Land planning land classification automatic judgment system based on AI image recognition

Abstract

The invention discloses a territorial planning land classification automatic judgment system based on AI image recognition, and relates to multi-source image data acquisition, preprocessing, AI image recognition, land classification judgment, data management and storage, system monitoring and maintenance, user interaction, intelligent error correction and multi-scale analysis modules. Quantum remote sensing and biological sensing are fused, block chain guarantee data is utilized, encryption and denoising topology analysis is applied, a quantum and GAN joint model is constructed, migration and reinforcement learning are performed, a cause and effect graph and a knowledge graph are combined for judgment and classification, quantum encryption and an account book architecture are adopted for data management, and through quantum sensing and deep learning monitoring, the data are acquired. A brain-computer interface and holographic projection interaction are fused, reinforcement learning intelligent error correction is combined, and multi-scale convolution and an attention mechanism are fused to analyze an image. According to the invention, image quality is improved, identification and determination are enhanced, data security is guaranteed, intelligent monitoring and maintenance are realized, innovative interaction experience is provided, and the accuracy and efficiency of land classification for land planning are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of national land planning land classification, and in particular to an automatic determination system for national land planning land classification based on AI image recognition. Background Art

[0002] Land use classification in national land planning is a crucial foundation for the rational development and utilization of land resources and for ensuring sustainable socioeconomic development. Traditional land use classification in national land planning relies primarily on manual field surveys and paper map analysis, which not only consumes significant manpower, material resources, and time, but also, due to human factors, makes it difficult to ensure the accuracy and consistency of classification results.

[0003] With the development of information technology, geographic information systems (GIS) and remote sensing technologies have been gradually applied to land use classification in national land planning. GIS can store, manage, and analyze geospatial data, while remote sensing technology can provide land cover information over large areas. However, these technologies still have certain limitations. For example, GIS data updates slowly, making it difficult to reflect land use changes in real time. Remote sensing image interpretation primarily relies on manual interpretation or simple computer algorithms, making it difficult to accurately identify complex land use types and subtle changes.

[0004] In recent years, artificial intelligence technology has made significant progress, with deep learning, in particular, demonstrating powerful capabilities in image recognition. Several studies have attempted to apply deep learning algorithms to land use classification for national land planning, achieving some success. However, existing land use classification methods based on deep learning still face several challenges. For one thing, deep learning models require a large amount of labeled data for training, which is difficult to obtain and expensive in the field of national land planning. Furthermore, existing models lack the ability to integrate multi-source data and are unable to fully utilize the complementary information from different types of data, resulting in a need for improved classification accuracy and reliability. Furthermore, existing land use classification systems lack intelligent error correction and multi-scale analysis capabilities, making them difficult to adapt to the complex and ever-changing needs of national land planning. Therefore, developing an automated land use classification system for national land planning based on AI image recognition is of great practical significance. Summary of the Invention

[0005] The present invention proposes an automated land classification determination system for national land planning based on AI image recognition to solve the problems mentioned in the above-mentioned prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an automated land classification determination system for national land planning based on AI image recognition, comprising:

[0007] Multi-source image data acquisition module: integrating quantum remote sensing and bio-sensing technology, combining low-altitude bionic aircraft to collect images, and introducing data richness index D rich , the formula is w i is the weight of each data source, d i Dimensions that provide unique information for the corresponding data source;

[0008] Image preprocessing module: adopts quantum encryption denoising algorithm, applies topological analysis and geometric correction, uses deep learning adaptive radiation enhancement technology, and introduces preprocessing consistency index C pre , the formula is I i-pre is the image eigenvalue after preprocessing, I ref is the reference eigenvalue;

[0009] AI image recognition module: builds a joint model of quantum neural network and generative adversarial network (GAN), adopts transfer and reinforcement learning training strategies, and introduces the recognition robustness index R rec , the formula is N correct-robust In order to correctly identify the number under complex interference, N total-robust is the total number of identifications;

[0010] Land use classification judgment module: Using the fusion technology of causal graph and knowledge graph, we build an intelligent reasoning engine for land use classification, make judgments based on the image recognition results, causal logic and domain knowledge, and introduce the judgment comprehensiveness index T judge , the formula is w i is the weight of each decision factor, t i is the contribution of the corresponding factor to the judgment result;

[0011] Data management and storage module: Using quantum encryption combined with distributed ledger architecture, using artificial intelligence to measure land changes, and introducing a comprehensive index of storage efficiency and security S safe-eff , the formula is α is the weight coefficient, For storage efficiency, For safe access ratio;

[0012] System monitoring and maintenance module: Use quantum sensors to monitor system hardware resources, build system performance prediction models through deep learning, introduce system self-repair mechanisms, and use system reliability indicators R sys , the formula is T stable is the system stable operation time, T total is the total running time;

[0013] User interaction module: Combining brain-computer interface and holographic projection technology, the operation is controlled by brain neural signals, and the classification results are presented by holographic projection. The user interaction satisfaction index Cuser is introduced, and the formula is w i is the weight of each interactive experience factor, c i Score the user satisfaction of the corresponding factors.

[0014] Furthermore, it also includes:

[0015] Intelligent error correction module: Use variational autoencoder (VAE) to generate potential feature space for land use classification, identify misclassification by comparing abnormal judgment results with normal feature distribution, and combine reinforcement learning to intelligently select error correction strategies. The intelligent error correction accuracy evaluation formula is: N corrected_right Indicates the number of successful corrections and correct results, N total_corrected Indicates the total number of corrections.

[0016] Furthermore, it also includes:

[0017] Multi-scale analysis module: It uses multi-scale convolution fusion and attention mechanism to analyze images of different resolutions, automatically focuses on key areas through the attention mechanism, and integrates features of different scales. The evaluation formula for multi-scale analysis effect is: Among them A before and A after They are the land use classification recognition accuracy before and after multi-scale analysis.

[0018] Furthermore, the multi-source image data acquisition module uses quantum dot sensors to detect trace elements and pollutants in the soil, and combines drone thermal imaging technology to obtain land temperature distribution and thermal anomaly information.

[0019] Furthermore, the AI image recognition module uses a quantum annealing algorithm to optimize the weights of the neural network. Through the principles of quantum mechanics, it utilizes the phenomenon of quantum fluctuations to explore the solution space, shortening the training time to s times the original time (s<1).

[0020] Furthermore, the land use classification determination module introduces uncertainty reasoning based on Bayesian networks, considers data uncertainty and ambiguity of determination rules, and performs probabilistic evaluation of land use classification results.

[0021] Furthermore, the data management and storage module adopts blockchain and edge computing collaborative technology. The edge device preliminarily screens and encrypts the collected data, and transmits and stores the data between the edge and the cloud through blockchain.

[0022] Furthermore, the system monitoring and maintenance module uses the generative adversarial network (GAN) to generate simulated fault data and conduct simulation tests on the system fault detection and repair mechanism.

[0023] Furthermore, the user interaction module introduces virtual reality tactile feedback technology, so that when users view the land use classification results, they can feel the virtual touch of different land use types through tactile devices.

[0024] Furthermore, the entire system introduces federated transfer learning technology. Different regions or departments use transfer learning to share model knowledge while protecting local data privacy. The evaluation formula for federated learning effect is: Among them A local is the accuracy of the local model, A federated is the accuracy of the model after federated learning.

[0025] Compared with the existing technology, the beneficial effects of the present invention are:

[0026] In terms of data collection, the integration of advanced technologies such as quantum remote sensing and bio-perception, combined with a cluster of low-altitude intelligent bionic aircraft, can obtain comprehensive, high-precision and unique land information, including microscopic material composition, ecological conditions, etc., providing a richer basis for land use classification.

[0027] The image preprocessing module uses technologies such as quantum encryption denoising, topological data analysis, and deep learning-driven adaptive radiation enhancement to effectively improve image quality, ensure the consistency and accuracy of the preprocessing effects, and lay a solid foundation for subsequent image recognition.

[0028] The AI image recognition module builds a joint model of quantum neural network and GAN, combining transfer learning and reinforcement learning, which greatly improves feature extraction efficiency and model generalization ability, while enhancing the robustness and anti-interference ability of recognition.

[0029] The land use classification determination module uses the fusion technology of causal graph and knowledge graph to build an intelligent reasoning engine, comprehensively considering causal logic and domain knowledge to make the determination results more scientific and comprehensive.

[0030] The data management and storage module adopts an architecture that combines quantum encryption and distributed ledgers, and uses artificial intelligence to automatically identify data changes, which not only ensures data security but also improves storage efficiency and the timeliness of data updates.

[0031] The system monitoring and maintenance module uses quantum sensors and deep learning models to achieve real-time monitoring and prediction of system performance, and has a self-repair mechanism to ensure the continuous and stable operation of the system.

[0032] The user interaction module combines brain-computer interface and holographic projection technology, as well as virtual reality tactile feedback technology, to provide users with a more natural, intuitive and realistic interactive experience, thereby improving user satisfaction.

[0033] In addition, the system also has functions such as intelligent error correction, multi-scale analysis, and federated transfer learning, which further improves the accuracy, adaptability and reliability of land use classification, and can effectively meet the complex and changing land planning needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a schematic block diagram of an automated land classification determination system for national land planning based on AI image recognition proposed by the present invention;

[0035] Figure 2 This is a schematic diagram for comparing data collection accuracy;

[0036] Figure 3 Schematic diagram for identification robustness comparison;

[0037] Figure 4 Schematic diagram of user interaction satisfaction survey. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0040] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below with reference to the accompanying drawings.

[0041] Reference Figures 1 to 4 : An automated land classification determination system for national land planning based on AI image recognition, including:

[0042] Multi-source image data acquisition module: innovative integration of quantum remote sensing and bio-sensing technology. Quantum remote sensing, with its ultra-high precision and resolution at the picometer level, can accurately capture the microscopic material composition and weak energy changes of the land, providing a more subtle feature basis for land use classification. Bio-sensing technology uses the stress response of specific organisms to different environments, deploys biosensors to monitor changes in vegetation, microbial communities, etc., and thereby infers the ecological status and utilization type of the land. Combined with a cluster of low-altitude intelligent bionic aircraft, simulating the flight path of birds, equipped with multi-spectral, high-resolution imaging and gas detection equipment, it can flexibly collect images of complex terrain and hidden areas. Utilize the distributed ledger and smart contracts of the blockchain to ensure that the entire data collection process is traceable and tamper-proof. Introduce the data richness index D rich , the formula is where w i is the weight of each data source, d i Provide unique information dimensions for corresponding data sources to ensure the comprehensiveness and uniqueness of collected data.

[0043] Image preprocessing module: Using a quantum encryption denoising algorithm, based on the non-cloning property of quantum states, it accurately identifies and removes image noise, increasing the signal-to-noise ratio by k times (k>1) without losing image detail. Using topological data analysis for geometric correction, it accurately restores the true geometric form of the image by analyzing the image's topological structure. Using deep learning-driven adaptive radiation enhancement technology, it intelligently adjusts radiation parameters based on image content to highlight ground features. Preprocessing consistency index C is introduced. pre , the formula is Among them I i-pre is the image eigenvalue after preprocessing, I refIt is used as a reference characteristic value to ensure the consistency and accuracy of the preprocessing effect.

[0044] AI image recognition module: Build a joint model based on quantum neural network and generative adversarial network (GAN). Quantum neural network uses the superposition and entanglement characteristics of quantum bits to quickly search for key features in high-dimensional complex image feature space, which increases the feature extraction efficiency by p times (p>1). GAN generates realistic land use image samples through adversarial training of generator and discriminator, expands the training data set, and improves the generalization ability of the model. The training strategy of combining transfer learning with reinforcement learning is adopted. After pre-training on a large-scale general image data set, the model interacts with the environment through reinforcement learning in the land use classification task, and continuously optimizes the classification strategy based on feedback. Introducing the recognition robustness index R rec , the formula is where N correct-robust In order to correctly identify the number under complex interference, N total-robust It is the total number of recognitions, which measures the stability and anti-interference ability of the model recognition.

[0045] Land use classification determination module: Utilizes the fusion technology of causal graph and knowledge graph. The causal graph clarifies the causal relationship between land use type and planning indicators and environmental factors, while the knowledge graph integrates professional knowledge, policies and regulations, historical cases and other information in the field of land planning. Based on this, an intelligent reasoning engine for land use classification is built, which makes judgments based not only on image recognition results but also on causal logic and domain knowledge. Introduces the comprehensiveness index T judge , the formula is where w i is the weight of each decision factor, t i To calculate the contribution of corresponding factors to the judgment results, and ensure that the judgment results are scientific and comprehensive.

[0046] Data management and storage module: A storage architecture that combines quantum encryption with distributed ledgers is used. Quantum encryption ensures data storage and transmission security, while distributed ledgers record data operation history, enabling data traceability throughout its lifecycle. AI is used to automatically identify data changes, and based on time series analysis and image semantic segmentation, it accurately detects changes in land use types, building construction, or demolition. A comprehensive storage efficiency and security indicator, S, is introduced. safe-eff , the formula is Where α is the weight coefficient, For storage efficiency, To ensure secure access ratio, optimize data management and storage performance.

[0047] System monitoring and maintenance module: Use quantum sensors to monitor the quantum state changes of system hardware resources (such as CPU, memory, GPU) in real time to detect potential failure risks in advance. Build a system performance prediction model through deep learning, analyze historical operation data based on long short-term memory network (LSTM), and predict future system performance trends. Introduce a system self-repair mechanism. When an anomaly is detected, it automatically calls the backup module and reconfigures the system parameters to ensure continuous and stable operation of the system. Introduce the system reliability index R sys , the formula is Where T stable is the system stable operation time, T total It is the total running time, which measures the overall reliability of the system.

[0048] User interaction module: Combining brain-computer interface and holographic projection technology. Through the brain-computer interface device, users can interact with the system by relying on brain nerve signals to perform operations such as image upload and result query. The accuracy of nerve signal recognition reaches q% (q is close to 100). Holographic projection technology presents the land use classification results of national land planning in naked-eye 3D form, allowing users to intuitively view the land use situation in different areas. Introducing user interaction satisfaction index C user , the formula is where w i is the weight of each interactive experience factor, c i Score user satisfaction scores for corresponding factors and continuously optimize the interactive experience.

[0049] The present invention also includes the following modules:

[0050] The intelligent error correction module utilizes variational autoencoders (VAEs) as its core technology to construct a latent feature space. VAEs are generative neural networks that learn from a large amount of correctly labeled land-use classification data to uncover underlying relationships between land-use features. During training, VAEs encode land-use image data into low-dimensional latent vectors that contain key characteristic information about land-use types. By continuously adjusting network parameters, VAEs are able to generate a latent feature space that closely resembles the distribution of real-world land-use features. When the system classifies a new land-use image, the intelligent error correction module compares the feature vectors corresponding to the classification result with the normal distribution of features in the latent feature space. If a feature vector for a classification result deviates from the normal distribution, it is considered an anomaly, indicating a possible classification error. For example, if industrial land is misclassified as agricultural land, the position of its feature vector in the latent feature space will differ significantly from the normal distribution of industrial land features. After identifying misclassifications, the module uses reinforcement learning to select an error correction strategy. Reinforcement learning allows the intelligent agent (error correction module) to try different error correction actions, giving rewards or penalties based on the correction results, and gradually learning the optimal error correction strategy. When a misjudgment is found, the intelligent agent can try to adjust the parameters of the classification model, re-extract features, or refer to other relevant data. Through continuous trial and error and optimization, the most effective error correction method is determined to correct ambiguous or erroneous judgments. In order to evaluate the performance of the intelligent error correction module, the system uses the formula Among them, N corrected_right Indicates the number of successful corrections and correct results, N total_corrected is the total number of corrections. By continuously calculating this accuracy metric and feeding it back into the module's learning process, the intelligent error correction module can continuously optimize itself and improve its error correction capabilities, thereby ensuring the accuracy and reliability of the automated land use classification system for national land planning based on AI image recognition.

[0051] The present invention also includes the following modules:

[0052] The Multi-Scale Analysis Module integrates multi-scale convolutional fusion with an attention mechanism to deeply mine key information from images of varying resolutions, improving land use classification accuracy. Multi-scale convolutional fusion is one of the core techniques employed in this module. When processing land use images for national land use planning, images of varying resolutions contain varying levels of detailed information. High-resolution images can reveal fine surface textures, such as the specific shapes of buildings and the subtle alignment of roads. Low-resolution images help capture the overall layout and macroscopic features of the land, such as the approximate extent of an area and the overall contours of the terrain. Multi-scale convolutional fusion employs convolution operations on images using kernels of varying sizes. Large kernels capture global image features, while smaller kernels focus on extracting local details. Feature maps generated from these convolutions at different scales are then fused, enabling the system to comprehensively understand the content of land images by simultaneously considering both macroscopic and microscopic information. Furthermore, the attention mechanism plays a crucial role in the multi-scale analysis module. Land use images for national land use planning often contain a significant amount of redundant information, and the attention mechanism enables the system to automatically focus on key areas. It calculates the weight of each area in the image, giving higher weight to areas that are critical to land use classification, while reducing the weight of insignificant background areas. For example, when analyzing an area containing farmland, roads, and a small number of buildings, the attention mechanism will highlight the farmland part because it may be more critical to land use classification. In order to measure the effectiveness of the multi-scale analysis module, the system adopts a scientific evaluation formula By comparing the land use classification recognition accuracy before and after multi-scale analysis before and A after , can intuitively understand the degree to which the module improves system performance, and then continuously optimize module parameters, so that it can more effectively analyze land use types in complex land planning scenarios and provide accurate and reliable basis for decision-making.

[0053] In this invention, the multi-source image data acquisition module integrates advanced quantum dot sensors with drone thermal imaging technology. As a new type of highly sensitive detection device, quantum dot sensors exhibit unique advantages in detecting trace elements and pollutants in soil. Quantum dots are nanoscale semiconductor materials with unique optical and electrical properties. When used for soil detection, quantum dot sensors can specifically interact with trace elements or pollutants in the soil through principles such as fluorescence resonance energy transfer. For example, specific quantum dots can bind to heavy metal ions in the soil and, when exposed to excitation light, emit a fluorescence signal related to the binding state. By precisely measuring and analyzing parameters such as the intensity and wavelength of these fluorescence signals, ultra-sensitive detection of trace elements and pollutants in the soil can be achieved. Even extremely small amounts of substances can be accurately identified, providing critical information on land quality and potential pollution risks for national land planning. Furthermore, drone thermal imaging technology offers a new perspective for land information collection. Drones equipped with high-resolution thermal imaging cameras can scan the land at different heights and angles. Thermal imaging cameras, based on the principle of infrared radiation, can capture differences in thermal radiation from the soil surface and generate temperature distribution images. By analyzing these images, we can clearly understand the temperature conditions in different areas of the land and identify possible areas of thermal anomalies. For example, in urban planning, thermal imaging technology can help identify the distribution and intensity of the urban heat island effect; in agricultural land planning, it can detect temperature differences caused by uneven irrigation or localized temperature changes caused by pests and diseases. By combining the land composition information detected by quantum dot sensors with the temperature distribution and thermal anomaly information obtained by drone thermal imaging technology, the multi-source image data acquisition module can provide more comprehensive and rich basic data for the automated land classification system for national land planning, effectively supporting subsequent land classification and planning decisions.

[0054] In this invention, the AI image recognition module uses a quantum annealing algorithm to optimize neural network weights. Determining optimal weights is a challenging task in traditional neural network training. Because the weight space of a neural network is extremely complex and contains numerous local optima, traditional optimization algorithms are easily trapped in these spaces, making it difficult to find the global optimal solution. Furthermore, the training process is time-consuming. However, the quantum annealing algorithm offers a new approach to addressing this challenge. Based on the principles of quantum mechanics, the quantum annealing algorithm exploits quantum fluctuations to explore the solution space. In this AI image recognition module, when using the quantum annealing algorithm to optimize neural network weights, the weight optimization problem is first mapped to a problem of minimizing an energy function. This energy function reflects the error or loss of the model under the current weights. At the beginning of the algorithm, the system is in a high-energy state, corresponding to the initial random distribution of the neural network weights. As the algorithm runs, quantum fluctuations cause the system to conduct a non-local search in the solution space. Unlike traditional algorithms that search only in a local neighborhood, this algorithm can skip local optima with a certain probability and directly explore a wider solution space. By continuously adjusting the weights and reducing the value of the energy function, the global optimal solution is gradually approached. When processing image recognition tasks for land use classification in national land planning, the quantum annealing algorithm can quickly search for the optimal weight combination of the neural network. This enables the model to more efficiently learn land use characteristics in complex image data, such as national land image data with different resolutions and lighting conditions. After optimization with the quantum annealing algorithm, the model training time was significantly reduced to only s times the original time (s<1), significantly improving the system's training efficiency and laying a solid foundation for rapid and accurate national land planning classification.

[0055] In the present invention, the land use classification determination module introduces uncertainty reasoning based on Bayesian networks. Bayesian networks are a technology based on probabilistic graphical models that represent the dependencies between variables in the form of directed acyclic graphs. In the context of land use classification in national land planning, numerous influencing factors are involved, such as the land's geographical location, surrounding environment, and historical use. These factors are complexly correlated, and the data itself is often uncertain. The determination rules are not absolutely precise and have a certain degree of ambiguity. In this module, various land use-related data are first collected and organized, including remote sensing image data, topographic data, and human and economic data. Then, based on this data, a Bayesian network structure is constructed to determine the causal relationship and conditional probability distribution between each node (representing different land use characteristics or influencing factors). For example, if an area is near a water source, the probability of it being classified as agricultural land or wetland may increase. Bayesian networks can quantify this probabilistic relationship. When making land use classification decisions, the system inputs the newly acquired data into the constructed Bayesian network. Based on Bayes' theorem and combined with the existing conditional probability distribution in the network, the probability of different land use types is calculated. Taking into account data uncertainty, such as feature recognition errors caused by the resolution limitations of remote sensing imagery, and the ambiguity of judgment rules, such as the difficulty in clearly defining overlapping land uses, the Bayesian network can integrate these factors and perform a probabilistic assessment of land use classification results. Ultimately, the system outputs not a simple deterministic classification result, but a probability value for each land use type, providing a more comprehensive and reliable basis for land planning decision-makers, enabling them to more scientifically weigh the possibilities of different land use classifications and make reasonable decisions.

[0056] In this invention, the data management and storage module utilizes blockchain and edge computing technology. Edge devices, serving as the frontline for data collection, are equipped with high-performance processors and advanced data processing algorithms. They are capable of performing preliminary screening of the massive amounts of land use planning data collected, such as land imagery and geographic coordinate information. By setting specific screening rules, such as those based on data accuracy and completeness, invalid or erroneous data is eliminated, retaining only data that meets the requirements. Furthermore, advanced encryption algorithms, such as asymmetric encryption, are used to encrypt the filtered data, ensuring it is protected from unauthorized theft or tampering during transmission. Blockchain technology plays a key role in data transmission and storage. Its distributed ledger nature creates an immutable and traceable storage environment for data. When data processed by edge devices is transmitted to the cloud, blockchain technology adds a timestamp and digital signature to each data block, linking these blocks in chronological order to form a complete chain. During transmission, blockchain uses consensus mechanisms, such as proof-of-work (PoW) or proof-of-stake (PoS), to ensure data consistency and security across nodes. Once data is stored in the cloud, any access or modification to it is recorded on the blockchain. Through the blockchain's chain structure and encryption algorithm, the source of the data and its operation history can be easily traced. By combining blockchain with edge computing, the system not only improves data processing efficiency and reduces computing pressure on the cloud, but more importantly, ensures the security and traceability of national land planning classification data throughout its lifecycle, providing a reliable data foundation for the system's stable operation and accurate judgment.

[0057] In this invention, the system monitoring and maintenance module utilizes generative adversarial network (GAN) technology. This network, composed of a generator and a discriminator, plays a unique role in the system's monitoring and maintenance module. The generator's task is to learn the distribution characteristics of real fault data. By deeply analyzing a large amount of historical fault data and relevant system operating parameters, it utilizes a complex neural network architecture and continuously adjusts its parameters to generate highly realistic simulated fault data. This simulated fault data covers a wide range of possible system failure scenarios, including hardware failures, software errors, and data transmission anomalies. Meanwhile, the discriminator is responsible for distinguishing between the simulated fault data output by the generator and real fault data. The discriminator, also based on advanced neural networks, continuously improves its ability to distinguish authenticity from falsified data by learning from a large number of training samples. In this process, the generator and the discriminator compete with each other, with the generator striving to generate data that is closer to real faults in order to deceive the discriminator, while the discriminator continuously improves its ability to discern the generator's "disguise." Using GAN-generated simulated fault data, the system can conduct comprehensive and in-depth simulation tests of its fault detection and repair mechanisms. During testing, the system's fault detection algorithm analyzes and processes this simulated fault data to assess its ability to accurately identify the fault type and location. Simultaneously, the repair mechanism performs simulated repair operations based on this simulated fault data to verify the effectiveness and efficiency of the repair strategy. Through continuous testing and optimization, the system can proactively identify potential issues and make targeted improvements to the fault detection and repair mechanisms, significantly improving system stability and ensuring that the AI-based image recognition-based automated land use classification system for national land planning can operate reliably and efficiently in practice.

[0058] In this invention, the user interaction module incorporates virtual reality haptic feedback technology, enabling deep interaction between the virtual environment and the user's sense of touch through advanced sensors and algorithms. When a user views land use classification results, the system generates corresponding virtual tactile signals based on different land use types, such as cultivated land, forest land, and construction land. Specifically, the haptic device incorporates multiple microelectromechanical system (MEMS) sensors that accurately sense the user's hand movements and position. Furthermore, the device's surface is densely packed with haptic feedback units, which generate tactile stimuli of varying frequencies, intensities, and textures based on system instructions. For example, when a user views cultivated land classification results, the haptic device simulates the fine texture and slight graininess of soil; while when viewing forest land, it creates the rough feel of tree bark and the gentle rustling of leaves. During the technical implementation process, the system first conducts in-depth analysis and modeling of land use type characteristics, extracting key tactile features that represent each type of land use. Then, using a deep learning algorithm, these features are mapped to the control parameters of the haptic feedback device, achieving precise tactile feedback. In this way, users can get a highly realistic tactile experience when interacting with the system, which greatly enhances the realism of the interaction and makes the viewing and analysis process of national land planning classification more intuitive, vivid and immersive.

[0059] In the present invention, the entire system introduces federated transfer learning technology, which provides an effective solution for improving system performance and protecting data privacy. The application architecture of federated transfer learning technology is sophisticated. Different regions or departments have their own unique land use data. These data often involve sensitive information, and data privacy protection is extremely important. Federated transfer learning allows all parties to share model knowledge without leaking local original data. Specifically, each participant first trains the model locally based on its own land use data. During the training process, the transfer learning technology is used to adapt the model parameters obtained from pre-training of large-scale general data to the local specific land planning land use classification task, so as to accelerate model convergence and improve initial performance. Subsequently, through the federated learning mechanism, all parties securely aggregate the model parameters in the training process. This aggregation process does not directly share the original data, but uses encryption technology and secure multi-party computing protocols to ensure the confidentiality of the data during the aggregation process. By continuously iterating the aggregated model parameters, the model can integrate the land use characteristics and classification experience of various places, and significantly improve the adaptability and accuracy in different regions. The effect of federated learning is evaluated by a specific formula, which is Among them A local Represents the accuracy of the local model, A federatedThis is the accuracy of the model after federated learning. This evaluation method can intuitively measure the performance improvement brought by federated learning to the model, thereby guiding the optimization and adjustment of the federated learning process, helping the AI image recognition-based land use classification system for national land planning to achieve greater effectiveness in complex and diverse real-world scenarios.

[0060] The above are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. An automated land classification determination system for national land planning based on AI image recognition, characterized in that: Includes the following modules: Multi-source image data acquisition module: integrating quantum remote sensing and bio-sensing technology, combining low-altitude bionic aircraft to collect images, and introducing data richness index D rich , the formula is w i is the weight of each data source, d i Dimensions that provide unique information for the corresponding data source; Image preprocessing module: adopts quantum encryption denoising algorithm, applies topological analysis and geometric correction, uses deep learning adaptive radiation enhancement technology, and introduces preprocessing consistency index C pre , the formula is I i-pre is the image eigenvalue after preprocessing, I ref is the reference eigenvalue; AI image recognition module: builds a joint model of quantum neural network and generative adversarial network (GAN), adopts transfer and reinforcement learning training strategies, and introduces the recognition robustness index R rec , the formula is N correct-robust In order to correctly identify the number under complex interference, N total-robust is the total number of identifications; Land use classification judgment module: Using the fusion technology of causal graph and knowledge graph, we build an intelligent reasoning engine for land use classification, make judgments based on the image recognition results, causal logic and domain knowledge, and introduce the judgment comprehensiveness index T judge , the formula is w i is the weight of each decision factor, t i is the contribution of the corresponding factor to the judgment result; Data management and storage module: Using quantum encryption combined with distributed ledger architecture, using artificial intelligence to measure land changes, and introducing a comprehensive index of storage efficiency and security S safe-eff , the formula is α is the weight coefficient, For storage efficiency, For safe access ratio; System monitoring and maintenance module: Use quantum sensors to monitor system hardware resources, build system performance prediction models through deep learning, introduce system self-repair mechanisms, and use system reliability indicators R sys , the formula is T stable is the system stable operation time, T total is the total running time; User interaction module: Combining brain-computer interface and holographic projection technology, the operation is controlled by brain neural signals, and the classification results are presented by holographic projection. The user interaction satisfaction index Cuser is introduced, and the formula is w i is the weight of each interactive experience factor, c i Score the user satisfaction of the corresponding factors.

2. The AI image recognition-based automated land classification determination system for national land planning according to claim 1 is characterized in that: Also includes: Intelligent error correction module: Use variational autoencoder (VAE) to generate potential feature space for land use classification, identify misclassification by comparing abnormal judgment results with normal feature distribution, and combine reinforcement learning to intelligently select error correction strategies. The intelligent error correction accuracy evaluation formula is: N corrected_right Indicates the number of successful corrections and correct results, N total_corrected Indicates the total number of corrections.

3. The AI image recognition-based automated land classification determination system for national land planning according to claim 1 is characterized in that: Also includes: Multi-scale analysis module: It uses multi-scale convolution fusion and attention mechanism to analyze images of different resolutions, automatically focuses on key areas through the attention mechanism, and integrates features of different scales. The evaluation formula for multi-scale analysis effect is: Among them A before and A after They are the land use classification recognition accuracy before and after multi-scale analysis.

4. The AI image recognition-based automated land classification determination system for national land planning according to claim 1 is characterized in that: The multi-source image data acquisition module uses quantum dot sensors to detect trace elements and pollutants in the soil, and combines drone thermal imaging technology to obtain land temperature distribution and thermal anomaly information.

5. The system for automatic determination of land use classification based on AI image recognition for national land planning according to claim 1 is characterized in that: The AI image recognition module uses a quantum annealing algorithm to optimize the weights of the neural network. Through the principles of quantum mechanics, it utilizes quantum fluctuations to explore the solution space, shortening the training time to s times the original time (s<1).

6. The AI image recognition-based automated land classification determination system for national land planning according to claim 1 is characterized in that: The land use classification determination module introduces uncertainty reasoning based on Bayesian networks, considers data uncertainty and fuzziness of determination rules, and performs probabilistic evaluation of land use classification results.

7. The system for automatic determination of land use classification based on AI image recognition for national land planning according to claim 1 is characterized in that: The data management and storage module uses blockchain and edge computing collaborative technology. The edge device preliminarily screens and encrypts the collected data, and transmits and stores the data between the edge and the cloud through blockchain.

8. The system for automatic determination of land use classification based on AI image recognition for national land planning according to claim 1 is characterized in that: The system monitoring and maintenance module uses the generative adversarial network (GAN) to generate simulated fault data and conduct simulation tests on the system fault detection and repair mechanism.

9. The system for automatic determination of land use classification based on AI image recognition for national land planning according to claim 1 is characterized in that: The user interaction module introduces virtual reality tactile feedback technology. When users view the land use classification results, they can feel the virtual touch of different land use types through tactile devices.

10. The system for automatic determination of land use classification based on AI image recognition for national land planning according to claim 1 is characterized in that: The entire system introduces federated transfer learning technology. Different regions or departments use transfer learning to share model knowledge while protecting local data privacy. The evaluation formula for federated learning effect is: Among them A local is the accuracy of the local model, A federated is the accuracy of the model after federated learning.

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