicated CV model and / or the unified semantic mapping relationship.

2. The method according to claim 1, characterized in that, In step S1, the preprocessing includes: compressing and standardizing the resolution of visual data, and standardizing the format of non-visual structured data; wherein, the visual data is compressed using the FIPER image compression algorithm based on factorized features, and the FIPER algorithm encodes the input image into a structured implicit representation containing structural factors, texture factors and semantic factors.

3. The method according to claim 1, characterized in that, In step S2, the pre-trained computer vision large model is the SegFormer model, and the incremental fine-tuning is implemented using the low-rank adaptive LoRA algorithm; wherein, the number of adaptation parameters of the LoRA algorithm does not exceed 0.2% of the total number of model parameters; the incremental fine-tuning uses the Lion optimizer for 10 to 20 rounds of iterative training.

4. The method according to claim 1, characterized in that, In step S3, the unified semantic mapping relationship is achieved by constructing a "visual feature-structured data" mapping table; the "visual feature-structured data" mapping table binds visual features (such as the outline of the water accumulation area) with pipeline network data (pipeline network ID, pipe diameter) and meteorological data (rainfall). The graph neural network is a GATv2 graph attention network, which is used to calculate the association weights between each data node after mapping.

5. The method according to claim 1, characterized in that, In step S4, the hierarchical reasoning strategy includes: S41. Utilize the lightweight backbone network MobileViTv3 to quickly analyze the preprocessed video stream and filter out target frames containing potential target objects; S42. Input the target frame into the dedicated CV model after TensorRT quantization for accurate recognition.

6. The method according to claim 5, characterized in that, In step S42, the dedicated CV model is quantized to INT8 precision; in step S41, the inference speed of the lightweight backbone network MobileViTv3 is not less than 45 FPS.

7. The method according to claim 1, characterized in that, In step S5, the multi-dimensional analysis results include: a risk distribution heatmap generated based on spatiotemporal aggregation and kernel density estimation, trend prediction results generated based on a lightweight time series model, and decision priority suggestions generated based on multi-attribute weighted sorting.

8. The method according to claim 1, characterized in that, In step S6, the triggering condition for model iteration is: the identification error rate in the collected feedback data is ≥5%, or the effective data volume of the newly added scene is ≥1000; the model iteration reuses the incremental fine-tuning process of step S2 and is completed within 24 hours.

9. A high-efficiency data processing system for urban governance based on a large computer vision model, characterized in that: The system includes: The data acquisition module is used to collect multi-source data on urban governance and preprocess the multi-source data to output a standardized dataset; the multi-source data includes at least visual data and non-visual structured data. The model fine-tuning module is connected to the scenario-based feature library and is used to incrementally fine-tune the pre-trained computer vision model based on the exclusive feature data in the scenario-based feature library, generate an exclusive CV model corresponding to a specific governance scenario and store it in the model library. The data fusion module is used to establish a unified semantic mapping relationship between visual features and the non-visual structured data, and to perform correlation analysis on the mapped multi-source data based on graph neural networks, and output the fusion analysis results. The reasoning and analysis module is connected to the model library and is used to load the dedicated CV model and perform reasoning and analysis on the visual data in the standardized dataset based on a hierarchical reasoning strategy to identify the target object. The data analysis and application module is used to mine and analyze historical monitoring data and inference results, generate multi-dimensional analysis results, and provide visual decision support. The model iteration optimization module is used to collect feedback data and automatically trigger the model fine-tuning module and data fusion module to perform iterative updates when preset conditions are met.

10. The system according to claim 9, characterized in that, The preprocessing unit of the data acquisition module is used to compress visual data using the FIPER image compression algorithm based on factorized features; The model fine-tuning module is configured to perform incremental fine-tuning based on the SegFormer model, using the LoRA low-rank adaptive algorithm and the Lion optimizer. The data fusion module includes a semantic mapping table for storing the correspondence between "visual features and structured data", and an association analysis model built based on the GATv2 graph attention network. The inference analysis module includes a MobileViTv3 lightweight backbone network for quickly filtering target frames, and a TensorRT optimization unit for performing INT8 quantization on the dedicated CV model. The model iteration and optimization module is configured to automatically trigger the iteration process when the recognition error rate is ≥5% or the amount of newly added scene data is ≥1000.

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